The Pennsylvania State University. The Graduate School. College of Health and Human Development BONE QUALITY, MUSCLE MASS, AND ACTIVITY:

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1 The Pennsylvania State University The Graduate School College of Health and Human Development BONE QUALITY, MUSCLE MASS, AND ACTIVITY: RELATIONSHIPS AND GENETIC INFLUENCE A Thesis in Kinesiology by Teresa Caroldean Lang 2003 Teresa Caroldean Lang Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2003

2 The thesis of Teresa Caroldean Lang has been reviewed and approved* by the following: Neil A. Sharkey Professor of Kinesiology Professor of Orthopaedics and Rehabilitation Thesis Advisor Chair of Committee Gerald E. McClearn Evan Pugh Professor of Health and Human Development Professor of Biobehavioral Health George P. Vogler Professor of Biobehavioral Health Director of The Center for Developmental and Health Genetics Robert B. Eckhardt Professor of Developmental Genetics and Evolutionary Morphology Philip E. Martin Professor of Kinesiology Head of the Department of Kinesiology *Signatures are on file in the Graduate School.

3 Abstract The primary aim of this study was to identify quantitative trait loci (QTL) for skeletal measures in C57BL/6J x DBA/2 F 2 and RI populations of mice. QTL analysis is based on a genome-wide search for regions of chromosomes that contain a locus or gene that influences the phenotype. Multiple points along the genome are analyzed to determine the likelihood of a QTL at each position influencing the trait based on finding an association between the genotype of a genetic marker and the trait. Many skeletal studies have reported strong correlations between muscle, skeletal, and body size phenotypes and these correlations add to the difficulty in identifying direct genetic influence on skeletal traits. Quantitative trait loci (QTL) have been identified for skeletal phenotypes and it is often the case that QTLs for body size (body weight, body length, and lean body mass) phenotypes also map to these same areas. A QTL identified as influencing bone could act indirectly through body size or muscle mass. Identifying QTLs that influence skeletal phenotypes that are mediated through body size related pathways is informative, however it is also important to identify QTLs that influence skeletal phenotypes independent of body size. Removing size effects has been an issue for researchers and traditionally one method of removing the size effect has been through the use of ratios. This technique and an alternate technique of multiple regression were performed on muscle and skeletal data and the differences in results from these methods of normalization are discussed. The second aim of this study was to investigate the relationships between muscle mass, skeletal integrity, physical activity/behavior, and the influence of specific genetic

4 iv loci that were identified for multiple traits in the same chromosomal region. Structural equation modeling (SEM) was used to investigate the direct and indirect relationships among activity, muscle and bone factors and five QTLs. This study has isolated and confirmed numerous QLTs influencing bone quality and provides evidence that many QTLs exert effects through complex pathways such as body size, muscle mass, and activity. This work fully illustrates the complex system responsible for the maintenance of skeletal integrity.

5 Table of Contents v List of Figures...ix List of Tables...xi Acknowledgements...xx Chapter 1 Introduction...1 Background...4 References...9 Chapter 2 Literature Review...10 Introduction...10 Inbred Mouse Strains and Their Progeny...11 Heritability...13 Quantitative Trait Loci (QTL) Analysis...14 Genetic Mapping...15 Genetic Studies of Skeletal phenotypes and Related Cofactors...17 Quantitative Trait Loci Studies in Humans...22 Quantitative Trait Loci Studies in Animals...25 C57BL/6 x DBA/2 Inbred Strain Crosses...26 C57BL/6 x Cast/EiJ Inbred Strain Crosses...31 C57BL/6J x C3H/HeJ Inbred Strain Crosses...33 SAMP6 and SAMP2 Inbred Mouse Crosses...35 MRL/MPJ x SJL/J Inbred Strain Crosses...37 Conclusions...39 References...41 Chapter 3 Methods...46 Animals and Animal Maintenance...46 Behavioral Assessments...47 Genotyping...48 Serum Alkaline Phosphatase and Serum Calcium...49 Body Size Measurements...49 Hind-limb Harvest and Dissection...50 Gross Dimensional Measurements...51 Flexural Testing of the Femoral Diaphysis...52 Shear Testing of the Femoral Neck...54 Flexural Testing of the Tibial Diaphysis...55 Tissue Processing and Histomorphometry...56 Compositional analysis...57

6 References...59 vi Chapter 4 Analyses...60 Sex, Strain, and Age Differences...60 Co-variance of Phenotypic Expression...61 Heritability Estimates...61 Quantitative Trait Loci (QTL) Analysis (Background)...61 Physical and Genetic Maps...68 Review of Quantitative Loci Analyses...68 Single Marker, Single QTL Analyses...69 Comparison of Marker Means...71 Comparison of the Means...72 Single Marker Single QTL Regression...73 Single Marker Likelihood Method...73 Interval Mapping Likelihood Method...75 Composite Interval Mapping (CIM)...80 Multiple Interval Mapping...84 Structural Equation Modeling...87 References...90 Chapter 5 Results...92 Sex Differences...92 Age by Strain Differences...94 Heritability...98 QTL Analyses...99 References Chapter 6 Statistical Issues Concerning Normalizing Data to Body Size: A Comparison of Methods Applied to Quantitative Trait Loci (QTL) Analysis Abstract Introduction Background Normalizing Biological Data to Body Size Materials and Methods Animals Genotyping Body Size Measurements Muscle Mass Skeletal Measures Gross Dimensional Skeletal Measurements Flexural Testing of the Right Femoral and Tibial Diaphysis Compositional Analysis of the Tibia and Ash Mass of the femur Quantitative Trait Loci (QTL) Analyses...148

7 Body Size Normalization Results Quantitative Trait Loci Analysis Results Conclusions Acknowledgements References Chapter 7 Quantitative Trait Loci Analyses (QTL) of Structural and Material Skeletal Phenotypes in C57BL/6J and DBA/2 F 2 and RI Populations Introduction Background Materials and Methods Animals and Genotyping Behavioral Assessments Tissue Harvest and Gross Dimensional Measurements Flexural Testing of the Femoral Diaphysis Flexural Testing of the Tibial Diaphysis Material Properties Shear Testing of the Femoral Neck Compositional Analysis Tissue Processing and Histomorphometry Preliminary Analyses Co-variance of Phenotypic Expression Heritability Estimates QTL Analyses Results and Discussion Preliminary Analyses Co-variance of Phenotypic Expression QTL Analyses Discussion Conclusions Acknowledgements References Chapter 8 Bone Quality, Muscle Mass and Activity: Structural Equation Modeling of their Relationships and Genetic Influence Introduction Background Genetic Studies of Skeletal Phenotypes: Methods Animal Activity/Behavioral Assessments Genotyping Muscle Mass vii

8 Skeletal Quality Gross Dimensional Measurements: Flexural Testing of the Right Femoral and Tibial Diaphysis: Compositional Analysis of the Tibia and Ash Mass of the femur: Analyses Quantitative Trait Loci (QTL) Analyses: Model Specification and Testing QTL Analyses Results Structural Equation Modeling Results Discussion Conclusions Acknowledgements References Chapter 9 Conclusions and Limitations References Appendix A Literature Search QTL Summaries Appendix B Measured and Calculated Parameters Preliminary Morphologic Skeletal Measurements Mechanical Testing of Tibial and Femoral Diaphysis and Femoral Neck Operation of Materials Testing System (MTS) Loading a Specimen (Procedures II and III three point bending test of femur and tibia) Loading a Specimen (Procedure IV Femoral Head Shear Test) Compositional Analysis of Distal Portion of Tibiae Obtain Wet Mass Obtain Dry Mass Obtain Ash Mass Tissue Processing and Histomorphometry Dehydration of the proximal tibia Infiltration and Embedding (proximal tibia and distal femur) Infiltration Procedures and Schedule Embedding Procedure Sectioning specimen after MMA has hardened Diaphyseal Architecture measurements (Femur and Tibiae) Appendix C Results viii

9 List of Figures ix Figure 3-1: Right hindlimb (top), gastrocnemius, soleus, EDL, and TA muscle (bottom left), tibia (bottom center), and femur (bottom right) Figure 3-2: Close-up of three point bending test of the femoral diaphysis Figure 3-3: Load displacement curve from mechanical testing. Mechanical parameters obtained from MatLab program...53 Figure 3-4: Shear test of the femoral neck...55 Figure 3-5: Close-up of three point bending flexural test of the tibial diaphysis Figure 3-6: Cross-section of femur and tibia mid-diaphysis Figure 4-1: F1 and B1 possible genotypes considering recombination...70 Figure 4-2: Interval mapping for QTL Q and flanking markers M and N...75 Figure 4-3: Proportion of gametes in a F1 population accounting for recombination Figure 6-1: Interval Mapping LOD score plots for the combined analyses of body size phenotypes Figure 6-2: Interval mapping results for chromosome Figure 6-3: Interval mapping results for chromosome Figure 7-1: Cross-sectional images of the tibial diaphysis for old and young DBA and B6 mice Figure 8-1: Interval Mapping results for chromosome Figure 8-2: Interval Mapping results for chromosome Figure 8-3: Interval Mapping results for chromosome Figure 8-4: Interval Mapping results for chromosome Figure 8-5: Interval Mapping results for chromosome Figure 8-6: Path diagram of femur model with factor loadings, residuals, and path coefficients for males (male values are bolded) and females

10 Figure 8-7: Path diagram of tibia model with factor loadings, residuals, and path coefficients for males (male values are bolded) and females Figure 8-8: Interval Mapping results for activity, muscle and bone factors on chromosome x

11 List of Tables xi Table 2-1: Mouse homologous chromosome regions for human regions containing skeletal QTLs...38 Table 5-1: Two-way ANOVA results for sex and strain differences between progenitor strains and one-way ANOVA results for sex differences in the F 2 cohort Table 5-2: Two-way ANOVA results for sex and strain differences between progenitor strains and one-way ANOVA results for sex differences in the F 2 cohort (continued)...94 Table 5-3: Two-way ANOVA for strain (B6 versus D2) and age (200 day versus 650 day) for skeletal and muscle phenotypes separated by sex...96 Table 5-4: Two-way ANOVA for strain (B6 versus D2) and age (200 day versus 650 day) for skeletal and muscle phenotypes separated by sex (continued) Table 5-5: Heritability estimates for select traits based on a one-way ANOVA on strain in the recombinant inbred cohort. Estimates were made for males and females separately...98 Table 5-6: Column descriptions for QTL result tables Table 5-7: Interval mapping results for female F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed redisduals Table 5-8: Interval mapping results for male F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight Table 5-9: Interval mapping results for combined (male and female sex corrected) F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight Table 5-10: Interval mapping results for combined (male and female sex corrected) F 2 phenotypes with LOD scores > 4.3 (continued) Table 5-11: Interval mapping results for female RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight

12 Table 5-12: Interval mapping results for male RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight Table 5-13: Interval mapping results for combined (male and female sex corrected) RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight Table 5-14: Female interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-15: Female interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-16: Female interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-17: Male interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-18: Male interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-19: Male interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-20: Male interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-21: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level xii

13 (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-22: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-23: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-24: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-25: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-27: Female composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-28: Female composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-29: Male composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-30: Male composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-31: Male composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant xiii

14 level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-32: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-33: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-34: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-35: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-36: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-37: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 5-38: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray Table 6-1: Pearson Correlation Coefficients for raw phenotypes (transformed using log (L) or square root (S) when appropriate) Table 6-2: Correlation Coefficients for normalized phenotypes using BMI ratio (D) (transformed using log (L) or square root (S) when appropriate) Table 6-3: Descriptive Statistics for select variables xiv

15 Table 6-4: Correlation Coefficients for normalized phenotypes using multiple regression on body weight and length (R) (transformed using log (L) or square root (S) when appropriate) Table 7-1: Skeletal characteristics of B6 and D2 progenitor strain mice and F day old mice. Means and standard deviations are presented, p-values reflect differences between the two strains and between genders as determined by two-way ANOVA Table 7-2: Phenotypic measures of B6 and D2 progenitor strain male mice. Means and standard deviations are presented; p-values determined by 2-way ANOVA, reflect differences within strain by age and between the two strains at 150 and 650 days Table 7-3: Heritability Estimates from one-way ANOVA on strain within the RI cohort Table 7-4: Pearson correlation coefficients for female body size, activity, muscle, and bone measures Table 7-5: Pearson correlation coefficients for male body size, activity, muscle, and bone measures Table 7-6: Column descriptions for QTL results tables Table 7-7: Interval mapping results for female F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight Table 7-8: Interval mapping results for female RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight Table 7-9: Interval mapping results for male F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight Table 7-10: Interval mapping results for male RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight Table 7-11: Interval mapping results for combined (male and female sex corrected) F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued) xv

16 Table 7-12: Interval mapping results for combined (male and female sex corrected) F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued) Table 7-13: Interval mapping results for combined (male and female sex corrected) RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued) Table 7-14: Female interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight Table 7-15: Male interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight Table 7-16: Male interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued) Table 7-17: Combined interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight Table 7-18: Combined interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued) Table 7-19: Female composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8) xvi

17 Table 7-20: Male composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8) Table 7-21: Combined composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8) Table 7-22: Combined composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8) Table 7-23: Combined composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8) Table 8-1: Marker name and centimorgan position for the markers that were included in the structural equation models Table 8-2: Path coefficients for the full femur models Table 8-3: Path coefficients for the full tibia models Table 8-4: Structural Equation Modeling results for the femur sub-models. Values presented in the table are the p-values that correspond to the difference in fit statistics obtained from the sub-model compared to the full model Table 8-5: Structural Equation Modeling results for the tibia sub-models. Values presented in the table are the p-values that correspond to the difference in fit statistics obtained from the sub-model compared to the full model Table 8-6: Pearson correlation coefficients for female body size, activity, muscle, and bone measures Table 8-7: Pearson correlation coefficients for male body size, activity, muscle, and bone measures Table 9-1: QTL results summary for all analyses Table 9-2: QTL results summary for all analyses (continued) Table 9-3: QTL results summary for all analyses (continued) xvii

18 Table 9-4: QTL results summary for all analyses (continued) Table A-1: QTL literature summary Table A-2: QTL literature summary (continued) Table A-3: QTL literature summary (continued) Table A-4: QTL literature summary (continued) Table A-5: QTL literature summary (continued) Table A-6: QTL literature summary (continued) Table A-7: QTL literature summary (continued) Table A-8: QTL literature summary (continued) Table A-9: QTL literature summary (continued) Table A-10: QTL literature summary (continued) Table A-11: QTL literature summary (continued) Table A-12: QTL literature summary (continued) Table A-13: QTL literature summary (continued) Table B-1: Femoral morphologic measures Table B-2: Tibial morphologic measurements Table B-3: Femur and tibia measures from mechanical testing of the diaphyseal shaft Table B-4: Shear test of the femoral neck Table B-5: Compositional Analysis of Distal tibiae and total ash mass of the femur Table B-6: Diaphyseal cross-sectional analysis of the tibia and femur Table C-1: One-way ANOVA results for sex differences in the 200 day F 2 cohort and between the two parental strains at 180 days of age Table C-2: One-way ANOVA results for sex differences in the 200 day F 2 cohort and between the two parental strains at 180 days of age (continued) xviii

19 Table C-3: One-way ANOVA results for sex differences in the 200 F 2 cohort and between the two parental strains at 180 days of age (continued) Table C-4: One-way ANOVA results for sex differences in the 200 F 2 cohort and between the two parental strains at 180 days of age (continued) Table C-5: Two-way ANOVA for strain (B6 versus D2) and age (180 day versus 650 day) separated by sex Table C-6: Two-way ANOVA for strain (B6 versus D2) and age (180 day versus 650 day) separated by sex (continued) Table C-7: Two-way ANOVA for strain (B6 versus D2) and age (180 day versus 650 day) separated by sex (continued) Table C-8: Two-way ANOVA for strain (B6 versus D2) and age (180 day versus 650 day) separated by sex (continued) Table C-9: Two-way ANOVA for strain (B6 versus D2) and age (180 day versus 650 day) separated by sex (continued) Table C-10: Two-way ANOVA for strain (B6 versus D2) and age (180 day versus 650 day) phenotypes separated by sex (continued) xix

20 Acknowledgements xx The research presented in this thesis was possible through the combined contribution of many individuals. I am grateful for the guidance and support that I have received at all levels. Without the team effort that I received from each individual, a project of this size and complexity would not have been possible. I also wish to recognize the financial support provided by grants from the National Institute on Aging of the National Institutes of Health (P01 AG14731 and R01 Ag21559). I would like to start by thanking my advisor and mentor, Dr. Neil A. Sharkey, who accepted me into the Biomechanics program in the Department of Kinesiology and the Center for Locomotion Studies. At the time of my acceptance I was a stay-at-home mother of four. Neil himself a father of four, understood all too well the demands of a large family. Despite this insight, he agreed to accept me as his student. I appreciated Neil s open door policy and on many occasions sought his guidance and advice. He provided me with many challenging and educational opportunities, which allowed me to grow as an independent researcher. I want to thank Neil for his constant encouragement, support, and most importantly, his understanding of the continuous struggle to balance both work and family. I would like to extend my gratitude to my committee members; Dr. Gerald E. McClearn, Dr. George P. Vogler, and Dr. Robert B. Eckhardt; for their continuous guidance and mentoring. As a mechanical engineer, I entered my graduate studies without any prior educational training in genetics or biology. I was very fortunate to receive a pre-doctoral training fellowship in Quantitative Genetics of Complex Traits at

21 The Center for Developmental and Health Genetics under the guidance of both Dr. xxi George Vogler and Dr. Gerald McClearn. I would like to thank George for his patience through many hours of mentoring in statistics including structural equation modeling. I want to thank Jerry for his guidance, support, and encouragement. Above all, I thoroughly enjoyed the discussions I had with Jerry about his thoughts on quantitative genetics, complex systems, and control theory. Jerry s enthusiasm has instilled in me an interest in chaos theory; an area that I wish to further explore. I will always be grateful for the opportunity I have had to work with Jerry and be included in the genetics center family. In addition, I wish to express thanks to the staff and students at The Center for Developmental and Health Genetics for their support; particularly Holly Mack, for her friendship and many hours of SAS and QTL Cartographer technical support. I would also like to thank the many staff and students at The Center for Locomotion Studies, especially Doug Tubbs for his continuous technical support and good humor for the numerous equipment design attempts that failed and were archived to the shop museum. I am also grateful to Nick Giacobe for taking the time to get me connected remotely from home. This has been a tremendous help in juggling home and work. I also want to thank Ahmet Erdemir, Rob Adamson, and Dr. Joseph Stitt for their help with Matlab code, and Andy Hoskins for his machining expertise. The amount of labor for this project was enormous. Over the last four years, we have mechanically tested over 1300 mouse and 100 rat femur and tibia diaphyseal shafts, and femur necks. This equals a total of 4200 mechanical tests, 2800 bones that were repeatedly weighed during the compositional analyses, 1200 bones that were embedded in methyl methacrylate and sectioned using a diamond wire saw, along with morphologic

22 xxii measures that were made on all 2800 bones. Obviously I could not have completed all of this work by myself. I was very fortunate to have many undergraduate students that were very dedicated and hard working. I am enormously grateful to all of the following undergraduates and wish them all the best in the future: Marc Benda, Bethany Baumbach, Karol Kijek, Karen Uston, May Yoneyama, Doug Corwin, Steve Minter, Kelly Newman, Lindsay Keller, Mary Ann Maximos, Abbey Bower, Ameila Sesma, Nicole Hollis, and Joanna Thomson. Finally, I would like to acknowledge my family to whom I am most grateful. My graduate experience has been a long journey for not only myself, but my family as well. I want to thank my husband, Scott, and my four children; Meagan, Morgan, Mitchell, and Matthew, for their love, support, and understanding. On many occasions the time I spent on my graduate work was time taken away from my family. I would especially like to thank Scott for his support of our family while I returned to school. I am very grateful for the opportunity I was given to pursue my graduate studies, and I realize that without Scott s support and commitment to myself and our family it would not have been possible. My time as a graduate student has also been a growing experience for my children who have become very independent and resourceful out of necessity. I hope that my children can look beyond their stressed out mom and see me as a role model the way I perceive my parents, Guy and Marie Hammer, who taught me how hard work and commitment can overcome any obstacles.

23 Chapter 1 Introduction This research is part of an NIA sponsored program project designed to locate Quantitative trait loci (QTLs) for biomarkers of aging across the lifespan of recombinant inbred (RI) and second filial (F 2 ) generation mice. The age groups included in the overall program project correspond to young adult, middle, and old age in humans. Preliminary age-related changes in skeletal, muscle, and activity phenotypes are reported. However, the QTL results reported in this thesis are limited to the young adult age group. Bone mass and composition are complex phenotypes that are known to be subjected to genetic influence. Results from studies on twins have shown that approximately 75% of the variance in bone density is attributable to genetic factors (Dequeker et al. 1987). Muscle mass has been shown to be positively correlated with increased bone mass (Kaye and Kusy, 1995), and physical activity has been shown to influence bone quality (Gordon et al. 1989). Kaye and Kusy (1995) investigated these relationships within several mouse strains and suggested that activity as well as muscle size and strength were influenced by genetics and that the genetic influence on bone size and strength could be acting through muscle and activity related pathways. To date the role that genetics plays in relationships between activity, muscle mass, and bone strength have been largely overlooked. Quantitative trait loci analysis is a statistical analysis applied to traits that have a continuous distribution. The continuous distribution could be the result of multiple genes

24 2 (polygenes) influencing the trait as well as environmental differences, gene-byenvironment interactions, and gene-by-gene interactions. Housing experimental mice in a controlled environment reduces environmental variance and increases the power to detect genetic influences. QTL analysis of inbred strain crosses is based on a genomewide search for regions of chromosomes that contain a locus or gene that influences the phenotype. This locus could be in a coding or non-coding region such as a gene promoter. Multiple points along the genome are analyzed individually to determine the likelihood of a QTL at each position influencing the trait. These analyses are based on finding an association between the genotype of a genetic marker and the trait. These markers are evenly spaced throughout the genome and allow QTLs to be mapped to a relative position along a chromosome. A major strength of this study is that it is using two populations: a second filial generation (F 2 ) cohort and a recombinant inbred (RI) cohort, both derived from the same two inbred progenitor strains. The intent is to identify QTLs in one population and confirm in the second. Hypothesis 1 is that there are QTLs within the mouse genome that influence skeletal phenotypes and these QTLs exert their influence in a sexdependent manner. Another strength that distinguishes this study from those that are currently in the literature is the multi-disciplinary design, which incorporates phenotypes such as behavioral activity measures, muscle mass, skeletal size and strength, and blood chemistry levels. The study therefore affords the opportunity to look at multiple phenotypes that might co-vary, possibly influencing complex skeletal phenotypes. The third major strength of the overall program project is that the same two model

25 populations (C57BL/6J and DBA/2 F 2 and RI mice) are studied at three different ages. 3 Changes in QTL identification across the lifespan will eventually be investigated and possibly lead to the detection of whether the influence is up-regulated, down-regulated, or whether different genes predominate at different ages. Hypothesis 2 is that QTLs influencing skeletal phenotypes are acting through both direct skeletal related pathways and indirect pathways that are mediated through muscle or activity. The second aim of this study is to investigate some of the underlying pathways of the genetic influence on bone quality. In particular, how the genetic influence on activity type behavioral patterns affects bone quality by dictating frequency and duration of loading, irrespective of muscle size and strength; and by altering muscle mass (strength) which in turn alters skeletal loading. Biological processes are known to be part of highly integrated systems of which the sum of the parts does not equal the whole. Many experimental studies investigating genetic pathways identify significant influences in isolated experiments but when investigated at the systems level, are unable to obtain significance. Biological organisms have many redundant pathways, which can affect experimental results. QTL analysis allows one to examine a phenotype within the context of the system. The limitation of this is that when a QTL is found one cannot state with certainty where in the complex pathway of the system it is exerting its influence. Investigating multiple phenotypes gives more insight into these complex pathways. Throughout this thesis distinctions are made with respect to direct and indirect genetic influence on skeletal phenotypes. In the context of this thesis indirect pathways refer to genetic influence mediated through body size, muscle mass and/or activity.

26 Direct skeletal pathways are all other pathways related to skeletal processes that do not 4 include body size, muscle mass or activity. These labels are misleading in that what could be classified as part of a direct skeletal pathway could in itself be an indirect effect. For example, if estrogen levels were influencing skeletal measures due to the genetic influence on the endocrine system this would be classified as a direct skeletal pathway (in this thesis) although it is in fact an indirect effect through the endocrine system. Along this line of reasoning, all genetic influence could be classified as indirect on some level and the distinction between direct and indirect would not be possible. These distinctions were made for discussion purposes only within the context of this thesis. Background Skeletal quality cannot be defined by a single measure but is a composite of many traits each contributing to a bone s ability to resist fracture and at the same time optimize size and shape to minimize the metabolic energy required to move the skeleton. Skeletal phenotypes are complex quantitative traits that are known to be under polygenic (multiple gene) control. As with many biological systems, skeletal integrity is the result of a dynamic system with many degrees of freedom. These redundant pathways can contribute to conflicting results when attempts are made to isolate single genes that influence skeletal phenotypes without considering these phenotypes from a polygenic reference frame and complex systems approach. The genetic variance of continuously distributed traits such as measures of skeletal strength is typically the result of multiple genes each having a small contribution

27 5 to the overall variance. The small effect size combined with interaction effects with other genes and with the environment, limits the power to detect the influence of individual genes. Recent advances in molecular genetics have made it possible to conduct genomewide scans for the approximate location of genes influencing quantitative traits as well as interaction effects between loci. With the aid of genetic markers, animal models have proven very useful in the field of quantitative genetics. Inbred mouse strains and their crosses allow for control of the genetic background and can significantly improve the odds of detecting genes that are influencing quantitative traits. Quantitative Trait Loci (QTL) analysis employs available markers in a genome-wide scan to search for chromosomal loci that contribute to the variance of a complex continuously distributed quantitative trait. A major focus of skeletal genetics has been bone mass acquisition as well as bone loss. Bone loss is known to occur with age. The extent to which this occurs often prompts clinicians as well as researchers to classify excessive bone loss as a pathologic condition known as osteoporosis. The actual classification in itself is often difficult due to the fact that measures of bone integrity whether they be bone mineral content, bone mineral density, or bone strength are all continuously distributed quantitative phenotypes. With quantitative phenotypes it is difficult to classify affected and unaffected with absolute certainty. While these diagnostic classifications are somewhat arbitrary and difficult to establish, it is often very practical to use some type of measurement to identify those individuals that are most at risk in an attempt to isolate those in need of intervention. Currently dual energy x-ray absorptiometry (DEXA) scans are used to identify individuals at increased risk of fracture. Base line DEXA scans are used as a

28 6 screening tool for very low-density levels and as a way to evaluate the rate of bone loss in subsequent years. For every 0.1 g/cm decline in bone mass there is an increase in fracture risk of 50 to 120 % for those individuals living in a retirement home compared with individuals living on their own (Hui et al. 1989). In the United States alone, 25 million people are affected by osteoporosis. It is estimated that osteoporosis is associated with at least 1.3 million fractures that occur annually. Osteoporosis affects both men and women but in unequal proportions; 12% of men and 40% of women (Ralston 1997). With the population as a whole living longer we are becoming increasingly aware of the devastating consequences of bone loss, particularly in the extreme cases that are classified as osteoporosis. Osteoporotic individuals are much more likely to fracture a hip or vertebrae during a fall. Twenty-five percent of women over the age of 65 will fracture their vertebrae and 32% of the women who reach the age of 90 will fracture their hip (Consensus 1993). Fractures in the elderly have been shown to mark the beginning of rapidly declining health. The success of rehabilitation after a hip fracture is very discouraging. Of the women aged 90+ who fracture their hip, 50% will be incapacitated and 5%-20% will die within one year of the fracture. Not only does bone loss drastically affect the health and well being of our aging population but it also puts a significant financial strain on our health care system. Increasing our understanding of bone strength and factors contributing to bone loss can potentially improve quality of life for millions. Low bone density in the elderly can be the result of two separate but related processes. Bone mass acquisition and maintenance contribute to our skeletal health as we age. Peak bone mass early in life has been shown to be a predictor of fracture risk later in

29 life. Consequently much interest has been generated in understanding what factors 7 contribute to peak bone mass. Individuals with higher peak bone mass have more bone to compensate for bone loss as they age compared with individuals with a lower peak bone mass and equal rates of loss. Peak bone mass is the result of skeletal growth and development, which entails bone modeling with net bone formation. Bone modeling is the process in which new bone is added to increase length and diameter, while older bone at other sites is removed to maintain proper shape. Osteoblasts function to add bone while osteoclasts work to remove bone. During modeling, the osteoclasts and osteoblasts work independently. The amount of bone that we retain as we age is not only influenced by the peak mass that we start with prior to the onset of bone loss but is also significantly affected by increases in the rate of bone loss. During development and throughout life bone is continually turned over to maintain mechanical integrity. This process is termed bone remodeling and is the coupled, sequential action of osteoclasts (bone resorbing cells) and osteoblasts (bone forming cells) at the same site. A group of osteoblasts and osteoclasts working together is termed a basic multicellular unit or BMU. Cells work as a team removing old bone and replacing it with new. During this process if more bone is removed than is replaced net bone loss will occur. Unequal coupling has been implicated in postmenopausal bone loss. In osteoporotic bone, this loss is manifested as a more porous bone matrix and is detected earlier in trabecular bone versus cortical bone. This is thought to occur because of a higher remodeling turnover rate in trabecular bone. Osteoblast and osteoclast activity results from four basic cellular processes. They are mitosis or cell proliferation, differentiation of osteoblasts from mesenchymal cells

30 8 and osteoclasts from mononuclear macrophages, migration of bone cells to the modeling or remodeling site, and the chemical processes involved with formation or resorption. Some of the processes are controlled remotely by hormones such as parathyroid (PTH), growth hormone, or estrogen, while others are controlled locally by proteins called cytokines or growth factors (Martin et al. 1998). These local processes are thought to couple resorption with formation while hormones are thought to initiate remodeling (Hansen et al. 1992). While peak bone mass has been shown to predict fracture risk, insight into the mechanisms that influence both bone acquisition and the rate of bone loss could provide a means for preventing osteoporosis and fracture. Many factors are thought to contribute to bone mass acquisition and maintenance, including: heredity, gender, dietary components (calcium and protein), endocrine factors (sex steroids, calcitonin, insulin-like growth factor I), and mechanical forces (Klein et al. 1998). The combined direct effect as well as interaction effects among these factors contributes to the complex nature of continuously distributed quantitative skeletal phenotypes and provides a challenging landscape for skeletal genetics research.

31 9 References Consensus (1993) Consensus development conference: diagnosis, prophylaxis and treatment of osteoporosis. American Journal of Medicine 94, Dequeker, J.; Nijs, J.; Verstraeten, A.; Geusens, P., and Gevers, G. (1987) Genetic determinants of bone mineral content at the spine and radius: a twin study. Bone. 8, Gordan, K.R., Perl, M., Levy, C. (1989) Structural alterations and breaking strength of mouse femora exposed to three activity regimens. Bone, 10(4), Hansen, M.A., Hassager, C., Jensen, S.B., and Christiansen, C. (1992) Is heritability a risk factor for postmenopausal osteoporosis? Journal of Bone and Mineral Research 7, Hui, S.L., Slemenda, C.W., and Johnston, C.C. Jr (1989) Baseline measurement of bone mass predicts fracture in white women. Ann Intern Med 111, Kaye, M. and Kusy, R. P. (1995) Genetic lineage, bone mass, and physical activity in mice. Bone. 17(2), Klein, R.F., Mitchell, S.R., Phillips, T.J., Belknap, J.K., and Orwoll, E.S. (1998) Quantitative trait loci affecting peak bone mineral density in mice. Journal of Bone and Mineral Research 13, Martin, R.B., Sharkey, N.A., and Burr, D.B. Skeletal Tissue Mechanics New York, NY, Springer-Verlag. Ralston, S.H. (1997) Genetic markers of bone metabolism and bone disease. Scand J Clin Lab Invest Suppl 227,

32 Chapter 2 Literature Review Introduction The impact of reduced bone mass in our aging population has heightened the interest in research on the genetic control of bone mass or bone mineral density (BMD). Past research on twins has shown that genetics may influences as much as 75% of the variance in bone density (Dequeker et al. 1987). Until recently, genetic research has focused on candidate genes with identifiable polymorphisms (differences in DNA sequence among individuals). Such an approach requires a prior hypothesis for candidate genes and is complicated by the fact that bone mass is a polygenic (more than one gene) continuous trait that is also affected by environmental factors (Kanders et al. 1988); (Matkovic et al. 1979); (Dequeker et al. 1987; Nilas and Christiansen 1987) Because of this complexity, the genetic analysis of BMD often produces conflicting results. For example, studies of the vitamin D-receptor (VDR) locus have yielded inconsistent findings, undoubtedly due to these complicating factors (Deng et al. 1999). To reduce environmental variance laboratory mice have become an integral part of today s research. Originally, recombinant inbred (RI) strains of mice were developed to detect major genes that are linked to bimodal traits (Bailey 1981). Recently RI strains have been used to detect quantitative trait loci (QTLs) that are associated with traits that show continuous RI strain distribution patterns (SDP). This QTL approach allows for the detection of many genes, each influencing a small amount of the genetic variance for a

33 given quantitative trait. One of the major advantages of the QTL approach is that a 11 genetic marker for a given trait can be identified without any prior hypothesis about how the phenotype is influenced (Klein et al. 1998). Inbred Mouse Strains and Their Progeny The use of recombinant inbred strains of mice as well as F 2 generation mice in quantitative trait loci (QTL) analysis makes it possible to find relationships between chromosomal loci (positions of genetic markers) and variations in the quantitative trait under investigation. A recombinant inbred (RI) strain is produced by first mating two highly inbred progenitor strains. For example, two common progenitor strains are the C57BL/6 and the DBA/2 strains. The inbred progenitor strains are homozygous (in like allelic state) for nearly all genes. An allele is an alternative form of a genetic locus. For example at a locus for coat color, the allele might produce white or black fur. This means for the progenitor strains, at the locus for coat color, the alleles might either be both white or both black. The two progenitor strains will be in different allelic state for some genes and in like allelic state for others. The resulting offspring from the mating of the highly inbred progenitor strains are the F 1 or first filial generation. At each locus, a single allele is inherited from each parent or each progenitor strain. The F 1 generation is a hybrid and will be heterozygous (having different alleles at a locus) for all loci where the progenitor strains had different allelic states (from each other). This means that the genome is known for the F 1 generation to the extent that the progenitor strain genomes are known and they are heterozygous at every locus where the two progenitor strains differ. The F 1

34 generation is then crossed and the resulting offspring are the F 2 or second filial 12 generation. One quarter of the mice in the F 2 generation will be homozygous in one state, one quarter homozygous in the other state, and one half heterozygous. Animals from the F 2 generation are then inbred under a strict regimen of mating and the new inbred strains resulting after 20 generations of brother and sister inbreeding are the recombinant inbred (RI) strains. The alleles for the RI strains will be the same as one of the progenitor strains and will have become fixed at each locus due to the re-inbreeding. Within the RI series several RI strains are produced and these strains can vary from each other as to which homozygous progenitor alleles are present along the genome. Using the example mentioned above the resulting inbred strains from the two progenitor strains (C57BL/6 and DBA/2) are classified as the BxD series. The resulting BxD series is a mixture of the two progenitor strain genomes or a recombination of the progenitor genomes (Bailey 1981; Plomin et al. 1991b). If the progenitors were different at a specific locus then half of the new RI strains will be homozygous for one allele and half will be homozygous for the other allele. If one of the polymorphic (different DNA sequence) loci has a major effect on a phenotype, or is close to a locus that has a major effect, two groups of RI strains will be obtained. This bimodal distribution is classified as a non-overlapping strain distribution pattern (SDP) and indicates a single-gene effect. A continuous trait distribution in the RI strains indicates multigenic inheritance. The location of loci contributing to the variance of a quantitative trait can be obtained by comparing the SDP of the quantitative trait of interest to that of known mapped markers. If the SDP of the trait and the SDP of a marker are the same or similar then a gene for the trait and the marker are linked. How

35 13 close the patterns match is an indication of how close the gene and marker are linked and the effect size. If the patterns have a perfect match for the BxD series RI strains, the gene for the trait is within about one cm (centimorgan) of the marker (Plomin and McClearn 1993). A centimorgan is a unit of measure of recombination frequency. The closer two genes are to each other the less likely they will be separated during cross-over from one generation to the next. One centimorgan is equal to a 1% chance that a marker at one locus will be separated from a marker at a second locus. In humans, this is equivalent to approximately one million base pairs (Gelehrter et al. 1998). The BxD series has been used extensively in behavioral genetics research and more recently in skeletal genetics research. As of 1998, there were over 1500 markers that had been identified in the BxD series. The BxD series consists of 36 RI strains obtained by crossing the C57BL/6 and the DBA/2 progenitor strains. The high number of inbred strains in the BxD series is essential for QTL analysis (Plomin and McClearn 1993). Heritability RI strains can be used to estimate heritability by comparing variances between RI strains to the variance within the RI strains. For RI strains, the variance within each strain is a measure of non-genetic variance. Because the individuals are genetically identical within any given RI strain, this variance provides an estimate of the environmental variance and experimental error. The genetic variance is estimated as the total variance (genetic plus environmental) minus the non-genetic variance. The

36 14 heritability is estimated as the proportion of total variance due to genetic variance. F 2 and F 1 generations can also be used to estimate heritability. The F 2 variance for a particular trait is a measure of the genetic plus environmental variance or total variance and because F 1 individuals are identical the variance between F 1 individuals is a measure of environmental variance. The QTL approach can detect candidate regions even when the heritability is low as long as the RI strain means are reliable (Plomin and McClearn 1993). Quantitative Trait Loci (QTL) Analysis Both RI and F 2 mice can be used in quantitative trait loci (QTL) analysis. RI inbred strains used for quantitative genetic analysis allows for the ability to make correlations for different measurements across different studies. Genetic correlations are estimated by correlating RI strain means for different measures within a RI series. These data can compare across studies and can correlate different biological processes (Plomin and McClearn 1993). Linkages between marker locations and quantitative traits have been made using a simplified approach by correlating the RI strain means and a marker. The marker is scored as 0 or 1 indicating an allele from one or the other progenitor strains. Correlations indicate associations between the marker and the quantitative trait. The correlation is also an indication of the strength of the associations. A weak association could mean that there is close linkage to a QTL with a small effect or a distant linkage to a QTL with a large effect (Plomin and McClearn 1993).

