Quantitative Genetics and Animal Breeding in the Age of Genomics Bruce Walsh

1. Quantitative Genetics and Animal Breeding in the Age of GenomicsBruce Walsh

2. Classical Quantitative Genetics • Quantitative genetics deals with the observed variation in a trait both within and between populations • Basic model (Fisher 1918): The phenotype (z) is the sum of (unseen) genetic (g) and environmental values (e) • z = g + e • The genetic value needs to be further decomposed into an additive part A passed for parent to offspring, separate from dominance (D) and epistatic effects (I) that are only fully passed along in clones • g = A + D + I • Var(g)/Var(z) is quantitative measure of nature vs. nurture • fraction of all trait variation due to genetic differences

3. Fisher’s great insight: Phenotypic covariances between relatives can estimate the variances of g, e, etc. • For example, in the simplest settings, • Cov(parent,offspring) = Var(A)/2 • Cov(Full sibs) = Var(A)/2 + Var(D)/4 • Cov(clones) = Var(g) = Var(A)+Var(D)+Var(I) • Random-effects model • Thus, in classical quantitative genetics, a few statistical descriptors describe the underlying complex genetics • This leaves an uneasy feeling among most of my molecular colleagues. • Does the age of genomics usher in the death knell of Quantitative Genetics?

4. “Classical” Animal Breeding • Using the records (phenotypes) of individuals from a known (often very complex) pedigree, estimates for the breeding values (A) for individuals are obtained • This is usually done using the machinery of BLUP -- best linear unbiased predictor. • To do this, we also have to estimate Var(A), typically this is done usingREML -- restricted (or residual) maximum likelihood • Predicted value of offspring from two parents is the average of the parental breeding values • How will genomics alter this classical approach?

5. Approximate costs of genome projects • Arabidopsis Genome Project ... $500 million • Drosophila Genome Project ... $1 billion • Human Genome Project ... $10 billion • Working knowledge of multivariate statistics ... Priceless

6. Neoclassical Quantitative Genetics • Use information from both an individual’s phenotype (z) and marker genotype (m) • z = u + Gm +g + e • Gm is genotypic value associated with the scored genotype (m ) • Obvious extensions: include Gm x e and Gm x g • Mixed model: can treat as the Gm as fixed effects; g and e as random • My molecular colleagues hope that Gm accounts for most of the variance in the trait • If true, then Var(g)/Var(z) trivial

7. Neoclassical Animal Breeding • Selection decisions are based on some weighted index of phenotype and genetic marker information • Base selection on an index, I = a E(BV) + b Gm • MAS = marker assisted selection • The larger the amount of phenotypic variance accounted for by the genetic marker information (Gm), the more selection is directly on the genotypes (i.e., much more weight on G than on the expected breeding value).

8. Limitations on Gm • The importance of particular genotypes may be quite fleeting • can easily change as populations evolve and as the biotic and abiotic environments change • If epistasis and/or genotype-environment interactions are significant, any particular genotype may be a good, but not exceptional, predictor of phenotype • Quantitative genetics provides the machinery necessary for managing all this uncertainty in the face of some knowledge of important genotypes • e.g., proper accounting of correlations between relatives in the unmeasured genetic values (g)

9. Limitations with MAS • Tradeoff between increased short-term response under MAS vs. decreased short-term response compared with phenotypic selection. • Reduced selection on phenotype • Reduction in effective population size • MAS may not be cost-effective compared to phenotypic selection • Optimal setting for MAS • Genes of major effect (e.g., scrapie (prion) resistance) • Sex-limited expression • Traits difficult/expensive to score directly (i.e., carcass traits)

10. How do we obtain Gm? • Ideally, we screen a number of candidate loci • QTL (Quantitative trait locus) mapping • Uses molecular markers to follow which chromosome segments are common between individuals • This allows construction of a likelihood function, e.g.,

