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Introduction Genomic selection is becoming common practice in animal breeding. It uses genome-wide dense markermaps, to accurately predict the genetic ability of animals, without the need to record phenotypic performance from theanimal itself or from close relatives. Presented applications of genomic selection have mainly been limited toimplementations where genomic breeding values are estimated using single trait models. A major breakthrough intraditional breeding value estimation was the application of multi-trait breeding value estimation, for instance to combinemastitis and somatic cell count information. Therefore, our objective was to develop multi-trait genomic breeding valueestimation methods.Materials and methods Four different multi-trait models were considered: 1) a model with a traditional pedigree basedrelationship matrix (A-BLUP), 2) a model where the traditional pedigree based relationship matrix is replaced by agenomic relationship matrix based on markers (G-BLUP) (e.g. VanRaden, 2008), 3) a model that includes SNP effectsdrawn from a single distribution (BayesA), and 4) a model that includes SNP effects drawn from two distributions todistinguish between SNPs that are (not) associated with QTL (BayesC) (a single trait implementation is presented by Caluset al., (2008)). The second model assumes equal contribution of each SNP to the total additive genetic (co)variance. Model3 and 4 explicitly estimate the (co)variance of the SNP effects, per sampled distribution of SNP effects. The additivegenetic (co)variance matrix was used as prior information for the SNP variances. The four models were applied to twosimulated traits with heritabilities of 0.9 and 0.6, to reflect e.g. de-regressed proofs, having a genetic correlation of 0.2, 0.5or 0.8 between them. In the simulated data, 2 generations of 500 animals each were available with phenotypes for bothtraits, and thus formed the reference population. Two additional generations of 500 animals were used as validation data,e.g. their breeding values were predicted while they had no phenotypic information of their own or from offspring in themodel.Results Increases in accuracy, due to applying multi-trait instead of single trait genomic breeding value estimation,depended on the genetic correlation between the traits. At a genetic correlation of 0.8, the accuracy of the breeding valuesof animals without phenotypes for the second trait increased by 0.03 to 0.07 (see Table 1). At a genetic correlation of 0.5,this increase ranged from 0.01 to 0.04. The highest increase was found using model BayesC, followed by BayesA, GBLUP,and A-BLUP respectively. Regression of the simulated on the estimated breeding values showed that the estimatedbreeding values were generally unbiased.Table 1 Increase in accuracies for breeding values of the first generation of animals without phenotypes, obtained frommultitrait versus single trait models.Genetic correlationModel 0.5 0.8A-BLUP 0.009 0.034G-BLUP 0.017 0.052BayesA 0.024 0.056BayesC 0.040 0.071Conclusions In a scenario where all animals in the reference population have phenotypes for all traits, multi-trait genomicbreeding value estimation showed an increase of up to 0.07 in accuracy for juvenile animals, compared to single traitanalysis. Thus, the application of multi-trait genomic selection in this scenario proved to be more accurate than single traitgenomic selection. In practice, higher accuracy increases are expected when one of the traits is measured on some of theanimals only.
|Publication status||Published - 2010|
|Event||BSAS/ARF Annual Conference, Belfast, Northern Ireland - |
Duration: 12 Apr 2010 → 14 Apr 2010
|Conference||BSAS/ARF Annual Conference, Belfast, Northern Ireland|
|Period||12/04/10 → 14/04/10|