37 F 2 populations can be scored similarly but must include a score for the 15 heterozygote allelic state. For an additive genetic model a genotype that is homozygous for one parental strain is scored as a 0, the heterozygote is scored as a 1 and the other parental homozygote is scored as a 2. As with the RI population, analyses can be conducted to determine the relationship of genotype to phenotype for genetic markers that are selected to cover the mouse genome. Because the F 2 animals are genotypically distinct from each other, each individual must be genotyped at each marker. Genetic Mapping Early genetic maps were based on visible phenotypes such as diseases and anatomical anomalies. As new techniques for studying genes became available, minor genetic differences or polymorphisms could be detected (Bailey 1981). These minor variations in DNA sequence provide a supply of markers used to follow inheritance. Restriction maps locate the positions on the DNA molecule that are cut by particular restriction enzymes. Restriction enzymes are proteins that recognize specific short nucleotide sequences of DNA and cut the DNA at precise locations. These locations are used as biochemical markers of specific areas along the chromosomes. Variations between individuals in DNA fragment sizes cut by specific restriction enzymes are called restriction fragment length polymorphisms (RFLPs). Polymorphic sequences that result in RFLPs are used as markers on both physical maps and genetic linkage maps. RFLPs are caused by mutations at a cutting site. RFLP linkage maps use large numbers of DNA probes, which make this process very time consuming.

38 16 Simple sequence repeat polymorphisms (SSRPs) or micro-satellites are another type of marker that is easier to type and can be automated. They are typed using polymerase chain reaction (PCR). SSRPs have enabled geneticists to develop dense genetic maps of RI murine strains. RI QTL analysis does not require genotyping. It is based on the strain distribution patterns of known markers. An additional method to detect QTLs is to use F 2 or secondgeneration crosses. Due to recombination, F 2 crosses have different combinations of chromosomes in different individuals and genotyping is required. Because genotyping is expensive and time consuming one method that has been employed is to test a large sample for the phenotype and then genotype two groups in the extremes. Linkage maps indicate the relative positions of genetic loci on a chromosome. The positions are determined based on how often the loci are inherited together. Interval mapping uses phenotypic and genotypic marker information to estimate the probable genotype and the most likely QTL effect at every point in the genome and is based on maximum-likelihood linkage analysis. Linkage analysis is based on developing a model that specifies the gene locus and a pattern of inheritance of a quantitative trait and comparing that to the null hypothesis. Linkage is determined from the likelihood ratio LR, which is the probability of obtaining the measured trait using the developed model divided by the probability of obtaining the measured trait using the null hypothesis. Linkage is usually described with LOD scores where the LOD score is log10 (LR) (Lander and Schork 1994).

39 Genetic Studies of Skeletal phenotypes and Related Cofactors 17 Inbred mouse strains can be used to investigate genetic influence without mapping the trait to specific chromosomal loci. If genetically distinct inbred strains raised in identical environments show differences in a phenotypic traits, these differences are an indication of genetic influence. Kaye and Kusy (1995) investigated relationships among physical activity, muscle mass, and bone mass within five strains of inbred mice including: A/J, BALB/CbyJ, C57BL/6J, DBA/2J, and PL/J. These strains were selected because they display similar weight gain characteristics. Strains C57BL/6J and A/J were studied in greater detail. Males from these strains were studied at 10 and 18 weeks of age and females at 8 and 13 weeks. Organ and bone weights were measured immediately after dissection, as was the muscle mass of the quadriceps group. Testosterone and estradiol levels were measured and bones were ashed at 650 degrees C for 16 hours to determine ash weight. Femur strength was determined using a three-point bending test. Whole bone properties were determined from force deflection curves. Stiffness, strength, ductility, and toughness were assessed and all correlated with bone ash weight, except for ductility. An ultrasonic sensor and automatic counter were used to measure animal activity in the home cage. To provide conditions that are standard, two animals were placed in each cage and activity was monitored for 24 hours including one light and one dark cycle. Body weight showed little variance between strains; however, bone weight and composition did vary between strains. Tibia and femur weight were higher in the C57BL/6J mice and tibia ash weight was higher in all C57BL/6J age groups except for

40 13-week females where body weight varied by 3 grams. A correlation was obtained 18 between femur weight and muscle weight (r = 0.85), and between muscle weight and activity (r = 0.60). Testicular weight and testosterone levels were higher in the C57BL/6J males but not significant. Ovarian weight and estradiaol levels were higher in the A/J females at 13 weeks but not at 8 weeks. Bone toughness was found to be higher in the C57BL/6J males and females and stiffness was also found to be higher in the C57BL/6J males and females. However, stiffness was only significant in the males. Overall, C57BL/6J males and females showed more activity than A/J animals. However, at age 10-weeks A/J and C57BL/6J male means were similar. Activity levels were not obtained for 13-week-old females. Therefore using the three groups available including the 10-week-old males; no correlation could be found between femur weight and activity. However if the 10 week old males were excluded a correlation of r=0.59 was obtained between femur weight and animal activity. The increased bone in the C57BL/6J animals could be caused by physical activity as demonstrated by this relationship. This study suggests that there is a relationship between muscle mass/strength and bone size and strength and that these traits are under genetic influence (Kaye and Kusy 1995). Arden and Spector (1997) investigated the relationship between bone mineral density, lean body mass, and muscle strength using a population of postmenopausal monozygotic (MZ) and dizygotic (DZ) human twin female pairs. Lean body mass and bone mineral density (BMD) were measured at multiple sites using DEXA. The correlation of BMD at multiple sites with leg extensor strength ranged from , with grip strength it ranged from , and with lean body mass it ranged from 0.2-

41 The heritabilities of lean body mass, leg strength, and grip strength were 0.52, 0.46, and 0.30 respectively. The muscle variables explained 20% of the genetic variance of BMD. These results provide support for the indirect genetic influence on bone mineral density mediated through muscle strength. Li et al. (2001)) assessed forearm muscle size and grip strength, bone mineral density, forearm bone size and humerus breaking strength in ten different inbred mouse strains (129/J Sencar/PtJ, C57BL/6J, CBA/J, FVB/NJ, NZB/BINJ, RIIIS/J, LP/J LG/J, and SWR/J). Heritability estimates from the ten inbred strains were: 0.60 (BMD), 0.68 (breaking strength), 0.83 (bone size), 0.63 (grip strength), 0.76 (muscle size), and 0.52 (body weight). The correlations among all muscle and bone related phenotypes were significant. The correlations of bone density with grip strength, muscle size and body weight were 0.54, 0.50, and 0.50 respectively. The correlations of breaking strength with grip strength, muscle size, bone density, and body weight were 0.63, 0.58, 0.69, and 0.52 respectively. Beamer et al. (1996) investigated the genetic variability in adult bone density in females from 11 different inbred strains including several of the strains discussed previously (AKR/J, BALB/cByJ, C3H/HeJ, C57BL/6J, C57/J, DBA/2J, NZB/B1NJ, SM/J, SJL/BmJ, SWR/BmJ, and 129/J). Peak bone mineral density of the femur was measured using peripheral quantitative computed tomography (pqct) at 12 months for all 11 strains. To investigate developmental differences, a subset of four strains including C3H/HeJ, DBA/2J, BALB/cByJ, and C57BL/6J were investigated at 2, 4, and 8 months of age. The density measurements were volumetric measures obtained by dividing total

42 20 mineral by total volume. Two of the strains investigated by Beamer et al. (DBA/2J and C57BL/6J) are the same strains under investigation in this thesis. There have been conflicting results in the literature concerning the rank order of bone mineral density between C57BL/6J and DBA/2J and several commonly used strains. The previous study by Kaye and Kusy (1995) reported C57BL/6J as having higher total tibia ash mass compared with BALB/c and DBA/2J. The results from Beamer et al. (1996) places C57BL/6J lowest in bone density out of all 11 strains, with BALB/c slightly less than DBA/2J but towards the middle of the distribution and C3H/HeJ at the top with the highest femoral mineral content and density. C57BL/6J had the highest volume and thinner cortical thickness. Beamer s developmental results indicated that femur length continued to increase until 12 months in three of the strains but the DBA/2J femur length plateaued at about 4 months. C57BL/6J and BALB/c had the longest femurs and DBA/2J had the shortest. Peak volumetric density was attained by 4 months for all four strains with C57BL/6J having the lowest density and C3H/HeJ the highest with DBA/2J in the middle (Beamer et al. 1996). Klein et al. (1998) reported whole body bone mineral density from DEXA for C57BL/6J and DBA/2J inbred strains. Their results indicated that C57BL/6J females had significantly higher whole body bone mineral density. The discrepancy between Klein et al. and Beamer et al. could be due to the type of measurements used to obtain bone mineral density. Klein et al. used an areal measurement whereas Beamer et al. (1996) used a volumetric bone mineral density measurement. Klein also reported whole body BMD measurements, whereas specific skeletal sites were reported in other studies.

43 21 Turner et al. (2002) used micro-computed tomography (µct) to analyze the microstructure of femur and vertebrae from C57BL/6J and C3H/HeJ mice at 4 months of age. These two strains showed the greatest difference in BMD as reported by Beamer et al. (1996). The µct results indicated that C3H/HeJ mice had thicker femoral and vertebral cortices than the C57BL/6J mice and that the bone was more highly mineralized. However, the number of trabeculi in the C3H/HeJ vertebral bodies was much less than that in the C57BL/6J mice. The femoral mid shaft was tested in three point bending and the proximal femoral neck was tested in shear by applying a load to the femoral head. The lumbar spine was also tested in compression. Ultimate force, stiffness, and work to failure were determined from load displacement curves. The results from the mechanical tests indicated that the C3H/HeJ femoral mid shaft was significantly stronger and stiffer than the C57BL/6J femur. In contrast, the C57BL/6J femoral neck was significantly stronger than the C3H/HeJ femoral neck. The lumbar vertebrae were not significantly different in strength but the C3H/HeJ were significantly stiffer. Bone formation rate (BFR) and mineral apposition rate (MAR) were greater for C3H/HeJ cortical bone in both the femur and tibia. Sheng et al. (2002) reported similar results for the femoral metaphysis, resulting in higher cancellous bone volume in C3H/HeJ femurs compared with those from C57BL/6J. Interestingly, bone volume and trabecular number were lower in the vertebral metaphysis of C3H/HeJ (Sheng et al. 2002; Sheng et al. 1999).

44 Quantitative Trait Loci Studies in Humans 22 QTL analysis is not limited to animal models. Several human studies have been conducted although replication of QTL results has typically been problematic. Wide individual differences in environmental factors such as diet and exercise can complicate human studies. Johnson et al. (1997) used an extended family-based design with both two-point and multipoint linkage analyses by using affected/unaffected and quantitativetrait models. The investigators were looking for a genetic locus in the human genome that was linked to very high bone density. The study included 28 individuals of maternal kindred who were of Caucasian (European) descent and was based on a patient who was referred to the Creighton Osteoporosis Research Center for evaluation of unusually dense bones. Radiographs of the patient revealed dense bones with thick cortices, but normal shape. Further investigation revealed that the high bone mass (HBM) was inherited from the patient s mother and further studies focused on maternal kindred. Bone-mineral density measurements of the spine, (L1-L4), hip and total body were performed with dual energy x-ray absorptiometry (DXA). The linkage marker set contained 345 markers that covered the human genome excluding the X and Y- chromosomes. The investigators were able to isolate a genetic locus (the HBM locus) on chromosome 11q Using multipoint analysis, a LOD score of 5.74 was obtained at marker D11S987m. It was noted that osteoporosis pseudoglioma syndrome (OPS) had also been mapped to this region of chromosome 11 (Gong et al. 2001). OPS is an autosomal (non-sex determining chromosome) recessive trait of juvenile osteoporosis and is expressed as the opposite phenotype of the HBM trait. These findings support either

45 23 that the two contrasting traits are caused by allelic variation in the same gene or that two genes regulating BMD are located in a very small region representing less than one percent of the human genome (Johnson et al. 1997). The locus on chromosome 11q12-13 was subsequently investigated to determine if it contributed to variation in BMD in the normal population. Multipoint linkage analysis of femoral neck BMD in 835 pre-menopausal Caucasian and African-American sisters produced a LOD score of 3.50 near the marker D11S987. This locus is in the same region as that identified in previous studies for autosomal dominant high bone mass, autosomal recessive osteoporosis-pseudoglioma, and autosomal recessive osteopetrosis (Koller et al. 1998). The previous study was extended to include an autosomal genome screen to search for additional loci. The search was conducted in two steps: first a genome screen was conducted in 429 Caucasian sister pairs and multipoint LOD scores were obtained for four distinct skeletal sites. Loci with LOD scores greater than 1.85 were then analyzed in an expanded study of 464 Caucasians and 131 African-Americans. Linkage results identified QTLs for BMD of the spine on 1q21-23 with a LOD score of 3.86, femoral neck on 5q33-35 with a LOD score of 2.23, spine on 6p11-12 with a LOD of 2.13, and the femoral neck on 11q12-13 with a LOD score of The LOD score for the locus at 11q12-13 was reduced from 3.5 with 835 individuals in the previous study to 2.13 with 595 individuals in the follow-up study (Koller et al. 2000). Devoto et al. (2001) identified a QTL for BMD of the femoral neck was identified on chromosome 1 at 1p36. This study also incorporated a two-step process where an initial genome screen of seven pedigrees identified a potential QTL at 1p36. They

46 24 followed up with additional markers spanning a 40 cm interval in 42 families. Linkage results produced a LOD score of 3.53 with a peak near marker D1S214 and heritability was estimated to be Genetic studies in human populations have been conducted using association or linkage approaches. Association studies test for a correlation between a disease and an allele in a population of unrelated affected and unaffected individuals. Linkage studies use pedigrees and look for correlated transmission in inheritance patterns of phenotypes and genotypes. Association studies often produce spurious associations due to population admixture and can lead to conflicting results. Linkage studies are often used for complex traits but are limited in statistical power, which results in problems with replication of results. The transmission disequilibrium test (TDT) is used to test candidate genes identified in an association study. A TDT tests whether an affected parent transmits an associated allele to an affected child more often than the non-associated allele (Lander and Schork 1994). When association of a candidate gene for a complex trait is tested in the presence of linkage, the TDT is not compromised by population admixture. When association and linkage are present the transmission disequilibrium test (TDT) can be used to test candidate genes as quantitative traits loci. (Deng et al. 2002) was the first to use association, linkage, and the TDT to test candidate genes. Loci for the vitamin D receptor (VDR), osteocalcin, and parathyroid hormone (PTH) genes were explored as regulators of bone mineral density. The study examined 630 subjects from 53 pedigrees selected from the bottom 10% of the population for bone mineral density variation. BMD was measured at the hip and spine and tested for linkage, association, and both

47 linkage and association for markers known to be inside the interval for the VDR, 25 osteocalcin, and PTH genes. Deng et al. reported significant results for all three tests for the VDR gene against spine BMD as well as the osteocalcin gene against hip BMD. This study was the first to test candidate genes as QTLs for bone mineral density. Genome wide linkage scans identified QTLs influencing body mass index (BMI), percent body fat, and body fat mass in the same population described previously. QTLs were reported on chromosome 2q14 near marker D2S347 with a LOD score of 4.44 for BMI and 2.0 for percent fat mass and body fat mass. The BMI QTL accounted for 28.2 percent of the variation in BMI. QTLs for BMI were also identified on chromosomes 1p36, 4q12, and 6q27 with corresponding LOD scores of 2.09, 2.09, and QTL for percent fat mass was identified on chromosome 8q24 with a LOD score of 1.54 and fat mass on chromosomes 6q27 and 20q13 with LOD scores of 1.59 and Lean body mass QTLs were also reported on chromosomes 12q14, 17p12, and 7p22 with LOD scores of 1.64, 1.64, and 1.52 (Deng et al. 2002). Quantitative Trait Loci Studies in Animals Several investigators have utilized genome wide scans to search for QTLs for skeletal phenotypes. Many QTLs have been identified across studies that could be confirming the same locus. In several studies BMD is used as the phenotype but the method and sites of measurement vary (e.g. pqct versus DEXA, spine versus femur). Other methods of quantifying skeletal quality include mechanical assessment of the midshaft of long bones, shear testing of the femoral neck, or compression testing of

48 26 vertebrae. Mineral content can be quantified using pqct or by ashing the bone to obtain ash fraction or total ash content. Each of these methods for measuring skeletal phenotypes can offer distinct insight into skeletal quality and often identify QTLs specific to that skeletal measure. However, it is also the case that these phenotypes overlap and often provide replication across studies for the same skeletal QTL. The skeletal site under investigation also varies and in many cases different site-specific QTLs have been identified in the same study. Several different progenitor strains and their progeny have been used in skeletal genetic studies. QTL genome scans have been performed using F 2, recombinant inbred (RI), recombinant congenic (RC), and backcross populations. QTLs also have been tested for confirmation in congenic and F 2 mice produced through RI segregation testing (RIST). The following review will group the animal QTL studies by the inbred progenitor strains used in investigation. C57BL/6 x DBA/2 Inbred Strain Crosses The C57BL/6 (B6) and DBA/2 (D2) mouse strains are the parental strains that produced the F 2 and RI cohorts used in the study presented in this thesis. Klein et al. (1998) was one of the first to perform a genome scan for skeletal QTLs. These investigators used QTL analysis to establish a genetic link to peak BMD and body weight in female mice. The study used B6 and D2 inbred mouse strains, and 24 of the BXD RI strains. Bone mineral density was measured by a densitometer and bones were weighed and then scanned using dual-energy X-ray absorptiometry. Whole bone mineral content

49 27 and bone mineral density were determined and then compared to values obtained from ashing the mouse carcass at 800 degrees for 24 hours. The residual material was dissolved in hydrochloric acid and whole body calcium was measured by automated titration on a Calcette calcium analyzer. Researchers compared these two methods of calculating whole body calcium and whole body BMC and obtained an r 2 of QTL analysis was performed by correlating the genetic marker information with body weight and whole body BMD. ANOVA was used to detect significant strain differences between the two progenitor strains and to provide an estimate of heritability. The authors viewed QTL analysis in the BXD RI strains as a preliminary screening with plans to follow-up in an F 2 population and therefore reported correlations of p < Peak bone mass in mice was found to occur at days. Therefore this study examined mice at approximately 84 days. As mentioned previously, past research has shown little difference between these progenitor strains; however, in this study bone mass was much greater in the B6 strain than in the D2 strain. Correlation analysis was conducted between body weight or whole body BMD and the allele (B6 or D2) at each of the 1522 polymorphic loci in their database. The results of the BXD RI strain QTL analysis identified 10 QTLs on chromosomes 1, 2, 7, 11, 14, 15, 16, 18, and 19 that were correlated with whole body BMD and 4 QTLs on chromosomes 4, 6, 9, and 14 that were correlated with body weight (Klein et al. 1998). Klein et al. (2001) also performed phenotypic selection for high and low BMD starting with a B6xD2 F 2 population. After three generations the high BMD line had 14% greater bone mineral density than the low BMD line. Femur cortical area and thickness, and cancellous vertebral bone volume was 16-30% greater in the high line.

50 28 Cancellous bone formation rates (BFR) were 35% lower in the high BMD line. Mineral apposition rate (MAR) was significantly reduced on the high BMD endosteal surface while periosteal MAR was increased in the high BMD line. Failure load and stiffness were greater in the high BMD line but material properties of the femur mid-shaft were similar suggesting that the difference in structural properties between the high and low lines was only due to geometric differences (Klein et al. 2001). In a subsequent study Klein et al. (2001) confirmed 4 of the QTLs identified in their first study using three independent populations derived from the same B6 and D2 parental strains. Researchers used selective genotyping encompassing the extreme 15% at each tail of the distribution for a BxD F 2 population consisting of 601 females and 393 males. In addition, they used short-term phenotypically selected lines and short-term genotypically selected lines derived from the same parental strains. The F 2 genome scan confirmed QTLs on chromosomes 1, 2, 4, and 11. For the QTLs on chromosomes 1, 2, and 4, the D2 allele contributed to increased BMD while the B6 allele contributed to increased BMD on chromosome 11. These results are inconsistent with their original results from the RI analysis that indicated that B6 mice had significantly greater BMD. The results of the short term phenotypically selected lines confirmed significant differences in allele frequencies for each of the 4 QTLs. The same allele affect was found in the selected line as that of the F 2 population where D2 alleles were associated with high BMD on chromosomes 1, 2, and 4 while B6 alleles increased BMD on chromosome 11. RI segregation testing (RIST) was used to fine map QTLs on chromosome 2 and 11. This method begins by selecting one of the 24 RI strains that had informative recombination sites within the QTL. RI BxD-8 was selected because it had

51 recombination at both QTLs on chromosome 2 and 11. RI BxD-8 mice were then 29 crossed with B6 and D2 mice producing two F 2 populations. The two F 2 populations were then analyzed to determine which population was still carrying the QTL. This method made it possible to locate the QTL position either above or below the recombination site in the RI BxD-8 strain. Using the RIST method Klein et al. were able to reduce the QTL interval for chromosome 2 and 11 down to 9-10 cm, as well as offer additional confirmation (Klein et al. 2001). Klein et al. (2002) also performed QTL analysis for femoral shaft cross sectional area using 964 F 2 male and female BxD mice as well as 18 B x D RI strains, all examined at 16 weeks. Only animals falling within the 15% extreme of each tail of the distribution were genotyped. Regressions between body weight and cross sectional area (CSA) revealed an r 2 value of 0.13 in males and 0.24 in females. QTL analysis was performed on CSA corrected for body weight by regressing on body weight and using the resulting residuals. The F 2 results indicated QTLs for femoral CSA on chromosomes 6, 8, 10, and X in both genders. These researchers reported gender differences in QTL results if the difference in LOD score exceeded 3.0. Based on this criterion they identified three gender specific QTLs on chromosomes 2 (males), 7 (females), and 12 (females). Although their RI population was limited in number (18), they were able to provide supporting evidence for the QTLs on chromosomes 2, 7, 8, 10, and 12. This study reported little or no correlation between CSA and BMD in both the RI and F 2 populations (Klein et al. 2002). Orwoll et al. (2001) reported sex specific BMD QTL results from the previous study by Klein et al. (2002) for both the F 2 and RI populations by analyzing the genders

52 separately. RI analyses indicated significant QTLs on chromosomes 1, 2, 7, 8, 11, and in females, and in males on chromosomes 2, 3, 7, 9, 11, 13, 17, 18, and X. The BxD F 2 population was also used in a bone density study by Drake et al. (2001). F 2 mice were fed an atherosclerosis-inducing diet for 4 months. At 16 months bone mineral mass and density were measured using computerized tomography and radiographs. Mechanical properties of the femur were also assessed using a torsion test on the shaft. Radiographic bone mineral content was normalized to bone size by averaging by total area or bone width. Normalizing radiographic bone mineral content to bone size resulted in a correlation of 0.77 with CT bone mineral content and 0.71 with CT bone mineral density. Bone mineral content determined by CT and ash weight produced an r 2 = Bone mass was found to be inversely correlated with atherosclorosis and directly correlated with body weight, length, and adipose tissue. Skeletal-related traits were mapped to chromosomes 2, 3, 6, 7, and both the proximal and distal ends of chromosome 15. Three of these QTLs were found to be adjacent to or overlap with QTLs that were identified for non-bone traits such as adipose tissue, plasma HDL and LDL, and body length. Drake et al. used a two step method to determine if these QTLs resulted from pleiotropic gene effects or multiple QTLs in close proximity. First, multi-trait composite interval mapping was used to determine if the significance of a QTL increased when multiple traits were included in the model. If the multi-trait analysis produced significant results pleiotropy versus tight linkage was tested using a statistical test by (Jiang and Zeng 1995). The pleiotropy versus tight linkage test provided support for pleiotropy of the QTL on chromosome 2 influencing radiographic BMD, adipose mass, and HDL cholesterol. The test also supported a pleiotropic effect of the QTL on chromosome 6 for radiographic

53 inter-trochanteric density, plasma HDL, and subcutaneous fat pad mass (Drake et al ). C57BL/6 x Cast/EiJ Inbred Strain Crosses C57BL/6 (B6) and Cast/EiJ (CAST) strain crosses have also been used extensively for skeletal QTL studies. Beamer et al. (1999) performed QTL analysis on 714 F 2 B6 x CAST 4 month old females for peak bone mineral density of the femur as measured using pqct. CAST body weight and femur length were found to be less than that of B6 mice. A significant correlation between body weight and BMD (r = 0.112) in the F 2 population was observed. QTL analyses identified significant QTLs on chromosomes 1, 5, 13, and 15 (Beamer et al. 1999). In a subsequent study, Gu et al. (2002a) reported gene expression data from a congenic strain that was developed based on a QTL on chromosome 1 that was identified in the previous study by Beamer et al. The congenic strain contained a QTL for bone density from CAST on the background of B6. Bone mineral density, serum insulin-like growth factor (IGF-I) and alkaline phosphatase (ALP) were measured in B6.CAST.1T congenic and in B6 controls at 16 weeks of age. BMD was significantly higher in the congenic strain whereas body weight and femur length were not significantly different. Skeletal alkaline phosphatase activity in serum was reduced in the congenic strain compared with B6. Microarray analysis was used to investigate at differences in gene expression patterns between the congenic and B6 strains. The congenic strain had significantly lower expression patterns in genes for bone formation compared with B6

54 mice. The microarray results also indicated that genes that could have a negative 32 regulatory effect on bone resorption were higher in the congenic strain than in the B6. These results indicate that the increase in bone mineral density of the congenic strain could be the result of decreased resorption as opposed to increased bone formation (Gu et al. 2002a). Gu et al. (2002b) have furthered this research by constructing a BAC contig for chromosome 1 that runs between cm. This region of the mouse genome is homologous to the human chromosome region 1q21-23, which was identified by (Koller et al. 2000) as a site controlling for spine BMD. Shults et al. (2003) recently reported the construction of 12 congenic lines for QTLs that have been previously identified. Six congenic lines were constructed from C3H donor regions on B6 backgrounds and six lines were constructed from CAST donor regions on B6 backgrounds. The C3H genetic regions were chromosomes 1 ( cm), 4 ( cm), 6 ( cm), 11 ( cm), 13 ( cm), and 18 ( cm). The CAST congenic lines consisted of regions on chromosomes 1 ( cm), 3 ( cm), 5 ( cm), 13 ( cm), 14 ( cm), and 15 ( cm). All congenic strains showed significant differences in BMD compared with B6 controls except B6.CAST.15T. The C3H/C3H alleles increased BMD in all congenic B6.C3H lines except B6.C3H.6T. The CAST/CAST alleles increased BMD in all congenic lines except B6.CAST.5T and B6.CAST.15T. Significant differences were also reported for body weight, femur length, and mid-diaphyseal periosteal circumference in several of the congenic strains. However, these results did not show consistent correlations with BMD. Eight additional sub-lines were constructed from C3H donors

55 and B6 recipients to fine-map the QTL on chromosome 1. Results from the chromosome 1 sublines indicated two QTLs at cm and cm (Shultz et al. 2003). 33 C57BL/6J x C3H/HeJ Inbred Strain Crosses In a study described previously, Beamer et al. (1996) assessed eleven different inbred strains for peak bone mineral density. The strains that scored highest and lowest for volumetric BMD were C3H and B6 respectively. Subsequently, Beamer et al. (2001) performed a genome wide scan for QTLs influencing femur and lumbar vertebral BMD by pqct in 986 F 2, 4 month old, females derived from C57BL/6J and C3H/HeJ inbred parental strains. The heritability for femoral and vertebral BMD was 0.83 and 0.72 respectively. QTLs were identified on chromosomes 1, 2, 4, 6, 11, 12, 13, 14, 16, and 18 for femur BMD and chromosomes 1, 4, 7, 9, 11, 14, and 18 for vertebral BMD. C3H alleles increased BMD at all QTL sites except for the QTLs on 7 and 9 for the vertebrae and 6 and 12 for the femur (Beamer et al. 2001). Serum insulin like growth factor-1 was measured in the F 2 population described in the previous study. The correlation between IGF-I and femur length and BMD was and respectively. A genome scan was performed and identified QTLs for serum IGF-I on chromosomes 6, 10, and 15. C3H alleles increased serum IGF-I on chromosomes 10 and 15 while B6 alleles increased IGF-I on chromosome 6 and an interaction effect was identified between the QTLs on chromosome 6 and 11. When the alleles on the QTL on chromosome 11 are C3/C3, the B6/B6 alleles on the QTL on chromosome 6 have no effect. The QTLs on chromosomes 6 and 11 also overlap with

56 34 the BMD QTL results described previously (Rosen et al. 2000). A congenic strain was produced for the QTL on chromosome 6. The chromosomal region from D6mit93 to D6mit150 of C3H was donated to the B6 recipient. The congenic strain had significantly lower serum IGF-I and BMD compared with B6 controls (Bouxsein et al. 2002). Yershov et al. (2001) performed QTL analyses on morphologic, compositional and mechanical measures of humeri from HcB/Dem recombinant congenic (RC) female mice at 6-7 months of age. The HcB/Dem RC mice were inbred strains produced after 3 generations of backcrossing with C3H as the background progenitor and C57BL/ScSnA (B10) as the donor progenitor. The HcB/Dem RC series consisted of 27 strains each containing about 12.5% of the B10 genome but with different combinations of the C3H and B10 genome. Cross sectional area, moment of inertia and bone mineral fraction were measured for the humerus and mechanical properties were assessed using a three point bending test of the humerus shaft. The cross sectional moment of inertia, failure load, and structural stiffness were higher in the B10 parentals. However, failure stress and young s modulus were significantly greater in C3H parental mice. Body mass was positively correlated with failure load and stiffness, percent ash mass, moment of inertia and cross sectional area, but negatively correlated with failure stress. Suggestive QTLs were reported on chromosomes 1, 2, 8, 10, 11, and 12 (Yershov et al. 2001).

57 SAMP6 and SAMP2 Inbred Mouse Crosses 35 Shimizu et al. (1999) performed QTL analyses on F 2 Senescence-Accelerated Mouse (SAM) strains produced from SAMP6 and SAMP2 parental strains that are known to have significantly low and high BMD respectively. The study examined 488 F 2 mice at 4 months of age. Measurements of femoral bone mass were made photometrically. A cortical thickness index (CTI) was used as the quantitative trait. The cortical width was taken as the total width of the femur minus the medullary width and CTI was calculated as cortical width divided by total width. The estimate of heritability for CTI was 53%. The genome scan was conducted in two phases. First, the F 2 males that were in the 10 % extremes of the CTI were genotyped across the whole genome. Using interval mapping two large QTLs were identified on chromosome 11 and 13 with LOD scores > 3.0 and a third QTL on chromosome 9 with a LOD score of 2.4. In the second phase all 246 F 2 males were genotyped on chromosomes 11, 13, and the X chromosome, while 56 mice were genotyped on chromosome 9 and two major QTLs were identified with LOD scores of 10.8 at 51.8 cm on chromosome 11 and 5.8 at 8.3 cm on chromosome 13 (Shimizu et al. 1999). In a follow-up study, Shimizu et al. (2002) produced a congenic strain with the chromosome 13 QTL region from the SAMP2 donor on the background of SAMP6. CTI was measured as described previously as well as areal BMD of the femur, whole body BMD using DEXA and volumetric BMD of the femur using pqct. All three methods produced significant differences between the congenic strain and the control. BMD was also measured in the AKR/J strain and 13 SAM recombinant-like strains derived from

58 AKR/J. The peak bone density 2 (Pbd2) locus that had previously been identified on 36 chromosome 13 (Shimizu et al. 2001) was confirmed in the congenic strain. A reciprocal congenic was also constructed with the Pbd2 interval from SAMP 6 on the background of SAMP2. These researchers identified an association of a CAG trinucleotide repeat within the bone morphogenic protein 6 (Bmp6) gene with peak bone mass in the SAM strains (Shimizu et al. 2002). Benes et al. (2000) also used the SAMP6 inbred strain crossed with two different strains to produce two separate F 2 populations, SAMR1 x SAMP6 and SAMP6 x AKR/J. BMDwas measured by DEXA in 250 animals at four months of age and the highest and lowest quartiles were genotyped. Single marker analysis identified QTLs on chromosomes 2, 11 and 13 for the AKR/JxSAMP6 F 2 population and QTLs on chromosomes 2, 7, and 16 in the SAMR1xSAMP6 F 2 population. The SAMP6 alleles for the QTL on chromosome 2 in the AKRxSAMP6 population were decreasing for BMD but on chromosome 11 the SAMP6 alleles were increasing. In the SAMR1xSAMP6 cross the alleles on the QTL for chromosome 7 were also increasing. Composite interval mapping confirmed a QTL on chromosome 2 in the SAMR1xSAMP6 cross, however the peak LOD score plots were about 20 cm apart. Of the five QTLs identified in this study SAMP6 alleles had decreasing affects on BMD only at the loci on chromosomes 2 and 16 which is consistent with their parental phenotypes (Benes et al. 2000).

59 MRL/MPJ x SJL/J Inbred Strain Crosses 37 BMD was also assessed in a F 2 cross from MRL/MPJ and SJL/J inbred parental strains. Masinde et al. (2002b) performed QTL analysis on BMD measures from 633 F 2 female mice at 7 weeks of age using pqct of the femur and DEXA measures for total skeletal density. A genome wide scan was conducted for all 633 mice. The DEXA BMD was corrected for body size, which led to increased LOD scores indicating that body weight could limit the ability to detect fine differences in uncorrected BMD. QTLs were identified on chromosomes 1, 3, 4, 9, 12, 17, and 18 for BMD of the femur using pqct and on chromosomes 1, 2, 4, 9, 11, 14, and 15 for total skeletal BMD using DEXA (Masinde et al. 2002b). QTLs for muscle size of the forelimb from pqct and body length were also assessed in this MRL/MPJ x SJL/J F 2 population. QTLs were identified on chromosome 7, 14, 15, 17, and 9 for muscle size. When muscle size data were corrected for body length QTLs were identified on chromosome 7, 11, 14, 17, and 9. QTLs for body length were found on chromosome 2, 9, 11, and 17 (Masinde et al. 2002a). Separating the effects of correlated phenotypes can be very problematic. In this study the researchers corrected muscle size for body length and the LOD score for muscle size increased on chromosome 9 and a new QTL was identified on chromosome 11. Both of these loci were identified as QTLs for body length. It is likely that while attempting to correct for body size these researchers have actually induced a greater correlation of muscle size with body size although the method of adjustment was not described.

