Table S1. Trait summary for Northern Leaf Blight resistance in the maize nested association mapping (NAM) population and individual NAM families
|
|
- Louisa Dennis
- 5 years ago
- Views:
Transcription
1 SUPPLEMENTAL MATERIAL Supplemental Tables: Table S1. Trait summary for Northern Leaf Blight resistance in the maize nested association mapping (NAM) population and individual NAM families "#$%&' (')*'%$+,-' ''."/$+0'1' ''."/$+0'2' ''."/$+0'3' )4+5,6-' 27' '' 789' :183;'278<=' '' 1189' :187;'>3=' '' 2?81' BA7'C'B73' 19A' ' 1>8A' :>81;'>189=' ' ' :13;'7A8<=' DEF1?3'C'B73' 177' ' A83' :189;'238>=' ' 1>83' ' DEF229'C'B73' 1<1' ' :2;'2?81=' ' 1187' :28A;'2>8A=' ' 228<' :98A;'><8>=' DEF2>7'C'B73' 192' ' 78<' ' 1189' :18<;'3389=' ' 238?' DEF277'C'B73' :?8<;'138A=' ' 981' :1;'2?8A=' ' DEF322'C'B73' ' 1189' :28>;'3?8A=' ' 1787' :3;'>387=' ' 3?8?' :A8>;'<28<=' DEF333'C'B73' 193' ' ' 218<' ' 328>' 1<3' ' 78<' :?87;'2181=' ' 1181' ' DEF<A'C'B73' 19<' ' <87' :?87;'2<8A=' ' 1?83' :182;'>387=' ' 1983' GH3?1'C'B73' 192' ' 2?81' :982;'><=' ' 3?8>' :13;'<38>=' ' :2787;'7<=' I%1>G'C'B73' 197' ' 1>81' :>81;'318<=' ' ' 3<81' J$11'C'B73' 1<9' ' A8?' :?83;'2782=' ' :?83;'3A8A=' ' 238>' J$3'C'B73' 1A1' ' <8?' ' 989' :182;'2187=' ' 1<8>' :>82;'3>87=' J&21'C'B73' 1A1' ' ' 2>8>' ' 3789' :1782;'<?8<=' E1<2K'C'B73' 197' ' :1;'178>=' ' A8<' :?89;'298A=' ' E37K'C'B73' 197' ' 78?' :18<;'2281=' ' :18A;'3<83=' ' 1A81' E)17'C'B73' 17A' ' 1189' ' 1789' ' 2A89' :1?82;'<?=' E)19K'C'B73' 173' ' ' 1>89' ' 2<81' :<83;'<?89=' EL71'C'B73' 1A1' ' ' 238>' ' :1181;'7A82=' 192' ' 1283' ' 198<' :283;'>?8<=' ' 2A8>' 192' ' 789' :282;'218<=' ' 1282' :289;'3<=' ' 2289' NO>3'C'B73' 19?' ' 138<' :<83;'2<8<=' ' 2?87' ' 328>' NO7B'C'B73' 17<' ' ' :98>;'7387=' ' >98A' :1983;'7A83=' P3A'C'B73' 17>' ' 1<81' ' 2>8<' ' 3A8?' :178A;'738>=' QC3?3'C'B73' 199' ' 128<' ' 1A8>' :>81;'3781=' ' 328>' QR$9'C'B73' 192' '' 1287' :18>;'>18<=' '' 198A' '' 3?8A' :781;'7387=' MSE' ><<7' ' 118>' ' 1782' :?83;'7387=' ' 2982' #,"+' :#$+;#"C='
2 Table S2. Broad-sense heritability for resistance to Northern Leaf Blight in the maize nested association mapping (NAM) population and individual NAM families "#$%&'."/$+0'1'."/$+0'2'."/$+0'3' 'I+5,C' DEF322'?872'?87A'?87<'?893' I%1>G'?8<?'?872'?872'?877' E)17'?8<9'?871'?8<A'?879' E)19K'?872'?89?'?87<'?893' QC3?3'?8<>'?871'?8<>'?87<' ST08'U$/O$+' "#$%&'?8<3'?871'?8<9'?877' )4+5,6-'?89?'?897'?899'?8A?'
