Fine mapping by composite genome-wide association analysis

Abstract

Genome-wide association (GWA) studies play a key role in current genetics research, unravelling genomic regions linked to phenotypic traits of interest in multiple species. Nevertheless, the extent of linkage disequilibrium (LD) may provide confounding results when significant genetic markers span along several contiguous cM. In this study, we have adapted the composite interval mapping approach to the GWA framework (composite GWA), in order to evaluate the impact of including competing (possibly linked) genetic markers when testing for the additive allelic effect inherent to a given genetic marker. We tested model performance on simulated data sets under different scenarios (i.e., qualitative trait loci effects, LD between genetic markers and width of the genomic region involved in the analysis). Our results showed that the genomic region had a small impact on the number of competing single nucleotide polymorphisms (SNPs) as well as on the precision of the composite GWA analysis. A similar conclusion was derived from the preferable range of LD between the tested SNP and competing SNPs, although moderate-to-high LD seemed to attenuate the loss of statistical power. The composite GWA improved specificity and reduced the number of significant genetic markers. The composite GWA model contributes a novel point of view for GWA analyses where testing circumscribed to the genomic region flanking each SNP (delimited by the nearest competing SNPs) and conditioning on linked markers increases the precision to locate causal mutations, but possibly at the expense of power.

Fig. 1.

Average number of competing SNPs included in the composite genome-wide association studies analysis; the whiskers extend the range of the results. Columns are organized in three independent groups depending on the linkage disequilibrium (r²) between competing SNPs and the QTL; within-group colour differences identify the size of the genomic region where competing SNPs were assessed, this being 10 cM (white), 30 cM (light grey) and 50 cM (dark grey) on each side of the tested SNP.

Fig. 2.

Average number of significant (p < 0·0005) SNPs under standard genome-wide association studies (GWAS) analysis and composite GWAS (GWASc) for small-effect QTLs (a), medium-effect QTLs (b) and large-effect QTLs (c); the whiskers extend the range of the results. Columns are organized in four independent groups depending on the analytical approach (GWAS vs. GWASc) and the linkage disequilibrium (r²) between competing SNPs and the QTL for GWASc analyses; within-group colour differences identify the size of the genomic region where competing SNPs were assessed, this being 10 cM (white), 30 cM (light grey) and 50 cM (dark grey) on each side of the tested SNP. The striped bar corresponds to the standard GWAS approach.

Fig. 3.

Average absolute distance between significant (p < 0·0005) SNPs and the QTL under standard genome-wide association studies (GWAS) analysis and composite GWAS (GWASc) for small-effect QTLs (a), medium-effect QTLs (b) and large-effect QTLs (c); the whiskers extend the range of the results. Columns are organized in four independent groups depending on the analytical approach (GWAS vs. GWASc) and the linkage disequilibrium (r²) between competing SNPs and the QTL for GWASc analyses; within-group colour differences identify the size of the genomic region where competing SNPs were assessed, this being 10 cM (white), 30 cM (light grey) and 50 cM (dark grey) on each side of the tested SNP. The striped bar corresponds to the standard GWAS approach.

Fig. 4.

Average percentage of significant (p < 0·0005) SNPs located not farther than 2·5 cM from the QTL under standard genome-wide association studies (GWAS) analysis and composite GWAS (GWASc) for large-effect QTLs; the whiskers extend the range of the results. Columns are organized in four independent groups depending on the analytical approach (GWAS vs. GWASc) and the linkage disequilibrium (r²) between competing SNP and the QTL for GWASc analyses; within-group colour differences identify the size of the genomic region where competing SNP were assessed, this being 10 cM (white), 30 cM (light grey) and 50 cM (dark grey) on each side of the tested SNP. The striped bar corresponds to the standard GWAS approach.

Fig. 5.

Representative examples of Manhattan plots from the standard genome-wide association analysis (upper panel) and the composite genome-wide association analysis (lower panel) for populations with small- (a), medium- (b) and large-effect QTLs (c). Competing SNPs for composite genome-wide association analyses were assessed in the whole chromosome and linkage disequilibrium (r²) with the tested SNP was restricted to 0·1 ⩽ r² ⩽ 0·9.