60 38 Chapter 6 will describe in detail the problems encountered with adjusting phenotypic data for body size using various methods. Additional assessments of this cohort for bone mechanical properties revealed several QTLs for femur breaking strength and work to failure. Mechanical properties were determined using a three point bending test. Li et al. (2002b) reported QTLs for femur breaking strength on chromosomes 1, 2, 8, 9, 10, and 17 and for femur work to failure on 2, 7, 8, 9, and X. The QTLs on 1, 9, and 17 were also identified for femur BMD (.Li et al. 2002a). Known human mouse homologous chromosome regions with chromosome number and centimorgan position are given in Table 2-1 for comparison of human QTL results to those of murine studies. The same murine chromosome region was identified on multiple homologous chromosome regions in the mouse genome database and the positions indicated in this table should be used as a general reference only. Table 2-1: Mouse homologous chromosome regions for human regions containing skeletal QTLs. Human Mouse 11q at 17cM 1q at 92 cm 5q at cm 6p cm 1p cm 2q cM 4q12 5 at 42cM 6q cM 8q cm 20q q cM 17p cM 7p cM

61 39 Conclusions A complete summary of the skeletal QTLs from the literature is presented in Appendix A. Appendix A also includes QTLs for muscle, lean body mass, body weight, length, fat measures, IGF-I, and activity. These QTLs are summarized by chromosome. This review clearly shows the search for genetic determinants of skeletal quality can be extremely complicated. Not only are there many biological processes involved in the regulation of bone modeling and remodeling but many environmental factors as well. Skeletal phenotypes are complex quantitative measures that are known to be influenced by many genes. Recombinant inbred mouse strains as well as F 2 cohorts derived from inbred strains are very promising tools for researchers for two important reasons. They provide a means for studying skeletal phenotypes while controlling for environmental factors, including dietary intake, and allow researchers to control and follow the inheritance of known markers that can be used for comparisons with patterns of inheritance of genetic phenotypes. It can also be seen that research on the genetics of skeletal phenotypes has yielded conflicting results. Without examining skeletal phenotypes from a polygenic reference frame (i.e. considering modulator genes), many candidate gene studies will be less than fruitful, as interaction effects can mask the effect of candidate genes. Multiple QTLs should be included in linkage analysis models to investigate epistatic interactions.

62 While QTL analysis can help to identify possible loci, it cannot tell us the 40 underlying mechanism whereby the effect is expressed. Further research using manipulated congenic or genotypically selected strains can be used to refine this pursuit and allow for scientifically sound hypothesis testing.

63 41 References Arden, N.K. and Spector, T.D. (1997) Genetic influences on muscle strength, lean body mass, and bone mineral density: a twin study. Journal of Bone and Mineral Research 12, Bailey, D.W. Recombinant inbred strains and bilineal congenic strains. Foster, H. L., Small, J. D., and Fox, J. G New York, NY, Academic Press. Beamer, W.G., Donahue, L.R., Rosen, C.J., and Baylink, D.J. (1996) Genetic variability in adult bone density among inbred strains of mice. Bone 18, Beamer, W.G., Shultz, K.L., Churchill, G.A., Frankel, W.N., Baylink, D.J., Rosen, C.J., and Donahue, L.R. (1999) Quantitative trait loci for bone density in C57BL/6J and CAST/EiJ inbred mice. Mammalian Genome 10, Beamer, W.G., Shultz, K.L., Donahue, L.R., Churchill, G.A., Sen, S., Wergedal, J.R., Baylink, D.J., and Rosen, C.J. (2001) Quantitative trait loci for femoral and lumbar vertebral bone mineral density in C57BL/6J and C3H/HeJ inbred strains of mice. Journal of Bone and Mineral Research 16, Benes, H., Weinstein, R.S., Zheng, W., Thaden, J.J., Jilka, R.L., Manolagas, S.C., and Shmookler Reis, R.J. (2000) Chromosomal mapping of osteopenia-associated quantitative trait loci using closely related mouse strains. Journal of Bone and Mineral Research 15, Bouxsein, M.L., Rosen, C.J., Turner, C.H., Ackert, C.L., Shultz, K.L., Donahue, L.R., Churchill, G., Adamo, M.L., Powell, D.R., Turner, R.T., Muller, R., and Beamer, W.G. (2002) Generation of a new congenic mouse strain to test the relationships among serum insulin-like growth factor I, bone mineral density, and skeletal morphology in vivo. Journal of Bone and Mineral Research 17, Deng, H.W., Deng, H., Liu, Y.J., Liu, Y.Z., Xu, F.H., Shen, H., Conway, T., Li, J.L., Huang, Q.Y., Davies, K.M., and Recker, R.R. (2002) A genomewide linkage scan for quantitative-trait loci for obesity phenotypes. Am J Hum Genet 70, Deng, H.W., Li, J., Li, J.L., Johnson, M., Gong, G., and Recker, R.R. (1999) Association of VDR and estrogen receptor genotypes with bone mass in postmenopausal Caucasian women: different conclusions with different analyses and the implications. Osteoporosis International 9, Deng, H.W., Shen, H., Xu, F.H., Deng, H.Y., Conway, T., Zhang, H.T., and Recker, R.R. (2002) Tests of linkage and/or association of genes for vitamin D receptor,

64 osteocalcin, and parathyroid hormone with bone mineral density. J Bone Miner Res 17, Dequeker, J., Nijs, J., Verstraeten, A., Geusens, P., and Gevers, G. (1987) Genetic determinants of bone mineral content at the spine and radius: a twin study. Bone 8, Devoto, M., Specchia, C., Li, HH., Caminis, J., Tenenhouse, A., Rodriguez, H., and Spotila, L.D. (2001) Variance component linkage analysis indicates a QTL for femoral neck bone mineral density on chromosome 1p36. Human Molecular Genetics 10, Drake, T.A., Schadt, E., Hannani, K., Kabo, J.M., Krass, K., Colinayo, V., Greaser, L.E., III, Goldin, J., and Lusis, A.J. (2001) Genetic loci determining bone density in mice with diet-induced atherosclerosis. Physiological Genomics 5, Gelehrter, T.D., Collins F.S., and Ginsburg D. (1998) Principles of Medical Genetics. Williams & Wilkins, Baltimore, MD. Gong, Y., Slee, R.B., Fukai, N., Rawadi, G., Roman-Roman, S., Reginato, A.M., Wang, H., Cundy, T., Glorieux, F.H., Lev, D., Zacharin, M., Oexle, K., Marcelino, J., Suwairi, W., Heeger, S., Sabatakos, G., Apte, S., Adkins, W.N., Allgrove, J., Arslan-Kirchner, M., Batch, J.A., Beighton, P., Black, G.C., Boles, R.G., Boon, L.M., Borrone, C., Brunner, H.G., Carle, G.F., Dallapiccola, B., De Paepe, A., Floege, B., Halfhide, M.L., Hall, B., Hennekam, R.C., Hirose, T., Jans, A., Juppner, H., Kim, C.A., Keppler-Noreuil, K., Kohlschuetter, A., LaCombe, D., Lambert, M., Lemyre, E., Letteboer, T., Peltonen, L., Ramesar, R.S., Romanengo, M., Somer, H., Steichen-Gersdorf, E., Steinmann, B., Sullivan, B., Superti-Furga, A., Swoboda, W., van den Boogaard, M.J., Van Hul, W., Vikkula, M., Votruba, M., Zabel, B., Garcia, T., Baron, R., Olsen, B.R., and Warman, M.L. (2001) LDL receptor-related protein 5 (LRP5) affects bone accrual and eye development. Cell 107, Gu, W., Li, X., Lau, K.H.W., Edderkaoui, B., Donahue, L.R., Rosen, C.J., Beamer, W.G., Shultz, K.L., Srivastava, A., Mohan, S., and Baylink, D.J. (2002a) Gene expression between a congenic strain that contains a quantitative trait locus of high bone density from CAST/EiJ and its wild-type strain C57BL/6J. Functional and Integrative Genomics 1, Gu, W.K., Li, X.M., Edderkaoui, B., Strong, D.D., Lau, K.H.W., Beamer, W.G., Donahue, L.R., Mohan, S., and Baylink, D.J. (2002b) Construction of a BAC contig for a 3 cm biologically significant region of mouse chromosome 1. Genetica 114, 1-9. Jiang, C. and Zeng, Z.B. (1995) Multiple trait analysis of genetic mapping for quantitative trait loci. Genetics 140,

65 Johnson, M.L., Gong, G., Kimberling, W., Recker, S.M., Kimmel, D.B., and Recker, R.B. (1997) Linkage of a gene causing high bone mass to human chromosome 11 (11q12-13). American Journal of Human Genetics 6, Kanders, B., Dempster, D.W., and Lindsay, R. (1988) Interaction of calcium nutrition and physical activity on bone mass in young women. Journal of Bone and Mineral Research 3, Kaye, M. and Kusy, R.P. (1995) Genetic lineage, bone mass, and physical activity in mice. Bone 17, Klein, R.F., Carlos, A.S., Vartanian, K.A., Chambers, V.K., Turner, R.J., Phillips, T.J., Belknap, J.KI., and Orwoll, E.S. (2001) Confirmation and fine mapping of chromosomal regions influencing peak bone mass in mice. Journal of Bone and Mineral Research 16, Klein, R.F., Mitchell, S.R., Phillips, T.J., Belknap, J.K., and Orwoll, E.S. (1998) Quantitative trait loci affecting peak bone mineral density in mice. Journal of Bone and Mineral Research 13, Klein, R.F., Shea, M., Gunness, M.E., Pelz, G.B., Belknap, J.K., and Orwoll, E.S. (2001) Phenotypic characterization of mice bred for high and low peak bone mass. Journal of Bone and Mineral Research 16, Klein, R.F., Turner, R.J., Skinner, L.D., Vartanian, K.A., Serang, M., Carlos, A.S., Shea, M., Belknap, J.K., and Orwoll, E.S. (2002) Mapping quantitative trait loci that influence femoral cross-sectional area in mice. J Bone Miner Res 17, Koller, D.L., Econs, M.J., Morin, P.A., Christian, J.C., Hui, S.L., Parry, P., Curran, M.E., Rodriguez, L.A., Conneally, P.M., Joslyn, G., Peacock, M., Johnston, C.C., and Foroud, T. (2000) Genome screen for QTLs contributing to normal varitaion in bone mineral density and osteoporosis. Journal of Clinical Endocrinology and Metabolism 85, Koller, D.L., Rodriguez, L.A., Christian, J.C., Slemenda, C.W., Econs, M.J., Hui, S.L., Morin, P., Conneally, P.M., Joslyn, G., Curran, M.E., Peacock, M., Johnston, C.C., and Foroud, T. (1998) Linkage of a QTL contributing to normal variation in bone mineral density to chromosome 11q Journal of Bone and Mineral Research 13, Lander, E.S. and Schork, N.J. (1994) Genetic dissection of complex traits. Science 265, Li, X., Masinde, G., Gu, W., Wergedal, J., Hamilton-Ulland, M., Xu, S., Mohan, S., and Baylink, D. (2002a) Chromosomal regions harboring genes for the work to femur failure in mice. Functional and Integrative Genomics 1,

66 Li, X., Masinde, G., Gu, W., Wergedal, J., Mohan, S., and Baylink, D.J. (2002b) Genetic dissection of femur breaking strength in a large population (MRL/MpJ X SJL/J) of F2 mice: single QTL effects, epistasis, and pleiotropy. Genomics 79, Li, X., Mohan, S., Gu, W., Wergedal, J., and Baylink, D.J. (2001) Quantitative assessment of forearm muscle size, forelimb grip strength, forearm bone mineral density, and forearm bone size in determining humerus breaking strength in 10 inbred strains of mice. Calcif Tissue Int 68, Masinde, G.L., Li, X., Gu, W., Hamilton-Ulland, M., Mohan, S., and Baylink, D.J. (2002a) Quantitative trait loci that harbor genes regulating muscle size in (MRL/MPJ x SJL/J) F(2) mice. Funct Integr Genomics 2, Masinde, G.L., Li, X., Gu, W., Wergedal, J., Mohan, S., and Baylink, D.J. (2002b) Quantitative trait Loci for bone density in mice: the genes determining total skeletal density and femur density show little overlap in f2 mice. Calcif Tissue Int 71, Matkovic, V., Kostial, K., Simonovic, I., Buzina, R., Brodarec, A., and Nordin, B.E. (1979) Bone status and fracture rates in two regions of Yugoslavia. The American Journal of Clinical Nutrition 32, Nilas, L. and Christiansen, C. (1987) Bone mass and its relationship to age and the menopause. J Clin Endocrinol Metab 65, Orwoll, E.S., Belknap, J.K., and Klein, R.F. (2001) Gender specificity in the genetic determinants of peak bone mass. Journal of Bone and Mineral Research 16, Plomin, R. and McClearn, G.E. (1993) Quantitative trait loci (QTL) analyses and alcohol-related behaviors. Behav Genet 23, Plomin, R., McClearn, G.E., Gora-Maslak, G.G., and Neiderhiser, J.M. (1991) Use of recombinant inbred strains to detect quantitative trait loci associated with behavior. Behavior Genetics 21, Rosen, C.J., Churchill, G.A., Donahue, L.R., Shultz, K.L., Burgess, J.K., Powell, D.R., and Beamer, W.G. (2000) Mapping quantitative trait loci for serum insulin-like growth factor-1 levels in mice. Bone 27, Sheng, M.H.C., Baylink, D.J., Beamer, W.G., Donahue, L.R., Lau, K.H.W., and Wergedal, J.E. (2002) Regulation of bone volume is different in the metaphyses of the femur and vertebra of C3H/HeJ and C57BL/6J mice. Bone 30, Sheng, M.H.C., Baylink, D.J., Beamer, W.G., Donahue, L.R., Rosen, C.J., Lau, K.H.W., and Wergedal, J.E. (1999) Histomorphometric studies show that bone formation 44

67 and bone mineral apposition rates are greater in C3H/HeJ (High-Density) than C57BL/6J (Low-Density) mice during growth. Bone 25, Shimizu, M., Higuchi, K., Bennett, B., Xia, C., Tsuboyama, T., Kasai, S., Chiba, T., Fujisawa, H., Kogishi, K., Kitado, H., Kimoto, M., Takeda, N., Matsushita, M., Okumura, H., Serikawa, T., Nakamura, T., Johnson, T., and Hosokawa, M. (1999) Identification of peak bone mass QTL in a spontaneously osteoporotic mouse strain. Mammalian Genome 10, Shimizu, M., Higuchi, K., Kasai, S., Tsuboyama, T., Matsushita, M., Matsumura, T., Okudaira, S., Mori, M., Koizumi, A., Nakamura, T., and Hosokawa, M. (2002) A congenic mouse and candidate gene at the Chromosome 13 locus regulating bone density. Mamm Genome 13, Shimizu, M., Higuchi, K., Kasai, S., Tsuboyama, T., Matsushita, M., Mori, M., Shimizu, Y., Nakamura, T., and Hosokawa, M. (2001) Chromosome 13 locus, Pbd2, regulates bone density in mice. J Bone Miner Res 16, Shultz, K.L., Donahue, L.R., Bouxsein, M.L., Baylink, D.J., Rosen, C.J., and Beamer, W.G. (2003) Congenic strains of mice for verification and genetic decomposition of quantitative trait loci for femoral bone mineral density. J Bone Miner Res 18, Turner, C.H., Hsieh, Y.F., Muller, R., Bouxsein, M.L., Baylink, D.J., Rosen, C.J., Grynpas, M.D., Donahue, L.R., and Beamer, W.G. (2000) Genetic regulation of cortical and trabecular bone strength and microstructure in inbred strains of mice. Journal of Bone and Mineral Research 15, Yershov, Y., Baldini, T.H., Villagomez, S., Young, T., Martin, M.L., Bockman, R.S., Peterson, M.G.E., and Blank, R.D. (2001) Bone strength and realted traits in HcB/Dem recombinant congenic mice. Journal of Bone and Mineral Research 16,

68 Chapter 3 Methods Animals and Animal Maintenance F 2 and recombinant inbred (RI) populations of mice were used for quantitative trait loci analyses of skeletal phenotypes. Breeding and maintenance of progenitor strains (C57BL/6 and DBA/2), recombinant inbred strains, as well as F 1 and F 2 animals derived from the progenitor strains were conducted in the barrier facility maintained by The Center for Developmental and Health Genetics at The Pennsylvania State University. The F 2 population included 200 male and 200 female mice and the RI population included 10 males and 10 females each for 23 different B x D RI strains. Additionally, 10 male and 10 female mice from each of the C57BL/6 and DBA/2 progenitor strains were included in all phenotypic measures and testing. The F 2, RI, and parental strains were euthanized at 200 days of age. Five male and five female C57BL/6 and DBA/2 650 day old mice were purchased from the National Institute on Aging breeding facility for preliminary age-related skeletal analyses. Rearing animals under barrier conditions reduces the potentially confounding effects of infectious disease. Animals that showed any of the following signs that are associated with moribund conditions were considered to be dying and consequently euthanized: impaired ambulation (unable to reach food or water easily), evidence of muscle atrophy or other signs of emaciation, any obvious illness including such signs as

69 47 lethargy, prolonged inappetence, bleeding, difficulty breathing, central nervous system disturbances, chronic diarrhea or constipation, an inability to remain upright, weight loss, poor grooming, and rapid growth of visible or palpable tumors. Mice were weaned into like-sex sibling groups at about 23 days of age with 4 animals per cage. They were fed a diet of autoclaved Purina Mouse Chow 5010 ad lib. The barrier facility was maintained under positive air pressure with a temperature and humidity controlled environment and a 12-hour light/dark cycle. Behavioral Assessments Several measures of activity-related behaviors were made on each animal at 150 days of age. Activity was measured in a 40 cm x 40 cm x 15 cm deep, black opaque plastic arena marked into four quadrants with a 15 mm hole cut in the center of each quadrant. Animals were individually placed into a clear plastic cylinder in the center of the arena. After ten seconds, the cylinder was lifted and each mouse was observed for five minutes. The number of quadrants entered, the number of times the animal reared onto its hindquarters, and the number of times it poked its head into the floor holes was recorded. The testing conducted in the activity box was repeated on three separate occasions for the F 2 mice and on one occasion for the RI mice. Each animal was placed on a dowel rod 89 cm long x 1.6 cm in diameter that was suspended 23 cm above a foam pad. The rod was marked into five sectors of 17.8 cm each. Animals were observed until falling or until one-minute had elapsed. The fall times and the number of excursions into different sectors were recorded. A cord drop test

70 48 was conducted in a similar fashion, except that animals were placed such that they hung from their forelimbs. Time to release was recorded. Rod and cord activity measures were repeated for three one-minute intervals on three separate occasions for a total of nine one-minute intervals for the F 2 mice. RI testing was conducted for a single oneminute interval on three separate occasions for a total of three one-minute intervals. The intervals were averaged for individual F 2 mice to reduce intra-individual variability and potentially increase the power to detect QTLs. RI QTL analysis was performed on strain means, which also reduces intra-individual differences because animals within a strain are genetically identical. Genotyping Four hundred mice were genotyped for microsatellite markers spaced at distances of approximately cm across all chromosomes. A small snip (approximately 2 mm) of their tail was taken at weaning for genetic analysis. DNA was extracted by standard lysis with Proteinase K digestion followed by phenol/sevag extraction and ethanol precipitation (Sambrook et al., 2002). A portion of the purified DNA was diluted to 10 ng/l into 96-well plates. Genotyping was carried out using markers from the MIT collection ( Primers were purchased from Research Genetics, Inc. (Huntsville, Alabama, USA) and the forward primer was fluorescently labeled for allele detection. The PCR reaction in a total volume of 10 µl consisted of 10ng of the template DNA, 2.5mM MgCl2, 10mM dntps,.04mm Spermidine and 0.5U AmpliTaq Gold

71 DNA polymerase (Applied Biosystems Inc., Foster City, CA, USA) and the buffer 49 supplied with the polymerase. Following a denaturation at 95C for 2 min, 35 cycles of PCR were carried out (95C, 45s/ 59C, 45s/ 72C, 60s). The samples were electrophoresed on an ABI 310 Genetic Analyzer (Applied Biosystems Inc., Foster City, CA, USA). Allele fragment sizes were determined by GeneScan software from Applied Biosystems. These sizes were converted to allele calls (either B or D for C57BL/6J or DBA/2J respectively) in ABI Genotyper software and exported into an Excel spreadsheet. Genotypic data for the BxD RI strains were obtained from Williams et al. (2001). Six hundred and seventy two known marker genotypes were used in the RI QTL analyses. Serum Alkaline Phosphatase and Serum Calcium Blood serum samples were taken from tail snips on three separate occasions: 100, 150, and 200 days. Blood samples were centrifuged, frozen and sent to the Clinical Blood Chemistry Laboratory at Wake Forest Medical Center for analysis. Serum was analyzed for alkaline phosphatase and calcium utilizing a Chem 1 Blood Analyzer (Bayer Corporation, NY). Body Size Measurements Prior to euthanism each animal was weighed to the nearest hundredth of a gram. After cervical dislocation body length was measured from the nose to anus.

72 Hind-limb Harvest and Dissection 50 At 200 days of age F 2 and RI mice were euthanized by cervical dislocation. The right hind limbs were harvested immediately following euthanasia by disarticulation at the hip joint. The dissection included isolating and removing the entire intact gastrocnemius, soleus, tibialis anterior, and extensor digitorum longus muscles from the harvested extremities, as well as isolating and carefully cleaning the femur and tibia (Figure 3-1). Muscles were carefully removed and individually weighed to the nearest hundred of a milligram. Individual bones were wrapped in saline soaked gauze and stored in test tubes at -5 C until mechanically tested. All bones underwent one freezethaw cycle; freezing has been found to have negligible effects on the material behavior of bone (Linde and Sorensen, 1993; Sedlin, 1965; Pelker et al., 1984).

73 51 Right Hindlimb Muscles Tibia Tibia Femur Femur Figure 3-1: Right hindlimb (top), gastrocnemius, soleus, EDL, and TA muscle (bottom left), tibia (bottom center), and femur (bottom right). Gross Dimensional Measurements At the time of testing, the bones were thawed at ambient temperature and any remaining bits of soft-tissue were removed. A digital caliper accurate to 0.01mm was used to measure femoral length from the most superior aspect of the greater trochanter to the most distal aspect of the intercondylar notch, femoral width at the center of the diaphysis in both the sagittal and coronal planes, and epiphyseal width at the widest point of the distal epiphysis in the coronal plane. Femoral head and femoral neck diameter were also measured at the center of the head and neck in the coronal plane (medial/lateral). The tibia was measured similarly, except that length was measured from

74 the intercondylar eminence of the tibia to its inferior articular surface and the proximal, rather than distal, epiphyseal width was measured. 52 Flexural Testing of the Femoral Diaphysis The mid-shaft of the femur was tested to failure in three point bending in an MTS MiniBionix testing apparatus using a support span of 8 mm and a deformation rate of 1 mm/min (Figure 3-2). Femurs were consistently oriented in the testing apparatus so that the nosepiece was posteriorly directed in respect to the femoral shaft. All testing was executed with the bones wet and at ambient temperature. Each femur was loaded to failure while recording load and actuator displacement at 20 Hz and a load-deformation curve was generated using MATLAB scientific software. Yield load, yield deformation, energy absorbed at yield (area under the load-deflection curve), failure load, failure deformation, energy absorbed at failure, and stiffness (initial slope of the load-deflection curve) were determined using a MATLAB program expressly written for this application (Figure 3-3). Figure 3-2: Close-up of three point bending test of the femoral diaphysis.

75 53 Flexural Test of Femoral Diaphysis Load at Failure ( N) Load at Yield ( N) Energy Absorbed at Yield (1.194 N-mm) (Area under load displacement curve to yield point) Energy Absorbed at Failure (1.679 N-mm) (Area under load displacement curve to failure point) Stiffness (2.217 N/mm) (Slope of Linear portion of load displacement curve) Displacement at Yield (0.203 mm) Displacement at Failure (0.230 mm) Figure 3-3: Load displacement curve from mechanical testing. Mechanical parameters obtained from MatLab program. These structural parameters were dependent on the shape and size of each bone, as well the material properties of the tissue itself. Yield stress, yield strain, failure stress, failure strain, and tissue modulus of the mid-diaphysis was subsequently calculated based upon histomorphometric measurements of cross-sectional area to assess the material properties of the tissue independent of geometry. Stress was calculated using Equation 3-1, where σ is the bending stress, F is yield or failure load, L is unsupported span length, c

76 54 is the distance from the cross-section centroid to the tensile periosteal surface and I is the cross-sectional moment of inertia. Strain was calculated using Equation 3-2, where c is the distance from the cross-section centroid to the tensile periosteal surface, d is the displacement, and L is the unsupported span length. Tissue modulus was calculated using Equation 3-3, where F, L, d, and I are described previously. (Turner and Burr 1993). σ = F L c / 4 I (3-1) Є = 12 c d /L 2 ) (3-2) E = F L 3 / d 48 I (3-3) Shear Testing of the Femoral Neck The proximal fragment of the femur produced by flexural testing of the diaphysis was used to measure the functional strength of the inter-trochanteric region and femoral neck under loading conditions similar to those that produce femoral neck fractures in osteoporotic individuals. The proximal femur was embedded vertically up to 3 mm below the top of the femoral head in a mounting pot containing low melting point alloy. The pot was positioned and secured in the MTS materials testing machine and the femoral head was loaded, parallel to the femoral shaft, at a rate of 1 mm/min (Figure 3-

77 4). Failure load, failure deformation, energy absorption to failure, were calculated using MATLAB routines similar to those described for flexural testing. 55 Figure 3-4: Shear test of the femoral neck. Flexural Testing of the Tibial Diaphysis The mid-shaft of the tibia was tested to failure in three point bending using a similar technique to that used for femoral testing except that the support span was 10 mm. A small section of the anterior flare of the proximal tibia was carefully removed before testing so that the tibia would lay flat on the support span and not roll during loading. This procedure was effective in preliminary tests and yielded consistent results without compromising the integrity of the diaphysis under study. Tibiae were consistently oriented across the support span so that the nosepiece was anteriorly directed with respect to the tibial shaft (Figure 3-5). All testing was executed with the bones wet and at ambient temperature. Each tibia was loaded to failure while recording load and actuator

78 56 displacement at 1 mm/min and a load-deformation curve was generated using MATLAB scientific software. Yield load, yield deformation, energy absorbed at yield, failure load, failure deformation, energy absorbed at failure, and stiffness were determined using a MATLAB program expressly written for this application. Yield stress and failure stress were calculated using the same approach as outlined for femoral testing. Figure 3-5: Close-up of three point bending flexural test of the tibial diaphysis. Tissue Processing and Histomorphometry After testing, the proximal end of each tibia was immersed and fixed in cold (5ºC) 40% ethanol for 48 hours. The bones were dehydrated over a one-week period using increasing concentrations of cold ethanol (from 70 to 100%) and the proximal tibia and the distal fragment of the femur were embedded in methylmethacrylate using a three-step three-solution approach (Recker 1983). A DDK diamond wire saw was used to cut 150 mm diaphyseal cross-sections. Digital images of each cross-section were collected using

79 a light microscope equipped with a 4X objective and a high-resolution CCD video 57 camera interfaced to a personal computer. Images were captured using NIH IMAGE software. Total area within the periosteal surface, medullary area within the endosteal surface, cortical area, centroid of the cross-section, cross-sectional moment of inertia, and average cortical width, and inner and outer radius at four perpendicular locations on the cross section were calculated using a MATLAB program ( Figure 3-6). These data, together with data from the flexural tests were used to calculate the failure and yield stress, strain, and elastic modulus of the femur and tibia. Direction of Applied Load Posterior Anterior Medial Lateral Medial Lateral Posterior Anterior Figure 3-6: Cross-section of femur and tibia mid-diaphysis. Compositional analysis After mechanically testing the femoral shaft and neck, the femoral fragments were gathered and placed together in a muffled furnace at 800ºC for 24 hours to remove water

80 58 and organic constituents from the tissue. Upon removal the fragments were brought to ambient temperature in a desiccator and weighed to the nearest 0.01 mg to determine the ash weight of the entire femur. The distal fragment of the femur was embedded and sectioned for diaphyseal area measurements. Following flexural testing of the tibia the distal fragment was blotted to remove excess moisture and weighed to the nearest 0.01 mg to obtain the wet weight (mw) of the bone fragment. After the initial weighing the tissue was dried in a vacuum oven at 100 ºC for 24 hours, cooled to room temperature in a desiccator, and weighed again (md). It was then be placed in a muffled furnace at 800ºC for 24 hours, removed, again brought to ambient temperature in a desiccator, and weighed a final time to determine its ash weight (ma). The percent composition of water (Equation 3-4), organic (Equation 3-5), and ash (Equation 3-6) within each bone fragment, as well as the percent mineralization (Equation 3-7) of the organic matrix were calculated as follows: Percent Water = 100(mw - md) / mw (3-4) Percent Organic = 100(md - ma) / mw (3-5) Percent Ash = 100ma / mw (3-6) Percent Tissue Mineralization = 100ma / md (3-7)

81 References 59 Linde, F. and Sorensen, H.C.F. (1993) The effect of different storage methods on the mechanical properties of trabecular bone. Journal of Biomechanics 26, Pelker, R.R., Friedlaender, G.E., Markham, T.C., Panjabi, M.M., and Moen, C.J. (1984) Effects of freezing and freeze-drying on the biomechanical properties of rat bone. Journal of Orthopaedic Research 1, Recker, R. (1983) Bone histomorphometry: techniques and interpretation. Franklin Book Company, Inc., Elkins Park, PA. Sambrook J., Fritsch E., Maniatis T. (1989) Molecular Cloning: A Laboratory Manual. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press. Sedlin, E.D. (1965) A rheologic model for cortical bone. A study of the physical properties of human femoral samples. Acta Orthopaedica Scandinavica Turner, C.H. and Burr, D.B. (1993) Basic biomechanical measurements of bone: a tutorial. Bone 14, Williams, R.W., Gu, J., Qi, S., Lu, L. (2001). The genetic structure of recombinant inbred mice: High-resolution consensus maps for complex trait analysis. Genome Biology, 2(11):research This article accompanies the BXN RI dataset, release 1 of January 15, 2001 at

82 Chapter 4 Analyses Sex, Strain, and Age Differences A number of preliminary analyses were conducted to determine the suitability of the BxD model for investigations of skeletal genetics as a function of sex and age. Although successful QTL analyses are not necessarily dependent on large interprogenitor differences in phenotypic expression, such differences may increase the likelihood of isolating new genetic loci that influence bone health in an age- and sexdependent manner. The bones and muscles of five male and five female mice from each progenitor strain were examined at 180 days of age using the techniques outlined in the methods. Two-way analyses of variance with sex and strain as independent factors were conducted on each response variable to screen for phenotypic differences as a function of these factors. In a separate analysis, twenty 650 day-old mice, 5 males and 5 females of each strain, were examined according to the procedures outlined previously. Phenotypic data from these animals were then combined with data from our 180 day-old parental strains and a second series of sex-specific two-way analyses of variance were conducted to determine the influence of age and strain on the skeletal, muscle, and activity phenotypes. P-values less than 0.05 were considered to reflect a significant difference in both sets of analyses.

