Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs

BMC Genetics

Research Article

Pernille Sarup¹  Just Jensen¹  Peter Sørensen¹  Tage Ostersen²  Mark Henryon² 

[1] Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, Aarhus University, Blichers Allé 20, 8830, Tjele, Denmark;SEGES Danish Pig Research Centre, Axeltorv 3, 1609, Copenhagen V, Denmark;

关键词: Genomic feature models;    GFBLUP;    Feed efficiency;    Average daily gain;    Meat percent;    Growth;    Genomic prediction;   

DOI  :  10.1186/s12863-015-0322-9

 received in 2015-09-08, accepted in 2015-12-20,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundIn animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that can evaluate the collective effects of sets of SNPs and thereby open the “black box.” The described genomic feature best linear unbiased prediction (GFBLUP) model has two components that are defined by genomic features.ResultsWe analysed data on average daily gain, feed efficiency, and lean meat percentage from 3,085 Duroc boars, along with genotypes from a 60 K SNP chip. In addition information on known quantitative trait loci (QTL) from the animal QTL database was integrated in the GFBLUP as a genomic feature. Our results showed that the most significant QTL categories were indeed biologically meaningful. Additionally, for high heritability traits, prediction accuracy was improved by the incorporation of biological knowledge in prediction models. A simulation study using the real genotypes and simulated phenotypes demonstrated challenges regarding detection of causal variants in low to medium heritability traits.ConclusionsThe GFBLUP model showed increased predictive ability when enough causal variants were included in the genomic feature to explain over 10 % of the genomic variance, and when dilution by non-causal markers was minimal. In the observed data set, predictive ability was increased by the inclusion of prior QTL information obtained outside the training data set, but only for the trait with highest heritability.

【 授权许可】

CC BY   
© Sarup et al. 2016

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