3 Table S3. Joint Linkage Model for Northern Leaf Blight Resistance in the maize nested association mapping population Model Effect Df Sum Sq F value Pr(>F) Chr. Marker Name factor(pop) < 2.2e- DaysToAnthesis E- 16 factor(pop):m E- 01 factor(pop):m E- 07 factor(pop):m E- 06 factor(pop):m E- 12 factor(pop):m E- 06 factor(pop):m E- 07 factor(pop):m E- 14 factor(pop):m E- 08 factor(pop):m E- 05 factor(pop):m E- 09 factor(pop):m E- 08 factor(pop):m E- 09 factor(pop):m < 2.2e- 05 factor(pop):m E- 16 factor(pop):m E- 06 factor(pop):m E- 12 factor(pop):m E- 12 factor(pop):m E- 12 factor(pop):m E- 12 factor(pop):m E- 14 factor(pop):m E- 06 factor(pop):m E- 06 factor(pop):m E- 15 factor(pop):m < 2.2e- 06 factor(pop):m E- 16 factor(pop):m < 2.2e- 16 factor(pop):m E- 16 factor(pop):m E- 05 factor(pop):m E- 06 Residuals NAM Marker Position (cm) Lower CI (cm) Upper CI (cm) BAC AGP Physical Map 1 PZB m AC c0365i PZA /2 m PZA m AC b0410i PZA m AC c0201i PZA m AC c0245c PZA m AC b0116l PZA m AC b0151l PZA m AC c0468p PZB /4 m PZA m AC b0498c PZA m AC c0276p PZA m AC b0457n PHM m PZA m PHM m AC b0318b PZA m AC c0337k PZA m AC c0422e PZA m AC b0395c PHM m AC c0190k PZA m AC b0623j PZA m AC c0411a PZA m AC c0207m PZA m AC c0447o PZA m AC c0206p PZA m AC b0358b PHM m AC c0384o PZA m AC c0384c PZA m AC c0325g PHM m
4 Supplemental Figures: % diseased leaf area CML277 Ki3 M162W CML69 CML52 M37W CML247 NC358 CML228 CML103 Ki11 Mo18W NC350 Mo17 CML322 Tzi8 Tx303 CML333 MS71 Oh43 B97 Il14H Ky21 P39 Hp301 Oh7B Fig. S1. Box-plot of trait variation of NLB resistance for individual families of NAM. The scale is percentage of total diseased leaf area at the third disease rating. For each family, the family median is shown as a bold line, the 25% and 75% percentiles with the box, and outliers with individual circles. Square-root transformation of the phenotype produced normal distributions for the families and were used for analysis (see text).
5 Family Average R 2 = Founder Phenotype Fig. S2. Comparison of NAM founder phenotypes and mean phenotype of progeny. For each NAM founder the observed phenotype was compared to the average observed phenotype for its respective family. Regression of the family means on the founder phenotype explained 80% of the variance.
6 Number of Alleles Allele Effect (change in % diseased leaf area) Fig. S3. Histogram of allele effects for all modeled alleles at QTL. The nested model give an estimated allele effect for each founder line at each QTL resulting in #QTL x #Founder estimates. The nesting of marker effects allows for pooling data among families to increase power in testing for QTL effects and estimating the total percentage of explained genetic variation accounted by the multiple SNP model of the NAM pop as a whole. Most of these allele effects are estimated to be close to zero and non-significant (grey). The significant positive and negative allele effects are shown in blue and red. Most observed effects were less than a 5% difference in diseased leaf area.
7 4 Allele Effect (change in % Diseased Leaf Area) CML103 P39 Ki3 Ky21 Ki11 Oh43 B97 Mo17 CML277 CML69 CML333 CML322 CML247 CML228 Hp301 M162W Tx303 Mo18W Tzi8 NC358 M37W NC350 MS71 Il14H CML52 Oh7B 4 Fig. S4. It was observed that many of the QTL (21 of 29) had allele effects that were estimated both above and below the B73 allele (the zero reference point). Shown here are the estimated allele effects for a single QTL (m616, Chr.5 at 69.5 cm, 88.6 Mb) showing both positive and negative alleles relative to B73. Confidence intervals (95%) are shown in brackets. We can conclude that there are at least three alleles at this locus by comparing to B73, and possibly more considering differences within both the positive and the negative allele groups.
8 Emperical Phenotype R 2 = Predicted Phenotype Fig. S5. Additive genetic model for NLB resistance gives accurate prediction of the observed phenotypes for the nested association mapping (NAM) population founders. Linear fit of predicted phenotypic values for each of the diverse founder inbred lines of NAM against the empirical phenotypic observations. Predicted phenotypes were determined by summing the allele effect for each respective founder line at all of the QTL identified while the empirical phenotypes are best linear unbiased predictions from three years of replicated field trials. Units are shown in percentage diseased leaf area.
9 m501 m Non Stiff Stalk m87 m m728 m Fig. S6. Test for allele origins showing QTL markers that had significant association with quantitative estimates of allele origins. The horizontal axis in each graph shows the origin of a given founder for either non-stiff stalk (first graph) or Tropical/Sub-tropical (remaining five graphs). The estimated allele effect is plotted on the vertical axis. The linear regression line is also shown.