83 Co-variance of Phenotypic Expression 61 Co-variation of animal activity, muscle mass, and bone geometry and strength was explored in the RI and F 2 cohorts using Pearson product moment correlations. A two-tailed significance level was used and results are reported at the p < 0.05 level. Heritability Estimates Estimates of heritability for the musculoskeletal traits were made using the phenotypic data from the male and female recombinant inbred strains. The broad sense heritability of the trait, h 2 is given in Equation 4-1 where V E is the environmental variance, V T is the variance due to the combined effects of genetics and environment, and V G is the genetic variance calculated as V E subtracted from V T. h 2 = V G / (V G + V E ) = V G / V T ( 4-1) With RI strains, heritability can be estimated by comparing the within-strain variance to the between-strain variance. The r 2 from a one-way ANOVA for strain is equivalent to the heritability and was used to estimate heritability in females and males separately. Quantitative Trait Loci (QTL) Analysis (Background) In general, QTL analysis is based on the probability of a QTL having a certain genotype (at a given location), given the genotype of a single marker or two flanking markers at that location, and the phenotype. A genome wide scan is performed to search

84 for regions of chromosomes that influence the phenotype. Multiple points along the 62 genome are analyzed individually to determine the likelihood of a QTL at that position influencing the data. The QTL genotype is not known and is estimated by the probability of having a certain QTL genotype given a single marker genotype or flanking marker genotypes in the case of interval mapping. With the F 2 design there are 3 different possible genotypes for each QTL. The probability of each QTL genotype is averaged within each of 9 possible marker classes (interval mapping) and the total likelihood for each individual is the product of the likelihood for each marker class. The total likelihood of all individuals is the product of each individual s likelihood. The likelihood is maximized over a grid of recombination fractions and the ratio of the likelihood of the null hypothesis to the likelihood of the given model is used as a test statistic for significance. QTL analysis is based on the assumption that the trait data are a mixture of normal distributions depending on the number of possible QTL genotypes. The probability of a QTL genotype given a particular marker genotype is defined through Bayesian analysis as: (the joint probability of the QTL genotype and marker genotype) / (the probability of the marker genotype). The presence of a QTL is typically evaluated with a likelihood ratio test statistic. A likelihood function is specified based on the normal density function and a mixture model for the QTL genotype probabilities. The likelihood function is evaluated over a grid of recombination fractions based on a map function. To obtain parameter estimates for the genetic effects, the mean of the QTL genotypes, and the variance, the likelihood function is maximized using an estimation and maximization algorithm. The likelihood function is again evaluated for the null

85 63 hypothesis (H 0 ) where the recombination fraction (r) = 0.5. The null hypothesis tests for a QTL present but not linked to the marker or marker interval in the case of interval mapping. The test statistic is obtained by taking the ratio of the maximum likelihood under the null hypothesis divided by the maximum likelihood over the grid of recombination fractions. The likelihood ratio follows a chi square distribution and can be used to obtain a p-value based on the degrees of freedom of the model. LOD scores are typically reported in the results of QTL studies. A LOD score is obtained by dividing the likelihood ratio by 4.6. In the past, QTLs were considered significant if they had a LOD score of 3.0 or higher. Recently there has been much debate over the proper significance threshold. If the threshold is too low the false positive error rate can increase and if the threshold is too high the false negative error rate can increase. Guidelines for reporting linkage results have been outlined by Lander and Kruglyak, (1995) and are generally used by researchers when publishing QTL results. These guidelines have been established to adjust the point-wise or nominal significance level for multiple testing in a genome wide search. The expected number of loci that exceed a linkage statistic threshold (T) is given by Equation 4-2. µ (T) = [C + 2 p G X ] α (T) ( 4-2) Where C is the number of chromosomes and G is the length of the genome in Morgans, X =(2 log e 10)*LOD, p = 1 (RI and F 2 additive only) and 4/3 for F 2 recessive and dominant), and 1.5 for F 2 with 2 degrees of freedom (additive and dominance). A 5% chance of obtaining one false positive in a genome wide scan is equal to a point wise

86 significance threshold of The following classifications were suggested by 64 (Lander and Kruglyak 1995). Suggestive linkage (expect to get value 1 time by chance in a genome scan) RI or F 2 population with additive genetic effect only: Genome wide p = 1.0 point wise p = and LOD = 1.9 F 2 population (additive and dominant genetic effect): Genome wide p = 1.0 point wise p = and LOD = 2.8 Significant linkage (expect to get value 0.05 times in a genome scan) RI or F 2 population with additive genetic effect only: Genome wide p = 0.05 point wise p = and LOD = 3.3 F 2 population (additive and dominant genetic effect): Genome wide p = 0.05 point wise p = and LOD = 4.3 Confirmed linkage (Significant linkage in an initial study with confirmation in a second study at a nominal or point wise p = 0.01 which is equivalent to a LOD score of 1.5. The uncertainty in deciding whether a QTL effect is present stems from the fact that the QTL genotype is unknown. The QTL genotype is predicted based on the probability given the marker genotype. Certain assumptions are made in the QTL analysis that could result in the inaccurate estimate of a QTL presence. The basic assumption is that the phenotypic data are normally distributed. Violations of normality could result in estimation errors. Transformations using log and square root functions can be used to correct for non-normality but in some cases the phenotypic data remain skewed despite the transformation. Another assumption is that the QTL genotypes are normally distributed. In calculating the genotype probabilities in each marker class we

87 make the assumption that the recombination between markers is independent of other 65 markers and in the case of interval mapping that the recombination between the first flanking marker and the QTL is independent of the recombination between the QTL and the second flanking marker. The Haldane mapping function that is used to estimate recombination based on marker interval distances assumes non-interference between markers. If interference is suspected a Kosambi mapping function can be used (Doerge et al. 1997). The results of most QTL analyses are model dependent and assume the correct model has been used. Inaccurate results could arise if the wrong model is used, (e.g. additive versus dominance, incorrect number of QTLs and or QTL interactions). Single marker analysis cannot distinguish between a QTL of large effect but distant to the marker from that of a QTL with small effect but very close to the marker. Interval mapping can eliminate this problem by using flanking markers and testing the interval between the markers. However, if there are any epistatic effects, the detection of a QTL could be hampered and might be missed altogether. There have been documented and modeled ghost effects that show multiple peaks in neighboring intervals as a result of a QTL. Ghosting effects can also be found on each side of a QTL with the ghost peaks higher than the QTL peak. If an interval is tested and is next to an interval that also contains a QTL or if there are two QTLs in the same interval, interval mapping may not detect the QTL. Composite interval mapping has been developed to control for the effects of other markers. It is based on regressing the phenotype on the potential QTL while holding all other markers as covariates. A likelihood function is developed based on the underlying distributions, and is used to estimate the regression weights of the QTL

88 66 and cofactors. The regression weights are estimated by taking the partial derivatives of the likelihood function with respect to each parameter and setting the partial derivative equal to zero. The maximum likelihood estimates are obtained by solving the partial derivative equations. The likelihood function is also evaluated over the null hypotheses and the regression weights are compared (Zeng 1994). The composite interval mapping approach is an improvement over interval mapping in that it controls for other markers as cofactors but it is still a one-dimensional search and cannot detect epistatic interactions. Multiple interval mapping (MIM) is a method of QTL analysis that includes multiple QTLs in the model. The MIM model increases the power to detect QTLs that might otherwise have been missed due to epistatic interactions of two QTLs with opposite effects or a QTL that is of small effect that is masked by another QTL with a large effect. Multiple interval mapping assumes a given number of QTLs in the model and then evaluates all the positions in the genome for their partial likelihood statistic, if above a certain threshold the QTL is added to the model in a stepwise fashion and the model is reevaluated. The new model with k+1 QTLs is compared to the previous model of k QTLs to evaluate the significance of the additional QTL. The MIM model allows the current QTLs in the model to be held as cofactors when evaluating additional QTL positions. This increases the power to detect QTLs by reducing the remaining variance. MIM also allows QTLs to be selected together to test for epistatic effects. A limitation of the MIM approach is that it only checks pair-wise epistatic effects for the QTLs that are in the model (Kao et al. 1999). Pseudomarker analysis is another interval mapping approach developed by Sen and Churchill (2001) based on Bayesian analysis that allows the evaluation of the

89 posterior probabilities for marker genotype, QTL location, and model parameters. 67 Pseudomarker divides the QTL analysis into two problems, that of estimation of model effects and linkage. The advantage of this application is that all pair-wise comparisons can be made across the genome. This method also allows for three way interactions (Sen and Churchill 2001). The various methods described each offer different advantages that increase the chances of detecting a QTL. However, with every method there is still the issue of significance level. Churchill and Doerge (1994) have addressed this issue by using permutations of the data (1000 for F 2 and 10,000 for RI) and then picking a significance level based on a 5% false positive rate. Other options such as simulating data have been recommended but the permutations of existing phenotypic data outlined by Churchill and Doerge take into account specifics that are unique to the experiment. As mentioned previously (Lander and Kruglyak 1995) recommended a genome-wide correction for a completely dense map based on the assumption that researchers will continue to search for positive results until they find something. There is much debate about selecting the proper threshold level and many suggest that simply reporting results along with the power to detect is adequate. Failure to replicate results that have been found in the literature could be due to differences in phenotyping, age, sex, and number of specimens. For example, two laboratories could both be investigating bone strength but using very different methodology. Even if the same assessment is used, there could be procedural differences that lead to varying results. Often the power to detect a significant QTL is limited by the sample size, and many times QTL results are sex-specific. The environment that the

90 animals are raised in can also influence QTL results. It also must be kept in mind that 68 QTL results are strain-dependent and can vary from study to study depending on where the parental strains differ with respect to genotype. Physical and Genetic Maps Knowing the relationship between markers is critical for locating QTL positions. There are two basic types of maps that can be constructed to indicate positions of genetic markers. Physical maps are based on the ordering of base pairs along the chromosome and can be determined by methods such as nucleotide sequencing. Genetic maps are based on the expected recombination between two points. Recombination between two loci on a chromosome is more likely to occur if the loci are far apart since the closer the loci are the more likely they are linked and segregate together. The distance between two loci is measured by the recombination fraction or how frequently recombination occurs. There are several types of genetic markers that can be used to map QTLs including single nucleotide polymorphisms (SNPs), microsatellites (Simple Sequence Repeats- SSRs), minisatellites (Variable Number of Tandem Repeats-VNTRs), and Restricted Fragment Length Polymorphisms (RFLPs). Review of Quantitative Loci Analyses The following review of various methods used in QTL analyses is based on reviews by Doerge (2002) and Doerge et al. (1997). The localization of a QTL position

91 69 is based on the transmission of genetic markers with a gene influencing the trait. Markers transmitted more often with specific trait values are more likely to be close to a gene for the trait. Several types of map functions can be used to estimate genetic map distance (x) measured in centimorgans (cm). The Haldane map function, Equation 4-3, assumes no interference from recombination at other sites. r = ½(1 - e -2x ) ( 4-3) Where r is the recombination fraction and x is the genetic distance in centimorgans (1 morgan is the distance over which one recombination occurs on a chromosome). The Kosambi mapping function assumes interference and the genetic map distance is estimated by Equation 4-4. x = ¼ ln((1 + 2r)/(1 2r)). ( 4-4) There are two null hypotheses that can be tested in QTL analyses. The first is that no QTL is present and the trait values follow a normal distribution. The second null hypothesis is that the QTL is present and unlinked to the testing position and the trait values follow a mixture of normal distributions. The alternative hypothesis for either of the null hypotheses is that the QTL is present and linked to the testing position and the trait values follow a mixture of normal distributions. Single Marker, Single QTL Analyses There are multiple methods that can be used to conduct QTL analyses. The following review of the various methods will be presented for a backcross population to

92 70 simplify the description of methods. F 2 analyses are similar but more complicated due to the presence of three possible marker and QTL genotypes versus two in a backcross. Backcross populations are produced from two inbred parental strains (P 1 and P 2 ) that are crossed to form a F 1 population. The F 1 population is then backcrossed with P 1 to form the backcross B 1. Figure 4-1 indicates the possible genotypes produced in the backcross taking into account recombination between two markers on a chromosome. Backcross Design P 1 P 2 M 1 Q 1 /M 1 Q 1 M 2 Q 2 /M 2 Q 2 F 1 M 1 Q 1 /M 2 Q 2 B 1 M 1 Q 1 /M 1 Q 1 M 1 Q 1 /M 1 Q 2 M 1 Q 1 /M 2 Q 1 M 1 Q 1 /M 2 Q 2 F 1 individuals Produce 4 possible gametes: M 1 Q 1 M 1 Q 2 M 2 Q 1 M 2 Q 2 Figure 4-1: F1 and B1 possible genotypes considering recombination.

93 Originally, QTL analyses were conducted on one marker at a time across the 71 genome. A simple comparison of the means of different genotype classes was tested using t-tests, regression, or likelihood. Comparison of Marker Means QTL mapping theory is based on the conditional probability of the QTL genotype given the observed marker genotype. The conditional probability given in Equation 4-5 is estimated from the joint and marginal probabilities that are functions of experimental design and linkage map (position of QTL relative to marker). Pr(Q k M j ) = Pr(Q k M j ) / Pr(M j ) ( 4-5) The distribution of trait values is examined separately for each possible marker class. In the case of a backcross the possible marker genotypes are: M 1 /M 1 or M 1 /M 2. Each marker-trait association test is performed independent of all other markers. A chromosome with n markers results in n separate single marker tests. Single marker analyses are used for the simple detection of a QTL linked to a marker rather than the estimation of the position and effects of the QTL. Parental strains, P 1 and P 2, and F 1 populations are genetically uniform and are assigned the same trait variance. Both B 1 and F 2 populations are mixtures of trait distributions and marker genotype classes. The mixing proportions depend on recombination. The Genotypic Array is made up of a proportion of the four possible QTL genotype classes and is given in Equation 4-6.

94 72 Genotypic Array = (1-r MQ )/2 * M 1 Q 1 /M 1 Q 1 + r MQ /2 * M 1 Q 1 /M 1 Q 2 + r MQ /2 * M 1 Q 1/ M 2 Q 1 + (1-r MQ )/2 * M 1 Q 1 /M 2 Q 2 ( 4-6) The two marker classes are: M 1 M 1 Mixture: Q 1 Q 1 Non-recombinants with mean µ 1 (P 1 ) Q 1 Q 2 Recombinants with mean µ 12 (F 1 ) M 1 M 2 Mixture: Q 1 Q 1 Non-recombinants with mean µ 1 (P 1 ) Q 1 Q 2 Recombinants with mean µ 12 (F 1 ) The marker class means are given in Equation 4-7. The expected difference between the marker class means is given in Equation 4-8. The hypothesis that the trait and the marker locus are unlinked (r MQ = 0.5) is equivalent to the hypothesis that the two marker classes have equal means: µ M1/M1 = µ M1/M2 µ M1/M1 = (1-r MQ )/2 * µ 1 + r MQ /2 * µ 12 µ M1/M2 = r MQ /2 * µ 1 + (1-r MQ )/2 * µ 12 (4-7) µ M1/M1 - µ M1/M2 = (1-2 r MQ ) (µ 1 - µ 12 ) (4-8) Comparison of the Means For single marker analyses a t-test can be used to compare the means of the two marker classes Equation 4-9. Where n M1/M1 is the sample size of marker class M1/M1, n M1/M2 is equal to the sample size of marker class M1/M2 and s 2 = pooled estimate of variance within the marker class.

95 t = (µ M1/M1 - µ M1/M2 ) / SQRT(s 2 (1/n M1/M1-1/n M1/M2 ) (4-9) 73 Single Marker Single QTL Regression Significance of the single marker can also be tested by regressing the trait value on the marker genotype. For a backcross population, B 1, the regression Equation 4-10 is: Y j = β 0 + β YX X j +ε j (4-10) where Y is the trait value, j is the jth individual, X is a coded value for the marker genotype (1 = M 1 / M 1, 2 = M 1 / M 2 ), εj is the random error term, β YX is the regression coefficient and is equal to (1-2r MQ )δ, where r MQ = recombination fraction and δ is equal to the difference between the P 1 and F 1 means. The regression coefficient for Y on X is the expected difference between the trait values in the two marker classes. One of the limitations of single marker regression is that the recombination frequency between the marker and the QTL not estimated. Single Marker Likelihood Method Single marker likelihood analysis takes into account the mixture of normal distributions within a marker class. The likelihood for an individual with phenotype value z given marker genotype M j is estimated from Equation 4-11, l(z M j ) = Σ φ (z, µq k, σ2) Pr(Q k M j ) (4-11)

96 74 where k is equal to the number of QTL genotypes and φ is the density function for the normal distribution given in Equation p(z) = (2П σ2) -1/2 exp[-(z-µ)2 / 2 σ2] (4-12) For a B 1 design with two QTL genotypes (Q 1 Q 1 and Q 1 Q 2 ) and two marker classes (M 1 M 1 and M 1 M 2 ) the likelihood of obtaining z given the marker genotype M 1 M 1 and M 1 M 2 is given by Equation 4-13 and Equation 4-14 respectively. l(z M 1 M 1 ) = [ Pr(Q 1 Q 1 M 1 M 1 ) φ (z, µ 1, σ2) + Pr(Q 1 Q 2 M 1 M 1 ) φ (z, µ 12, σ2) ] (4-13) l(z M 1 M 2 ) = [ Pr(Q 1 Q 1 M 1 M 2 ) φ (z, µ 1, σ2) + Pr(Q 1 Q 2 M 1 M 2 ) φ (z, µ 12, σ2) ] (4-14) Where the likelihood of z, (l (z)) = Π l (zi Mi) = which is the product of the likelihood for each individual. Parameter estimates for µ 1, µ 12, σ2, and r MQ are obtained using maximum likelihood. An estimation and maximization (EM) algorithm is used to obtain the estimates. The hypotheses for H0:2 are tested with the likelihood ratio statistic (LR) given in Equation 4-15 as λ: λ = - 2 ln [L(µ 1, µ 12, σ2, r MQ =0.5) / L (µ 1, µ 12, σ2, r MQ )] (4-15) An alternative procedure is to evaluate r MQ over a grid of values and a LOD score is calculated based on Equation LOD = - log10 [L(µ 1, µ 12, σ2, r MQ =0.5) / L (µ 1, µ 12, σ2, r MQ )] (4-16)

97 75 Interval Mapping Likelihood Method Interval mapping estimates the QTL position using two flanking markers (M and N in Figure 4-2). This allows for the separation of recombination and size effect. A separate analysis is performed for each pair of adjacent marker loci. Figure 4-2: Interval mapping for QTL Q and flanking markers M and N. For a backcross design the F 1 individuals consisting of four possible gamete genotypes are backcrossed to the parental strain with one possible genotype. The proportion of each gamete depends on the recombination fraction given in Figure 4-3

98 Figure 4-3: Proportion of gametes in a F1 population accounting for recombination. 76

99 77 Because P 1 individuals have only one possible genotype, the frequency of marker genotype classes in the B 1 population is equal to the frequency of marker genotypes in the F 1 gametes. This results in four possible marker classes in the backcross population: B 1 Marker Class and Frequency: M 1 N 1 (1-r mn )/2 M 1 N 2 r mn /2 M 2 N 1 r mn /2 M 2 N 2 (1-r mn )/2 There are two possible QTL genotypes in a backcross population (Q 1 Q 1 and Q 1 Q 2 ). The likelihood of obtaining the trait data (z) within a marker class is a mixture of distributions within each marker class based on the probability of the QTL genotype. The likelihood for each marker class is given by the following: Equation 4-17 (marker class M1N1M1N1), Equation 4-18 (marker class M 1 N 1 M 1 N 2 ), Equation 4-19 (marker class M1N1M2N1), and Equation 4-20 (marker class M1N1M2N2). l(z M 1 N 1 M 1 N 1 )= [ Pr(Q 1 Q 1 M 1 N 1 M 1 N 1 ) φ (z, µ Q1Q1, σ2) + r(q 1 Q 2 M 1 N 1 M 1 N 1 ) φ (z, µ Q1Q2, σ2) ] (4-17) l(z M 1 N 1 M 1 N 2 ) = [ Pr(Q 1 Q 1 M 1 N 1 M 1 N 2 ) φ (z, µ Q1Q1, σ2) + Pr(Q 1 Q 2 M 1 N 1 M 1 N 2 ) φ (z, µ Q1Q2, σ2) ] (4-18) l(z M 1 N 1 M 2 N 1 ) = [ Pr(Q 1 Q 1 M 1 N 1 M 2 N 1 ) φ (z, µ Q1Q1, σ2) + Pr(Q 1 Q 2 M 1 N 1 M 2 N 1 ) φ (z, µ Q1Q2, σ2) ] (4-19)

100 78 l(z M 1 N 1 M 2 N 2 ) = [ Pr(Q 1 Q 1 M 1 N 1 M 2 N 2 ) φ (z, µ Q1Q1, σ2) + Pr(Q 1 Q 2 M 1 N 1 M 2 N 2 ) φ (z, µ Q1Q2, σ2) ] (4-20) For example, the probability of F1 gametes in the marker class M 1 N 1 M 1 N 1 is obtained by first calculating the frequency of M 1 N 1 in Equation 4-21 plus Equation Pr(M 1 Q 1 N 1 ) = (1 r MQ )(1-r QN ) / 2 (4-21) Pr(M 1 Q 2 N 1 ) = (r MQ )(r QN ) / 2 (4-22) For the backcross the P 1 donates M 1 Q 1 N 1 genotype therefore the joint probability for M 1 Q 1 N 1 from P 1 and M 1 Q 1 N 1 from F 1 is given by Equation 4-23, and the joint probability of M 1 Q 1 N 1 from P 1 and M 1 Q 2 N 1 from F 1 is given in Equation Pr(M 1 Q 1 N 1 / M 1 Q 1 N 1 ) = (1 r MQ )(1-r QN ) / 2 (4-23) Pr(M 1 Q 1 N 1 / M 1 Q 2 N 1 ) = (r MQ )(r QN ) / 2 (4-24) The probability of obtaining the QTL genotype Q 1 Q 1 within the marker class M 1 M 1 N 1 N 1 is given by Equation 4-25: Pr(Q 1 Q 1 M 1 N 1 M 1 N 1 ) = Pr(M 1 Q 1 N 1 / M 1 Q 1 N 1 ) / Pr(M 1 M 1 N 1 N 1 ) (4-25) where Pr(M 1 M 1 N 1 N 1 ) is equal to the marker class frequency. Substituting the frequencies into Equation 4-25 gives the frequency of Q 1 Q 1 in marker class M 1 N 1 M 1 N 1 (Equation 4-26). Pr(Q 1 Q 1 M 1 N 1 M 1 N 1 ) = [(1 r MQ )(1-r QN ) / 2] / (1-rmn)/2 (4-26)

101 79 Equation The probability of obtaining the QTL genotype Q 1 Q 2 is similarly obtained by Pr(Q 1 Q 2 M 1 N 1 M 1 N 1 ) = Pr(M 1 Q 1 N 1 / M 1 Q 2 N 1 ) / Pr(M 1 M 1 N 1 N 1 ) (4-27) Equation 4-28 And the frequency of QTL genotype in the M 1 N 1 M 1 N 1 marker class is given in Pr(Q 1 Q 2 M 1 N 1 M 1 N 1 ) = [(r MQ )(r QN ) / 2] / (1-rmn)/2 (4-28) The likelihood of obtaining the trait value given the marker class M 1 N 1 M 1 N 1 can now be estimated by adding the probability of obtaining the two QTL genotypes within the marker class M 1 N 1 M 1 N 1 by Equation l(z M 1 N 1 M 1 N 1 ) = [ Pr(Q 1 Q 1 M 1 N 1 M 1 N 1 ) φ (z, µ Q1Q1, σ2) + Pr(Q 1 Q 2 M 1 N 1 M 1 N 1 ) φ (z, µ Q1Q2, σ2) ] (4-29) The likelihood is calculated for each marker class as outlined above for the marker class M 1 N 1 M 1 N 1. For the B1 population there are four marker classes and in the F2 population there are nine marker classes. Each individual belongs to one marker class. Therefore the likelihood of obtaining the trait data given the marker genotypes is the product of the likelihood for each individual (Equation 4-30). l (z) = Π l (zi Mi) (4-30) The use of two marker genotypes results in n-1 separate tests of marker-trait associations for a chromosome with n markers (one for each marker interval). Interval Mapping increases the power of detection and more accurately estimates of QTL effects and positions compared to single marker analysis.

102 Composite Interval Mapping (CIM) 80 Both single marker and interval mapping cannot distinguish multiple linked QTL effects when two or more QTLs are on same chromosome and can result in incorrect mapping positions. QTLs on the same chromosome will bias the QTL effect estimates. CIM combines interval mapping (using two flanking markers) and multiple regression (using other markers besides the flanking markers as cofactors). Composite interval mapping was developed by Z. B. Zeng and is described in detail in Zeng (1993) and Zeng (1994).. The test statistic is independent of the effects of QTLs at other regions of the chromosome. This increases the precision of mapping as well as the power to detect QTLs by removing the genetic variance of unlinked markers. The statistical model used in composite interval mapping is a multiple regression model and is given in Equation 4-31: yj = b0 + b* xj* + Σ bk xjk + ej (4-31) where j = 1, 2,..., n (n = # of individuals), b0 is the mean of the model, b* is the effect of the QTL (being tested in interval), xj* is coded as a 1 or 0 (QTL genotype based on probability of flanking marker genotype and testing position), bkis the partial regression coefficient of y on the kth marker, and xjk is coded as a 1 or 0 depending on genotype of kth marker. The advantage of multiple regression is that the partial regression coefficient depends only on the QTL within the interval of the two neighboring markers. In addition, conditioning on unlinked markers (on other chromosomes) reduces the

103 81 sampling variance of the test statistic by removing genetic variation, which increases the power. Conditioning on linked markers (on the same chromosome) will reduce the interference of multiple linked QTLs but could cause a loss of statistical power. Regression coefficients for two markers are uncorrelated with each other unless they are on adjacent markers. The multiple regression Equation 4-32 for composite interval mapping combines multiple regression with interval mapping: yj = b0 + b* xj* + Σ bk xjk + ej (4-32) xj* is the QTL genotype dependent on the flanking marker genotype and testing position. The probability of the QTL having genotype (1) or (0) is given by Equation For the backcross design there are two QTL genotypes and four marker classes. L (β0, β*, {βk}, σ 2 ) = Π [p j (1)f j (1) + p j (0)f j (0)] (4-33) Parameter Estimates are obtained using an expectational / conditional maximization algorithm. Hypothesis testing evaluates the likelihood function for b* = 0. The likelihood ratio is then given as minus two times the natural log of the likelihood function evaluated under the null hypothesis and the maximum likelihood estimate calculated using Equation LR = -2 ln (L0/L1) (4-34) Assuming no epistasis, QTL position (p) and effect (b*) are unaffected by other linked QTLs if markers separate those QTLs from the QTL being tested. There can still be interference on testing and estimation between two QTLs in adjacent intervals.

104 82 For multiple regression, the test statistic under the null hypothesis is uncorrelated for different intervals. The nominal significance level is equal to alpha divided by the number of intervals for a sparse map. The distribution of the test statistic under the null hypothesis is not clear and depends on the sample size, number of markers in model, and the genetic size of map. When n is small and a large number of markers are in the model the test statistic deviates from a chi square distribution. Zhao-Bang Zeng (1994) suggests using 5 markers as cofactors. These markers can be pre-identified with stepwise regression to explain most of the variance. The partial regression coefficient (b yist ) of the phenotype, y, on the ith marker conditional on all other markers is equal to: (σyist/σ2 ist ) which is equal to the conditional covariance of the phenotype and marker divided by the conditional variance of the marker. The sampling variance of (b yist) is equal to (σys 2 /n σ2 ist ). Since σ2 ist is a function of only the size of the neighboring intervals, removing some immediate linked markers will increase the denominator and decrease the numerator, which will increase the power. This is because the conditional test depends on the number of recombinants and increasing the interval increases the number of recombinants. However, this increases the chance of interference from linked QTLs. Composite interval mapping can be performed in the software package QTL Cartographer (Basten et al. 1994; Basten et al. 2002). Several models are available to allow options for the markers that are included in the model. Model 1 includes all markers in model. Model 2 is semi-composite interval mapping with all unlinked

105 83 markers (markers not on the chromosome that is being tested). Model 3 is equivalent to interval mapping with two flanking markers in model. Model 1 and 3 do not control for other QTLs on the same chromosome. Model 2 removes the residual variance of QTLs on other chromosomes, which increases statistical power. Model 2 and 3 are not considered interval tests and might not accurately locate the QTL because of bias from other linked QTLs. Model 1 is the only model that is a true interval test that removes variation from linked QTLs. Model 1 is a conditional test on linked markers and can reduce interference from linked QTLs but could also cause an increase in sampling variance which can reduce the statistical power compared with models 2 and 3. A combination of model 1 and 2 provides the best probability of detecting QTLs and mapping them with precision. Only the markers linked to QTLs will be informative in removing genetic variance. Step-wise regression can be used to select the markers used as cofactors to control for genetic background. Model 4 is an ad-hoc model with 1 marker for each chromosome (except for the chromosome being tested). Model 5 is also an ad-hoc model with 2 markers per chromosome and all other markers on the chromosome that is being tested that are 10 cm away from the flanking markers. Model 6 allows the user to pick the number of markers to use as cofactors and a window size that is used to exclude markers close to the test site. Options are available to pick the method of selection used to identify the markers to control for background then at each test position only the number of cofactors that have been specified are used in the model with markers within the specified window excluded as cofactors (the window blocks out markers on each side of test site). Jansen and Stam (1994) recommend that the number of cofactors should not

106 exceed two times the square root of number of individuals in the analysis (n). The 84 defaults that have been suggested for model 6 are a 10 cm window with 5 markers as cofactors in model. Increasing n will increase precision of mapping. Model 7 uses interval mapping and then uses virtual markers as estimates of QTL positions as covariates in the model. Model 8 is similar to 7, but uses flanking markers of putative QTLs as cofactors instead of virtual markers. Multiple Interval Mapping Multiple QTL models also called Multiple Interval Mapping (MIM) or Multipoint Mapping simultaneously take into consideration all linked markers on a chromosome. Multiple interval mapping uses Cockerham s genetic model to partition the genetic variance into additive, dominance, and epistatic variance. Cockerham used orthogonal contrasts to partition the epistatic variance. Orthogonal contrasts ensure that the solutions are linearly independent. For a backcross population with m QTLs (Q 1, Q 2,.., and Q m ) there are two possible QTL genotypes for each of the m QTLs. Therefore there are 2 m possible genotypes in the backcross population. Cockerham s genetic model is used to estimate the genetic parameters and model the relationship between the genotypic value and the genetic parameters. Equation 4-35 defines the relationship between the genotypic value G i for individual i and the genetic parameters (Kao et al. 1999;Kao and Zeng 2002). G i = µ + Σ a j x ij + Σ w jk ( x ij x jk ) (4-35) j=1 j/=1

107 85 Where x ij is coded as ½ or ½ if the genotype of Q j is Q j Q j or Q j q j, a j is the main effect of Q j, and w jk is the epistatic effect between Q j and Q k. The genetic model for multiple interval mapping is given in Equation 4-36, y i = µ + Σ a j x ij * + Σ δ jk ( w jk x ij * x jk * )+ ε i (4-36) where µ is the mean, x ij * is the coded variable for the genotype of Qj, a j is the main effect of Q j, w jk is the epistatic effect between Q j and Q k, δ jk is an indicator variable for epistasis that is 1 if QTLs Qj and Qk interact or 0 if they do not, and ε i is the environmental deviation (Kao et al. 1999). The genotype of each QTL is unknown but its distribution is inferred based on the recombination frequency between the two flanking markers. Under the assumption of no recombination interference, the conditional probability distribution of each QTL genotype is independent from each other and the joint conditional probability of the m QTLs is the product of the individual QTL probability (Equation 4-37), Prob (Q 1, Q 2,..., Q m I 1, I 2,..., I m ) = Π prob ( Qi Ii ) (4-37) where I i is the interval containing the QTL Q j. The likelihood function for the estimated parameters of the model given the phenotypic trait data and marker data for n individuals is given in Equation 4-38, L (θ Y, X) = Π [Σ p ij φ (y i - µ ij )/ σ ] (4-38) where θ is equal to the estimated parameters in the genetic model (QTL positions p 1, p 2,, p m, genetic affects a 1,, a m, epistatic effects w jk,, and variance σ 2 ), φ is the standard normal probability density function, the µ ij s are the genotypic values for the 2 m

108 86 different QTL genotypes and the p ij s are the joint conditional probabilities. The density of each individual is a mixture of 2 m possible normal densities with means µ ij s and mixing proportions p ij s. The density referred to here is the distribution of the probability of the genotypic value for each individual. An estimation and maximization algorithm is used to obtain the maximum likelihood estimates for the genetic parameters (Kao and Zeng 1997). An advantage of multiple interval mapping is that it allows for the modeling of epistatic QTLs. If epistasis is present and ignored the variance attributable to the interaction is added to the residual error. This increases the sampling variance and reduces the power to detect QTLs. If the QTLs are unlinked, Cockerham s model is not biased if epistasis is present but ignored. Multiple interval mapping can be implemented using QTL Cartographer (Basten et al. 1994; Basten et al. 2002). The modeling process starts with no QTL in the genetic model (m=0). QTLs are then added to the model using stepwise regression. A likelihood ratio test (LRT) statistic (Equation 4-39) is selected as the criterion for QTLs that enter or leave the model. The genome is then scanned for the position of a QTL that when added to the model (m=1) results in a LRT greater than the entering value. LRT = -2 log L 0 /L 1 (4-39) If no position above the entry value is found then the position with the highest LRT is selected to get the process started. A region can also be added based on a suspicion that two closely linked QTLs with opposite effects are present.

109 The model is now evaluated with m = k +1 QTLs in the model. Again the 87 position with the highest LRT above entry level is added to the model. Stepwise selection evaluates all QTLs and deletes any QTLs that are less than the specified staying level. Stepwise selection ends when no further positions above entry level are found. The shape of the LR curve can indicate multiple linked QTLs. Several peaks or a change in direction of the QTL effect in the same region can be an indication of multiple linked QTLs. Linked QTLs can be separated by comparing a multiple QTL model to a single QTL model for the suspected region. After all main effect and epistatic interactions are identified the QTL positions are fine tuned for the final model by doing a multidimensional search around the regions of the QTLs. Heritability can be estimated by Equation Where V g is the genetic variance and V p is the phenotypic variance. The heritability is equal to the model sum of squares divided total sum of squares = R 2. h 2 = V g /V p (4-40) One of the advantages of MIM over CIM is that CIM cannot control for two linked QTLs when searching for new QTLs, it only includes 1 of the two and the variance remains in the residual variance in subsequent tests. Structural Equation Modeling To investigate the relationships between the three domains of activity, muscle, bone, and genetics, a structural equation model was developed to include a genetic

110 88 marker representing a quantitative trait loci (QTL) and three latent variables: (activity, muscle, and bone). Each of these factors loaded on measured parameters. Activity loaded on headpokes, rears, squares and rod sector entries (see methods for description). The muscle factor loaded on the measured masses of the gastrocnemius, soleus, extensor digitorum longus, and tibialis anterior. For the femur, the bone factor loaded on femur length, femur width (sagittal), head diameter, ultimate load, stiffness, and ash mass. For the tibia model, the bone factor loaded on tibia length, tibia width (sagittal), tibia width (coronal), tibia ultimate load, stiffness, and tibia percent ash. The marker included in the model was the peak marker for each QTL that had been identified as having pleiotropic effects across at least two of the three domains. If the QTL mapped between two flanking markers both markers were included in the models one at a time. Structural equation modeling (SEM) was performed on raw data and separated by sex using Mx Statistical software. For those phenotypes that were not normally distributed a log or square root transformation was used. If neither of these transformations improved the distribution the analyses were based on the raw data. Parameter estimates were made using maximum likelihood (Neale et al. 2002). Eight full models were run for five QTLs (chr. 3, chr. 4, chr. 5, chr. 9 and chr. 11) each loading on femur and tibia separately. Submodels were tested by calculating the 2 * log likelihood difference between each submodel and the full model. The difference in 2*log likelihood between the full model and nested sub-model is distributed as a χ 2. The degrees of freedom (df) for the χ 2 test of significance are the number of parameter estimates in the full model minus the number of parameter estimates in the sub-model. A significant difference between the sub-model and the full model indicates that the differences between the models are significant.

111 Significant differences indicate that the parameter removed in the sub-model is 89 significant (Neale et al. 2002).

112 90 References Basten, C. J., Weir, B. S., & Zeng, Z.-B. (1994). Zmap-a QTL cartographer. In Proceedings of the 5th World Congress on Genetics Applied to Livestock Production: Computing Strategies and Software, edited by C. Smith, J. S. Gavora, B. Benkel, J. Chesnais, W. Fairfull, J. P. Gibson, B. W. Kennedy and E. B. Burnside. Volume 22, pages Published by the Organizing Committee, 5th World Congress on Genetics Applied to Livestock Production, Guelph, Ontario, Canada. Basten, C.J., Weir, B. S., & Z.-B. Zeng, Z.-B. (2002). QTL Cartographer, Version 2.0. Department of Statistics, North Carolina State University, Raleigh, NC. Churchill, G.A. and Doerge, R.W. (1994) Empirical threshold values for quantitative trait mapping. Genetics 138, Doerge, R.W. (2002) Mapping and analysis of quantitative trait loci in experimental populations. Nature Reviews. Genetics 3, Doerge, R.W., Zeng, Z.-B., and Weir, B.S. (1997) Statistical issues in the search for genes affecting quantitative traits in experimental populations. Statistical Science 12, Jansen, R.C. and Stam, P. (1994) High-resolution of quantitative traits into multiple loci via interval mapping. Genetics 136, Kao, C.-H. and Zeng, Z.-B. (1997) General formulas for obtaining the MLEs and the asymptotic variance-covariance matrix in mapping quantitative trait loci when using the EM algorithm. Biometrics 53, Kao, C.-H. and Zeng, Z.-B. (2002) Modeling epistasis of quantitative trait loci using Cockerham's model. Genetics 160, Kao, C.-H., Zeng, Z.-B., and Teasdale, R.D. (1999) Multiple interval mapping for quantitative trait loci. Genetics 152, Lander, E.S. and Kruglyak, L. (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nature Genetics 11, Neale, M. C., Boker, S. M., Xie, G., & Maes, H. H. (2002). Mx: Statistical Modeling. VCU Box , Richmond, VA 23298: Department of Psychiatry. 6 th Edition. Sen, S. and Churchill, G.A. (2001) A statistical framework for quantitative trait mapping.

