Accuracy of Whole-Genome Prediction Using a Genetic Architecture-Enhanced Variance-Covariance Matrix
文献类型： 外文期刊
作者： Zhang, Zhe 1 ; Erbe, Malena 2 ; He, Jinlong 1 ; Ober, Ulrike 3 ; Gao, Ning 1 ; Zhang, Hao 1 ; Simianer, Henner; Li, Ji 1 ;
作者机构： 1.South China Agr Univ, Natl Engn Res Ctr Breeding Swine Ind, Guangdong Prov Key Lab Agroanim Genom & Mol Breed, Coll Anim Sci, Guangzhou 510642, Guangdong, Peoples R China
2.Univ Gottingen, Dept Anim Sci, Anim Breeding & Genet Grp, D-37075 Gottingen, Germany
3.Univ Gottingen, Dept Anim Sci, Anim Breeding & Genet Grp, D-37075 Gottingen, G
关键词： whole-genome prediction;genetic architecture;trait specific relationship matrix;BLUP vertical bar GA;GenPred;shared data resource
期刊名称：G3-GENES GENOMES GENETICS （ 影响因子：3.154； 五年影响因子：3.369 ）
ISSN： 2160-1836
年卷期： 2015 年 5 卷 4 期
页码：
收录情况： SCI
摘要： 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 vertical bar 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 vertical bar GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP vertical bar 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 vertical bar 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 vertical bar GA and GBLUP significantly correlate with the distance between the T and G matrices. The new strategy applied in BLUP vertical bar 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 vertical bar GA in WGP would ease the burden of model selection.
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