Understandable Statistics
Understandable Statistics correlated to the Advanced Placement Program Course Description for Statistics Prepared for Alabama CC2 6/2003 2003 Understandable Statistics 2003 correlated to the Advanced Placement
More informationFigure S2. Distribution of acgh probes on all ten chromosomes of the RIL M0022
96 APPENDIX B. Supporting Information for chapter 4 "changes in genome content generated via segregation of non-allelic homologs" Figure S1. Potential de novo CNV probes and sizes of apparently de novo
More informationMeaning-based guidance of attention in scenes as revealed by meaning maps
SUPPLEMENTARY INFORMATION Letters DOI: 1.138/s41562-17-28- In the format provided by the authors and unedited. -based guidance of attention in scenes as revealed by meaning maps John M. Henderson 1,2 *
More informationSupplementary Figure 1: Attenuation of association signals after conditioning for the lead SNP. a) attenuation of association signal at the 9p22.
Supplementary Figure 1: Attenuation of association signals after conditioning for the lead SNP. a) attenuation of association signal at the 9p22.32 PCOS locus after conditioning for the lead SNP rs10993397;
More informationChapter 11: Advanced Remedial Measures. Weighted Least Squares (WLS)
Chapter : Advanced Remedial Measures Weighted Least Squares (WLS) When the error variance appears nonconstant, a transformation (of Y and/or X) is a quick remedy. But it may not solve the problem, or it
More informationAn Introduction to Quantitative Genetics I. Heather A Lawson Advanced Genetics Spring2018
An Introduction to Quantitative Genetics I Heather A Lawson Advanced Genetics Spring2018 Outline What is Quantitative Genetics? Genotypic Values and Genetic Effects Heritability Linkage Disequilibrium
More informationLecture 9: Hybrid Vigor (Heterosis) Michael Gore lecture notes Tucson Winter Institute version 18 Jan 2013
Lecture 9: Hybrid Vigor (Heterosis) Michael Gore lecture notes Tucson Winter Institute version 18 Jan 2013 Breaking Yield Barriers for 2050 Phillips 2010 Crop Sci. 50:S-99-S-108 Hybrid maize is a modern
More informationMULTIPLE REGRESSION OF CPS DATA
MULTIPLE REGRESSION OF CPS DATA A further inspection of the relationship between hourly wages and education level can show whether other factors, such as gender and work experience, influence wages. Linear
More informationThe Association Design and a Continuous Phenotype
PSYC 5102: Association Design & Continuous Phenotypes (4/4/07) 1 The Association Design and a Continuous Phenotype The purpose of this note is to demonstrate how to perform a population-based association
More informationResearch Methods in Forest Sciences: Learning Diary. Yoko Lu December Research process
Research Methods in Forest Sciences: Learning Diary Yoko Lu 285122 9 December 2016 1. Research process It is important to pursue and apply knowledge and understand the world under both natural and social
More informationMBG* Animal Breeding Methods Fall Final Exam
MBG*4030 - Animal Breeding Methods Fall 2007 - Final Exam 1 Problem Questions Mick Dundee used his financial resources to purchase the Now That s A Croc crocodile farm that had been operating for a number
More informationDiscontinuous Traits. Chapter 22. Quantitative Traits. Types of Quantitative Traits. Few, distinct phenotypes. Also called discrete characters
Discontinuous Traits Few, distinct phenotypes Chapter 22 Also called discrete characters Quantitative Genetics Examples: Pea shape, eye color in Drosophila, Flower color Quantitative Traits Phenotype is
More informationNature Genetics: doi: /ng Supplementary Figure 1. SEER data for male and female cancer incidence from
Supplementary Figure 1 SEER data for male and female cancer incidence from 1975 2013. (a,b) Incidence rates of oral cavity and pharynx cancer (a) and leukemia (b) are plotted, grouped by males (blue),
More informationbivariate analysis: The statistical analysis of the relationship between two variables.
bivariate analysis: The statistical analysis of the relationship between two variables. cell frequency: The number of cases in a cell of a cross-tabulation (contingency table). chi-square (χ 2 ) test for
More informationYour DNA extractions! 10 kb
Your DNA extractions! 10 kb Quantitative characters: polygenes and environment Most ecologically important quantitative traits (QTs) vary. Distributions are often unimodal and approximately normal. Offspring
More informationTable of Contents. Plots. Essential Statistics for Nursing Research 1/12/2017
Essential Statistics for Nursing Research Kristen Carlin, MPH Seattle Nursing Research Workshop January 30, 2017 Table of Contents Plots Descriptive statistics Sample size/power Correlations Hypothesis
More informationWDHS Curriculum Map Probability and Statistics. What is Statistics and how does it relate to you?
