Role of Genomics in Selection of Beef Cattle for Healthfulness Characteristics Dorian Garrick dorian@iastate.edu Iowa State University & National Beef Cattle Evaluation Consortium
Selection and Prediction Breeding Values Breeding Objective Agenda Conventional Prediction of Breeding Values Genomic Prediction of Breeding Values Concept and Theory Application and Practice Present Status for Healthfulness Characteristics Knowledge Gaps (and Research Needs)
Selection and Prediction Genetic change results from using candidates that differ from population average as parents of the next generation The key to genetic change is selection more intense selection will provide faster change (1-2% pa) The key to artificial selection is quantifying the breeding merit for attributes of interest This is achieved using breeding values
Breeding Values A breeding value is twice the deviation in performance of the offspring relative to offspring of average parents adjusted for the merit of the mates adjusted for non-genetic influences on performance (eg age at measurement)
Breeding Objectives If you re not breeding for profit, we wish you well with your hobby
Breeding Objectives Two components First, the list of traits that influence income &or costs These are the traits for which we need breeding values Second, the relative emphasis of each trait in the list Value of a unit change in that trait, all other traits held constant $INDEX=r 1 BV 1 +r 2 BV 2 +.r n BV n
Breeding Objectives If you re not breeding for profit, we wish you well with your hobby Income that allows for profit in the beef industry derives from consumer satisfaction in the eating experience
Conventional Prediction We cannot observe Breeding Values (BV) We predict them and refer to the estimates as EBV Beef Industry uses its own jargon, a term known as EPD, for Expected Progeny Difference EPD= ½ EBV
Suppose we get progeny on a bull Sire Progeny
Performance of the Progeny +30 lb +15 lb -10 lb Sire Offspring of one sire exhibit more than ¾ diversity of the entire population Progeny + 5 lb +10 lb +10 lb
We learn about parents from progeny +30 lb +15 lb -10 lb Sire (EPD is shrunk ) Sire EPD +8-9 lb Progeny + 5 lb +10 lb +10 lb
EPDs on widely-used (old) sires are accurate Sire With enough progeny, this is usually close to the bulls true EPD Sire EPD +8-9 lb
EPD on offspring are parent average EPD +9 GV + EPD +1 EPD +5 +5 +5 +5 +5 +5 +5 +5 +5 +5
Current Circumstances EPD +9 GV + EPD +1 EPD +5 +5 +5 +5 +5 +5 +5 +5 +5 +5 True +1 +20 +0-5 -10 +20-5 -25 +50 +5
Current Circumstances EPD +9 GV + EPD +1 EPD +5 +5 +5 +5 +5 +5 +5 +5 +5 +5 True +1 +20 +0-5 -10 +20-5 -25 +50 +5 But identifying those better than parent average requires phenotypes
But genes determine the EPD Part of 1 pair of chromosomes From sire From dam Cattle usually have 30 pairs of chromosomes One member of each pair was inherited from the sire, one from the dam Each chromosome has about 100 million base pairs (A, G, T or C) Blue base pairs represent genes Yellow represents markers inherited from the sire Orange represents markers inherited from the dam
Definition of Breeding Value BV is sum of average effects of alleles, summed over the pair of alleles at each locus and over all loci influencing the trait Mendelian Viewpoint
EPD is half sum of the gene effects +3-3 -6 +6 +5 +5 Sum=+2 Sum=+8 Blue base pairs represent genes The EPD is half the sum of all these genetic values (half because offspring inherit a random half sample of each parents chromosomes) But what is the genomic architecture of various traits?
EPD is half sum of the gene effects +3-6 +5-3 +6 +5 If we knew the number, location and effect of the genes, we could obtain EPD directly, before the bull was breeding age
Gene Assisted Selection (GAS) Q q EPD +9 EPD +1 Q q Suppose Q is +5 better than q Sort by: Q Q Q q q q
Gene Assisted Selection (GAS) Q q EPD +9 EPD +1 Q q Suppose Q is +5 better than q +10 +10 +5 +5 +5 +5 +5 0 0 0 Sort by: Q Q Q q q q
Genomic Prediction Involves finding the location and effects of the genes (known as QTL=Quantitative Trait Loci) that cause variation in the trait of interest in a discovery phase Then using this information to determine the genetic merit of a new individual that need not be recorded itself for the phenotype of interest
Regress EPD on QTL Accurate EPD (eg from AI sires) genotype Variation due to other genes Slope=average effect of allele qq Qq QQ
December 2004 February 2001 April 2009
Genotypes are now a commodity!
Practice regress EPD on SNP EPD or phenotype Use SNP genotypes at locus 1 (in high LD) as surrogates for QTL A 1 A 1 A 1 B 1 B 1 B 1
Practice BV on SNP True Breeding Value Use SNP genotypes at locus 2 (in low LD) as surrogates for QTL A 2 A 2 A 2 B 2 B 2 B 2
Linkage Disequilibrium on Bos Taurus autosome 1 LD indicates the ability of observed SNP to act as surrogates (of other SNP) Hope this reflects the LD between SNP and QTL 1,000 mixed breeds half-sib groups
Birth Weight on BTA6 in 3,500 Angus bulls
Major Fatty Acids (Proportions in Phospholipids)
Informative 1cM Regions
Cross Validation Partition the dataset (by sire) into say three groups Training G1 G2 G3 Validation G1 Derive g-epd Compute the correlation between predicted genetic merit from g-epd and observed performance
Cross Validation Every animal is in exactly one validation set Training G1 G2 G3 Validation G1 G2 G3
Fatty Acids are a System
Distribution of Joint Effects C14:0 and C16:0 (1cM windows)
Knowledge Gaps What are the key healthfulness targets? These dictate the list of traits to predict From which depots? (Longissimus dorsi, subcutaneous etc) What is their relative importance? To each other and to productive/reproductive traits How can we more cheaply phenotype them? Important for validation and beef marketing Can we construct a small panel of (causal?) SNP that operate across breed? Only practically deliverable through a genomics option Can a value chain be created that can take this science from the computer to the consumer?
Summary Science is showing considerable promise in being able to predict genetic merit so that the concentration of healthfulness traits can be increased or decreased by directed selection We need more clarity on the targets We need more fine-mapping studies and validation of these targets expression array work would help We need an entrepreneurial framework to deliver the prototype concept in meaningful quantities to test the market potential
Acknowledgments Pfizer Animal Genetics funded the healthfulness study Drs Jim Reecy and JR Tait managed the overall study and laboratory data collection at ISU Drs Raluca Mateescu and Deb VanOverbeke at Oklahoma State, and Alison Van Eenennaam at UC Davis managed data collection in some of the cohorts Dr Clara Diaz, INIA Madrid provided the results of the genomic analyses of the fatty acids