Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens

Abstract

The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.

Meta‐analysis of genome‐wide association from genomic prediction models

“…In this paper, we show that for GBLUP‐based SNP tests, z‐ scores should be combined based on specific weights, and we also show the correct way to compute the optimal weights assuming a fixed SNP test derived from recently published work on transforming animal evaluations into SNP effects (Wang et al . 2012, 2014; Gualdrón Duarte et al . 2014).…”

“…A common practice after estimation of GEBV from a genomic evaluation consists of performing genome‐wide association (GWA) analyses (Wang et al . 2012, 2014; Gualdrón Duarte et al . 2014).…”

“…2010), which has been recently adapted to perform GWA (Wang et al . 2012, 2014; Gualdrón Duarte et al . 2014).…”

Genome-Wide Association Study for Identifying Loci that Affect Fillet Yield, Carcass, and Body Weight Traits in Rainbow Trout (Oncorhynchus mykiss)

“…This method combines phenotype, genotype, and pedigree data in a joint analysis and is implemented in the BLUPF90 software (Misztal et al, 2002; Aguilar et al, 2010; Wang et al, 2012). The wssGWAS avoids spurious solutions of SNPs, uses phenotypes from non-genotyped individuals included in the pedigree, and allows multi-trait analyses (Fragomeni et al, 2014; Wang et al, 2014). However, multi-trait analysis was not performed in this study due to convergence issues associated with the balance of the information.…”

“…The ssGWAS2 scenario described by Wang et al (2014) was used to estimate genomic breeding values and iteratively estimate and weight SNP effects. Windows of 20 adjacent SNPs based on the new genetic linkage map were created using POSTGSF90 (Aguilar et al, 2014).…”