WDHS Curriculum Map Probability and Statistics Time Interval/ Unit 1: Introduction to Statistics 1.1-1.3 2 weeks S-IC-1: Understand statistics as a process for making inferences about population parameters
More informationSimple Linear Regression
Simple Linear Regression Assoc. Prof Dr Sarimah Abdullah Unit of Biostatistics & Research Methodology School of Medical Sciences, Health Campus Universiti Sains Malaysia Regression Regression analysis
More informationUnit 1 Exploring and Understanding Data
Unit 1 Exploring and Understanding Data Area Principle Bar Chart Boxplot Conditional Distribution Dotplot Empirical Rule Five Number Summary Frequency Distribution Frequency Polygon Histogram Interquartile
More informationfraction (soluble sugars, starch and total NSC). Conditional and marginal R 2 values are given for
1 2 Martínez-Vilalta, Sala, Asensio, Galiano, Hoch, Palacio, Piper and Lloret. Dynamics of non- structural carbohydrates in terrestrial plants: a global synthesis. Ecological Monographs. 3 4 APPENDIX S2.
More informationChapter 1: Exploring Data
Chapter 1: Exploring Data Key Vocabulary:! individual! variable! frequency table! relative frequency table! distribution! pie chart! bar graph! two-way table! marginal distributions! conditional distributions!
More informationComplex Trait Genetics in Animal Models. Will Valdar Oxford University
Complex Trait Genetics in Animal Models Will Valdar Oxford University Mapping Genes for Quantitative Traits in Outbred Mice Will Valdar Oxford University What s so great about mice? Share ~99% of genes
More informationChapter 3 CORRELATION AND REGRESSION
CORRELATION AND REGRESSION TOPIC SLIDE Linear Regression Defined 2 Regression Equation 3 The Slope or b 4 The Y-Intercept or a 5 What Value of the Y-Variable Should be Predicted When r = 0? 7 The Regression
More informationIDENTIFICATION OF QTLS FOR STARCH CONTENT IN SWEETPOTATO (IPOMOEA BATATAS (L.) LAM.)
Journal of Integrative Agriculture Advanced Online Publication: 2013 Doi: 10.1016/S2095-3119(13)60357-3 IDENTIFICATION OF QTLS FOR STARCH CONTENT IN SWEETPOTATO (IPOMOEA BATATAS (L.) LAM.) YU Xiao-xia
More informationIntroduction of Genome wide Complex Trait Analysis (GCTA) Presenter: Yue Ming Chen Location: Stat Gen Workshop Date: 6/7/2013
Introduction of Genome wide Complex Trait Analysis (GCTA) resenter: ue Ming Chen Location: Stat Gen Workshop Date: 6/7/013 Outline Brief review of quantitative genetics Overview of GCTA Ideas Main functions
More informationLecture 6B: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression
Lecture 6B: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression! Equation of Regression Line; Residuals! Effect of Explanatory/Response Roles! Unusual Observations! Sample
More informationValidation of consistency of Mendelian sampling variance in national evaluation models
Validation of consistency of Mendelian sampling variance in national evaluation models Nordic Cattle Genetic A.-M. Tyrisevä 1, E.A. Mäntysaari 1, J. Jakobsen 2, G.P. Aamand 3, J. Dürr 2, W.F. Fikse 4 and
More informationPerforming. linkage analysis using MERLIN
Performing linkage analysis using MERLIN David Duffy Queensland Institute of Medical Research Brisbane, Australia Overview MERLIN and associated programs Error checking Parametric linkage analysis Nonparametric
More information1.4 - Linear Regression and MS Excel
1.4 - Linear Regression and MS Excel Regression is an analytic technique for determining the relationship between a dependent variable and an independent variable. When the two variables have a linear
More informationGenomic Selection for Fusarium Head Blight Resistance in Barley
Genomic Selection for Fusarium Head Blight Resistance in Barley Kevin Smith University of Minnesota Aaron Lorenz, U Nebraska Jean Luc Jannink USDA Ithaca, NY Shiaoman Chao USDA Fargo, ND, Vikas Vikram
More informationHZAU MULTIVARIATE HOMEWORK #2 MULTIPLE AND STEPWISE LINEAR REGRESSION
HZAU MULTIVARIATE HOMEWORK #2 MULTIPLE AND STEPWISE LINEAR REGRESSION Using the malt quality dataset on the class s Web page: 1. Determine the simple linear correlation of extract with the remaining variables.
