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

Quantitative Genetics and Animal Breeding in the Age of Genomics Bruce Walsh. Classical Quantitative Genetics. Quantitative genetics deals with the observed variation in a trait both within and between populations

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

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  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

  15. 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)

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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

  22. 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

  23. 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.

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