113 Genetics 159, Zeng, Z.B. (1993) Theoretical basis of separation of multiple linked gene effects on mapping quantitative trait loci. Proceedings of the National Academy of Sciences USA 90, Zeng, Z.B. (1994) Precision mapping of quantitative trait loci. Genetics 136(4),

114 92 Chapter 5 Results This chapter presents a short summary of the most significant and important results. A complete list of results from the analyses for sex, strain and age differences is included in Appendix C. Particularly interesting data are also presented in chapters 6, 7, and 8, which are each designed as stand-alone full-text manuscripts to be submitted for publication. These chapters also summarize pertinent aspects of the background literature, methods, and analyses that were previously explored in detail within chapters 1-4. As mentioned previously in chapter 4, proper correction of skeletal measures for body size is not well appreciated and can be problematic. A thorough exploration of this issue is therefore presented in chapter 6. Skeletal QTL results from the F 2 and RI analyses are presented in chapter 7 and structural equation modeling of the relationships between activity, muscle, and bone, as well as the genetic influence is presented, in chapter 8. Sex Differences A one-way analysis of variance (ANOVA) was used to test for sex differences in the F 2 cohort, which included 200 male and 200 female mice, and within the two progenitor strains using 5 male and 5 female mice for each strain. Many of the skeletal

115 93 phenotypes investigated in this study revealed significant sex differences. A summary of the results for select phenotypes is given in Tables 5-1 and 5-2 and the complete list is given in Appendix C. Table 5-1: Two-way ANOVA results for sex and strain differences between progenitor strains and one-way ANOVA results for sex differences in the F 2 cohort. Skeletal characteristics of C57BL/6 and DBA/2 progenetor strain mice and F day old mice. Means and standard deviations are presented; p-values reflect differences between the two strains and between genders as determined by two-way ANOVA and one-way ANOVA for the F 2 analysis. C57BL/6 DBA/2 F day old Quantitative Measures Female Male Female Male Female Male n = 5 n = 5 n = 5 n = 5 n = 200 n = 200 Body Weight (gm) Gender Effects p < p < p < Strain Effects p = Body Length (cm) Gender Effects p < ns p < Strain Effects ns Overall Femoral Length (mm) Gender Effects p = ns p=0.029 Strain Effects p = Femoral Shaft Width (mm) (Coronal Plane) Gender Effects p < p = p < Strain Effects p = Femoral Head Diameter (mm) Gender Effects p < ns p < Strain Effects p = Femoral Shaft Yield Load (N) Gender Effects ns p=0.002 Strain Effects p < 0.001

116 Table 5-2: Two-way ANOVA results for sex and strain differences between progenitor strains and one-way ANOVA results for sex differences in the F 2 cohort (continued). Skeletal characteristics of C57BL/6 and DBA/2 progenetor strain mice and F day old mice. Means and standard deviations are presented; p-values reflect differences between the two strains and between genders as determined by two-way ANOVA and one-way ANOVA for the F 2 analysis. C57BL/6 DBA/2 F day old Quantitative Measures Female Male Female Male Female Male n = 5 n = 5 n = 5 n = 5 n = 200 n = 200 Femoral Shaft Yield Displacement (mm) Gender Effects ns p=0.004 Strain Effects p < Femur Ash Weight (mg) Gender Effects p = ns p < Strain Effects ns Overall Tibial Length (mm) Gender Effects p < ns p = Strain Effects p = Tibial Shaft Width (mm) (Sagittal Plane) Gender Effects p = ns Strain Effects p = p < Tibial Shaft Failure Load (N) Gender Effects p < ns p < Strain Effects Tibial Shaft Stiffness (N/mm) Gender Effects p < ns p < Strain Effects p < Age by Strain Differences A subset of testing and analyses were performed for 10 male and 10 female mice from each of the parental strains DBA/2 and C57BL/6 at 650 days of age. These animals were then compared to the 5 male and 5 female DBA/2 and C57BL/6 animals at 200 days of age to investigate age related differences between the strains. Two-way ANOVA was

117 used to investigate mean differences in age and strain within each sex separately. The 95 results of these analyses are summarized in Table 5-3 and Table 5-4 and a complete list is presented in Appendix C.

118 96 Table 5-3: Two-way ANOVA for strain (B6 versus D2) and age (200 day versus 650 day) for skeletal and muscle phenotypes separated by sex. Skeletal characteristics of C57BL/6 and DBA/2 progenetor strain male and female mice. Means and standard deviations are presented, p-values reflect differences within strain by age and between the two strains at 650 and 200 days old. P - values were determined by 2-way ANOVA for males and females separately. Male C57BL/6 DBA/2 Female C57BL/6 DBA/2 Quantitative Measures 200 days 650 days 200 days 650 days Quantitative Measures 200 days 650 days 200 days 650 days n = 5 n = 5 n = 5 n = 5 n = 5 n = 5 n = 5 n = 5 Body Length (cm) Body Length (cm) Age Effects ns ns Age Effects p < p = Strain Effects p = p = Strain Effects ns ns Body Weight (gm) Body Weight (gm) Age Effects p = p = Age Effects p = ns Strain Effects p < p = Strain Effects ns ns Extensor Digitorum (mg) Extensor Digitorum (mg) Age Effects p = p < Age Effects p = ns Strain Effects p < p < Strain Effects p = ns Tibialis Anterior (mg) Tibialis Anterior (mg) Age Effects p = p< Age Effects p = p = Strain Effects p < p < Strain Effects p < p < Overall Femoral Length (mm) Overall Femoral Length (mm) Age Effects ns ns Age Effects ns ns Strain Effects p = p = Strain Effects ns ns Femoral Shaft Width (mm) Femoral Shaft Width (mm) (Sagittal Plane) (Sagittal Plane) Age Effects p < p = Age Effects p = p = Strain Effects p = ns Strain Effects p = p = 0.023

119 97 Table 5-4: Two-way ANOVA for strain (B6 versus D2) and age (200 day versus 650 day) for skeletal and muscle phenotypes separated by sex (continued). Skeletal characteristics of C57BL/6 and DBA/2 progenetor strain male and female mice. Means and standard deviations are presented, p-values reflect differences within strain by age and between the two strains at 650 and 200 days old. P - values were determined by 2-way ANOVA for males and females separately. Male C57BL/6 DBA/2 Female C57BL/6 DBA/2 Quantitative Measures 200 days 650 days 200 days 650 days Quantitative Measures 200 days 650 days 200 days 650 days n = 5 n = 5 n = 5 n = 5 n = 5 n = 5 n = 5 n = 5 Femoral Shaft Yield Load (N) Femoral Shaft Yield Load (N) Age Effects p < p < Age Effects ns p = Strain Effects p = p = Strain Effects ns p = Tibial Shaft Width (mm) Tibial Shaft Width (mm) (Coronal Plane) (Coronal Plane) Age Effects ns p = Age Effects p < ns Strain Effects ns p = Strain Effects p < ns Tibial Shaft Yield Load (N) Tibial Shaft Yield Load (N) Age Effects p < ns Age Effects p = ns Strain Effects ns p = Strain Effects ns ns Tibial Shaft Stiff. (N/mm) Tibial Shaft Stiff. (N/mm) Age Effects ns ns Age Effects ns ns Strain Effects p = p = Strain Effects p = ns Tibial Bone Organic (%) Tibial Bone Organic (%) Age Effects p = p = Age Effects p < p = Strain Effects ns ns Strain Effects ns ns Tibial Bone Mineraliz. (%) Tibial Bone Mineraliz. (%) Age Effects ns ns Age Effects p = ns Strain Effects ns ns Strain Effects p = ns Femur Ash Weight (mg) Femur Ash Weight (mg) Age Effects p = p < Age Effects ns ns Strain Effects p = ns Strain Effects ns ns

120 98 Heritability Heritabilities were estimated by comparing the within strain variance to the between strain variance within the RI cohort. Separate heritabilities were estimated for female and male phenotypes by performing one-way ANOVAs with strain as the factor for the RI cohort. The r 2 values from the one-way ANOVAs provided the heritability estimates given in Table 5-5. Table 5-5: Heritability estimates for select traits based on a one-way ANOVA on strain in the recombinant inbred cohort. Estimates were made for males and females separately. Female h 2 Male h 2 Female h 2 Male h 2 Body Size Muscle Mass Body Mass Index Extensor Digitorum Longus Body Weight Gastrocnemius Body Length Tibialis Anterior Adipose Weight Gastrocnemius Femur Overall Dimensions Tibia Overall Dimensions Femur Length Tibial Length Femur Coronal Width Tibial Coronal Length Femur Head Diameter Femur Medullary and Cortical Area and Thickness Tibia Medullary and Cortical Area and Thickness Femur Medullary Area Tibia Medullary Area Femur Cortical Area Tibia Cortical Area Femur Total Area TibiaTotal Area Femur Shaft Strength / Measures Tibia Shaft Strength / Measures Femur Ultimate Load Tibia Ultimate Load Femur Ultimate Stress Tibia Ultimate Stress Femur Ultimate Strain Tibia Ultimate Strain Femur Modulus of Elasticity Tibia Modulus of Elasticity Femur Stiffness Tibia Stiffness Femur Neck Strength / Measures Skeletal Ash Mass and Composition Shear Test Ultimate Load Femur Ash Mass Tibia % Water Serum Tibia %Mineral Alkaline Phosphatase Tibia % Organic Serum Calcium Tibia % Ash

121 QTL Analyses 99 QTL analyses were performed on males and females separately as well as on a combined group corrected for sex differences using QTL Cartographer (Basten et al. 1994; Basten et al. 2002). All phenotypes were corrected for body size using multiple regression on body weight and body length to identify QTLs influencing skeletal phenotypes independent of body size. The resulting residuals were used as the body size corrected phenotypes. The following QTL results include both raw data and body size regressed phenotypes. The result tables include a column with variable codes that identify regressed phenotypes (R). All phenotypes were screened for normality and when necessary a log (L) or square root (S) transformation was used and is also indicated in the code column of the table. The following tables include the chromosome number, trait code, peak cm position, LOD score, additive genetic effect for RI and additive and dominant genetic effects for F 2, and percent variance explained by the QTL. The column labels correspond to the descriptions given in Table 5-6. Table 5-6: Column descriptions for QTL result tables. Chart Code Description Hypotheses tested: Chr. Chromosome H0: a = 0, d = 0 Code R-regressed on weight and length, H1: a not = 0, d = 0 (RI) L-log transformed, S-square root transformed H3: a not = 0, d not = 0 (F2) Peak cm LOD H1:a H3:a H3:d Var. Position in centimorgans where LOD score LOD score for H3/H0 (F2) and H1/H0 (RI) Estimate of a (the additive effect) under H1 Estimate of a (the additive effect) under H3 Estimate of d (the dominance effect) under H3 Percent variance attributed to QTL

122 100 The significance threshold level for linkage analysis must be adjusted to correct for multiple testing across the genome. A 5% chance of obtaining one false positive in a genome wide scan is equal to a point wise significance threshold of The following classification s were suggested by (Lander and Kruglyak 1995) for declaring suggestive and significant linkage: Suggestive linkage (expect to get value 1 time by chance in a genome scan) RI or F 2 population with additive genetic effect only: Genome wide p = 1.0 point wise p = and LOD = 1.9 F 2 population (additive and dominant genetic effect): Genome wide p = 1.0 point wise p = and LOD = 2.8 Significant linkage (expect to get value 0.05 times in a genome scan) RI or F 2 population with additive genetic effect only: Genome wide p = 0.05 point wise p = and LOD = 3.3 F 2 population (additive and dominant genetic effect): Genome wide p = 0.05 point wise p = and LOD = 4.3 Confirmed linkage (Significant linkage in an initial study with confirmation in a second study at a nominal or point wise p = 0.01 which is equivalent to a LOD score of 1.5. The QTL analyses revealed many QTLs exceeding the recommended significant threshold level. The QTLs that exceeded a LOD score of 4.3 at the significant level for the F 2 analyses with both additive and dominant genetic effects are summarized in Tables 5-7 for females, 5-8 for males, and Tables 5-9 and, 5-10 for the combined group. QTLs that exceeded the significant level in the RI analyses with LOD scores greater than

123 3.3 are summarized in Tables 5-11 for females, Table 5-12 for males and Table 5-13 for the combined group. 101 Table 5-7: Interval mapping results for female F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed redisduals. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. F2 F 2 Femur Cortical Thick-medial LR F2 F 2 Femur Cortical Thick-medial L F2 F 4 Alkaline Phosphatase SR F2 F 4 Alkaline Phosphatase S F2 F 4 Rears Mean SR F2 F 4 Rears Mean S F2 F 4 Squares Mean S F2 F 4 Squares Mean SR F2 F 6 Femur Coronal Width F2 F 11 Body Length L F2 F 12 Femur Head Diameter R F2 F 12 Femur Head Diameter F2 F 15 Rod Sections F2 F 16 Femur Outer Radius-lateral L F2 F 16 Femur Outer Radius-medial LR F2 F 16 Femur Outer Radius-medial L F2 F 20 Calcium (Serum) F2 F 20 Calcium (Serum) R

124 102 Table 5-8: Interval mapping results for male F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. F2 M 1 Adipose Weight F2 M 1 Adipose Weight R F2 M 1 Femur Length F2 M 1 Femur Length R F2 M 1 Tibia CSMI-PB SR F2 M 1 Tibia Length F2 M 1 Tibia Length R F2 M 4 Alkaline Phosphatase S F2 M 4 Alkaline Phosphatase R F2 M 4 Tibia Ultimate Load F2 M 6 Femur Coronal Width R F2 M 6 Gastrocnemius R F2 M 6 Shear Test Yield Load R F2 M 6 Shear Ultimate Displ. L F2 M 6 Shear Ultimate Displ. LR F2 M 7 Adipose Weight F2 M 7 Femur Ultimate Load R F2 M 7 Femur Yield Load R F2 M 8 Tibia % Mineral F2 M 11 Adipose Weight R F2 M 13 Body Weight Table 5-9: Interval mapping results for combined (male and female sex corrected) F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. F2 C 1 Adipose Weight R F2 C 1 Femur Length F2 C 1 Femur Length R F2 C 1 Femur Sagittal Width L F2 C 1 Tibia Length F2 C 1 Tibia Length R F2 C 1 Tibia Medullary Area L F2 C 1 Tibia Medullary Area LR F2 C 2 Femur Length R F2 C 2 Femur Yield Load R F2 C 2 Femur Yield Load

125 103 Table 5-10: Interval mapping results for combined (male and female sex corrected) F 2 phenotypes with LOD scores > 4.3 (continued). F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. F2 C 3 Cord Drop Mean S F2 C 3 Cord Drop Mean R F2 C 3 Gastrocnemius F2 C 3 Tibia Length F2 C 4 Alkaline Phosphatase S F2 C 4 Alkaline Phosphatase R F2 C 4 Rears Mean S F2 C 4 Rears Mean SR F2 C 4 Squares Mean S F2 C 4 Squares Mean SR F2 C 6 Femur Coronal Width R F2 C 6 Femur Coronal Width F2 C 6 Shear Ultimate Displ. LR F2 C 6 Tibia Length R F2 C 6 Tibia Outer Radius-posterior R F2 C 6 Tibia Ultimate Load R F2 C 6 Tibia Ultimate Load F2 C 7 Adipose Weight S F2 C 7 Femur Ultimate Load LR F2 C 7 Femur Yield Load R F2 C 8 Tibia % Mineral F2 C 8 Tibia % Mineral R F2 C 11 Cord Drop Mean S F2 C 12 Femur Head Diameter R F2 C 12 Femur Head Diameter F2 C 13 Adipose Weight S F2 C 13 Body Mass Index L F2 C 13 Body Weight L F2 C 13 Rears Mean S F2 C 13 Squares Mean S F2 C 13 Squares Mean SR F2 C 13 Tibia Length F2 C 14 Femur Coronal Width R F2 C 14 Femur Coronal Width F2 C 16 Femur Coronal Width F2 C 16 Femur Coronal Width R F2 C 16 Femur Outer Radius-lateral LR F2 C 17 Femur Stiffness L F2 C 17 Femur Stiffness LR F2 C 18 Tibia Coronal Width

126 104 Table 5-11: Interval mapping results for female RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight.. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. RI F 1 Femur Stiffness LR RI F 1 Tibia Outer Radius-anterior L RI F 1 Tibia Outer Radius-anterior LR RI F 2 Tibialis Anterior Mass RI F 2 Tibialis Anterior Mass R RI F 4 Alkaline Phosphatase S RI F 4 Alkaline Phosphatase SR RI F 4 Tibia Outer Radius-medial RI F 4 Tibia Outer Radius-medial R RI F 5 Femur Epiphyseal Width RI F 5 Tibia Inner Radius-posterior RI F 5 Tibia Inner Radius-posterior R RI F 5 Tibia Outer Radius-posterior LR RI F 7 Femur Outer Radius-anterior RI F 7 Femur Outer Radius-anterior R RI F 9 Femur Stiffness L RI F 9 Femur Stiffness LR RI F 9 Tibia Length R RI F 13 Femur Cortical Thick-medial RI F 15 Tibia Length RI F 15 Tibia Length R RI F 17 Femur Inner Radius-posterior RI F 17 Femur Inner Radius-posterior R RI F 17 Femur Inner Radius-medial RI F 17 Femur Inner/Outer Rad-posterior RI F 17 Femur Inner/Outer Rad-posterior R RI F 17 Gastrocnemius RI F 17 Gastrocnemius R RI F 17 Rod Drop Mean RI F 18 Femur Stiffness LR RI F 18 Rears Mean RI F 19 Extensor Digit. Longus SR

127 105 Table 5-12: Interval mapping results for male RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. RI M 1 Femur Cortical Thick-medial L RI M 1 Femur Cortical Thick-medial LR RI M 1 Femur Length RI M 1 Femur Length R RI M 1 Tibia Length R RI M 9 Squares Mean R RI M 13 Calcium (Serum) RI M 13 Calcium (Serum) R RI M 13 Femur Yield Load RI M 13 Femur Yield Load R RI M 13 Femur Yield Work S RI M 13 Femur Yield Work SR RI M 15 Tibia Length RI M 15 Tibia Yield Work RI M 15 Tibia Yield Work R RI M 17 Femur Outer Radius-anterior L RI M 18 Tibia Inner/Outer Rad.-anterior RI M 18 Tibia Inner/Outer Rad.-anterior R RI M 19 Extensor Digit. Longus L RI M 19 Extensor Digit. Longus LR RI M 19 Tibia Cortical Area RI M 19 Tibia Cortical Area R

128 106 Table 5-13: Interval mapping results for combined (male and female sex corrected) RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. RI C 1 Femur Length R RI C 2 Tibialis Anterior Mass RI C 2 Tibialis Anterior Mass R RI C 3 Femur Cortical Thick-medial LR RI C 4 Alkaline Phosphatase SR RI C 4 Alkaline Phosphatase S RI C 4 Tibia Outer Radius-medial L RI C 9 Squares Mean R RI C 9 Tibia % Mineral L RI C 9 Tibia % Mineral LR RI C 15 Tibia Length RI C 17 Femur Cortical Thick-medial LR RI C 17 Femur Outer Radius-posterior RI C 17 Femur Outer Radius-posterior R RI C 17 Gastrocnemius RI C 17 Rod Drop Mean RI C 19 Extensor Digit. Longus L RI C 19 Extensor Digit. Longus LR RI C 19 Extensor Digit. Longus S RI C 19 Extensor Digit. Longus SR The QTL results presented in the following tables include those QTLs that meet the criteria for confirmed linkage as outlined by Lander and Kruglyak (1995) with LOD scores greater than 4.3 in the F 2 analyses and confirmation in the RI analyses with LOD scores greater than 1.5. QTLs that meet the criteria for confirmed linkage are identified as confirmed significant and are shaded in dark gray. A second category is identified as confirmed suggestive and includes QTLs that were nominated in the F 2 analyses with LOD scores ranging from 2.8 to 4.3 and confirmed in the RI analyses with LOD scores greater than 1.5. QTLs that met the criteria of confirmed suggestive are shaded in light gray. A third category of QTLs that were interesting includes QTLs that were

129 107 nominated in the F 2 with LOD scores between 1.9 and 2.8 and confirmed in the RI with a LOD scores greater than 1.5. Although the final category does not meet the suggestive threshold, including these QTLs in the results summary provides additional information when exploring trends in the data across phenotypes and between sex cohorts. For example, the absence of significant QTL results in one sex could lead to the identification of sex specific QTLs when in fact the QTL in the other sex just missed the cut off for significance. The interval mapping QTL results are summarized in Tables 5-14, 5-15 and 5-16 for females, Tables 5-17, 5-18, 5-19 and 5-20 for males, and Tables 5-21, 5-22, 5-23, 5-24 and 5-25 for the sex corrected combined analyses. Composite interval mapping was performed using QTL Cartographer with the default settings of 5 cofactors and a window size of 10 cm. QTLs that were nominated in the F 2 cohort (LOD=1.9) and confirmed in the RI (LOD = 1.5) are listed in Tables 5-26, 5-27 and 5-28 for females, Tables 5-29, 5-30 and 5-31 for males and Tables 5-32, 5-33, 5-34, 5-35, 5-36, 5-37, and 5-38 for the sex corrected combined analyses.

130 108 Table 5-14: Female interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a F 1 Femur Length R R F 1 Femur Length F 1 Femur Ultimate Work R R F 1 Rod Sections S F 1 Tibia Length LR R F 1 Tibialis Anterior Mass R R F 1 Tibialis Anterior Mass F 2 Femur Ultimate Load LR LR F 2 Femur Yield Load R R F 2 Femur Yield Load F 2 Femur Yield Work S F 2 Femur Yield Work R SR F 2 Rears Mean S F 3 Shear Test Ult. Displ. R LR F 3 Shear Test Ult. Displ L F 3 Shear Test Ult. Work SR LR F 4 Adipose Weight S L F 4 Alkaline Phosphatase SR SR F 4 Alkaline Phosphatase S S F 4 Body Weight L L F 4 Extensor Digit. Longus R LR F 4 Femur Stiffness L L F 4 Squares Mean S F 4 Squares Mean SR R

131 109 Table 5-15: Female interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a F 4 Tibia Length LR R F 4 Tibialis Anterior Mass R R F 5 Adipose Weight R LR F 5 Tibia Coronal Width F 5 Tibia Coronal Width R R F 6 Tibia Inner Radius-posterior S F 6 Tibia Inner Radius-posterior SR R F 8 Femur Ultimate Load L L F 8 Femur Ultimate Load LR LR F 8 Femur Yield Load R R F 8 Tibia Ultimate Displ L F 9 Alkaline Phosphatase SR SR F 9 Femur Length F 9 Femur Outer Radius-anterior L F 9 Tibia Epiphyseal Width R R F 9 Tibia Length LR R F 9 Tibia Length F 9 Tibia Ultimate Work R LR F 9 Tibia Ultimate Work L F 11 Adipose Weight R LR F 11 Rod Drop Mean F 11 Tibia Length F 11 Tibia Length LR R F 13 Femur Stiffness L L

132 110 Table 5-16: Female interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a F 14 Femur Outer Radius-posterior L F 14 Tibia Coronal Width F 14 Tibia Coronal Width R R F 14 Tibia Inner Radius-medial S F 14 Tibia Outer Radius-medial R R F 14 Tibia Outer Radius-medial F 15 Cord Drop Mean S F 15 Femur Outer Radius-posterior LR R F 15 Femur Outer Radius-posterior L F 15 Femur Ultimate Load LR LR F 15 Femur Ultimate Load L L F 16 Tibia Inner Radius-posterior SR R F 16 Tibia Inner Radius-posterior S F 17 Femur Length F 17 Femur Length R R F 17 Femur Sagittal Width L F 17 Femur Sagittal Width LR R F 17 Tibia Inner/Outer Rad.-lateral F 17 Tibialis Anterior Mass R R F 17 Tibialis Anterior Mass F 18 Femur Length F 18 Femur Ultimate Strain L F 20 Tibia Yield Load R R

133 111 Table 5-17: Male interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 1 Femur Inner Radius-anterior L L M 1 Femur Length M 1 Femur Length R R M 1 Rears Mean SR R M 1 Tibia Inner Radius-medial LR R M 1 Tibia Inner Radius-medial L M 1 Tibia Inner/Outer Rad.-medial LR R M 1 Tibia Inner/Outer Rad.-medial L M 1 Tibia Length M 1 Tibia Length R R M 1 Tibia Medullary Area L M 1 Tibia Medullary Area LR R M 1 Tibia Outer Radius-medial L L M 1 Tibia Stiffness L M 1 Tibia Stiffness LR R M 1 Tibia Thickness-posterior L M 1 Tibia Total Area R R M 2 Extensor Digit. Longus L L M 2 Extensor Digit. Longus LR LR M 3 Adipose Weight L M 4 Alkaline Phosphatase S S M 4 Alkaline Phosphatase R SR M 4 Body Length M 4 Femur Outer Radius-lateral R R

134 112 Table 5-18: Male interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 4 Femur Total Area LR LR M 4 Femur Total Area L L M 4 Squares Mean S M 4 Squares Mean SR R M 4 Tibia Yield Load M 4 Tibia Yield Load R R M 4 Tibia Yield Work M 6 Femur Ultimate Load R R M 6 Femur Yield Load R R M 6 Gastrocnemius M 6 Head Pokes SR SR M 6 Shear Test Ult. Displ. L L M 6 Shear Test Ult. Work L L M 6 Shear Test Ult. Work LR LR M 7 Femur Epiphyseal Width LR R M 7 Femur Epiphyseal Width L M 7 Femur Head Diameter M 7 Femur Head Diameter R R M 7 Shear Test Ult. Load M 8 Extensor Digit. Longus LR LR M 8 Extensor Digit. Longus L L M 8 Femur Yield Load M 8 Femur Yield Load R R M 8 Tibia Thickness-lateral

135 113 Table 5-19: Male interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 8 Tibia Ultimate Load M 8 Tibia Ultimate Load R R M 9 Adipose Weight R SR M 9 Femur Neck Diameter R R M 9 Femur Ultimate Work M 9 Gastrocnemius M 9 Tibia % Mineral L M 9 Tibia % Mineral R LR M 11 Adipose Weight L M 11 Adipose Weight R SR M 11 Femur CSMI-PB LR LR M 11 Femur CSMI-PB L L M 11 Femur Inner Radius-lateral R R M 11 Femur Inner Radius-lateral M 11 Femur Outer Radius-lateral R R M 11 Femur Total Area L L M 11 Rod Sections L M 12 Tibia % Ash R R M 12 Tibia % Ash M 13 Tibia Inner/Outer Rad.-lateral M 14 Femur Coronal Width R R M 14 Femur Head Diameter R R M 14 Femur Head Diameter

136 114 Table 5-20: Male interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 14 Femur Inner/Outer Rad.-anterior M 15 Femur Inner Radius-posterior L M 16 Tibia % Organic M 16 Tibia % Organic R R M 17 Body Mass Index L M 17 Body Weight L M 17 Femur Stiffness LR R M 17 Femur Stiffness L M 17 Femur Yield Displ L M 17 Femur Yield Displ. R LR M 17 Femur Yield Displ L M 17 Femur Yield Displ. R LR M 17 Gastrocnemius M 17 Rears Mean SR R M 17 Tibia Length M 17 Tibialis Anterior Mass M 19 Femur Epiphyseal Width L M 19 Tibia Cortical Area M 19 Tibia Cortical Area R R M 19 Tibia Total Area R R M 19 Tibia Total Area M 19 Tibia Total Area M 19 Tibia Total Area R R

137 115 Table 5-21: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 1 Femur Length C 1 Femur Length R R C 1 Gastrocnemius R R C 1 Tibia Outer Radius-medial L L C 1 Tibia CSMI-PB S L C 1 Tibia CSMI-PB SR LR C 1 Tibia Inner Radius-medial L C 1 Tibia Inner Radius-medial LR SR C 1 Tibia Length C 1 Tibia Length R R C 1 Tibia Medullary Area L L C 1 Tibia Medullary Area LR LR C 1 Tibia Thickness-anterior L L C 1 Tibia Thickness-anterior LR LR C 1 Tibialis Anterior Mass LR R C 2 Extensor Digit. Longus LR LR C 2 Extensor Digit. Longus L L C 2 Femur Yield Load R R C 2 Femur Yield Load C 2 Femur Yield Work S S C 2 Femur Yield Work SR SR C 2 Head Pokes S S C 2 Rears Mean S C 2 Squares Mean S

138 116 Table 5-22: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 2 Tibia Epiphyseal Width R R C 4 Alkaline Phosphatase S S C 4 Alkaline Phosphatase R SR C 4 Femur Cortical Area LR LR C 4 Femur Cortical Area L L C 4 Femur CSMI -B LR LR C 4 Femur CSMI -PB SR LR C 4 Femur Outer Radius-lateral LR LR C 4 Femur Total Area LR LR C 4 Gastrocnemius R R C 4 Squares Mean S C 4 Squares Mean SR R C 4 Tibia Length R R C 4 Tibia Stiffness L C 4 Tibia Ultimate Load R LR C 4 Tibia Ultimate Load L C 4 Tibia Yield Load C 4 Tibialis Anterior Mass LR R C 5 Femur CSMI -PB SR LR C 5 Femur Modulus S C 5 Femur Outer Radius-lateral LR LR C 5 Femur Ultimate Stress R R C 5 Femur Yield Stress C 5 Head Pokes S S

139 117 Table 5-23: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 5 Head Pokes SR SR C 5 Soleus Mass S C 5 Soleus Mass R SR C 5 Tibia CSMI-PB S L C 6 Tibia Thickness-posterior L L C 7 Femur Epiphyseal Width L C 7 Shear Test Ult. Load L C 7 Shear Test Ult. Load R LR C 8 Extensor Digit. Longus LR LR C 8 Extensor Digit. Longus L L C 8 Femur Thickness-lateral LR LR C 8 Femur Thickness-lateral L L C 8 Femur Ultimate Load LR LR C 8 Femur Ultimate Load L C 8 Femur Yield Load C 8 Femur Yield Load R R C 8 Femur Yield Work S S C 8 Femur Yield Work SR SR C 8 Tibia Ultimate Load L C 8 Tibia Ultimate Work R LR C 9 Adipose Weight R LR C 9 Alkaline Phosphatase S S C 9 Alkaline Phosphatase R SR C 9 Femur Length R R

140 118 Table 5-24: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 9 Femur Length C 9 Femur Ultimate Displ C 9 Femur Ultimate Displ. R R C 9 Tibia Outer Radius-anterior L S C 9 Tibia % Mineral R LR C 9 Tibia % Mineral L C 9 Tibia Length R R C 9 Tibia Length C 9 Tibialis Anterior Mass C 9 Tibialis Anterior Mass LR R C 11 Adipose Weight R LR C 11 Femur Epiphyseal Width LR R C 11 Tibia Outer Radius-medial L L C 11 Tibia Inner Radius-medial LR SR C 11 Tibia Inner Radius-medial L C 11 Tibia Length C 11 Tibia Length R R C 12 Shear Test Ult. Displ. L L C 13 Tibia Inner Radius-lateral LR LR C 13 Tibia Inner Radius-lateral L L C 13 Tibia Inner/Outer Rad.-lateral C 14 Femur Coronal Width R SR C 14 Femur Coronal Width S C 14 Femur Length R R

141 119 Table 5-25: Combined interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Sex Chr. Phenotypic Nomination in F 2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 14 Femur Length C 15 Femur Stiffness L L C 15 Femur Ultimate Load LR LR C 15 Femur Ultimate Load L C 15 Tibia % Ash C 15 Tibia % Ash R R C 15 Tibia Coronal Width C 16 Femur Inner Radius-lateral L C 16 Tibia % Organic C 16 Tibia % Organic R R C 16 Tibia Thickness-lateral R R C 16 Tibia Thickness-lateral C 17 Body Mass Index L L C 17 Body Weight L L C 17 Femur Stiffness L L C 17 Femur Stiffness LR LR C 17 Femur Yield Displ. R R C 17 Femur Yield Displ C 17 Femur Yield Displ. R R C 17 Femur Yield Displ C 17 Gastrocnemius C 18 Femur Length C 19 Adipose Weight S L

142 120 Table 5-26: Female composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a F 1 Femur Cortical Area L F 1 Femur CSMI-PB L F 1 Femur Length F 1 Femur Sagital Width L F 1 Femur Sagital Width LR R F 1 Tibia % Water R SR F 2 Femur Yield Work S F 2 Femur Yield Work R SR F 2 Shear Test Ultimate Displ. R LR F 2 Soleus Mass R R F 2 Tibia Avg. Cortical Thickness F 2 Tibia Medullary Area LR LR F 3 Alkaline Phosphatase S S F 3 Alkaline Phosphatase SR SR F 3 Shear Test Ultimate Displ. R LR F 3 Tibia % Water R SR F 3 Tibia Coronal Width R R F 3 Tibia Modulus LR LR F 3 Tibia Ultimate Stress LR LR F 4 Alkaline Phosphatase S S F 4 Alkaline Phosphatase SR SR F 4 Body Mass Index L L F 4 Body Weight L L F 4 Femur Medullary Area LR R F 4 Femur Neck Diameter F 4 Femur Total Area LR R

143 121 Table 5-27: Female composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a F 4 Tibia Modulus L L F 4 Tibia Tensile Distance L F 5 Femur Ash Mass LR LR F 5 Tibia % Mineral R LR F 6 Body Weight L L F 6 Femur Coronal Width R R F 6 Femur Total Area LR R F 6 Shear Test Ultimate Displ. R LR F 6 Tibia Stiffness L F 7 Femur Ultimate Work R R F 8 Calcium Mean R R F 8 Femur Ultimate Load L L F 8 Femur Yield Displ. R R F 8 Tibia CSMI-PB LR LR F 8 Tibia Ultimate Displ L F 9 Femur Ash Mass LR LR F 9 Tibia CSMI-B S S F 10 Shear Test Ultimate Displ. R LR F 10 Tibia Total Area R LR F 11 Tibia Length F 11 Tibia Length LR R F 12 Extensor Digitorum Longus L F 15 Femur Medullary Area L F 15 Femur Ultimate Load LR LR F 15 Tibia Ultimate Stress L L F 15 Tibia Yield Stress S L

144 122 Table 5-28: Female composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a F 16 Femur Coronal Width F 16 Femur CSMI-PB L F 16 Femur Medullary Area L F 16 Femur Medullary Area LR R F 16 Femur Total Area L F 16 Tibia Cortical Area L F 17 Femur Length R R F 17 Femur Sagital Width L F 17 Femur Sagital Width LR R F 17 Tibia % Water R SR F 17 Tibia Modulus L L F 17 Tibia Tensile Distance L F 17 Tibialis Anterior Mass R R F 18 Femur Modulus F 18 Femur Sagital Width L F 18 Femur Sagital Width LR R F 18 Tibia Coronal Width R R F 18 Tibia Stiffness L F 19 Femur Sagital Width L F 19 Femur Sagital Width LR R F 19 Tibia % Mineral R LR F 19 Tibia Total Area L F 19 Tibia Ultimate Stress LR LR F 19 Tibia Ultimate Stress L L F 19 Tibia Yield Stress SR LR F 20 Alkaline Phosphatase SR SR F 20 Femur Yield Stress R R

145 123 Table 5-29: Male composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 1 Femur Length R R M 1 Tibia % Water R SR M 1 Tibia Length M 1 Tibia Length R R M 1 Tibia Sagittal Width M 2 Body Mass Index L M 2 Femur Coronal Width R M 2 Femur Coronal Width R S M 2 Femur Total Area LR LR M 2 Tibia Epiphyseal Width M 2 Tibia Length M 2 Tibia Ultimate Work S M 3 Shear Test Ultimate Load R R M 4 Femur Cortical Area L L M 4 Femur Ultimate Stress M 4 Tibia CSMI-PB S L M 4 Tibia Stiffness L M 4 Tibia Total Area M 4 Tibia Yield Load M 5 Femur Epiphyseal Width L M 5 Tibia Cortical Area R R M 5 Tibia CSMI-PB SR LR M 5 Tibia Total Area R R M 6 Femur Neck Diameter R R M 6 Femur Ultimate Load M 6 Femur Ultimate Stress M 6 Femur Ultimate Stress R R M 6 Gastrocnemius R R

146 124 Table 5-30: Male composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 6 Shear Test Ultimate Displ. LR LR M 6 Tibia Coronal Width R R M 6 Tibia Ultimate Strain M 6 Tibia Ultimate Strain R R M 7 Body Mass Index L M 7 Femur Ash Mass R LR M 7 Femur Avg. Cortical Thickness LR LR M 7 Femur Cortical Area LR LR M 7 Femur Epiphyseal Width L M 7 Femur Epiphyseal Width LR R M 7 Femur Head Diameter M 7 Femur Ultimate Work R R M 8 Extensor Digit. Longus LR SR M 8 Femur Avg. Cortical Thickness L L M 8 Femur Yield Load R R M 8 Tibia Ultimate Load R R M 9 Femur Epiphyseal Width L M 9 Gastrocnemius M 9 Shear Test Ultimate Work L L M 9 Tibia CSMI-PB S L M 10 Femur Neck Diameter M 11 Extensor Digit. Longus LR SR M 11 Femur CSMI-PB LR LR M 11 Femur Total Area L L M 11 Tibia Ultimate Load R R M 11 Tibia Ultimate Strain M 12 Femur Head Diameter R R M 12 Tibia Cortical Area