More informationNORTH SOUTH UNIVERSITY TUTORIAL 2
NORTH SOUTH UNIVERSITY TUTORIAL 2 AHMED HOSSAIN,PhD Data Management and Analysis AHMED HOSSAIN,PhD - Data Management and Analysis 1 Correlation Analysis INTRODUCTION In correlation analysis, we estimate
More informationIntroduction to Statistical Data Analysis I
Introduction to Statistical Data Analysis I JULY 2011 Afsaneh Yazdani Preface What is Statistics? Preface What is Statistics? Science of: designing studies or experiments, collecting data Summarizing/modeling/analyzing
More informationEffect of Source and Level of Protein on Weight Gain of Rats
Effect of Source and Level of Protein on of Rats 1 * two factor analysis of variance with interaction; 2 option ls=120 ps=75 nocenter nodate; 3 4 title Effect of Source of Protein and Level of Protein
More informationSupplementary Figures
Supplementary Figures Supplementary Figure 1. Heatmap of GO terms for differentially expressed genes. The terms were hierarchically clustered using the GO term enrichment beta. Darker red, higher positive
More informationIntroduction to linkage and family based designs to study the genetic epidemiology of complex traits. Harold Snieder
Introduction to linkage and family based designs to study the genetic epidemiology of complex traits Harold Snieder Overview of presentation Designs: population vs. family based Mendelian vs. complex diseases/traits
More informationUNIT 1CP LAB 1 - Spaghetti Bridge
Name Date Pd UNIT 1CP LAB 1 - Spaghetti Bridge The basis of this physics class is the ability to design an experiment to determine the relationship between two quantities and to interpret and apply the
More informationBig Data Training for Translational Omics Research. Session 1, Day 3, Liu. Case Study #2. PLOS Genetics DOI: /journal.pgen.
Session 1, Day 3, Liu Case Study #2 PLOS Genetics DOI:10.1371/journal.pgen.1005910 Enantiomer Mirror image Methadone Methadone Kreek, 1973, 1976 Methadone Maintenance Therapy Long-term use of Methadone
More informationQuantitative Genetics. Statistics Overview: Mean. Statistics Overview: Variance. Statistics Overview: Distributions. Chapter 22
Quantitative Genetics Chapter Statistics Overview: Distributions Phenotypes on X axis, Frequencies on Y axis Statistics Overview: Mean Measure of central tendency (average) of a group of measurements X
More informationCHAPTER 3 Describing Relationships
CHAPTER 3 Describing Relationships 3.1 Scatterplots and Correlation The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers Reading Quiz 3.1 True/False 1.
More informationSimple Linear Regression the model, estimation and testing
Simple Linear Regression the model, estimation and testing Lecture No. 05 Example 1 A production manager has compared the dexterity test scores of five assembly-line employees with their hourly productivity.
More informationSTATISTICS & PROBABILITY
STATISTICS & PROBABILITY LAWRENCE HIGH SCHOOL STATISTICS & PROBABILITY CURRICULUM MAP 2015-2016 Quarter 1 Unit 1 Collecting Data and Drawing Conclusions Unit 2 Summarizing Data Quarter 2 Unit 3 Randomness
More informationBIOL 364 Population Biology Fairly testing the theory of evolution by natural selection with playing cards
BIOL 364 Population Biology Fairly testing the theory of evolution by natural selection with playing cards Game I: The Basics Scenario: Our classroom is now a closed population (no immigration or emigration)
More informationSupplementary Information. Supplementary Figures
Supplementary Information Supplementary Figures.8 57 essential gene density 2 1.5 LTR insert frequency diversity DEL.5 DUP.5 INV.5 TRA 1 2 3 4 5 1 2 3 4 1 2 Supplementary Figure 1. Locations and minor
More informationTo test the possible source of the HBV infection outside the study family, we searched the Genbank
Supplementary Discussion The source of hepatitis B virus infection To test the possible source of the HBV infection outside the study family, we searched the Genbank and HBV Database (http://hbvdb.ibcp.fr),
More informationIntroduction to regression
Introduction to regression Regression describes how one variable (response) depends on another variable (explanatory variable). Response variable: variable of interest, measures the outcome of a study
More informationMangrove: Genetic risk prediction on trees
Mangrove: Genetic risk prediction on trees Luke Jostins March 8, 2012 Abstract Mangrove is an R package for performing genetic risk prediction from genotype data. You can use it to perform risk prediction
More informationApplication of Local Control Strategy in analyses of the effects of Radon on Lung Cancer Mortality for 2,881 US Counties
Application of Local Control Strategy in analyses of the effects of Radon on Lung Cancer Mortality for 2,881 US Counties Bob Obenchain, Risk Benefit Statistics, August 2015 Our motivation for using a Cut-Point
More informationBusiness Statistics Probability
Business Statistics The following was provided by Dr. Suzanne Delaney, and is a comprehensive review of Business Statistics. The workshop instructor will provide relevant examples during the Skills Assessment
More information2.75: 84% 2.5: 80% 2.25: 78% 2: 74% 1.75: 70% 1.5: 66% 1.25: 64% 1.0: 60% 0.5: 50% 0.25: 25% 0: 0%
Capstone Test (will consist of FOUR quizzes and the FINAL test grade will be an average of the four quizzes). Capstone #1: Review of Chapters 1-3 Capstone #2: Review of Chapter 4 Capstone #3: Review of
More informationLecture 12: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression
Lecture 12: more Chapter 5, Section 3 Relationships between Two Quantitative Variables; Regression Equation of Regression Line; Residuals Effect of Explanatory/Response Roles Unusual Observations Sample
More informationInvestigating causality in the association between 25(OH)D and schizophrenia
Investigating causality in the association between 25(OH)D and schizophrenia Amy E. Taylor PhD 1,2,3, Stephen Burgess PhD 1,4, Jennifer J. Ware PhD 1,2,5, Suzanne H. Gage PhD 1,2,3, SUNLIGHT consortium,
More informationTHE EFFECT OF WEIGHT TRIMMING ON NONLINEAR SURVEY ESTIMATES
THE EFFECT OF WEIGHT TRIMMING ON NONLINER SURVEY ESTIMTES Frank J. Potter, Research Triangle Institute Research Triangle Institute, P. O Box 12194, Research Triangle Park, NC 27709 KEY WORDS: sampling
More informationChapter 2--Norms and Basic Statistics for Testing
Chapter 2--Norms and Basic Statistics for Testing Student: 1. Statistical procedures that summarize and describe a series of observations are called A. inferential statistics. B. descriptive statistics.
