title: Accuracy of whole-genome prediction using a genetic architecture-enhanced variance-covariance matrix.
abstract: Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|GA (BLUP-given genetic architecture), which can use genetic architecture information within the dataset at hand rather than from public sources. This is achieved by using a trait-specific covariance matrix ( T: ), which is a weighted sum of a genetic architecture part ( S: matrix) and the realized relationship matrix ( G: ). The algorithm of BLUP|GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP|GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches. Results show that BLUP|GA outperformed GBLUP in 20 of 21 scenarios in the dairy cattle dataset and outperformed GBLUP, BayesA, and BayesB in 12 of 13 traits in the analyzed public datasets. Further analyses showed that the difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T: and G: matrices. The new strategy applied in BLUP|GA is a favorable and flexible alternative to the standard GBLUP model, allowing to account for the genetic architecture of the quantitative trait under consideration when necessary. This feature is mainly due to the increased similarity between the trait-specific relationship matrix ( T: matrix) and the genetic relationship matrix at unobserved causal loci. Applying BLUP|GA in WGP would ease the burden of model selection.
authors: National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-Universität Göttingen, Göttingen 37075, Germany.
authors: Zhe
authors: Z
authors: Zhang
authors: Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-Universität Göttingen, Göttingen 37075, Germany.
authors: Malena
authors: M
authors: Erbe
authors: National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
authors: Jinlong
authors: J
authors: He
authors: Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-Universität Göttingen, Göttingen 37075, Germany.
authors: Ulrike
authors: U
authors: Ober
authors: National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
authors: Ning
authors: N
authors: Gao
authors: National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
authors: Hao
authors: H
authors: Zhang
authors: Department of Animal Sciences, Animal Breeding and Genetics Group, Georg-August-Universität Göttingen, Göttingen 37075, Germany hsimian@gwdg.de jqli@scau.edu.cn.
authors: Henner
authors: H
authors: Simianer
authors: National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China hsimian@gwdg.de jqli@scau.edu.cn.
authors: Jiaqi
authors: J
authors: Li
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001947:Breeding; D002417:Cattle; D030541:Databases, Genetic; D014644:Genetic Variation; D016678:Genome; D055106:Genome-Wide Association Study; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci
keywords: BLUP|GA; GenPred; genetic architecture; shared data resource; trait specific relationship matrix; whole-genome prediction