147 125 Table 5-31: Male composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 12 Tibia Sagittal Width R R M 12 Tibia Yield Stress LR LR M 13 Body Length M 13 Body Weight L M 13 Femur Ultimate Load R R M 13 Femur Ultimate Strain L M 14 Femur Coronal Width R R M 14 Femur Head Diameter R R M 15 Body Weight L M 15 Femur Yield Strain L M 16 Femur Coronal Width S M 16 Tibia % Organic R R M 16 Tibia Epiphyseal Width M 16 Tibia Modulus L L M 16 Tibia Ultimate Displ. R R M 16 Tibia Yield Strain M 17 Femur Avg. Cortical Thickness L L M 17 Femur Epiphyseal Width L M 17 Femur Stiffness L M 17 Gastrocnemius M 17 Shear Test Ultimate Displ. LR LR M 17 Shear Test Ultimate Work LR LR M 17 Tibia Tensile Distance R R M 17 Tibialis Anterior Mass M 18 Femur Neck Diameter R R M 18 Tibia Ultimate Stress L S M 19 Femur Ultimate Stress M 19 Tibia Avg. Cortical Thickness M 19 Tibia Avg. Cortical Thickness R R

148 126 Table 5-32: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 1 Tibialis Anterior Mass LR R C 1 Femur Length R R C 1 Tibia Length C 1 Tibia Length R R C 1 Body Length C 1 Femur Ultimate Displacement R R C 2 Femur Average Cortical Thickness LR LR C 2 Femur CSMI-B LR LR C 2 Femur Total Area L L C 2 Femur Sagittal Width L C 2 Femur Sagittal Width LR R C 2 Femur Length R R C 2 Femur Yield Load C 2 Tibia Epiphyseal Width R R C 2 Tibia Average Cortical Thickness R R C 2 Tibia Length R R C 2 Femur Yield Load R R C 2 Tibia Yield Load C 2 Femur Ultimate Load L C 3 Body Weight L L C 3 Gastrocnemius Mass C 3 Tibia Epiphyseal Width C 3 Femur CSMI-B L L

149 127 Table 5-33: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 3 Tibia Percent Water S C 3 Tibia Percent Water R SR C 4 Femur Yield Displacement C 4 Body Weight L L C 4 Tibia CSMI-PB S L C 4 Femur CSMI-B L L C 4 Femur CSMI-B LR LR C 4 Femur Medullary Area LR R C 4 Femur Modulus of Elasticity S C 4 Femur Total Area L L C 4 Femur Ultimate Stress R R C 4 Femur Yield Stress R R C 4 Femur Coronal Width S C 4 Femur Yield Stress C 4 Body Length C 4 Femur Cortical Area LR LR C 4 Tibia Ultimate Load R LR C 4 Femur CSMI-PB S L C 4 Tibia Ultimate Load L C 4 Femur Cortical Area L L C 4 Tibia Yield Load C 4 Femur CSMI-PB SR LR C 4 Tibia Percent Mineral R LR

150 128 Table 5-34: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 5 Femur Yield Strain C 5 Femur CSMI-PB S L C 5 Femur Cortical Area L L C 5 Femur Ultimate Strain L C 5 Soleur Muscle Mass R SR C 5 Femur Ultimate Stress C 6 Tibia CSMI-B S L C 6 Tibia Tensile Distance R LR C 6 Tibia Stiffness L C 6 Tibia Yield Load C 6 Tibia Stiffness LR R C 6 Soleur Muscle Mass R SR C 6 Tibia Inner/Outer Radius-lateral C 6 Tibia Inner/Outer Radius-lateral R R C 6 Femur Sagittal Width LR R C 6 Femur Sagittal Width L C 6 Tibia Total Area L C 7 Femur Inner/Outer Radius-posterior R R C 7 Femur Ultimate Load L C 7 Femur Yield Load R R C 7 Body Mass Index L C 7 Femur Ultimate Work R R

151 129 Table 5-35: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 7 Femur Work at Yield SR SR C 7 Femur Work at Yield S S C 7 Tibialis Anterior Mass C 7 Femur Ash Mass SR LR C 7 Femur Length R R C 8 Femur Ash Mass S L C 8 Femur Inner/Outer Radius-lateral R R C 8 Tibia Work at Ultimate Load R LR C 8 Tibia Percent Organic C 8 Tibia Percent Organic R R C 8 Extensor Digitorum Longus LR LR C 8 Tibia Percent Mineral R LR C 9 Shear Test Ultimate Load R LR C 9 Tibia Percent Ash C 9 Tibia Cortical Area L C 9 Femur Ultimate Displacement C 9 Tibialis Anterior Mass LR R C 9 Femur Epiphyseal Width L C 9 Tibia Epiphyseal Width C 9 Tibia Percent Mineral R LR C 9 Tibia Percent Mineral L C 9 Tibia Length R R

152 130 Table 5-36: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 9 Tibia Length C 9 Tibia Ultimate Load L C 10 Femur Length C 10 Tibia Inner/Outer Radius-lateral R R C 11 Tibialis Anterior Mass LR R C 11 Tibia Coronal Width R R C 11 Tibia Coronal Width C 11 Femur Coronal Width S C 11 Tibia Ultimate Strain R R C 11 Tibia Length C 11 Femur CSMI-PB SR LR C 11 Femur Length R R C 11 Tibia Work at Ultimate Load L C 11 Femur Epiphyseal Width LR R C 11 Femur Inner/Outer Radius-medial S S C 12 Tibia Sagittal Width LR R C 12 Tibia CSMI-B SR LR C 12 Tibia Medullary Area L L C 12 Femur Head Diameter C 12 Gastrocnemius Mass R R C 12 Shear Test Ultimate Displ. LR LR C 12 Tibia Percent Ash R R

153 131 Table 5-37: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 12 Tibia Percent Ash C 13 Femur Stiffness LR LR C 13 Body Mass Index L C 13 Femur Ultimate Load L C 13 Tibia Inner/Outer Radius-posterior C 13 Tibia Ultimate Load L C 14 Femur Head Diameter C 14 Tibia Length C 14 Femur Coronal Width R SR C 14 Femur Coronal Width S C 15 Femur Length C 15 Tibia Length C 15 Tibia Inner/Outer Radius-anterior C 15 Tibia Inner/Outer Radius-anterior R R C 15 Tibia Coronal Width C 15 Tibia Work at Ultimate Load R LR C 15 Tibia Coronal Width R R C 15 Femur Stiffness L L C 15 Femur Yield Load R R C 15 Tibia Percent Ash C 15 Femur Medullary Area LR R C 15 Femur Modulus of Elasticity S

154 132 Table 5-38: Combined composite interval mapping results nominated in F 2 (LOD > 1.9) and confirmed in RI (LOD > 1.5). QTLs nominated at significant level (LOD > 4.3) are shaded in dark gray and at the suggestive level (LOD > 2.8) light gray. Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 15 Femur Yield Load C 15 Femur Ultimate Load L C 15 Femur Ultimate Load LR LR C 16 Tibia Ultimate Strain R R C 16 Tibia Ultimate Strain C 16 Tibia Percent Organic R R C 16 Femur Coronal Width S C 17 Femur Ultimate Displacement R R C 17 Femur Ultimate Displacement C 17 Femur Yield Displacement C 17 Body Mass Index L C 17 Femur Yield Load C 17 Femur Yield Load R R C 17 Gastrocnemius Mass C 17 Body Weight L L C 17 Tibia Modulus of Elasticity L L C 17 Femur Stiffness L L C 17 Shear Test Ultimate Displ. LR LR C 17 Shear Test Ultimate Displ. L L C 17 Femur Yield Displacement C 18 Tibia Epiphyseal Width C 19 Femur Ultimate Load L

155 133 References Lander, E.S. and Kruglyak, L. (1995) Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nature Genetics 11, Basten, C. J., Weir, B. S., & Zeng, Z.-B. (1994). Zmap-a QTL cartographer. In Proceedings of the 5th World Congress on Genetics Applied to Livestock Production: Computing Strategies and Software, edited by C. Smith, J. S. Gavora, B. Benkel, J. Chesnais, W. Fairfull, J. P. Gibson, B. W. Kennedy and E. B. Burnside. Volume 22, pages Published by the Organizing Committee, 5th World Congress on Genetics Applied to Livestock Production, Guelph, Ontario, Canada. Basten, C.J., Weir, B. S., & Z.-B. Zeng, Z.-B. (2002). QTL Cartographer, Version 2.0. Department of Statistics, North Carolina State University, Raleigh, NC.

156 134 Chapter 6 Statistical Issues Concerning Normalizing Data to Body Size: A Comparison of Methods Applied to Quantitative Trait Loci (QTL) Analysis Abstract Twin studies (Dequeker et al. 1987) as well as inbred mouse studies Beamer et al. 1996; Klein et al. 1998; Shimizu et al. 2001) have confirmed that bone properties are under genetic influence. The continuous distribution of skeletal phenotypes indicates that they are complex traits with multiple influences. These influences are known to include environmental differences such as diet and exercise and genes. Previous studies have also shown that muscle mass is associated with increased bone mass (Kaye and Kusy 1995). Kaye and Kusy investigated these relationships within several mouse strains and suggested that activity as well as muscle size and strength were influenced by genetics and that the genetic influence on bone size and strength could be acting through muscle and activity related pathways. Body weight and length have also been shown to have significant genetic influence (Ishikawa et al. 2002; Keightley et al. 1996; Klein et al. 1998) and are also significantly correlated with skeletal measures. Many skeletal studies have reported strong correlations between muscle, skeletal, and body size phenotypes and these correlations add to the difficulty in identifying direct genetic influence on skeletal traits. Quantitative trait loci (QTL) have been identified for skeletal phenotypes and it is often the case that QTLs for body size phenotypes also map to these same areas. A QTL identified as influencing bone mineral density could act

157 135 indirectly through body size or muscle mass. Identifying QTLs that influence skeletal phenotypes indirectly through body size related pathways is informative, however it is also important to identify QTLs that influence skeletal phenotypes independent of body size. Removing size effects has been an issue for researchers and traditionally one method of removing the size effect has been through the use of ratios. This technique and an alternate technique of regression was performed on muscle and skeletal data used in a QTL analysis and the differences in results from the various methods of normalization are discussed. The aim of this study was to compare methods of adjusting phenotypic data for body size and discuss the problems encountered with using these methods when applied to quantitative trait loci (QTL) analyses in a C57/BL6 x DBA2 F 2 population of mice. Introduction Many factors are known to influence bone mass acquisition, including diet, gender, endocrine factors, mechanical loading and genetics. The mechanical strength of bone is not based solely on density (bone matrix per volume) alone but the result of complex interactions among size, shape, cross-sectional distribution, and mechanical integrity of the matrix itself. As a compensational mechanism for bone loss, cortical expansion of long bones has been shown to occur with age. The distribution of bone away from the axis of bending helps to resist bending and therefore increases bending strength.

158 136 The mechanostat theory by Frost et al. (2002a) was developed to explain a bone s response to its loading environment. The mechanostat is the response of bone to its total environment including estrogen, muscle strength, marrow composition, and other factors that can influence signaling pathways. The mechanostat is based on thresholds that are thought to control modeling and remodeling in an overriding driving force to conserve bone mass and at the same time provide enough bone strength to meet demands of normal activity (Frost, 2000a). It has been shown that the highest normal loads experienced by bone are from muscle forces used for movement. High muscle forces are the result of the length of the moment arm for the muscle. Skeletal muscles function at a mechanical disadvantage and much more force is required by the muscle to move a given load imposed by the environment. As muscle mass increases the loads applied to bones increase and the strain on the bone increases. If bone is growing and developing in response to a strain threshold then the principal signal could be due to the loads produced by muscle. A study by Zanchetta et al. (1995) of boys and girls between the ages of 2 and 20 showed that bone formation continued as long as muscle mass was increasing (Frost, 2000b). Under the assumptions outlined by Frost, as muscle mass (an index of muscle strength) increases, the strain on bone will increase and thus stimulate bone formation. As muscle mass decreases, the strain on bone is reduced, and bone loss through absorption will occur. Therefore, variations in peak bone mass and rates of bone loss could also be influenced by reduced muscle mass or muscle strength as well as variations in the frequency and duration of activity (Frost et al., 1998).

159 137 While muscle forces are thought to be the biggest source of strain on our bones, body weight is the second biggest source of mechanical loading experienced by the skeleton. The effect of body size is complex and could interact with activity, which also plays a role in the loading environment. Larger individuals may be less active and therefore are not loading their bones by muscular contractions as often as smaller individuals, while at the same time their larger body size applies a greater constant load on the skeleton. This paper discusses statistical issues associated with normalizing skeletal measures to body size. Quantitative trait loci (QTL) analysis is used to identify genetic influence on quantitative continuously distributed traits. Recently there have been many studies in the literature that have reported QTLs for skeletal measures such as bone mineral density, bone strength, or cortical thickness. These measures can be correlated with body size and QTLs could be influencing bone indirectly through overall body size or muscle mass. Identifying these QTLs are informative in themselves, however, normalizing data to body size would allow for the investigation of QTLs that influence skeletal measures independent of body size and or muscle mass. Historically biological studies have attempted to normalize data by using the ratio of the target variable over a second variable such as a measure of body size. It is the case that spurious correlations can in fact be induced and the size effect can be greater after the data are normalized. The results of using ratios as well as alternative methods of regression are compared and discussed in detail.

160 Background 138 Many studies have shown significant correlations between muscle mass, body weight, and skeletal measures. In an examination of several inbred mouse strains, Kaye and Kusy (1995) reported correlation coefficients of 0.85 between femur weight and muscle weight, 0.60 between muscle weight and activity and 0.59 between femur weight and animal activity. These results suggest a relationship between muscle mass or strength and bone size and strength and that these traits are under genetic influence (Kaye and Kusy, 1995). Arden et al. (1997) investigated the relationship between bone mineral density, lean body mass, and muscle strength using a population of postmenopausal monozygotic (MZ) and dizygotic (DZ) twin female pairs. Lean body mass and bone mineral density (BMD) were measured at multiple sites using duel energy x-ray absorptiometry (DEXA). The correlation of BMD with leg extensor strength was between 0.16 and BMD also correlated with grip strength (0.12 to 0.21), and lean body mass (0.2 to 0.39). The heritability estimates of lean body mass, leg strength, and grip strength were 0.52, 0.46, and 0.30 respectively. Muscle variables explained 20% of the genetic variance of BMD (Arden and Spector, 1997). These results provide support for the indirect genetic influence of muscle strength on bone mineral density. Li et al. (2001) assessed forelimb muscle size and grip strength, bone mineral density, forelimb bone size and humerus breaking strength in ten different inbred mouse strains (129/J Sencar/PtJ, C57BL/6J, CBA/J, FVB/NJ, NZB/BINJ, RIIIS/J, LP/J LG/J, and SWR/J). Heritability estimates from the ten inbred strains were: 0.60 (BMD), 0.68

161 139 (breaking strength), 0.83 (bone size), 0.63 (grip strength), 0.76 (muscle size), and 0.52 (body weight). The correlations among all muscle and bone related phenotypes were significant. The correlations of bone density with grip strength, muscle size and body weight were 0.54, 0.50, and 0.50 respectively. The correlations of breaking strength with grip strength, muscle size, bone density, and body weight were 0.63, 0.58, 0.69, and 0.52 respectively (Li et al., 2001). Several investigators have utilized genome wide scans to search for QTLs for skeletal phenotypes. Many of the QTLs identified across studies often localize in the same chromosomal region and could be confirming the same loci. The skeletal site under investigation often varies and in some cases site specific QTLs have been identified in the same study. QTLs identified in skeletal studies often co-localize with QTLs for nonskeletal phenotypes. It could be the case that a QTL for bone could also be a QTL for body size, activity, or muscle size or strength and the effect on bone could be an indirect effect through these other cofactors. Li et al. (2002) reported significant QTLs for femur breaking strength, femur length, periosteal circumference, lean mass and body weight all mapping to 54.6 cm on chromosome 2 in a genome wide scan of MRLxSJL F 2 mice. Similar results were also reported for femur breaking strength, BMD of the femur, and body weight on chromosome 9 at 41.5 cm and for BMD, periosteal circumference, and body weight on chromosome 11 at 47.6 cm. They also found pleiotropic results for femur BMD, femur length, breaking strength, and body weight on chromosome 12 at 34.0 cm, BMD measured with a PIXIMUS densitometer, lean body mass, and body weight on

162 140 chromosome 13 at 9.0 cm, and femur breaking strength, femur BMD and length, and body weight on chromosome 17 at 6.6 cm (Li et al., 2002). Klein et al. (1998)reported a QTL for both whole body BMD and body weight on chromosome 14 in the BXD RI analyses. QTL analysis was also performed for femur cross-sectional area using BxD F 2 mice as well as 18 BxD RI strains at 16 weeks. An r 2 of 0.13 in males and 0.24 in females between body weight and cross sectional area (CSA) was reported. QTL analysis was performed on CSA corrected for body weight by regressing on body weight and using the resulting residuals (Klein et al., 2002). The B6 x D2 F 2 population was also used in a bone density QTL study by Drake et al. (2001). F 2 mice were fed an atherosclerosis-inducing diet for 4 months. Bone mass was found to be inversely correlated with atherosclorosis and directly correlated with body weight, length, and adipose tissue. Skeletal related traits were mapped to chromosome 2, 3, 6, 7, and proximal and distal 15. Three of these QTLs were found to be adjacent to or overlap with QTLs that were identified for non-bone traits such as adipose tissue, plasma HDL and LDL, and body length. Drake et al. used a two step method to determine if these QTLs resulted from pleiotropic gene effects or multiple QTLs in close proximity. Multi-trait composite interval mapping was used to determine if the significance of a QTL increased when multiple traits were included in the model. If the multi-trait analysis produced significant results pleiotropy versus tight linkage was tested using a statistical test by Jiang and Zeng (1995). The pleiotropy versus tight linkage test provided support for pleiotropy of the QTL on chromosome 2 influencing radiographic BMD, adipose mass, and HDL cholesterol. The test also supported a

163 pleiotropic effect of the QTL on chromosome 6 for radiographic inter-trochanteric 141 density, plasma HDL, and subcutaneous fat pad mass (Drake et al., 2001). The C57BL/6 and Cast/EiJ strain crosses have also been used extensively for skeletal QTL studies and significant correlations between body weight and BMD (r = 0.112) in an F 2 population have been reported (Beamer et al., 1999). Shultz et al. (2003) recently reported the construction of 12 congenic strains for QTLs that have previously been identified. Six congenic lines were constructed from C3H donor regions on B6 backgrounds and six lines were constructed from CAST donor regions on B6 backgrounds. All congenic strains showed significant differences in BMD compared with B6 controls except B6.CAST.15T. Significant differences were also reported for body weight, femur length, and mid-diaphyseal periosteal circumference in several of the congenic strains. However, the results did not show consistent correlations with BMD. Eight additional sub-lines were constructed from C3H donor and B6 recipient to fine-map the QTL on chromosome 1. Results from the chromosome 1 sublines indicated two QTLs at 36.9 to 49.7 cm and 73.2 to cm. Pleiotropic results were reported for femur BMD, body weight and length, and periosteal circumference at both loci on chromosome 1, as well as chromosome 6 at cm and femur BMD, body weight on chromosome 14 at cm and chromosome 18 at cm (Shultz et al., 2003). BMD was also assessed in an F 2 cross from MRL/MPJ and SJL/J inbred parental strains. Masinde et al. (2002b) performed QTL analysis on BMD measures from 633 F 2 female mice at 7 weeks of age using pqct of the femur and PIXIMUS measures for total skeletal density. A genome wide scan was conducted for all 633 mice. The PIXIMUS

164 142 BMD was corrected for body size, which led to increased LOD scores indicating that body weight could limit the ability to detect fine differences in uncorrected BMD. QTLs were identified on chromosomes 1, 3, 4, 9, 12, 17, and 18 for BMD of the femur using pqct and on chromosomes 1, 2, 4, 9, 11, 14, and 15 for total skeletal BMD using PIXIMUS (Masinde et al., 2002b). QTLs for muscle size of the forelimb from pqct and body length were also assessed in this MRL/MPJ x SJL/J F 2 population. QTLs were identified on chromosome 7, 14, 15, 17, and 9 for muscle size. Muscle size was corrected for by body size by adjusting for body length. The procedure used to adjust lean body mass to body length was the ratio method described previously. Lean body mass was divided by body length in an attempt to remove the effects of body length. QTLs were identified on chromosome 7, 9, 11, 14, and 17. QTLs for body length were found on chromosome 2, 9, 11, and 17 (Masinde et al., 2002a). This method of using ratios is one of the main topics of this study and will be shown to give spurious results. Separating the effects of correlated phenotypes can be very problematic. In this study the researchers corrected muscle size for body length and the LOD score for muscle size increased on chromosome 9 and a new QTL was identified on chromosome 11. Both of these loci were identified as QTLs for body length. It is likely that while attempting to correct for body size these researchers have actually induced a greater correlation of muscle size with body size although the method of adjustment was not described. Many of the results reported by Masinde et al. (2002a) show overlapping QTL regions for multiple co-factors. The ratio of lean mass to body length and body length both mapped to chromosome 2 at 50.3 cm, two regions on chromosome 9 at 7.7 and 31.7

165 143 cm, and to two regions on chromosome 13 at 2.2 and 23 cm. It is interesting to note that Li et al. (2002) reported a body weight QTL in this same test cohort at 41.5 cm on chromosome 9. Body length and whole body BMD using PIXIMUS densitometry mapped to 72.1 and 78.7 cm on chromosome 2 and on chromosome 11 at 43.7 cm. Normalizing Biological Data to Body Size Many biological phenomena vary with body size resulting in confounding effects when researchers are investigating differences in measures resulting from factors other than body size. Variables of interest are often normalized or scaled to a measure of body size specific to the biological function such as leg length for locomotion measures, body weight for lung function, or limb length for muscle mass. Standard measures of body size are body weight, height, and body mass index (BMI; weight/length 2 ). It is common practice to use ratios to scale experimental data and it has also been well documented that such ratios can lead to spurious correlations and inaccurate conclusions. However, ratios continue to be used (Packard and T.J. Boardman, 1987). Atchley et al. (1976) discuss these issues in detail and uses simulated data to show how spurious correlations arise from using ratios as a method of scaling. For example, if QTL analysis is to be performed on measures for body size (X 2 ), gatrocnemius muscle mass (X 1 ) and tibial diaphyseal length (X 3 ) and the intent is to investigate whether the same genes are influencing muscle mass and bone length independent of body size, the bone and muscle measures could be divided by body mass index (BMI). The new variables would be muscle mass scaled (Y=X 1 /X 2 ) and bone

166 length scaled (Z=X 3 /X 2 ). When two normalized variables are made using a common 144 denominator as in the example above, a spurious correlation can be induced if the coefficient of variation of the denominator is not equal to the coefficient of variation of the numerator. The coefficient of variation (C.V.) is given in Equation 6-1 (Atchley et al., 1976). C.V. = (standard deviation / mean ) * 100 (6-1) The coefficient of variation measures the variability relative to the mean and allows for the comparison of the dispersion of different types of data. The ratio of the coefficient of variation of the variable of interest to the coefficient of variation of the denominator (scaling variable, i.e. body size) can be used as an indicator of how much spurious correlation is induced relative to the initial correlation of two different variables before scaling with the same denominator. Results from simulated data showed that as the ratio of the coefficients of variations (δ 1 / δ 2 ) decreased from 2 to 0.1 the induced correlation between the new ratio variables increased. The initial correlation between X 1 and X 3 was varied from 0.75 to 0.75 while holding the correlation of X 1 to X 2 and X 3 to X 2 equal to zero. All cases resulted in an increase due to spurious correlations. As the correlation of X 1 to X 2 and X 3 to X 2 increased to 0.5 and 0.5, the ratio of the coefficients of variation decreased from 2 to 0.1, and the induced correlation increased. Atchley showed that using ratios to remove size effect actually induced more of a size effect. The larger the coefficient of variation of the denominator, relative to the numerator, the greater the induced correlation. By attempting to remove body size from the muscle and bone measures the result could actually be an increase in the size affect, an increase in

167 145 correlation of muscle to bone, and an increase in the correlation of muscle to body size and bone to body size. This paper presents the possibly erroneous QTL results that could arise from scaling phenotypes using this ratio method as well as an alternative method of regression. Materials and Methods Animals This study investigated body size, muscle mass and skeletal phenotypes for 400 F 2 (200 male and 200 female) F 2 progeny of DBA/2 (D2) and C57BL/6J (B6) inbred mouse strains. These animals were established and maintained in a barrier facility maintained by The Center for Developmental and Health Genetics at The Pennsylvania State University. Mice were weaned into like-sex sibling groups at about 23 days of age with 4 animals per cage. They were fed a diet of autoclaved Purina Mouse Chow 5010 ad lib. The barrier facility was maintained under positive pressure with a temperature and humidity controlled environment and a 12-hour light/dark cycle. Genotyping Four hundred F 2 mice were genotyped in-house using 96 microsatellite markers distributed throughout the genome with an average spacing of centimorgans (cm). Marker analyses were conducted on purified DNA samples procured from tail snips using

168 an automated, fluorescence-based detection system described in detail in Vandenbergh et al. (2003). 146 Body Size Measurements Prior to euthanism, each animal was weighed and body weight was recorded to the nearest hundredth of a gram. After cervical dislocation body length was measured from the nose to anus. Muscle Mass At sacrifice, the right hind limb was harvested and the gastrocnemius, soleus, tibialis anterior, and extensor digitorum longus muscles were dissected and weighed to the nearest hundredth of a milligram. The tibia and femur were wrapped in saline soaked gauze and frozen for future mechanical testing. Skeletal Measures Gross Dimensional Skeletal Measurements At the time of testing, the femur and tibia were thawed at ambient temperature. A digital caliper accurate to 0.01mm was used to measure femoral length from the most superior aspect of the greater trochanter to the most inferior surface of the intercondylar notch, femoral width at the center of the diaphysis in both the sagittal and coronal planes,

169 and epiphyseal width at the widest point of the distal epiphysis in the coronal plane. 147 Femoral head and femoral neck diameter were also measured. The tibia was measured similarly, except that length was measured from the intercondylar eminence of the tibia to its inferior articular surface and the proximal, rather than distal, epiphyseal width was measured. Flexural Testing of the Right Femoral and Tibial Diaphysis The mid-shaft of the right femur and tibia were tested to failure in three point bending in an MTS Mini Bionix testing apparatus using a support span of 8 mm and 10 mm respectively and a deformation rate of 1 mm/min. Femurs were oriented in the testing apparatus so that the nosepiece was posteriorly directed in respect to the femoral shaft. Tibiae were oriented so that the nosepiece was anteriorly directed in respect to the tibial shaft. A small section of the anterior flare of the proximal tibia was carefully removed before testing so that the tibia would lay flat on the support span and not roll during loading. Each femur and tibia was loaded to failure while recording load and actuator displacement at 20 Hz and a load-deformation curve was generated using MATLAB scientific software. Yield load, yield deformation, energy absorbed at yield (area under the load-deflection curve), failure load, failure deformation, energy absorbed at failure, and stiffness (initial slope of the load-deflection curve) were determined.

170 Compositional Analysis of the Tibia and Ash Mass of the femur 148 The distal tibia was dried and ashed to determine composition. The percent composition of water, organic, and ash (mineral) within each bone fragment, as well as the percent mineralization of the organic matrix was calculated based on wet, dry and ash mass. The fragments from the mechanical testing of the femur were ashed to obtain total femoral ash mass. Quantitative Trait Loci (QTL) Analyses QTL analyses were performed on the F 2 cohort to locate chromosomal regions influencing phenotypic variables. QTL analyses were conducted using QTL Cartographer software to perform interval mapping (Basten et al. 1994; Basten et al. 2002). All QTL analyses were conducted on male and female combined data that were corrected for sex differences by subtracting the difference between the male and female mean from each individual male measurement. All phenotypes were tested for normality and natural log or square root transformations were used when necessary. The results that were based on transformed variables are indicated with an (L) for log transformed or an (S) for square root transformed. Body Size Normalization Two methods were used to normalize skeletal and muscle phenotypic measurements to body size measures. Initially skeletal and muscle measures were

171 normalized to body mass index (BMI; weight / length 2 ). This normalization was 149 performed using the ratio method described previously and consisted of dividing (D) each phenotypic measure by BMI. Skeletal and muscle phenotypic measures were also normalized to body size by using an alternative method of multiple regressing against body weight and body length. These measurements are indicated with a (R). QTL analyses were performed on raw data, data normalized by dividing through with BMI, and data normalized by regressing against weight and length and using the residuals. Results Muscle, skeletal, and body size phenotypes show strong correlations in the raw data. Table 6-1 includes two muscle mass measures, select skeletal measures and body size measures. These measures were selected to illustrate the typical initial correlations between skeletal, muscle, and body size and the subsequent changes in correlations after the body size normalizations. Pearson correlation coefficients are presented in the table. Correlations significant at p < 0.01 are shaded in gray and those at a p < 0.05 are designated with a bolded border.

172 Table 6-1: Pearson Correlation Coefficients for raw phenotypes (transformed using log (L) or square root (S) when appropriate). 150 Pearson Correlation BMI Body Body Gastroc. Tibialis Tibia Fem. Ult. Femur Femur Coefficients Weight Length Anterior Length Load Stiff. (L) Ash (S) BMI 1 Body Weight Body Length Gastroc Tibialis Anterior Tibia Length Femur Ult. Load Femur Stiffness (L) Femur Ash (S) Tibia Stiffness (L) As indicated in the table, skeletal and muscle phenotypes are strongly correlated as are both skeletal and muscle traits with body size. It is interesting to point out that femur ultimate load and femur ash had correlations of 0.26 and 0.35 with body length but when compared to BMI (a commonly used scaling variable) the correlations were no longer significant. This is due to BMI (weight / length 2 ) having a correlation of 0.85 with body weight but only 0.2 with body length. The correlations between the ratio-normalized phenotypes are given in Table 6-2 and were consistently higher for ratio normalized data compared with the raw data. For example, the correlation of tibial length to tibialis anterior muscle mass increased from 0.43 (raw) to 0.88 (ratio). Phenotypes that were initially uncorrelated such as femur ash mass and BMI went from 0.06 to 0.88 as a ratio variable. While attempting to remove size effect the ratio variables result in an increase in correlation with BMI compared with the raw variables. Initially tibialis anterior muscle mass and BMI were correlated 0.28 and this correlation increased to 0.85 for the ratio variable. As will be seen in the QTL

173 151 analyses, this increase in correlation results in erroneous QTLs for multiple traits identified at the same location as BMI. Table 6-2: Correlation Coefficients for normalized phenotypes using BMI ratio (D) (transformed using log (L) or square root (S) when appropriate). Pearson Correlation BMI Body Body Gastroc. Tibialis Tibia Fem. Ult. Femur Femur Coefficients Weight Length (D) Ant. (SD) Length (D) Load (D) Stiff. (LD) Ash (SD) BMI 1 Body Weight Body Length Gastroc. (D) Tibialis Anterior (SD) Tibia Length (D) Femur Ult. Load (LD) Femur Stiffness (LD) Femur Ash (SD) Tibia Stiffness (LD) Table 6-3 includes body size, muscle mass, and skeletal measures and their corresponding standard deviation, mean, coefficient of variation, and the ratio of the coefficient of variations for each trait over CV-BMI. As shown in the table the ratio (CV / (BMI-CV) is close to 0.1 for many of the traits. This is due BMI s CV being relatively large compared with the CV of many of the traits. The spurious correlations as shown in Table 6-2 are consistent with what Atchley et al. outlined.

174 152 Table 6-3: Descriptive Statistics for select variables. Phenotype Mean Standard Coefficient of Variance Deviation Variation (CV) CV / (BMI-CV) BMI Body Weight Body Length Gastrocnemius Tibialis Anterior (S) Femur Length Tibia Length Femur Ult. Load (L) Femur Stiffness (L) Femur Ash (S) Tibia Stiffness (L) In general although not listed, most morphological measures such as skeletal width and length were more correlated with body length than with body weight and skeletal strength measures such as tibia ultimate load and muscle mass were more correlated with body weight. However, this was not always the case and several measures were equally correlated with body length and body weight. What was consistent is that in all cases every trait was correlated more with either body length or body weight than BMI. BMI has traditionally been used as a way to scale body size, so that the differences between a very heavy but short individual was distinguished from a very heavy but long individual. Initially BMI was used in the regression normalization method. However, because BMI and body weight are correlated 0.85 while BMI and length are only correlated 0.2 the residualized data are more correlated with body length than body weight after regression regardless of whether they were more correlated with length or weight prior to regression. Regression using BMI is effective at removing all the

175 153 variance associated with BMI, but because BMI is correlated with body weight r=0.8, and length r=0.2, the residualized variables remain significantly correlated with both body weight and length. To remove completely the covariance of body weight and length, the phenotypes were instead normalized using multiple regression on both body weight and body length. The correlation coefficients for the multiple regression-residualized phenotypes are given in Table 6-4. Table 6-4: Correlation Coefficients for normalized phenotypes using multiple regression on body weight and length (R) (transformed using log (L) or square root (S) when appropriate). Pearson Correlation BMI Body Body Gastroc. Tibialis Tibia Fem. Ult. Femur Femur Coefficients Weight Length (R) Ant. (R) Len. (R) Load (LR) Stiff. (LR) Ash (SR) BMI 1 Body Weight Body Length Gastroc. (R) Tibialis Anterior (R) Tibia Length (R) Femur Ult. Load (LR) Femur Stiffness (LR) Femur Ash (SR) Tibia Stiffness (LR) After normalizing to body length and body weight using multiple regression, most of the skeletal phenotypes continued to be significantly correlated with muscle phenotypes. However the correlation was considerably reduced.

176 Quantitative Trait Loci Analysis Results 154 QTL analyses were performed on raw phenotypes, normalized with ratios, and normalized with multiple regression phenotypes. The interval mapping results are displayed graphically with the LOD score plotted against centimorgan position along each chromosome. Figure 6-1 is a graph of the LOD score for BMI, body weight and body length for the entire genome. Chromosome number is indicated along the bottom of the graph. As shown in the graph, these size variables show up on many of the same chromosomes. However, there are some differences.

177 155 Figure 6-1: Interval Mapping LOD score plots for the combined analyses of body size phenotypes. Interval mapping results are presented for chromosomes 13 and 7 to illustrate the results that are produced when ratio normalization is used. In Figures 6-2 and 6-3 the results for the ratio phenotypes are presented on the left and of the traits that were identified on the ratio graph the corresponding interval mapping results for the raw and

178 regressed phenotypes are presented on the right. BMI, body weight, and body length are also presented on the graph to the right. 156 Figure 6-2: Interval mapping results for chromosome 13. Figure 6-3: Interval mapping results for chromosome 7.

179 157 What is clear from these graphs and the previous correlations is that using ratios to remove a size effect can lead to erroneous results. Many phenotypes that did not map to these chromosomes as raw phenotypes mapped to the same region as body size phenotypes on the ratio graph. For example, tibial coronal width did not map to chromosome 13 initially but by using ratios it now has a LOD score greater than 5. On chromosome 13, only three of the traits that mapped on the ratios graph were also on the raw graph and all three of these showed a decrease in LOD score when normalized using the multiple regression method. It appears that multiple regression is removing the size effect and the ratios method, with increased spurious correlations, is inducing a size effect that was not there initially. However, the ratio method does not appear to increase the size effect for the three traits that mapped to chromosome 13 initially. The LOD score for tibialis anterior, tibial length, and femur stiffness did not increase on the ratios graph. Going back to our original example, tibialis anterior ratios had a marked increase in the spurious correlation with BMI and a less striking but still significant increase from 0.41 to 0.62 for body weight. However, on the ratio graph, tibialis anterior scored the lowest out of the phenotypes listed. The results from chromosome 7 show a similar pattern for the ratio data with many traits mapping to the region of the chromosome for the body size QTLs that were not present in the raw data. Most of the traits that were seen on chromosomes 7 and 13 that mapped to the body size QTL positions had coefficient of variation ratios (CVR) less than 0.5 and are consistent with induced correlations. Less clear are the multiple regression method results from chromosome 7. For tibialis anterior, femur ultimate load, and femur ash mass, there was a significant increase in the LOD score using the multiple

180 158 regression method. These three traits were the most affected by multiple regression and they were also the three traits that scored the highest on the ratios graph. Could this be an indication that the QTL or multiple QTLs on chromosome 7 has a direct effect on body size, muscle mass and skeletal strength as apposed to acting indirectly through body size as could be the case with the QTL on chromosome 13. Multiple regression significantly reduced the LOD score on 13 while increasing the LOD score on 7. If two closely linked QTLs are present on chromosome 7 with one significantly influencing body size and the other influencing skeletal strength then removing the variance from skeletal strength that is associated with body size could increase the significance of the linked skeletal QTLs. Conclusions The use of ratios to remove size effect has been shown to actually increase the size effect by inducing correlations. Atchley et al. (1976) as well as others have discussed this in detail yet it is still common in practice. The QTL results also indicated that using ratios could lead to inaccurate results. Multiple regression can be used to remove the variance of co-factors to allow for the investigation of genetic influence independent of correlated phenotypes. Interpretation of the results are however complex. In general, the multiple regressed residualized variables mapped very closely to the raw variables. However, in many cases the LOD scores were significantly different between raw and regressed phenotypes. The results presented here were limited to chromosome 7 and 13. However, similar results were found on other chromosomes as well.