More informationHow to interpret scientific & statistical graphs
How to interpret scientific & statistical graphs Theresa A Scott, MS Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott 1 A brief introduction Graphics:
More informationSUPPLEMENTARY INFORMATION
SUPPLEMENTRY INFORMTION Supplementary Figure 1. Q-Q plot of RE/NIMH autism family TDT results. The quantile-quantile plot of the expected and observed P-values is shown. The blue circles represent the
More informationNotes for laboratory session 2
Notes for laboratory session 2 Preliminaries Consider the ordinary least-squares (OLS) regression of alcohol (alcohol) and plasma retinol (retplasm). We do this with STATA as follows:. reg retplasm alcohol
More informationChapter 5 A Dose Dependent Screen for Modifiers of Kek5
Chapter 5 A Dose Dependent Screen for Modifiers of Kek5 "#$ ABSTRACT Modifier screens in Drosophila have proven to be a powerful tool for uncovering gene interaction and elucidating molecular pathways.
More informationGenome-wide association study dissects the genetic architecture of
Supplementary Information for Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels Hui Li 1,6, Zhiyu Peng 2,6, Xiaohong Yang 1,6, Weidong Wang 1,6, Junjie
More informationGenetics of extreme body size evolution in mice from Gough Island
Genetics of extreme body size evolution in mice from Gough Island Karl Broman Biostatistics & Medical Informatics, UW Madison kbroman.org github.com/kbroman @kwbroman bit.ly/apstats2017 This is a collaboration
More informationDescribe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo
Please note the page numbers listed for the Lind book may vary by a page or two depending on which version of the textbook you have. Readings: Lind 1 11 (with emphasis on chapters 10, 11) Please note chapter
More informationWorksheet 6 - Multifactor ANOVA models
Worksheet 6 - Multifactor ANOVA models Multifactor ANOVA Quinn & Keough (2002) - Chpt 9 Question 1 - Nested ANOVA - one between factor In an unusually detailed preparation for an Environmental Effects
More informationMethod Comparison for Interrater Reliability of an Image Processing Technique in Epilepsy Subjects
22nd International Congress on Modelling and Simulation, Hobart, Tasmania, Australia, 3 to 8 December 2017 mssanz.org.au/modsim2017 Method Comparison for Interrater Reliability of an Image Processing Technique
More informationVARIATION IN MEASUREMENT OF HIV RNA VIRAL LOAD
VARIATION IN MEASUREMENT OF HIV RNA VIRAL LOAD SOURCES OF VARIATION (RANDOM VS SYSTEMATIC) MAGNITUDE OF EACH SOURCE CONSEQUENCES FOR CONFIDENCE LIMITS AROUND MEASUREMENTS AND CHANGES DATA FROM ROCHE HIV
More informationStatistical Methods and Reasoning for the Clinical Sciences
Statistical Methods and Reasoning for the Clinical Sciences Evidence-Based Practice Eiki B. Satake, PhD Contents Preface Introduction to Evidence-Based Statistics: Philosophical Foundation and Preliminaries
More information25.1 QUANTITATIVE TRAITS
CHAPTER OUTLINE 5.1 Quantitative Traits 5. Polygenic Inheritance 5.3 Heritability 5 QUANTITATIVE In this chapter, we will examine complex traits characteristics that are determined by several genes and
More informationMidterm STAT-UB.0003 Regression and Forecasting Models. I will not lie, cheat or steal to gain an academic advantage, or tolerate those who do.
