Abstract
Utilizing the whole genomic variation of complex traits to predict the yet-to-be observed phenotypes or unobserved genetic values via whole genome prediction (WGP) and to infer the underlying genetic architecture via genome wide association study (GWAS) is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding. Though thousands of significant loci for several species were detected via GWAS in the past decade, they were not used directly to improve WGP due to lack of proper models. Here, we propose a generalized way of building trait-specific genomic relationship matrices which can exploit GWAS results in WGP via a best linear unbiased prediction (BLUP) model for which we suggest the name BLUP|GA. Results from two illustrative examples show that using already existing GWAS results from public databases in BLUP|GA improved the accuracy of WGP for two out of the three model traits in a dairy cattle data set, and for nine out of the 11 traits in a rice diversity data set, compared to the reference methods GBLUP and BayesB. While BLUP|GA outperforms BayesB, its required computing time is comparable to GBLUP. Further simulation results suggest that accounting for publicly available GWAS results is potentially more useful for WGP utilizing smaller data sets and/or traits of low heritability, depending on the genetic architecture of the trait under consideration. To our knowledge, this is the first study incorporating public GWAS results formally into the standard GBLUP model and we think that the BLUP|GA approach deserves further investigations in animal breeding, plant breeding as well as human genetics.
Highlights
Predicting the yet-to-be observed phenotypes or unobserved genetic values for complex traits and inferring the underlying genetic architecture utilizing genomic data is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding [1,2,3]
Predictive ability of the whole genome prediction (WGP) was measured via different crossvalidation procedures, applying the best linear unbiased prediction (BLUP)|GA approach with genetic covariance structure given by the trait-specific variancecovariance matrix T as proposed in equation (3)
The weights h in T were chosen based on counts of how often a marker was reported to be within a significant quantitative trait locus (QTL) region during association studies previously carried out in the literature, a knowledge we will retrieve from publicly available QTL databases
Summary
Predicting the yet-to-be observed phenotypes or unobserved genetic values for complex traits and inferring the underlying genetic architecture utilizing genomic data is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding [1,2,3] In this context, two predominant approaches were proposed: (i) whole genome prediction (WGP) [2,4] and (ii) genome wide association studies (GWAS) [5,6] or quantitative trait locus (QTL) mapping studies[7,8,9]. A prominent example is human height, for which tens of loci explain only ,5% of the genetic variance [25], a phenomenon called ‘‘missing heritability’’ in the literature [26,27]
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