11. A typical QTL map from a likelihood analysis

12. Genomics and candidate loci • Typical QTL confidence interval 20-50 cM • The big question: how do we find suitable candidates? • The hope is that a genomic sequence will suggest candidates

13. Genomics tools to probe for candidates • Dense marker maps • Complete genome sequence • Expression data (microarrays) • Proteomics • Metablomics

14. The accelerating pace of genomics • Faster and cheaper sequencing • Rapid screening of thousands of loci via DNA chips • Phylogenetic bootstrapping from model systems to distant relatives

16. Prediction of Candidate Genes • Try homologous candidates from other species • Examine all Open Reading Frames (ORFs) within a QTL confidence interval • Expression array analysis of these ORFs • Lack of tissue-specific expression does not exclude a gene • Proteomics • Specific protein motifs may provide functional clues • Cracking the regulatory code (in silico genetics)

17. Searching for Natural Variation • This may be the area where genomics has the largest payoff • Source (natural and/or weakly domesticated) populations contain more variation than the current highly domesticated lines • Key is to first detect and localize importance variants, then introgress them into elite lines

18. The impact of other biotechnologies • Cloning, other reproductive technologies • Maintain elite lines as cell cultures? • Trans-species maintenance of tissue cultures • Embryo transplantation into elite maternal lines? • Creation of permanently heterotic lines • Transgenics • Important tool in both breeding and evolutionary biology • Complications: • Silencing of multiple copies in some species • Strong position effects • Currently restricted to major genes • Major genes can have deleterious effects on other characters • Importance of quantitative genetics for selecting for background polygenic modifiers

19. Useful Tools for Quantitative Genetic analysis • Four subfields of Quantitative Genetics • Plant breeding • Animal breeding (forest genetics) • Evolutionary Genetics • Human Genetics • Restricted communications between fields • Important tools often unknown outside a field

20. Tools from Plant Breeding • Special features dealt with by plant breeders • Diversity of mating systems (esp. selfing) • Sessile individuals • Issues • Creation and selection of inbred lines • Hybridization between lines • Genotype x Environment interactions • Competition • Plant breeding tools useful in Animal Breeding • Field-plot designs • G x E analysis models: AMMI and biplots • These designs are also excellent candidates for the analysis of microarray expression data • Covariance between inbred relatives • Line cross analysis

21. Animal Breeding • Special features • Complex pedigrees • Large half-sib (more rarely full-sib) families • Long life spans • Overlapping generations • Tree breeders face many of these same issues • Animal breeding tools useful in other fields • BLUP (best linear unbiased predictors) for genotypic values • REML (restricted maximum likelihood) for variance components • BLUP/REML allow for arbitrary pedigrees, very complex models • Maternal effects designs • Endosperm work of Shaw and Waser • Selection response in structured populations

22. Evolutionary Genetics • Issues • Estimating the nature and amount of selection • Population-genetic models of evolution • Tools • Estimation of the nature of natural selection on any specified character • Lande-Arnold fitness estimation; cubic splines • Using DNA sequences to detect selection on a locus • Example: teosinte-branched 1 • Coalescent theory • The genealogy of DNA sequences within a random sample • Analysis of finite-locus and non-Gaussian models of selection response • Barton and Turelli; Burger

23. Human Genetics • Issues • Very small family sizes • Lack of controlled mating designs • Tools of potential use • Sib-pair approaches for QTL mapping • QTL mapping in populations • Transmission-disequilibrium test (TDT) • Account for population structure • Linkage-disequilibrium mapping • Use historical recombinations to fine-map genes • Random-effects models for QTL mapping • BLUP/REML-type analysis over arbitrary pedigrees

24. Conclusions • Genomics will increase, not decrease, the importance of quantitative genetics • The machinery of classical quantitative genetics is easily modified to account for massive advances in genomics and other fields of biotechonology • Useful and powerful tools have been developed to address specific issues in the various subfields of quantitative genetics • The future of animal breeding is a natural fusion of genomic information into an expanded quantitative-genetics framework, exploiting advances in reproductive biotechnologies.