Midterm STAT-UB.0003 Regression and Forecasting Models The exam is closed book and notes, with the following exception: you are allowed to bring one letter-sized page of notes into the exam (front and
More informationChapter 1: Explaining Behavior
Chapter 1: Explaining Behavior GOAL OF SCIENCE is to generate explanations for various puzzling natural phenomenon. - Generate general laws of behavior (psychology) RESEARCH: principle method for acquiring
More informationAnalysis of single gene effects 1. Quantitative analysis of single gene effects. Gregory Carey, Barbara J. Bowers, Jeanne M.
Analysis of single gene effects 1 Quantitative analysis of single gene effects Gregory Carey, Barbara J. Bowers, Jeanne M. Wehner From the Department of Psychology (GC, JMW) and Institute for Behavioral
More informationQuantitative Methods in Computing Education Research (A brief overview tips and techniques)
Quantitative Methods in Computing Education Research (A brief overview tips and techniques) Dr Judy Sheard Senior Lecturer Co-Director, Computing Education Research Group Monash University judy.sheard@monash.edu
More informationTwo-Way Independent Samples ANOVA with SPSS
Two-Way Independent Samples ANOVA with SPSS Obtain the file ANOVA.SAV from my SPSS Data page. The data are those that appear in Table 17-3 of Howell s Fundamental statistics for the behavioral sciences
More informationAdjustment for Heterogeneous Herd-Test-Day Variances
Adjustment for Heterogeneous Herd-Test-Day Variances G. J. Kistemaker and L.R. Schaeffer Centre for Genetic Improvement of Livestock University of Guelph, Guelph, Ontario, Canada Currently lactation records
More informationDr. Kelly Bradley Final Exam Summer {2 points} Name
{2 points} Name You MUST work alone no tutors; no help from classmates. Email me or see me with questions. You will receive a score of 0 if this rule is violated. This exam is being scored out of 00 points.
More informationBangor University Laboratory Exercise 1, June 2008
Laboratory Exercise, June 2008 Classroom Exercise A forest land owner measures the outside bark diameters at.30 m above ground (called diameter at breast height or dbh) and total tree height from ground
More informationEffects of Nutrients on Shrimp Growth
Data Set 5: Effects of Nutrients on Shrimp Growth Statistical setting This Handout is an example of extreme collinearity of the independent variables, and of the methods used for diagnosing this problem.
More informationNormal Q Q. Residuals vs Fitted. Standardized residuals. Theoretical Quantiles. Fitted values. Scale Location 26. Residuals vs Leverage
Residuals 400 0 400 800 Residuals vs Fitted 26 42 29 Standardized residuals 2 0 1 2 3 Normal Q Q 26 42 29 360 400 440 2 1 0 1 2 Fitted values Theoretical Quantiles Standardized residuals 0.0 0.5 1.0 1.5
More informationThe Efficiency of Mapping of Quantitative Trait Loci using Cofactor Analysis
The Efficiency of Mapping of Quantitative Trait Loci using Cofactor G. Sahana 1, D.J. de Koning 2, B. Guldbrandtsen 1, P. Sorensen 1 and M.S. Lund 1 1 Danish Institute of Agricultural Sciences, Department
More informationStats 95. Statistical analysis without compelling presentation is annoying at best and catastrophic at worst. From raw numbers to meaningful pictures
Stats 95 Statistical analysis without compelling presentation is annoying at best and catastrophic at worst. From raw numbers to meaningful pictures Stats 95 Why Stats? 200 countries over 200 years http://www.youtube.com/watch?v=jbksrlysojo
More informationRoadmap. Inbreeding How inbred is a population? What are the consequences of inbreeding?
1 Roadmap Quantitative traits What kinds of variation can selection work on? How much will a population respond to selection? Heritability How can response be restored? Inbreeding How inbred is a population?
More informationAP Statistics. Semester One Review Part 1 Chapters 1-5
AP Statistics Semester One Review Part 1 Chapters 1-5 AP Statistics Topics Describing Data Producing Data Probability Statistical Inference Describing Data Ch 1: Describing Data: Graphically and Numerically
More informationChapter 7: Pedigree Analysis B I O L O G Y
Name Date Period Chapter 7: Pedigree Analysis B I O L O G Y Introduction: A pedigree is a diagram of family relationships that uses symbols to represent people and lines to represent genetic relationships.
More informationQTL detection for traits of interest for the dairy goat industry
QTL detection for traits of interest for the dairy goat industry 64 th Annual Meeting EAAP 2013 26 th -30 th august Nantes, France C. Maroteau, I. Palhière, H. Larroque, V. Clément, G. Tosser-Klopp, R.
