Bayesian methods for estimating GEBVs of threshold traits

Estimation of genomic breeding values is the key step in genomic selection (GS). Manymethods have been proposed for continuous traits, but methods for threshold traits arestill scarce. Here we introduced threshold model to the framework of GS, and specifically,we extended the three Bayesian methods BayesA, BayesB and BayesCπ on the basis ofthreshold model for estimating genomic breeding values of threshold traits, and theextended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computingprocedures of the three BayesT methods using Markov Chain Monte Carlo algorithm werederived. A simulation study was performed to investigate the benefit of the presentedmethods in accuracy with the genomic estimated breeding values (GEBVs) for thresholdtraits. Factors affecting the performance of the three BayesT methods were addressed. Asexpected, the three BayesT methods generally performed better than the correspondingnormal Bayesian methods, in particular when the number of phenotypic categories was small.In the standard scenario (number of categories=2, incidence=30%,number of quantitative trait loci=50, h²=0.3), theaccuracies were improved by 30.4%, 2.4%, and 5.7% points,respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies andboth performed better than BayesTA. In conclusion, our work proved that threshold modelfits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be themethod of choice for GS of threshold traits.     

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

The efficiency of marker-assisted prediction of phenotypes has been studied intensively for different types of plant breeding populations. However, one remaining question is how to incorporate and counterbalance information from biparental and multiparental populations into model training for genome-wide prediction. To address this question, we evaluated testcross performance of 1652 doubled-haploid maize (Zea mays L.) lines that were genotyped with 56,110 single nucleotide polymorphism markers and phenotyped for five agronomic traits in four to six European environments. The lines are arranged in two diverse half-sib panels representing two major European heterotic germplasm pools. The data set contains 10 related biparental dent families and 11 related biparental flint families generated from crosses of maize lines important for European maize breeding. With this new data set we analyzed genome-based best linear unbiased prediction in different validation schemes and compositions of estimation and test sets. Further, we theoretically and empirically investigated marker linkage phases across multiparental populations. In general, predictive abilities similar to or higher than those within biparental families could be achieved by combining several half-sib families in the estimation set. For the majority of families, 375 half-sib lines in the estimation set were sufficient to reach the same predictive performance of biomass yield as an estimation set of 50 full-sib lines. In contrast, prediction across heterotic pools was not possible for most cases. Our findings are important for experimental design in genome-based prediction as they provide guidelines for the genetic structure and required sample size of data sets used for model training.     

Synthesis and parameters of new populations of meat-type chickens

Summary Sire and dam populations of meat-type chickens were developed at the Animal Research Centre, Ottawa to provide unselected control populations suitable for modern broiler research, estimate genetic parameters of broiler traits, and initiate selection experiments. The populations were synthesized from 16 commercial broiler parent stocks (nine sire and seven dam stocks) during two generations of crossbreeding and one generation of random mating. The estimated genetic contribution per stock to the respective population ranged from 8.3 to 16.7% for autosomal genes, from 5.5 to 17.8% for genes on Z chromosomes, and from 0 to 25.0% for genes on W chromosomes. Genetic differences among the original stocks had not been fully diffused among individuals within the populations at the time parameters were estimated. Consequently, genetic parameter estimates are probably slightly biased. Estimated heritabilities of broiler traits in these populations were generally high, 0.39 to 0.63. Hence, rapid genetic change of these traits is possible. The genetic correlation between 28 to 49 day feed efficiency and corresponding weight gain was 0.69 and between feed efficiency and abdominal fat percentage was -0.68. Increasing body weight gain, improving feed efficiency and reducing abdominal fatness of broilers by selection appears feasible.Contribution No. 1203, Animal Research Centre     

Genome-Wide Prediction of Traits with Different Genetic Architecture Through Efficient Variable Selection

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.