181 159 The pathways involved in bone s response to its loading environment are complex and involve both direct skeletal related pathways and indirect pathways that are mediated through muscle, activity or body size. The correlations between body size, muscle mass, physical activity, and skeletal phenotypes could arise through different pathways and in some cases produce conflicting responses. For example, differences in bone mineral density and cortical thickness could arise from a scaling effect due purely to body size differences. Alternatively bigger muscles produce greater forces and increase the load on the skeleton. Greater loads produce increased bone and increased bone and muscle mass increase body weight. Distinguishing between a scaling effect and a response effect are critical in understanding the causal relationships in these complex pathways. One could argue whether body size is a causal influence on bone measures or is the measurement of body mass really a measure of bone mass. Could the correlation of body size with skeletal measures be due to the percent of bone in the body mass measurement or is it influenced by the load produced by body weight? Acknowledgements This work was supported by grants P01 AG14731 and R01 Ag21559 from the National Institute on Aging of the National Institutes of Health.

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185 163 Chapter 7 Quantitative Trait Loci Analyses (QTL) of Structural and Material Skeletal Phenotypes in C57BL/6J and DBA/2 F 2 and RI Populations Introduction The impact of reduced bone mass on our aging population has heightened the interest in research on the genetic influence of bone mass or bone mineral density (BMD). Past research on twins has shown that genetic influence may control as much as 75% of the variance in bone density (Dequeker et al., 1987). Until quite recently, genetic research focused on candidate genes with identifiable polymorphisms. Such an approach requires a prior hypothesis for candidate genes and often produces conflicting results for continuously distributed phenotypes such as skeletal measures. The problem stems from complex polygenic control and the confounding effects of environment. Extrinsic factors such as nutrition, lifestyle, and skeletal loading are impossible to control over substantial time periods in human populations. The use of experimental mice in environmentally controlled laboratories can eliminate much of the phenotypic variance associated with nutrition and environment. Recombinant inbred (RI) strains, which were originally developed to detect major genes linked to bimodal traits (Bailey, 1981), are now being used in QTL analysis to detect genetic loci associated with phenotypic traits that show continuous strain distribution patterns. Applying this technique to inbred strains of mice as well as F 2 (second

186 164 generation) mice, identically housed in the same facility over their lifespan, minimizes the confounding effects of diverse environments. We here report the results from QTL analyses that were used to elucidate the genetic regulation of musculoskeletal strength and architecture in 200 day old C57BL/6J X DBA/2 (BXD) recombinant inbred (RI) strains of mice, as well as F 2 cohorts derived from the same parental strains. This is the first phase of an ongoing Program Project at the Center for Developmental and Health Genetics at The Pennsylvania State University. The aim of the Program Project is to identify QTLs for biomarkers of aging across the life span. Background Several investigators have utilized genome wide scans to search for QTLs for skeletal phenotypes. Many QTLs with similar loci have been identified across studies, lending credibility to the technique. In several such studies, similar phenotypes are often used but the method of measurement varies (e.g. pqct versus DEXA for BMD measures). Other methods of quantifying skeletal quality include mechanical testing of the mid-shaft of long bones, shear testing of the femoral neck, or compression testing of vertebrae. Mineral content has been be obtained using pqct or by ashing the bone to obtain ash fraction or total ash content. Each of these methods for measuring skeletal phenotypes can offer distinct insight into skeletal quality and often identify QTLs specific to that skeletal measure. However, it is also the case that these phenotypes overlap and often provide replication across studies for the same skeletal QTL. The skeletal site

187 165 under investigation also varies and in many cases site specific QTLs have been identified in the same study. Inbred mouse strains used in the present study have been integral to genetic studies for many years. Klein et al. (1998) was one of the first to perform a genome scan for skeletal QTLs. These investigators used QTL analysis to establish a genetic link to peak BMD and body weight in female mice. The study used C57BL/6 and DBA/2 inbred mouse strains, and 24 of the BXD RI strains. The results of the BXD RI strain QTL analysis identified 10 QTLs on chromosomes 1, 2, 7, 11, 14, 15, 16, 18, and 19 that were correlated with whole body BMD and 4 QTLs on chromosomes 4, 6, 9, and 14 that were correlated with body weight (Klein et al. 1998). Klein et al. (2001) also performed phenotypic selection for high and low BMD starting with an F 2 population. After three generations the high BMD line had 14% greater bone mineral density than the low BMD line. Femur cortical area and thickness, and cancellous vertebral bone volume was 16-30% greater in the high line. Cancellous bone formation rates (BFR) were 35% lower in the high BMD line. Mineral apposition rate (MAR) was significantly reduced on the endosteal surface of the high BMD line whereas periosteal MAR was increased in the high BMD line. Failure load and stiffness were greater in the high BMD line but material properties of the femur mid-shaft were similar in both lines, suggesting that the difference in structural properties between the high and low lines was only due to geometric differences (Klein et al., 2001). In a subsequent study, Klein et al. (2001) confirmed 4 of the QTLs identified in their previous study using three independent populations derived from the same B6 and D2 parental strains. Selective genotyping was conducted on the animals comprising the

188 166 extreme15% of each tail of the BxD F 2 population distribution, consisting of 601 females and 393 males. In addition, short-term phenotypically selected lines and short-term genotypically selected lines derived from the same parental strains were used in QTL analyses. The F 2 genome scan confirmed QTLs on chromosomes 1, 2, 4, and 11. For the QTLs on chromosomes 1, 2, and 4, the D2 allele contributed to increased BMD while the B6 allele contributed to increased BMD on chromosome 11. These results are inconsistent with the investigator s original results from their RI analysis, which indicated that B6 mice had significantly greater BMD. The results of the short term phenotypically selected lines confirmed significant differences in allele frequencies for each of the four QTLs. The allele affect found in the selected line was the same as that of the F 2 population. D2 alleles were associated with high BMD on chromosomes 1, 2, and 4 whereas B6 alleles increased BMD on chromosome 11. RI segregation testing (RIST) was used to fine map QTLs on chromosome 2 and 11. Using the RIST method, Klein et al. were able to reduce the QTL interval for chromosome 2 and 11 down to 9-10 cm, as well as offer additional confirmation of the QTLs at these sites (Klein et al., 2001). Klein et al. (2002) also performed QTL analyses for femur cross sectional area using 964 F 2 male and female B x D mice as well as 18 B x D RI strains at 16 weeks. Animals comprising the extreme 15% in each tail of the distribution were genotyped. Regressions between body weight and cross sectional area (CSA) demonstrated an r 2 of 0.13 for males and 0.24 for females. QTL analysis was performed on CSA corrected for body weight by regressing on body weight and using the resulting residuals. The F 2 results indicated QTLs for femur CSA on chromosomes 6, 8, 10, and X in both genders. These researchers defined gender differences in QTL results as a difference in LOD

189 167 scores between males and females exceeding 3.0. Based on this criterion they identified three gender specific QTLs on chromosomes 2 (males), 7 (females, and 12 (females). Although their RI population was limited in number (18 strains), they were able to provide support for the QTLs on chromosomes 2, 7, 8, 10, and 12. This study reported little or no correlation between CSA and BMD in both the RI and F 2 populations (Klein et al., 2002). The B6 x D2 F 2 population was also used in a study of bone density conducted by Drake et al. (2001) F 2 mice were fed an atherosclerosis-inducing diet for 4 months. At 16 months bone mineral mass and density were measured using computerized tomography and radiographs. Mechanical properties of the femoral shaft were assessed using a torsion test on the shaft. Bone mass was found to be inversely correlated with atherosclerosis and directly correlated with body weight, length, and adipose tissue. Skeletal related traits were mapped to chromosome 2, 3, 6, 7, and proximal and distal portions of chromosome 15. Three of these QTLs were found to be adjacent to or to overlap with QTLs that were identified for non-bone traits such as adipose tissue, plasma HDL and LDL, and body length. Drake et al. used a two step method to determine if these QTLs resulted from pleiotropic gene effects or from multiple QTLs in close proximity. Multi-trait composite interval mapping provided support for pleiotropy of the QTL on chromosome 2, which influenced radiographic BMD, adipose mass, and HDL cholesterol. The test also supported a pleiotropic effect of the QTL on chromosome 6 for radiographic inter-trochanteric density, plasma HDL, and subcutaneous fat pad mass (Drake et al., 2001).

190 168 Shults et al. (2003) recently reported the construction of 12 congenic lines for QTLs that have been previously identified. Six congenic lines were constructed from C3H donor regions on B6 backgrounds and six lines were constructed from CAST donor regions on B6 backgrounds. The C3H genetic regions were chromosomes 1 ( cm), 4 ( cm), 6 ( cm), 11 ( cm), 13 ( cm), and 18 ( cm). The CAST congenic lines consisted of regions on chromosomes 1 ( cm), 3 ( cm), 5 ( cm), 13 ( cm), 14 ( cm), and 15 ( cm). All congenic strains showed significant differences in BMD compared with B6 controls except B6.CAST.15T. The C3H/C3H alleles increased BMD in all congenic B6.C3H lines except B6.C3H.6T. The CAST/CAST alleles increased BMD in all congenic lines except B6.CAST.5T and B6.CAST.15T. Significant differences were also reported for body weight, femur length, and mid-diaphyseal periosteal circumference in several of the congenic strains. However, these results did not show consistent correlations with BMD. Eight additional sub-lines were constructed from C3H donors and B6 recipients to fine-map the QTL on chromosome 1. Results from the chromosome 1 sublines indicated two QTLs at cm and cm (Shultz et al., 2003). Materials and Methods Animals and Genotyping Breeding and maintenance of C57BL/6J and DBA/2 progenitor strains of mice, twenty-two BXD recombinant inbred strains as well as F 1 and F 2 animals derived from

191 169 the progenitor strains, was conducted in a barrier facility. One of the long-term aims of this study is to determine genetic regulation as a function of aging. Rearing animals under barrier conditions provides interpretational clarity of age-related changes with the potentially confounding effects of infectious disease eliminated. This report is primarily focused on 200 day-old animals, an age equivalent to young adulthood. Animals with ages representative of midlife and elderly populations (500 day-old and 800 day-old) will be assessed in the near future. Genotypic data for over 700 microsatellite markers were obtained from Williams et al. (2001) for use in the subsequent QTL analyses for the BxD RI strains. F 2 animals were genotyped in-house using 96 microsatellite markers distributed throughout the genome with an average spacing of centimorgans (cm). Marker analyses were conducted on purified DNA samples procured from tail snips using an automated, fluorescence-based detection system described in detail in Vandenbergh et al. (2003). Behavioral Assessments Several measures of activity-related behaviors were made on each animal at 150 days of age. Activity was measured in a 40 cm x 40 cm x 15 cm deep, black opaque plastic arena marked into four quadrants with a 15 mm hole cut in the center of each quadrant. Animals were individually placed into a clear plastic cylinder in the center of the arena. After ten seconds, the cylinder was lifted and each mouse was observed for five minutes. The number of quadrants entered, the number of times the animal reared onto its hindquarters, and the number of times it poked its head into the floor holes was

192 recorded. The testing conducted in the activity box was repeated on three separate 170 occasions for the F 2 mice and on one occasion for the RI mice. Each animal was placed on a dowel rod 89 cm long x 1.6 cm in diameter that was suspended 23 cm above a foam pad. The rod was marked into five sectors of 17.8 cm each. Animals were observed until falling or until one-minute had elapsed. The fall times and the number of excursions into different sectors were recorded. A cord drop test was conducted in a similar fashion, except that animals were placed such that they hung from their forelimbs. Time to release was recorded. Rod and cord activity measures were repeated for three one-minute intervals on three separate occasions for a total of nine one-minute intervals for the F 2 mice. RI testing was conducted for a single oneminute interval on three separate occasions for a total of three one-minute intervals. Tissue Harvest and Gross Dimensional Measurements After cervical dislocation animal weight and nose-to-anus length was recorded. The right hind limb of each animal was harvested by disarticulation at the hip joint. The extracted limb was tacked in full extension to a dissection board and the gastrocnemius, soleus, tibialis anterior and extensor digitorum longus muscles were carefully isolated, removed in their entirety, and weighed on a precision microbalance. The femur and tibia were cleaned of remaining muscle and soft tissue. Individual bones were wrapped in saline soaked gauze and stored in test tubes at -5 o C until mechanically tested. All bones underwent one freeze-thaw cycle.

193 At the time of testing, the bones were thawed at ambient temperature and any 171 remaining bits of soft-tissue were removed. A digital caliper accurate to 0.01 mm was used to measure femoral length from the most superior aspect of the greater trochanter to the intercondylar notch, femoral width at the center of the diaphysis in both the sagittal and coronal planes, and epiphyseal width at the widest point of the distal epiphysis in the coronal plane. Femoral head and femoral neck diameter were also measured. Flexural Testing of the Femoral Diaphysis The midshaft of the femur was tested to failure in three-point-bending in an MTS MiniBionix testing apparatus using a support span of 8 mm and a displacement rate of 1 mm/min. Femurs were consistently oriented in the testing apparatus so that the nosepiece was posteriorly directed in respect to the femoral shaft. All testing was executed with the bones wet and at ambient temperature. Each femur was loaded to failure while recording load and actuator displacement at 20 Hz and a load-displacement curve was generated using MATLAB scientific software. Yield load, yield displacement, energy absorbed at yield (area under the load-displacement curve), failure load, failure displacement, energy absorbed at failure, and stiffness (initial slope of the load-displacement curve) were determined.

194 Flexural Testing of the Tibial Diaphysis 172 The mid-shaft of the tibia was tested to failure in three-point bending using a similar technique to that used for femoral testing except that the support span was 10 mm. A small section of the anterior flare of the proximal tibia was carefully removed before testing so that the tibia would lie flat on the support span and not roll during loading. This procedure was effective in preliminary tests and yielded consistent results without compromising the integrity of the diaphysis under study. Tibiae were consistently oriented across the support span so that the nosepiece was anteriorly directed with respect to the tibial shaft. All testing was executed with the bones wet and at ambient temperature. Each tibia was loaded to failure while recording load and actuator displacement at 1 mm/min and a load-deformation curve was generated using MATLAB scientific software. Yield load, yield deformation, energy absorbed at yield, failure load, failure deformation, energy absorbed at failure, and stiffness were determined using a MATLAB program expressly written for this application. Yield stress and failure stress were calculated using the same approach as outlined for femoral testing. Material Properties The structural parameters outlined above are dependent on the shape and size of each bone, as well the material properties of the tissue itself. Yield stress, yield strain, failure stress, failure strain and tissue modulus of the mid-diaphysis were subsequently calculated based upon histomorphometric measurements of cross-sectional area to assess the material properties of the tissue independent of geometry. Stress was calculated using

195 173 Equation 7-1, where σ is the bending stress, F is yield or failure load, L is unsupported span length, c is the distance from the cross-section centroid to the tensile periosteal surface and I is the cross-sectional moment of inertia. Strain was calculated using Equation 7-2, where c is the distance from the cross-section centroid to the tensile periosteal surface, d is the displacement, and L is the unsupported span length. Tissue modulus was calculated using Equation 7-3, where F, L, d, and I are described previously (Turner and Burr, 1993). σ = F L c / 4 I (7-1) Є = 12 c d /L 2 (7-2) E = F L 3 / d 48 I (7-3) Shear Testing of the Femoral Neck The proximal fragment of the femur was used to measure the functional strength of the inter-trochanteric region and femoral neck. The proximal femur was embedded vertically to three mm below the top of the femoral head in a mounting pot containing low melting point alloy. The pot was positioned and secured in the MTS materials testing machine and the femoral head was loaded, parallel to the femoral shaft, at a rate of 1 mm/min. Yield load, yield displacement, energy absorption to yield, failure load,

196 failure displacement, energy absorption to failure, and stiffness were calculated using MATLAB routines similar to those described for flexural testing. 174 Compositional Analysis After mechanically testing the femoral shaft and neck the femoral fragments were gathered and placed together in a muffled furnace at 800 o C for 24 hours. Upon removal the fragments were brought to ambient temperature in a desiccator and weighed to the nearest 0.01 mg to determine the ash weight of the entire femur. Following the flexural testing of the tibia the distal fragment was blotted to remove excess moisture and weighed to the nearest 0.01 mg to obtain the wet weight (mw) of the bone fragment. After the initial weighing the tissue was dried in a vacuum oven at 100 ºC for 24 hours, cooled to room temperature in a desiccator, and weighed again (md). It was then placed in a muffled furnace at 800ºC for 24 hours, removed, again brought to ambient temperature in a desiccator, and weighed a final time to determine its ash weight (ma). Percent water, organic, ash and mineralization were obtained based on the wet, dry and ash mass for the tibia. Tissue Processing and Histomorphometry After testing, the proximal end of the tibia was immersed and fixed in cold (5ºC) 40% ethanol for 48 hours. The bones were dehydrated over a one-week period using increasing concentrations of cold ethanol (from 70 to 100%) and the proximal tibia and

197 distal femur were embedded in methylmethacrylate using a three-step three-solution 175 approach (1983). A DDK diamond wire saw was used to cut 150 mm diaphyseal crosssections. Digital images of each cross-section were collected using a light microscope equipped with a 4X objective and a high-resolution CCD video camera interfaced to a personal computer. Images were captured using NIH IMAGE software. Total area within the periosteal surface, medullary area within the endosteal surface, cortical area, centroid of the cross-section, cross-sectional moment of inertia, and average cortical width, and inner and outer radius at four perpendicular locations on the cross section were calculated using a MATLAB program. These data, together with data from the flexural tests were used to calculate the failure and yield stress, strain, and elastic modulus of the femur and tibia. Preliminary Analyses A number of preliminary analyses were conducted to determine the suitability of the BXD model for investigations of skeletal genetics as a function of sex and age. Although successful QTL analyses are not necessarily dependent on large progenitor strain differences in phenotypic expression, such differences may increase the likelihood of isolating new genetic loci that influence bone health in an age- and sex-dependent manner. The bones and muscles of five male and five female mice from each progenitor strain were examined at 180 days of age using the above techniques. Two-way analyses of variance with sex and strain as independent factors were conducted for the parental

198 176 strains and one-way ANOVA was performed in the F 2 cohort on each response variable to screen for phenotypic differences as a function of these factors. In a separate analysis, twenty 650 day-old mice, 5 males and 5 females of each strain, were examined according to the procedures outlined previously. Phenotypic data from these animals were then combined with data from our 180 day-old parental strains and a second series of sexspecific two-way analyses of variance were conducted to determine the influence of age and strain on the skeletal characteristics examined. P-values less than 0.05 were considered to reflect a significant difference in both sets of analyses and the results for representative phenotypes are given in. Co-variance of Phenotypic Expression Co-variation of animal activity and muscle mass, with bone geometry and strength were explored in the F 2 cohort using Pearson product moment correlations. A two-tailed significance level was used and results are reported at the p < 0.05 level. Heritability Estimates Estimates of heritability for the musculoskeletal traits were made using the phenotypic data from the male and female recombinant inbred (RI) strains. The broad sense heritability of the trait, h 2 is given in Equation 7-4, where V E is the variance due to non-genetic factors such as environment, V T is the variance due to the combined effects

199 of genetics and environment, and V G is the genetic variance calculated as V E subtracted from V T. 177 h2 = VG / (VG + VE) = VG / VT (7-4) With RI strains, heritability can be estimated by comparing the within strain variance to the between strain variance. The r 2 from a one-way ANOVA for strain is equivalent to the heritability and was used to estimate heritability in females and males separately. QTL Analyses Separate sex-specific QTL analyses were performed on both the RI and F 2 cohorts to locate chromosomal regions influencing phenotypic variables. QTL analyses were also performed on males and females combined but corrected for sex differences. RI analyses employed data from 10 male and 10 female mice for each of twenty-two RI strains. QTL analyses were performed using strain means. The F 2 analyses employed approximately 400 mice, equally divided between male and female. QTL analyses were performed on F 2 and RI data using QTL Cartographer (Basten et al. 1994; Basten et al. 2002). Interval mapping was performed on all skeletal, muscle and behavioral measures and composite interval mapping was performed on muscle, and skeletal phenotypes using the default settings of 5 cofactors and a window size of 10 cm. The F 2 population was used to nominate QTLs and the RI cohort was used for confirmation. QTLs that were nominated in the F 2 with LOD scores of 4.3 or greater and confirmed in the RI with LOD scores of 1.5 or greater were considered as confirmed

200 linkage (Lander and Kruglyak, 1995), whereas QTLs nominated in the F 2 with LOD 178 scores between 2.8 and 4.3 and confirmed in the RI with a LOD score greater than 1.5 were considered confirmed suggestive. Results and Discussion Preliminary Analyses Many phenotypic measures were strain and sex dependent suggesting that the BXD recombinant inbred series of mice is an appropriate tool for assessing skeletal genetics. The results from the analyses for strain and sex differences are given in Table 7-1. Though limited in size, the sample presented here suggests some very interesting inter-strain and inter-gender relationships. Femoral length and diaphyseal width and femoral head diameter were significantly larger in the B6 male strain than in the D2 male strain. Contrarily, the dimensions of the femoral neck and epiphysis were similar in each strain. Such a contrast cannot be explained by simple geometrically scaled size differences between the two strains. A better postulate is that different genetic factors are exerting their influences at different skeletal sites and perhaps on different types of bone tissue (trabecular versus cortical). An even more interesting observation is that despite the significantly enlarged size of the femoral diaphysis in the B6 animals, these femurs were significantly weaker than femurs from the D2 animals. Although we have not yet analyzed the cross-sectional data for strain and sex differences, we hypothesize that B6 femora must have considerably thinner diaphyseal cortices than

201 D2 femora and that the expanded diaphyseal diameter measured in B6 animals may 179 actually reflect compensation for cortical thickness. The lack of a statistical difference in femoral ash weight between the two strains tends to support this hypothesis. Additional support for this hypothesis can be found in a study by (Beamer et al., 1996) that compared the femoral length, density, and volume of four different inbred mouse strains, including the B6 and D2 strains. They indicate that B6 animals had longer bones and more volume, but considerably thinner cortices than DBA animals, which demonstrated higher density, greater mineral content, and thicker cortices. Similar results were seen in the cross sections from the parental used in this study Figure 7-1.

202 180 Table 7-1: Skeletal characteristics of B6 and D2 progenitor strain mice and F day old mice. Means and standard deviations are presented, p-values reflect differences between the two strains and between genders as determined by two-way ANOVA. C57BL/6 DBA/2 F day old Quantitative Measures Female Male Female Male Female Male n = 5 n = 5 n = 5 n = 5 n = 200 n = 200 Body Weight (gm) Gender Effects p < p < p < Strain Effects p = Body Length (cm) Gender Effects p < ns p < Strain Effects ns Overall Femoral Length (mm) Gender Effects p = ns p=0.029 Strain Effects p = Femoral Shaft Width (mm) (Coronal Plane) Gender Effects p < p = p < Strain Effects p = Femoral Head Diameter (mm) Gender Effects p < ns p < Strain Effects p = Femoral Shaft Yield Load (N) Gender Effects ns p=0.002 Strain Effects p < Femoral Shaft Yield Displacement (mm) Gender Effects ns p=0.004 Strain Effects p < Femur Ash Weight (mg) Gender Effects p = ns p < Strain Effects ns Overall Tibial Length (mm) Gender Effects p < ns p = Strain Effects p = Tibial Shaft Width (mm) (Sagittal Plane) Gender Effects p = ns Strain Effects p = p < Tibial Shaft Failure Load (N) Gender Effects p < ns p < Strain Effects Tibial Shaft Stiffness (N/mm) Gender Effects p < ns p < Strain Effects p < 0.001

203 181 Image 1: Male B6 (young) Image 5: Male B6 (old) Image 2: Male D2 (young) Image 6: Male D2 (old) Image 3: Female B6 (young) Image 7: Female B6 (old) Image 4: Female D2 (young) Figure 7-1: Cross-sectional images of the tibial diaphysis for old and young DBA and B6 mice.

204 182 A summary of significant comparisons for the male mice is presented in Table 7-2. Female data were similar but less pronounced due to higher variability. These data provide strong support for use of the BXD RI model in genetic studies of skeletal aging. Many phenotypic variables were adversely affected by age and the magnitude of the age effects varied across strain. For example, the load required to break the femur fell precipitously in the C57BL/6 mice over the time period examined but did not change appreciably in the DBA/2 animals. Many other examples of differential behavior are presented in the table. Heritabilities calculated from RI variances fell between 0.26 and 0.71 Table 7-3.

205 183 Table 7-2: Phenotypic measures of B6 and D2 progenitor strain male mice. Means and standard deviations are presented; p-values determined by 2-way ANOVA, reflect differences within strain by age and between the two strains at 150 and 650 days. Male C57BL/6 DBA/2 Quantitative Measures 150 days 650 days 150 days 650 days n = 5 n = 5 n = 5 n = 5 Body Length (cm) Age Effects ns ns Strain Effects p = p = Body Weight (gm) Age Effects p = p = Strain Effects p < p = Extensor Digitorum (mg) Age Effects p = p < Strain Effects p < p < Tibialis Anterior (mg) Age Effects p = p< Strain Effects p < p < Overall Femoral Length (mm) Age Effects ns ns Strain Effects p = p = Femoral Shaft Width (mm) (Sagittal Plane) Age Effects p < p = Strain Effects p = ns Femoral Shaft Yield Load (N) Age Effects p < p < Strain Effects p = p = Tibial Shaft Width (mm) (Coronal Plane) Age Effects ns p = Strain Effects ns p = Tibial Shaft Yield Load (N) Age Effects p < ns Strain Effects ns p = Tibial Shaft Stiff. (N/mm) Age Effects ns ns Strain Effects p = p = Femur Ash Weight (mg) Age Effects p = p < Strain Effects p = ns

206 184 Table 7-3: Heritability Estimates from one-way ANOVA on strain within the RI cohort. Female h 2 Male h 2 Female h 2 Male h 2 Body Size Muscle Mass Body Mass Index Extensor Digitorum Longus Body Weight Gastrocnemius Body Length Tibialis Anterior Adipose Weight Gastrocnemius Femur Overall Dimensions Tibia Overall Dimensions Femur Length Tibial Length Femur Coronal Width Tibial Coronal Length Femur Head Diameter Femur Medullary and Cortical Area and Thickness Tibia Medullary and Cortical Area and Thickness Femur Medullary Area Tibia Medullary Area Femur Cortical Area Tibia Cortical Area Femur Total Area TibiaTotal Area Femur Shaft Strength / Measures Tibia Shaft Strength / Measures Femur Ultimate Load Tibia Ultimate Load Femur Ultimate Stress Tibia Ultimate Stress Femur Ultimate Strain Tibia Ultimate Strain Femur Modulus of Elasticity Tibia Modulus of Elasticity Femur Stiffness Tibia Stiffness Femur Neck Strength / Measures Skeletal Ash Mass and Composition Shear Test Ultimate Load Femur Ash Mass Tibia % Water Serum Tibia %Mineral Alkaline Phosphatase Tibia % Organic Serum Calcium Tibia % Ash Co-variance of Phenotypic Expression Correlations, with coefficients often exceeding 0.60, were found between skeletal parameters and muscle mass, and 0.25 between skeletal measures and activity level. The correlation coefficients from the female F 2 results are listed in Table 7-4 and the males are given in Table 7-4. Correlations significant at p < 0.01 are shaded in gray and those at p < 0.05 have bold borders. (Kaye and Kusy, 1995) found stronger correlations for

207 femur weight vs. muscle weight (r = 0.85), activity vs. muscle weight (r = 0.6), and 185 activity vs. femur weight (males r = 0.49 and females r = 0.59) for C57BL/6 and A/J mouse strains. Table 7-4: Pearson correlation coefficients for female body size, activity, muscle, and bone measures. Pearson Correlation Coefficients Females Body Weight Body Length Gastroc. Tibialis Anterior Femur Length Tibia Length Femur Ult. Load Femur Stiff. (L) Body Weight 1 Body Length Gastroc Tibialis Anterior Femur Length Tibia Length Femur Ult. Load Femur Stiffness (L) Femur Ash (S) Tibia Stiffness (L) Head Pokes Rears Squares Rod Sections Table 7-5: Pearson correlation coefficients for male body size, activity, muscle, and bone Pearson Correlation Coefficients Males measures. Body Weight Body Length Gastroc. Tibialis Anterior Femur Length Tibia Length Tibia Ult. Load Femur Stiff. (L) Body Weight Body Length Gastroc Tibialis Anterior Femur Length Tibia Length Femur Ult. Load Femur Stiffness (L) Femur Ash (S) Tibia Stiffness (L) Head Pokes Rears 0.25 Squares 0.22 Rod Sections

208 QTL Analyses 186 QTL analyses included males and females separately as well as combined and corrected for sex differences. The QTL analyses included both raw and weight and length regressed phenotypes and results for both are listed in the following tables with variable codes to distinguish non-regressed phenotypes (N) from regressed phenotypes (R). All phenotypes were screened for normality and when necessary a log (L) or square root (S) transformation was used and is also indicated in the code column of the table. The column headers for the QTL results are described in Table 7-6. Table 7-6: Column descriptions for QTL results tables. Chart Code Description Hypotheses tested: Chr. Code Peak cm LOD H1:a H3:a H3:d % Var. Chromosome H0 a = 0, d = 0 R - regressed on body weight and length H1 a not = 0, d = 0 (RI cohort) L - log transformed H3 a not = 0, d not = 0 (F2 cohort) S - square root transformed Position in centimorgans where LOD score peaks LOD score for H3/H0 (F2) and H1/H0 (RI) Estimate of a (the additive effect) under H1 Estimate of a (the additive effect) under H3 Estimate of d (the dominant effect) under H3 Percent variance attributed to QTL The QTL analyses revealed many QTLs exceeding the recommended significant threshold level. The QTLs that exceeded a LOD score of 4.3 at the significant level for the F 2 analyses with both additive and dominant genetic effects are summarized in Table 7-7 for female F 2, Table 7-8 for female RI, Table 7-9 for male F 2, and Table 7-10

209 187 for male RI, and Table 7-11 and Table 7-12 for combined F 2 and Table 7-13 for combined RI. Table 7-7: Interval mapping results for female F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. F2 F 2 Femur Cortical Thick-medial LR F2 F 2 Femur Cortical Thick-medial L F2 F 4 Alkaline Phosphatase SR F2 F 4 Alkaline Phosphatase S F2 F 4 Rears Mean SR F2 F 4 Rears Mean S F2 F 4 Squares Mean S F2 F 4 Squares Mean SR F2 F 6 Femur Coronal Width F2 F 11 Body Length L F2 F 12 Femur Head Diameter R F2 F 12 Femur Head Diameter F2 F 15 Rod Sections F2 F 16 Femur Outer Radius-lateral L F2 F 16 Femur Outer Radius-medial LR F2 F 16 Femur Outer Radius-medial L F2 F 20 Calcium (Serum) F2 F 20 Calcium (Serum) R

210 188 Table 7-8: Interval mapping results for female RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. RI F 1 Femur Stiffness LR RI F 1 Tibia Outer Radius-anterior L RI F 1 Tibia Outer Radius-anterior LR RI F 2 Tibialis Anterior Mass RI F 2 Tibialis Anterior Mass R RI F 4 Alkaline Phosphatase S RI F 4 Alkaline Phosphatase SR RI F 4 Tibia Outer Radius-medial RI F 4 Tibia Outer Radius-medial R RI F 5 Femur Epiphyseal Width RI F 5 Tibia Inner Radius-posterior RI F 5 Tibia Inner Radius-posterior R RI F 5 Tibia Outer Radius-posterior LR RI F 7 Femur Outer Radius-anterior RI F 7 Femur Outer Radius-anterior R RI F 9 Femur Stiffness L RI F 9 Femur Stiffness LR RI F 9 Tibia Length R RI F 13 Femur Cortical Thick-medial RI F 15 Tibia Length RI F 15 Tibia Length R RI F 17 Femur Inner Radius-posterior RI F 17 Femur Inner Radius-posterior R RI F 17 Femur Inner Radius-medial RI F 17 Femur Inner/Outer Rad-posterior RI F 17 Femur Inner/Outer Rad-posterior R RI F 17 Gastrocnemius RI F 17 Gastrocnemius R RI F 17 Rod Drop Mean RI F 18 Femur Stiffness LR RI F 18 Rears Mean RI F 19 Extensor Digit. Longus SR

211 189 Table 7-9: Interval mapping results for male F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. F2 M 1 Adipose Weight F2 M 1 Adipose Weight R F2 M 1 Femur Length F2 M 1 Femur Length R F2 M 1 Tibia CSMI-PB SR F2 M 1 Tibia Length F2 M 1 Tibia Length R F2 M 4 Alkaline Phosphatase S F2 M 4 Alkaline Phosphatase R F2 M 4 Tibia Ultimate Load F2 M 6 Femur Coronal Width R F2 M 6 Gastrocnemius R F2 M 6 Shear Test Yield Load R F2 M 6 Shear Ultimate Displ. L F2 M 6 Shear Ultimate Displ. LR F2 M 7 Adipose Weight F2 M 7 Femur Ultimate Load R F2 M 7 Femur Yield Load R F2 M 8 Tibia % Mineral F2 M 11 Adipose Weight R F2 M 13 Body Weight

212 190 Table 7-10: Interval mapping results for male RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body length and body weight. F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. RI M 1 Femur Cortical Thick-medial L RI M 1 Femur Cortical Thick-medial LR RI M 1 Femur Length RI M 1 Femur Length R RI M 1 Tibia Length R RI M 9 Squares Mean R RI M 13 Calcium (Serum) RI M 13 Calcium (Serum) R RI M 13 Femur Yield Load RI M 13 Femur Yield Load R RI M 13 Femur Yield Work S RI M 13 Femur Yield Work SR RI M 15 Tibia Length RI M 15 Tibia Yield Work RI M 15 Tibia Yield Work R RI M 17 Femur Outer Radius-anterior L RI M 18 Tibia Inner/Outer Rad.-anterior RI M 18 Tibia Inner/Outer Rad.-anterior R RI M 19 Extensor Digit. Longus L RI M 19 Extensor Digit. Longus LR RI M 19 Tibia Cortical Area RI M 19 Tibia Cortical Area R Table 7-11: Interval mapping results for combined (male and female sex corrected) F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued). F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. F2 C 1 Adipose Weight R F2 C 1 Femur Length F2 C 1 Femur Length R F2 C 1 Femur Sagittal Width L F2 C 1 Tibia Length F2 C 1 Tibia Length R F2 C 1 Tibia Medullary Area L F2 C 1 Tibia Medullary Area LR F2 C 2 Femur Length R F2 C 2 Femur Yield Load R F2 C 2 Femur Yield Load

213 191 Table 7-12: Interval mapping results for combined (male and female sex corrected) F 2 phenotypes with LOD scores > 4.3. Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued). F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. F2 C 3 Cord Drop Mean S F2 C 3 Cord Drop Mean R F2 C 3 Gastrocnemius F2 C 3 Tibia Length F2 C 4 Alkaline Phosphatase S F2 C 4 Alkaline Phosphatase R F2 C 4 Rears Mean S F2 C 4 Rears Mean SR F2 C 4 Squares Mean S F2 C 4 Squares Mean SR F2 C 6 Femur Coronal Width R F2 C 6 Femur Coronal Width F2 C 6 Shear Ultimate Displ. LR F2 C 6 Tibia Length R F2 C 6 Tibia Outer Radius-posterior R F2 C 6 Tibia Ultimate Load R F2 C 6 Tibia Ultimate Load F2 C 7 Adipose Weight S F2 C 7 Femur Ultimate Load LR F2 C 7 Femur Yield Load R F2 C 8 Tibia % Mineral F2 C 8 Tibia % Mineral R F2 C 11 Cord Drop Mean S F2 C 12 Femur Head Diameter R F2 C 12 Femur Head Diameter F2 C 13 Adipose Weight S F2 C 13 Body Mass Index L F2 C 13 Body Weight L F2 C 13 Rears Mean S F2 C 13 Squares Mean S F2 C 13 Squares Mean SR F2 C 13 Tibia Length F2 C 14 Femur Coronal Width R F2 C 14 Femur Coronal Width F2 C 16 Femur Coronal Width F2 C 16 Femur Coronal Width R F2 C 16 Femur Outer Radius-lateral LR F2 C 17 Femur Stiffness L F2 C 17 Femur Stiffness LR F2 C 18 Tibia Coronal Width

214 192 Table 7-13: Interval mapping results for combined (male and female sex corrected) RI phenotypes with LOD scores > 3.3. Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued). F2-RI Sex Chr. Phenotype Code Peak cm LOD H1:a H3:d % Var. RI C 1 Femur Length R RI C 2 Tibialis Anterior Mass RI C 2 Tibialis Anterior Mass R RI C 3 Femur Cortical Thick-medial LR RI C 4 Alkaline Phosphatase SR RI C 4 Alkaline Phosphatase S RI C 4 Tibia Outer Radius-medial L RI C 9 Squares Mean R RI C 9 Tibia % Mineral L RI C 9 Tibia % Mineral LR RI C 15 Tibia Length RI C 17 Femur Cortical Thick-medial LR RI C 17 Femur Outer Radius-posterior RI C 17 Femur Outer Radius-posterior R RI C 17 Gastrocnemius RI C 17 Rod Drop Mean RI C 19 Extensor Digit. Longus L RI C 19 Extensor Digit. Longus LR RI C 19 Extensor Digit. Longus S RI C 19 Extensor Digit. Longus SR QTLs from the interval mapping results that were nominated in the F 2 (LOD > 2.8) and confirmed in the RI (LOD > 1.5) are summarized in Table 7-14 for females, and Table 7-15 and 7-16 for males and Tables 7-17 and 7-18 for the combined sex corrected analyses. Composite interval mapping was performed using QTL Cartographer with the default settings of 5 cofactors and a window size of 10 cm. QTLs that were nominated in the F 2 cohort (LOD=1.9) and confirmed in the RI are listed in Table 7-19 for females and Table 7-20 for males and, and Tables 7-21, 7-22, and 7-23 for combined sex corrected.