More informationChapter 9. Factorial ANOVA with Two Between-Group Factors 10/22/ Factorial ANOVA with Two Between-Group Factors
Chapter 9 Factorial ANOVA with Two Between-Group Factors 10/22/2001 1 Factorial ANOVA with Two Between-Group Factors Recall that in one-way ANOVA we study the relation between one criterion variable and
More informationEstimating genetic variation within families
Estimating genetic variation within families Peter M. Visscher Queensland Institute of Medical Research Brisbane, Australia peter.visscher@qimr.edu.au 1 Overview Estimation of genetic parameters Variation
More informationMath 215, Lab 7: 5/23/2007
Math 215, Lab 7: 5/23/2007 (1) Parametric versus Nonparamteric Bootstrap. Parametric Bootstrap: (Davison and Hinkley, 1997) The data below are 12 times between failures of airconditioning equipment in
More informationDecomposition of the Genotypic Value
Decomposition of the Genotypic Value 1 / 17 Partitioning of Phenotypic Values We introduced the general model of Y = G + E in the first lecture, where Y is the phenotypic value, G is the genotypic value,
More informationExamining Relationships Least-squares regression. Sections 2.3
Examining Relationships Least-squares regression Sections 2.3 The regression line A regression line describes a one-way linear relationship between variables. An explanatory variable, x, explains variability
More informationSection 3 Correlation and Regression - Teachers Notes
The data are from the paper: Exploring Relationships in Body Dimensions Grete Heinz and Louis J. Peterson San José State University Roger W. Johnson and Carter J. Kerk South Dakota School of Mines and
More informationC) Show the chromosomes, including the alleles on each, in the F1 hybrid progeny at metaphase of Meiosis 1 and mitosis.
On my honor, this is my work GENETICS 310 EXAM I all, 2017 I. Australian daises have 4 chromosomes (2 pairs). A gene on chromosome 1 affects petal color where M M is magenta, M M is pink and MM flowers
More informationSection 3.2 Least-Squares Regression
Section 3.2 Least-Squares Regression Linear relationships between two quantitative variables are pretty common and easy to understand. Correlation measures the direction and strength of these relationships.
More informationExperimental Inference
Experimental Inference Proof reading and error rate An investigator does a study on proofreading for typographical errors. There are three levels of the independent variable (three conditions), distinguished
More informationInbreeding and Crossbreeding. Bruce Walsh lecture notes Uppsala EQG 2012 course version 2 Feb 2012
Inbreeding and Crossbreeding Bruce Walsh lecture notes Uppsala EQG 2012 course version 2 Feb 2012 Inbreeding Inbreeding = mating of related individuals Often results in a change in the mean of a trait
More informationStatistical Tests for X Chromosome Association Study. with Simulations. Jian Wang July 10, 2012
Statistical Tests for X Chromosome Association Study with Simulations Jian Wang July 10, 2012 Statistical Tests Zheng G, et al. 2007. Testing association for markers on the X chromosome. Genetic Epidemiology
More informationanalysed for its genotype-environmental interactions. This use as independent
GENOTYPE-ENVIRONMENTAL INTERACTIONS IN SCHIZOPHYLLUM COMMUNE II. ASSESSING THE ENVIRONMENT YVONNE J. FRIPP0 Department of Genetics, University of Birmingham, Birmingham, BlS 2TT Received l.vii.71 1. INTRODUCTION
More informationAppendix B Statistical Methods
Appendix B Statistical Methods Figure B. Graphing data. (a) The raw data are tallied into a frequency distribution. (b) The same data are portrayed in a bar graph called a histogram. (c) A frequency polygon
More informationChoosing a Significance Test. Student Resource Sheet
Choosing a Significance Test Student Resource Sheet Choosing Your Test Choosing an appropriate type of significance test is a very important consideration in analyzing data. If an inappropriate test is
More informationSPRING GROVE AREA SCHOOL DISTRICT. Course Description. Instructional Strategies, Learning Practices, Activities, and Experiences.
SPRING GROVE AREA SCHOOL DISTRICT PLANNED COURSE OVERVIEW Course Title: Basic Introductory Statistics Grade Level(s): 11-12 Units of Credit: 1 Classification: Elective Length of Course: 30 cycles Periods
More informationMultiple Linear Regression Analysis
Revised July 2018 Multiple Linear Regression Analysis This set of notes shows how to use Stata in multiple regression analysis. It assumes that you have set Stata up on your computer (see the Getting Started
More information2.4.1 STA-O Assessment 2
2.4.1 STA-O Assessment 2 Work all the problems and determine the correct answers. When you have completed the assessment, open the Assessment 2 activity and input your responses into the online grading
More informationINVESTIGATING EFFICIENCY OF SELECTION USING UNIVARIATE AND MULTIVARIATE BEST LINEAR UNBIASED PREDICTORS. Richard Kerr Tony McRae
INVESTIGATING EFFICIENCY OF SELECTION USING UNIVARIATE AND MULTIVARIATE BEST LINEAR UNBIASED PREDICTORS Richard Kerr Tony McRae . demonstration of the limited advantage in using multivariate analysis I
More information