Improving the Accuracy of Whole Genome Prediction for Complex Traits Using the Results of Genome Wide Association Studies

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.

Measuring missing heritability: Inferring the contribution of common variants

“…Several recent papers (13,21,30,(32)(33)(34)(35) focused on improving the estimation of the genetic correlation matrices, by accounting for sparsity (33,34), LD and LD-dependent genetic architecture (30), prior information regarding effect sizes of different SNPs (35), or the relationship between minor allele frequency and effect size (30). As better methods for estimating the genetic correlation matrix are developed, they may be used in PCGC regression in place of the correction method of Yang et al (9).…”

Genomic Prediction Models for Count Data

“…Predicting yet-to-be observed phenotypes or unobserved genetic values for complex traits and inferring the underlying genetic architecture utilizing genomic data are interesting and fast developing areas in the context of plant and animal breeding, and even in human diseases (Goddard and Hayes 2009;de los Campos et al 2010de los Campos et al , 2013aRiedelsheimer et al 2012;Zhang et al 2014). Rapid genetic progress requires that such predictions are accurate and can be produced early in life.…”

“…Real data analysis and simulation studies promote the use of this methodology for increasing genetic progress in less time. For continuous phenotypes, models have been developed to regress phenotypes on all available markers using a linear model (Zhang et al 2014;de los Campos et al 2013b). However, in plant breeding, the response variable in many traits is a count (y = 0, 1, 2, .…”

Identification of molecular markers linked to Fusarium ear rot genes in maize plantsZea maysL

“…[35] A further recent study by Zhang et al [36] proposed a method for increasing genome-based prediction accuracy by systematically exploiting the wealth of published QTL information through integration into the genomic relationship matrix.…”