215 193 Table 7-14: Female interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight. Sex Chr. Phenotypic Nomination in F2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a F 1 Femur Length R R F 1 Femur Length F 2 Femur Ultimate Load LR LR F 2 Femur Yield Load R R F 2 Femur Yield Load F 4 Adipose Weight S L F 4 Alkaline Phosphatase SR SR F 4 Alkaline Phosphatase S S F 4 Squares Mean S F 4 Squares Mean SR R F 5 Adipose Weight R LR F 6 Tibia Inner Radius-posterior S F 6 Tibia Inner Radius-posterior SR R F 9 Tibia Epiphyseal Width R R F 9 Tibia Length F 11 Tibia Length F 15 Femur Ultimate Load LR LR F 17 Femur Length R R

216 194 Table 7-15: Male interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight. Sex Chr. Phenotypic Nomination in F2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 1 Femur Length M 1 Femur Length R R M 1 Tibia Inner Radius-medial L M 1 Tibia Inner/Outer Rad.-medial LR R M 1 Tibia Inner/Outer Rad.-medial L M 1 Tibia Length M 1 Tibia Length R R M 1 Tibia Medullary Area L M 1 Tibia Medullary Area LR R M 1 Tibia Thickness-posterior L M 1 Tibia Total Area R R M 2 Extensor Digit. Longus L L M 2 Extensor Digit. Longus LR LR M 4 Alkaline Phosphatase S S M 4 Alkaline Phosphatase R SR M 4 Body Length M 4 Femur Outer Radius-lateral R R M 4 Femur Total Area L L M 4 Squares Mean S

217 195 Table 7-16: Male interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued). Sex Chr. Phenotypic Nomination in F2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 4 Squares Mean SR R M 4 Tibia Yield Load M 6 Gastrocnemius M 6 Head Pokes SR SR M 6 Shear Test Ult. Displ. L L M 6 Shear Test Ult. Work L L M 6 Shear Test Ult. Work LR LR M 9 Adipose Weight R SR M 9 Gastrocnemius M 11 Adipose Weight L M 11 Adipose Weight R SR M 11 Rod Sections L M 12 Tibia % Ash R R M 12 Tibia % Ash M 14 Femur Coronal Width R R M 17 Femur Stiffness LR R M 17 Femur Stiffness L M 17 Gastrocnemius M 19 Tibia Total Area R R

218 196 Table 7-17: Combined interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight. Sex Chr. Phenotypic Nomination in F2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 1 Femur Length C 1 Femur Length R R C 1 Tibia CSMI-PB S L C 1 Tibia CSMI-PB SR LR C 1 Tibia Length C 1 Tibia Length R R C 1 Tibia Medullary Area L L C 1 Tibia Medullary Area LR LR C 2 Extensor Digit. Longus LR LR C 2 Extensor Digit. Longus L L C 2 Femur Yield Load R R C 2 Femur Yield Load C 2 Rears Mean S C 4 Alkaline Phosphatase S S C 4 Alkaline Phosphatase R SR C 4 Femur CSMI -B LR LR C 4 Femur CSMI -PB SR LR C 4 Femur Total Area LR LR C 4 Squares Mean S C 4 Squares Mean SR R C 4 Tibia Ultimate Load L C 5 Femur Outer Radius-lateral LR LR C 8 Extensor Digit. Longus LR LR

219 197 Table 7-18: Combined interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Code key: L = log transformed, S = square root transformed, R = regressed on body size and body weight (continued). Sex Chr. Phenotypic Nomination in F2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 8 Extensor Digit. Longus L L C 8 Femur Ultimate Load LR LR C 8 Femur Ultimate Load L C 8 Femur Yield Load C 8 Femur Yield Load R R C 8 Femur Yield Work S S C 8 Femur Yield Work SR SR C 9 Femur Length C 11 Tibia Length C 11 Tibia Length R R C 14 Femur Coronal Width R SR C 14 Femur Coronal Width S C 15 Femur Ultimate Load LR LR C 16 Femur Inner Radius-lateral L C 16 Tibia % Organic C 16 Tibia % Organic R R C 17 Body Mass Index L L C 17 Femur Stiffness L L C 17 Femur Stiffness LR LR C 17 Femur Yield Displ C 17 Femur Yield Displ. R R C 17 Femur Yield Displ

220 198 Table 7-19: Female composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Sex Chr. Phenotypic Nomination in F2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a F 1 Femur Length F 2 Femur Yield Work R SR F 3 Tibia Ultimate Stress LR LR F 3 Alkaline Phosphatase SR SR F 4 Femur Medullary Area LR R F 4 Alkaline Phosphatase SR SR F 4 Alkaline Phosphatase S S F 5 Tibia % Mineral R LR F 6 Body Weight L L F 6 Shear Test Ultimate Displ. R LR F 6 Femur Coronal Width R R F 8 Calcium Mean R R F 8 Tibia Ultimate Displ L F 10 Shear Test Ultimate Displ. R LR F 11 Tibia Length F 11 Tibia Length LR R F 15 Femur Ultimate Load LR LR F 16 Femur Medullary Area LR R F 16 Femur Medullary Area L F 16 Femur CSMI-PB L F 16 Femur Coronal Width F 17 Femur Length R R F 17 Tibia Modulus L L F 18 Femur Sagital Width L F 18 Tibia Coronal Width R R F 19 Femur Sagital Width LR R F 19 Femur Sagital Width L

221 199 Table 7-20: Male composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Sex Chr. Phenotypic Nomination in F2 Cohort Confirmation in RI Cohort Trait Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a M 1 Tibia % Water R SR M 1 Tibia Length M 1 Tibia Length R R M 1 Femur Length R R M 2 Tibia Length M 3 Shear Test Ultimate Load R R M 4 Tibia CSMI-PB S L M 4 Femur Ultimate Stress M 4 Tibia Yield Load M 6 Femur Ultimate Stress R R M 6 Gastrocnemius R R M 6 Shear Test Ultimate Displ. LR LR M 7 Femur Ultimate Work R R M 7 Femur Ash Mass R LR M 7 Femur Cortical Area LR LR M 7 Femur Head Diameter M 9 Gastrocnemius M 12 Tibia Cortical Area M 13 Body Weight L M 14 Femur Coronal Width R R M 17 Gastrocnemius M 17 Tibialis Anterior Mass M 17 Femur Stiffness L M 17 Shear Test Ultimate Displ. LR LR M 19 Tibia Avg. Cortical Thickness R R

222 200 Table 7-21: Combined composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 1 Tibialis Anterior Mass LR R C 1 Femur Length R R C 1 Tibia Length C 1 Tibia Length R R C 1 Body Length C 2 Femur Length R R C 2 Femur Yield Load C 2 Tibia Average Cortical Thickness R R C 2 Tibia Length R R C 2 Femur Yield Load R R C 2 Femur Ultimate Load L C 3 Gastrocnemius Mass C 4 Body Weight L L C 4 Femur CSMI-B L L C 4 Femur CSMI-B LR LR C 4 Femur Medullary Area LR R C 4 Femur Total Area L L C 4 Femur Yield Stress R R C 4 Body Length C 4 Femur CSMI-PB S L

223 201 Table 7-22: Combined composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 4 Tibia Ultimate Load L C 4 Femur CSMI-PB SR LR C 5 Femur CSMI-PB S L C 5 Femur Cortical Area L L C 6 Tibia Stiffness LR R C 6 Femur Sagittal Width LR R C 6 Femur Sagittal Width L C 7 Femur Ultimate Load L C 7 Femur Yield Load R R C 7 Body Mass Index L C 7 Femur Ultimate Work R R C 7 Femur Work at Yield SR SR C 7 Femur Work at Yield S S C 7 Femur Ash Mass SR LR C 8 Extensor Digitorum Longus LR LR C 8 Tibia Percent Mineral R LR C 9 Shear Test Ultimate Load R LR C 9 Femur Epiphyseal Width L C 9 Tibia Percent Mineral R LR C 9 Tibia Percent Mineral L

224 202 Table 7-23: Combined composite interval mapping results nominated in F 2 (LOD > 2.8) and confirmed in RI (LOD > 1.5). QTLs nominated at the significant level (LOD > 4.3) are shaded in light gray, all others are at the suggestive level (LOD > 2.8). Composite Interval Mapping F 2 Nomination RI Confirmation Sex Chr. Phenotype Code Peak cm LOD H3:a H3:d Code Peak cm LOD H1:a C 11 Tibialis Anterior Mass LR R C 11 Femur Coronal Width S C 11 Tibia Length C 11 Femur Inner/Outer Radius-medial S S C 12 Tibia Medullary Area L L C 12 Femur Head Diameter C 12 Tibia Percent Ash R R C 12 Tibia Percent Ash C 13 Body Mass Index L C 14 Femur Coronal Width R SR C 14 Femur Coronal Width S C 15 Femur Length C 15 Femur Yield Load R R C 15 Femur Ultimate Load L C 15 Femur Ultimate Load LR LR C 16 Tibia Percent Organic R R C 16 Femur Coronal Width S C 17 Femur Yield Displacement C 17 Gastrocnemius Mass C 17 Femur Stiffness L L C 17 Shear Test Ultimate Displ. L L C 17 Femur Yield Displacement

225 203 Discussion Our stringent method of nominating and confirming QTLs within two separate cohorts proves beneficial in eliminating false positives, but it runs considerable risk of generating false negatives and thereby excluding viable QTLs. Many interesting sexdependent QTLs were nominated in the separate RI and F 2 analyses, but not confirmed according to our criteria. Although these sites were not definitively confirmed, it is interesting to note that often times the same locus influenced muscle mass, skeletal dimensions, and skeletal mechanics. The co-localization of these QTLs suggests that the same gene or group of genes exerted its effects on both muscle and bone simultaneously or its action on one was transmitted to the other through a cascade of events. QTLs were found on nearly every chromosome within both the RI and F 2 cohorts. Recognizing that many of the phenotypes are strongly correlated, we hypothesize that bone strength and size are influenced indirectly by QTLs controlling body size, muscle mass, or behavior. Skeletal measures that are included in this discussion but not listed in the QTL summaries, had LOD scores between 3.3 and 4.6 and meet the suggestive criteria outlined by Lander and Kruglyak (1995). Several of the phenotypes mapped to chromosomal locations close to QTL positions that have been mapped in recent QTL studies cited in the literature. Chromosome 1 at 74 cm had the highest skeletal QTL LOD score of 9.9 for tibia length in the combined interval mapping analyses. Many skeletal measures mapped to this region though not necessarily meeting the stringent significance criteria outlined here.

226 These include tibia CSMI, femur inner/outer radius, femur medullary area, and the 204 percent organic composition of the tibia. QTLs on chromosome 1 have been reported for skeletal traits by several studies in the literature including a QTL for BMD of the femur at 95.8 cm and 81.6 cm (Beamer et al., 1999; Beamer et al., 2001) and whole body BMD at 74.1 cm and 95.0 cm (Klein et al. 1998; Klein et al. 2001). Gu et al. (2002a) reported gene expression data from a congenic strain that was developed based on a QTL on chromosome 1, identified in the previous study by Beamer et al. (2001). The congenic strain contained a QTL for bone density from CAST on the background of B6. Bone mineral density, serum insulin-like growth factor (IGF-I) and alkaline phosphatase (ALP) were measured in B6.CAST-1T congenic and B6 controls at 16 weeks of age. BMD was significantly higher in the congenic strain while body weight and femur length were not significantly different while skeletal alkaline phosphatase activity in serum was reduced in the congenic strain compared with B6. Microarray analysis was used to investigate differences in gene expression patterns between the congenic and B6 strains. The congenic strain had significantly lower expression patterns in genes for bone formation compared with B6 mice. The microarray results also indicated that genes that could have a negative regulatory effect on bone resorption were higher in the congenic strain versus B6. These results indicated that the increase in bone mineral density of the congenic strain could be the result of decreased resorption as opposed to increased bone formation (Gu et al., 2002a). Gu et al. (2002b) have furthered this research by construction of a BAC contig for chromosome 1 from cm (Gu et al., 2002b). This region of the mouse genome is homologous to human chromosome region 1q This was identified by Koller et al. (2000) for spine BMD presented

227 previously. The present study identified a QTL for femur medullary area in the 205 combined F 2 analyses with a LOD score of 4.2 at 93 cm. As mentioned previously, the results from (Shultz et al., 2003) congenic sublines for femur BMD and circumference on chromosome 1 indicated two QTLs at cm and cm. Composite interval mapping identified a QTL (LOD=7.5) for RI female femur cortical area at 47.6 cm. The LOD score for the F 2 cohort was 1.9 but did not meet our confirmed criteria. On chromosome 2 at 79 cm, femur yield load was nominated and confirmed in the female and combined interval mapping analyses. In the combined analyses, femur length and tibia ultimate load also mapped to this region as well as femur length and cortical thickness in the female analyses. This site has been identified in previous studies including BMD of the femur at 86.3 cm (Beamer et al., 2001), BMC at 105 cm (Drake et al., 2001), and 87 cm for whole body BMD (Klein et al. 1998). On chromosome 4 femur CSMI and femur total cross-sectional area and tibia ultimate load were nominated and confirmed at approximately cm. Femur length, and coronal width as well as body length also map to this region although not confirmed. Many of the results presented here are for structural measures such as size, geometry, mechanical load. Composite interval mapping identified a suggestive QTL for male femur ultimate stress in this same chromosome region indicating that material as well as structural properties were influenced by this QTL. A QTL on chromosome 5 at 17 cm was nominated and confirmed for the combined analyses of the outer radius of the femur. Femur average thickness also mapped to this position but was not confirmed in the RI. Beamer et al. (1999) reported a

228 206 QTL for BMD of the femur at 24.5 cm on chromosome 5 and is very close to the position reported here. Tibia length in both the female and combined analyses was nominated and confirmed at 42 cm on chromosome 11 in the interval mapping analyses. Male adipose weight also mapped to this region as well as female body length. QTLs for whole body BMD, carcass ash mass, femur cortical thickness as well as body length and growth rate have been reported in the literature (Masinde et al., 2002; Klein et al., 2001; Shimizu et al., 1999). It is interesting to note that a QTL for stem cell proliferation was identified at 47 cm on chromosome 11(Haan et al, 2002). A QTL for femur ultimate load in both the female and combined analyses was identified at 49 cm on chromosome 15 in the interval mapping results and at 39 cm in composite interval mapping analyses. Male and female tibia length and male tibia yield work were also identified at the suggestive level in the interval mapping analyses. There have been several studies that have reported skeletal QTL in this region of chromosome 15 including femur BMD at 42.8 cm (Beamer et al., 1999), whole body BMD at 44.8 cm (Klein et al., 1998a), percent fat at 43.3 cm (Keightly et al. 1998), and IGF-1 at 56 cm (Brockman et al. 2002). Femur stiffness and yield displacement were nominated and confirmed in the combined analyses on chromosome 17 at 56 cm in the F 2 and 30 cm in the RI. Femur stiffness was also confirmed in the male analyses along with gastrocnemius muscle mass. A QTL for femur inner radius was identified in the female interval mapping analyses at this same site and the female composite interval mapping results identified a QTL for the

229 modulus of elasticity of the tibia at 54 cm. Corva et al. (2000) reported QTLs for carcass ash mass and femur length at 48 cm on chromosome Conclusions Multiple QTLs for skeletal phenotypes have been identified that meet strict criteria for nomination and confirmation at the suggestive and significant levels. When exploring the results of these confirmed QTLs combined with QTLs that were identified at the suggestive level but not confirmed, multiple correlated phenotypes were observed to map to the same general chromosome location. Many of the results presented here also map to the same region as QTLs that have been identified for other skeletal measures indicating that the same QTL could be influencing separate skeletal measures that have the same underlying genetic influence. The majority of the QTL results are for structural skeletal measures. However, several suggestive QTLs were identified for material properties as well. The significant strain and age differences provides convincing evidence that the extended QTL study for biomarkers of aging related to skeletal measures should prove fruitful in identifying QTLs related to skeletal aging. Acknowledgements This work was supported by grants P01 AG14731 and R01 Ag21559 from the National Institute on Aging of the National Institutes of Health.

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233 211 Chapter 8 Bone Quality, Muscle Mass and Activity: Structural Equation Modeling of their Relationships and Genetic Influence. Introduction Skeletal quality cannot be defined by a single measure but is a composite of many traits each contributing to a bone s ability to resist fracture. Skeletal phenotypes are complex quantitative traits that are known to be under polygenic (multiple gene) control. As with many biological systems, skeletal integrity is the result of a dynamic system with many degrees of freedom. Redundant pathways can contribute to conflicting results when isolating single genes that influence skeletal phenotypes without looking at these phenotypes from a polygenic reference frame and complex systems approach. The field of biology has been revolutionized in the way it investigates organisms in the context of their environment by a change in scientific thought concerning form and function. Scientists began to see biological organisms as self-regulating systems capable of adapting to their environment. These adaptations could be of a more permanent type such as genetic changes that have occurred through natural selection in the evolutionary process or temporary changes (somatic). Somatic changes allow the organism to adapt to environmental changes without permanent genetic changes (Martin et al., 1998). Bone is now thought of as a self-regulating system with complex pathways. Genes are involved with both kinds of adaptation whether evolutionary or somatic. The ability of bone to

234 212 adapt to its environment is controlled by many pathways each of which are regulated by gene expression. Bone quality is a combination of composition, size, geometry, and density. Differences in composition can affect bone strength. More highly mineralized bone is stronger but stiffer and can become brittle. The self-regulating system of bone adaptation can be observed in the optimization of strength and efficiency outlined by Wolffe s law. If a bone is too compliant part of the energy produced by the muscle has to go into deforming the bone instead of moving it. If bones are too big and too mineralized more metabolic energy has to go into carrying around the mass of the bone. The pathways involved in bone s response to its loading environment are complex and involve both direct and indirect mechanisms. Correlations between body size, muscle mass, physical activity, and skeletal phenotypes arise through different pathways and in some cases produce conflicting responses. For example, differences in bone mineral density and cortical thickness could arise from a scaling effect solely due to body size differences. Larger individuals also have the added masses that the muscles must move, ultimately producing higher forces on the skeleton, which stimulate increases in bone mineral density and cortical thickness due to adaptation to the loading environment. Very large individuals may not be as active and in turn may not load their bones as often, which may also lead to bone adaptations in response to decreased frequency of loading. Identifying where in the pathway the correlation between covariates originate would allow for greater insight into a bone s response to its environment. The aim of this study is to investigate the relationships between muscle mass, skeletal integrity and the influence of physical activity/behavior, as well as the influence

235 of specific genetic loci. Multiple measures were obtained for phenotypes that are 213 indicative of activity and behavior, muscle strength (implied by muscle mass), and bone size, shape and strength for F 2 (second-generation) progeny of C57BL/6J (B6) and DBA/2 (D2) inbred mouse strains. These phenotypes were used in Quantitative Trait Loci (QTL) analyses and significant QTLs were identified. Structural equation modeling (SEM) was then used to investigate the relationships between activity, muscle and bone as well as the influence of five QTL that had multiple traits map to the same general position. Background Bone loss is known to occur with disuse. This has been seen in astronauts on extended space flights and within individuals who have been immobilized or bed ridden. Bone loss is also known to occur with age and is accelerated in postmenopausal women. (Hui et al., 1989) showed that for every 0.1 g/cm decline in bone mass there is an increase in fracture risk of 50 to 120 % for those individuals living in a retirement home compared with younger individuals living on their own. In the United States alone, 25 million people are affected by osteoporosis. It is estimated that osteoporsis is associated with at least 1.3 million fractures annually. Osteoporosis is known to affect both men and women but in unequal proportions roughly equal to 12% of men and 40% of women (Ralston, 1997). With the population as a whole living longer we are becoming more aware of the devastating consequences of bone loss, particularly in the extreme cases that we classify

236 as osteoporosis. Osteoporotic individuals are much more likely to fracture a hip or 214 vertebrae during a fall. Women over the age of 65 have a significant increase in fracture risk, 25% of the women will fracture their vertebrae and 32% of the women who reach the age of 90 will fracture their hip (Consensus, 1993). Fractures in the elderly have been shown to mark the beginning of rapidly declining health. The success of rehabilitation after a hip fracture is very discouraging. Of the women aged 90+ who fracture their hip, 50% will be incapacitated. The remaining time that these patients have is often compromised and quality of life is significantly diminished. It has also been shown that 5%-20% of these women will die within one year of the fracture. Not only does bone loss drastically affect the health and well-being of our aging population but it is also a significant financial strain on our health care system. Increasing our understanding of bone strength and the factors contributing to bone loss can potentially improve the quality of life for millions. While peak bone mass has been shown to predict fracture risk, insight into the mechanisms that influence both bone acquisition and the rate of loss could provide a means for preventing bone loss and increased fracture risk. The mechanostat theory of (Frost et al., 1998) was developed to explain bone s response to its loading environment. The mechanostat is based on thresholds that are thought to control modeling and remodeling in an effort to conserve bone mass and at the same time provide enough bone strength to meet the demands of normal activity. Frost et al. predicted that estrogen and other related factors can modulate the thresholds for the mechanostat and the point was illustrated through a study by Zanchetta et al. (1995) in which whole body bone mineral content and lean body mass was

237 215 measured with DEXA in boys and girls between the ages of 2 and 20. They plotted bone mineral content (BMC) against lean body mass for each sex separately and found that for similar lean mass (presumably muscle mass) girls increase BMC at a faster rate than boys and at puberty this rate was markedly increased. Boys increase BMC at a constant rate until 15 and then the rate is increased and maintained at an accelerated rate until age 20. It has been shown that the highest normal loads experienced by bone are from muscle forces used for movement. High muscle forces are needed to counteract the short moment arm through which muscles function. Skeletal muscles have a mechanical disadvantage in that more force is required to balance a given load imposed by the environment. As muscle mass increases the loads applied to bones increase and the strain in bone increases proportionately. If bone is growing and developing in response to a strain threshold, then the predominating signal originates through loads produced by muscle. Frost s concluding assumptions are that estrogen lowers the threshold for remodeling and basic multi-cellular units are activated on all surfaces, but that other, as yet unidentified factors in marrow reduce e relative to resorption in each BMU. In girls, as estrogen increases near puberty, the remodeling dependent bone loss prior to puberty decreases, while bone gains due to modeling continue. The result is an increase in the rate of bone formation compared with pre-pubertal rates, and higher rates than in boys with similar muscle strength. Gains in bone mass continue as long as muscle mass continues to increase. When muscle mass plateaus and bone strain drops below the modeling threshold, modeling turns off, but conservation-mode remodeling continues. These assumptions are consistent with the results of the Zanchetta study and provide an

238 216 explanation for the increase in the rate of bone acquisition at puberty and the continued acquisition of bone in boys relative to girls. The model is also consistent with what is seen in postmenopausal osteoporosis. When estrogen levels drop the remodeling threshold is increased and disuse-mode remodeling is turned on which removes bone close to marrow. Once bone strength is lowered to the point that the strain levels are above the disuse-mode remodeling threshold, it is turned off and conservation-mode is turned back on at a new steady-state (Frost et al., 1998). If the ideas concerning the mechanostat regulating mechanism are correct, muscle force could be a driving mechanism for bone modeling and remodeling (post-natal), along with modulating factors such as estrogen. Under the assumptions outlined by Frost, as muscle mass (an index of muscle strength) increases, the strain in bone increases and this increases bone formation. As muscle mass decreases, the strain on bone is reduced, and bone loss occurs. Therefore, variations in peak bone mass and rates of bone loss could also be influenced by reduced muscle mass or muscle strength, as well as variations in the frequency and duration of activity. Genetic Studies of Skeletal Phenotypes: Twin studies (Dequeker et al., 1987) as well as inbred mouse studies (Beamer et al., 1996; Klein et al., 1998; Shimizu et al., 2001) have confirmed that bone properties are influenced by genetics. The continuous distribution of skeletal phenotypes indicates that they are complex traits with multiple influences. These influences are known to include environmental factors such as diet and exercise and genes. Previous studies have

239 217 also shown that muscle mass is associated with increased bone mass (Kaye and Kusy, 1995) and physical activity can influence skeletal quality (Gordon et al., 1989). Kaye and Kusy investigated these relationships within several mouse strains and reported correlation coefficients of 0.85 between femur weight and muscle weight, 0.60) for muscle weight and activity and 0.59) for femur weight and animal activity; suggesting that activity as well as muscle size and strength are genetically influenced and part of the genetic influence on bone size and strength is modulated through activity and muscle size. Body weight and length have also been shown to have significant genetic influence (Klein et al., 1998) and are also significantly correlated with skeletal measures (Keightley et al., 1996; Ishikawa et al., 2000). (Arden and Spector, 1997) investigated the relationship between bone mineral density, lean body mass, and muscle strength using a population of postmenopausal monozygotic (MZ) and dizygotic (DZ) twin female pairs. Lean body mass and bone mineral density (BMD) were measured at multiple sites using DEXA. The correlation of BMD with leg extensor strength was , with grip strength was , and with lean body mass was The heritability of lean body mass, leg strength, and grip strength was 0.52, 0.46, and 0.30 respectively. The muscle variables explained 20% of the genetic variance of BMD. Many of the QTLs identified in skeletal studies often co-localize with QTLs for non-skeletal phenotypes. It could be the case that a QTL for bone could also be a QTL for body size, activity, or muscle size or strength and the effect on bone could be modulated through these other cofactors. Li et al. (2001) assessed forelimb muscle size and grip strength, bone mineral density, forelimb bone size and humerus breaking

240 strength in ten different inbred mouse strains (129/J Sencar/PtJ, C57BL/6J, CBA/J, 218 FVB/NJ, NZB/BINJ, RIIIS/J, LP/J LG/J, and SWR/J). Heritability estimates from the ten inbred strains were: 0.60 (BMD), 0.68 (breaking strength), 0.83 (bone size), 0.63 (grip strength), 0.76 (muscle size), and 0.52 (body weight). The correlations among all muscle and bone related phenotypes were significant. The correlations of bone density with grip strength, muscle size and body weight were 0.54, 0.50, and 0.50 respectively. The correlations of breaking strength with grip strength, muscle size, bone density, and body weight were 0.63, 0.58, 0.69, and 0.52 respectively (Li et al., 2001). These results provide support for the indirect genetic influence of muscle strength on bone mineral density. The data strongly support the hypothesis that genetic control of skeletal integrity functions through both direct and indirect pathways. The indirect pathways may include the affect of activity acting either directly on bone or indirectly through muscle to bone. Some activities may increase muscle mass and or strength, which could produce greater forces applied to bone. Other activities may increase the frequency of the loads that the muscles produce on bone. The second biggest source of loading experienced by bone is that due to body weight. Body size and weight are known to be significantly influenced by genetics and these could also be part of indirect pathways of genetic influence on bone. Methods Behavioral measures, muscle mass, and skeletal phenotypes were assessed in 400 F 2 (200 males and 200 females) progeny of DBA/2 (D2) and C57BL/6J (B6) inbred

241 219 mouse strains. Breeding and maintenance of progenitor strains and F 2 animals derived from the progenitor strains were conducted in the barrier facility maintained by The Center for Developmental and Health Genetics at The Pennsylvania State University. Mice were weaned into like-sex sibling groups at about 23 days of age with 4 animals per cage. They were fed a diet of autoclaved Purina Mouse Chow 5010 ad lib. The barrier facility was maintained under positive pressure with a temperature and humidity controlled environment and a 12-hour light/dark cycle. Animal Activity/Behavioral Assessments Several measures of activity-related behaviors were made on each animal at 150 days of age. Activity was measured in a 40 cm x 40 cm x 15 cm deep, black opaque plastic arena marked into four quadrants with a 15 mm hole cut in the center of each quadrant. Animals were individually placed into a clear plastic cylinder in the center of the arena. After ten seconds, the cylinder was lifted and each mouse was observed for five minutes. The number of quadrants entered, the number of times the animal reared onto its hindquarters, and the number of times it poked its head into the floor holes was recorded. The testing conducted in the activity box was repeated on three separate occasions. Each animal was placed on a dowel rod 89 cm long x 1.6 cm in diameter that was suspended 23 cm above a foam pad. The rod was marked into five sectors of 17.8 cm each. Animals were observed until falling or until one-minute had elapsed. The fall times and the number of excursions into different sectors were recorded. Rod measures

242 were repeated for three one-minute intervals on three separate occasions for a total of nine one-minute intervals. 220 Genotyping Four hundred F 2 mice were genotyped in-house using 96 microsatellite markers distributed throughout the genome with an average spacing of centimorgans (cm). Marker analyses were conducted on purified DNA samples procured from tail snips using an automated, fluorescence-based detection system described in detail in Vandenbergh et al. (2003). Genotypic data for the BxD RI strains were obtained from Williams et al. (2001). Over 700 known marker genotypes were used in the RI QTL analyses. Muscle Mass At sacrifice, the right hind limb was harvested and the gastrocnemius, soleus, tibialis anterior, and extensor digitorum longus muscles were dissected and weighed to the nearest hundredth of a milligram. The tibia and femur were wrapped in saline soaked gauze and frozen for future mechanical testing.

243 Skeletal Quality 221 Gross Dimensional Measurements: At the time of testing, the femur and tibia were thawed at ambient temperature. A digital caliper accurate to 0.01mm was used to measure femoral length from the most superior aspect of the greater trochanter to the intercondylar notch, femoral width at the center of the diaphysis in both the sagittal and coronal planes, and epiphyseal width at the widest point of the distal epiphysis in the coronal plane. Femoral head and femoral neck diameter were also measured. The tibia was measured similarly, except that length was measured from the intercondylar eminence of the tibia to its inferior articular surface and the proximal, rather than distal, epiphyseal width was measured. Flexural Testing of the Right Femoral and Tibial Diaphysis: The midshaft of the right femur and tibia were tested to failure in three point bending in an MTS MiniBionix testing apparatus using a support span of 8 mm and 10 mm respectively and a deformation rate of 1 mm/min. Femurs were oriented in the testing apparatus so that the nosepiece was posteriorly directed in respect to the femoral shaft. Tibiae were oriented so that the nosepiece was anteriorly directed in respect to the tibial shaft. This was necessary to prevent the tibia from rolling during testing. Each femur and tibia was loaded to failure while recording load and actuator displacement at 20 Hz and a load-deformation curve was generated using MATLAB scientific software. Yield load, yield deformation, energy absorbed at yield (area under the load-deflection

244 curve), failure load, failure deformation, energy absorbed at failure, and stiffness (initial slope of the load-deflection curve) were determined. 222 Compositional Analysis of the Tibia and Ash Mass of the femur: The distal tibia was dried and ashed to determine the composition. The percent composition of water, organic, and ash (mineral) within each bone fragment, as well as the percent mineralization of the organic matrix was calculated based on wet, dry and ash mass. The fragments from the mechanical testing of the femur were ashed to obtain total femoral ash mass. Analyses Quantitative Trait Loci (QTL) Analyses: Separate sex-specific QTL analyses were performed on the F 2 cohort to locate chromosomal regions influencing phenotypic variables. QTL analyses were conducted using QTL Cartographer software to perform interval mapping (Basten et al. 1994; Basten et al. 2002). The analyses assumed an additive genetic model. LOD scores of 3.3 or greater were considered significant, while scores between 1.9 and 3.3 were considered suggestive. All QTL analyses were conducted on each sex separately.

245 Model Specification and Testing 223 To investigate the relationships between the three domains of activity, muscle and bone, a structural equation model was developed to include three latent variables: (activity, muscle, and bone). Each of these factors loaded on measured phenotypes. The activity factor loaded on four behavioral measures: headpokes, rears, squares and rod sector entries (see methods for description). The muscle factor loaded on gastrocnemius, soleus, extensor digitorum longus, and tibialis anterior. For the femur, the bone factor loaded on femur length, femur width (sagittal), head diameter, ultimate load, stiffness, and ash mass. For the tibia model, the bone factor loaded on tibia length, tibia sagittal width, tibia coronal width, tibia ultimate load, stiffness, and tibia percent ash. The model included the genotypes of one genetic marker representing the QTL that was identified in the QTL analysis. The marker was selected as the peak marker for each QTL that had been identified as having pleiotropic effects across at least two of the three domains of activity, muscle, and bone. If the peaks generally fell between two markers both flanking markers were tested in the model separately. Structural equation modeling (SEM) was performed on raw data separated by sex using Mx Statistical software. Parameter estimates were made using maximum likelihood (Neale et al. 2002). The femur and tibia were modeled separately which entailed running two full models for each QTL identified in the QTL analyses. Submodels were tested by calculating the 2 * log likelihood difference between each submodel and the full model. The difference in 2*log likelihood between the full model and nested sub-model is distributed as a χ 2. The degrees of freedom (df) for the χ 2 test

246 224 of significance are the number of parameter estimates in the full model minus the number of parameter estimates in the sub-model. A significant difference between the sub-model and the full model indicates that the path that was dropped in the sub-model is significant. QTL Analyses Results The results of our QTL analyses in the 200-day F 2 cohort revealed many sex specific QTL with multiple traits associated with the same region of a chromosome. Figures 8-1, 8-2, 8-3, 8-4, and 8-5 summarize the results of our QTL analyses for chromosomes 3, 4, 5, 9 and 11 for males and females. These chromosomes were selected because of the multiple phenotypes that mapped in close proximity of each other. Figure 8-1: Interval Mapping results for chromosome 3.

247 225 Figure 8-2: Interval Mapping results for chromosome 4. Figure 8-3: Interval Mapping results for chromosome 5.

248 226 Figure 8-4: Interval Mapping results for chromosome 9. Figure 8-5: Interval Mapping results for chromosome 11.