Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits

Abstract

BackgroundDense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants.ResultsWe introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits.ConclusionsBayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2443-6) contains supplementary material, which is available to authorized users.

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

“…This provides objective evidence for the probability that coding sites are more likely to alter phenotype than non-coding sites. The Bayes RC approach can improve both the precision of QTL mapping and accuracy of genomic prediction provided there is good prior biological information available [20]. Figure 3 compares the results from Bayes RC, Bayes R and GWAS analyses of bovine milk protein percentage (data described in [20]) in the region of the LALBA gene.…”

“…The Bayes RC approach can improve both the precision of QTL mapping and accuracy of genomic prediction provided there is good prior biological information available [20]. Figure 3 compares the results from Bayes RC, Bayes R and GWAS analyses of bovine milk protein percentage (data described in [20]) in the region of the LALBA gene. LALBA is a candidate gene because it codes for alpha-lactalbumin, a key regulator of lactose synthesis [24].…”

“…By contrast, the Bayes R model, in which many SNPs have no effect, gives a large effect to SNPs that best track the QTL. Figure 1 compares partial results of a genome-wide analysis of fat percentage in bovine milk in a region around the FASN (fatty acid synthase) gene (data described in [20]), using either Bayes R or the common GWAS method (SNP fitted singly as a fixed effect using the linear mixed model).…”

“…LALBA is a candidate gene because it codes for alpha-lactalbumin, a key regulator of lactose synthesis [24]. In this Bayes RC analysis (figure 3a), prior knowledge of candidate genes for milk production and variant annotation was used to allocate variants to categories (details in [20]). The genotype data included approximately 1 million genome-wide sequence coding variants and SNP from a high-density array.…”

“…Typically, in the analysis of GWAS, this information is used after the statistical analysis and in a subjective manner. We have modified Bayes R to include a priori information about the polymorphic sites (Bayes RC [20]). The sites are placed in categories and the mixing proportions for each category are estimated in the analysis.…”