Genomic breeding value prediction and QTL mapping of QTLMAS2011 data using Bayesian and GBLUP methods

Background: The goal of this study was to apply Bayesian and GBLUP methods to predict genomic breeding values (GEBV), map QTL positions and explore the genetic architecture of the trait simulated for the 15 QTL-MAS workshop. Methods. Three methods with models considering dominance and epistasis inheritances were used to fit the data: (i) BayesB with a proportion = 0.995 of SNPs assumed to have no effect, (ii) BayesC, where is considered as unknown, and (iii) GBLUP, which directly fits animal genetic effects using a genomic relationship matrix. Results: BayesB, BayesC and GBLUP with various fitted models detected 6, 5, and 4 out of 8 simulated QTL, respectively. All five additive QTL were detected by Bayesian methods. When two QTL were in either coupling or repulsion phase, GBLUP only detected one of them and missed the other. In addition, GBLUP yielded more false positives. One imprinted QTL was detected by BayesB and GBLUP despite that only additive gene action was assumed. This QTL was missed by BayesC. None of the methods found two simulated additive-by-additive epistatic QTL. Variance components estimation correctly detected no evidence for dominance gene-action. Bayesian methods predicted additive genetic merit more accurately than GBLUP, and similar accuracies were observed between BayesB and BayesC. Conclusions: Bayesian methods and GBLUP mapped QTL to similar chromosome regions but Bayesian methods gave fewer false positives. Bayesian methods can be superior to GBLUP in GEBV prediction when genomic architecture is unknown

Strategies for implementing genomic selection in family-based aquaculture breeding schemes: double haploid sib test populations

<p>Abstract</p> <p>Background</p> <p>Simulation studies have shown that accuracy and genetic gain are increased in genomic selection schemes compared to traditional aquaculture sib-based schemes. In genomic selection, accuracy of selection can be maximized by increasing the precision of the estimation of SNP effects and by maximizing the relationships between test sibs and candidate sibs. Another means of increasing the accuracy of the estimation of SNP effects is to create individuals in the test population with extreme genotypes. The latter approach was studied here with creation of double haploids and use of non-random mating designs.</p> <p>Methods</p> <p>Six alternative breeding schemes were simulated in which the design of the test population was varied: test sibs inherited maternal (<it>Mat</it>), paternal (<it>Pat</it>) or a mixture of maternal and paternal (<it>MatPat</it>) double haploid genomes or test sibs were obtained by maximum coancestry mating (<it>MaxC</it>), minimum coancestry mating (<it>MinC</it>), or random (<it>RAND</it>) mating. Three thousand test sibs and 3000 candidate sibs were genotyped. The test sibs were recorded for a trait that could not be measured on the candidates and were used to estimate SNP effects. Selection was done by truncation on genome-wide estimated breeding values and 100 individuals were selected as parents each generation, equally divided between both sexes.</p> <p>Results</p> <p>Results showed a 7 to 19% increase in selection accuracy and a 6 to 22% increase in genetic gain in the <it>MatPat</it> scheme compared to the <it>RAND</it> scheme. These increases were greater with lower heritabilities. Among all other scenarios, i.e. <it>Mat, Pat, MaxC</it>, and <it>MinC</it>, no substantial differences in selection accuracy and genetic gain were observed.</p> <p>Conclusions</p> <p>In conclusion, a test population designed with a mixture of paternal and maternal double haploids, i.e. the <it>MatPat</it> scheme, increases substantially the accuracy of selection and genetic gain. This will be particularly interesting for traits that cannot be recorded on the selection candidates and require the use of sib tests, such as disease resistance and meat quality.</p