Detailed phenotyping identifies genes with pleiotropic effects on body composition

Abstract

Background: Genetic variation in both the composition and distribution of fat and muscle in the body is important to human health as well as the healthiness and value of meat from cattle and sheep. Here we use detailed phenotyping and a multi-trait approach to identify genes explaining variation in body composition traits.

Results: A multi-trait genome wide association analysis of 56 carcass composition traits measured on 10,613 sheep with imputed and real genotypes on 510,174 SNPs was performed. We clustered 71 significant SNPs into five groups based on their pleiotropic effects across the 56 traits. Among these 71 significant SNPs, one group of 11 SNPs affected the fatty acid profile of the muscle and were close to 8 genes involved in fatty acid or triglyceride synthesis. Another group of 23 SNPs had an effect on mature size, based on their pattern of effects across traits, but the genes near this group of SNPs did not share any obvious function. Many of the likely candidate genes near SNPs with significant pleiotropic effects on the 56 traits are involved in intra-cellular signalling pathways. Among the significant SNPs were some with a convincing candidate gene due to the function of the gene (e.g. glycogen synthase affecting glycogen concentration) or because the same gene was associated with similar traits in other species.

Conclusions: Using a multi-trait analysis increased the power to detect associations between SNP and body composition traits compared with the single trait analyses. Detailed phenotypic information helped to identify a convincing candidate in some cases as did information from other species.

Keywords: Body composition; GWAS; Genes; Human; Meta-analysis; Multi-trait; Pleiotropy; Sheep.

Fig. 1

Pie chart showing percentages of total of 10,613 animals in each of sheep populations

Fig. 2

Quantile-quantile plot of P-values from single SNP genome wide association study of HGRFAT (darkorange), CEMA (red), SHEARF1 (skyblue), and FA_C22_5n3 (magenta), and from multi-trait analysis (olivegreen1). Observed and expected P-values would fall on the light blue line if there was no association. The top horizontal line is P < 0.0001, middle horizontal line is P < 0.001, and the bottom horizontal line is P < 0.05

Fig. 3

The Manhattan plot showing the –log₁₀ (P-values) of SNPs of the multi-trait test of the whole genome (except the X chromosome) before (a) and after (b) fitting 23 lead SNPs in the model

Fig. 4

The –log₁₀ (P-values) of single SNP regressions for 6 traits and multi-trait chi-squared statistic on a region of OAR 11

Fig. 5

Correlation matrix between the 23 lead SNPs calculated from SNP effects on 56 traits

Fig. 6

Dendrogram drawn based on correlation matrix between the effects of the lead SNPs and their linear index SNPs within each group: a Group 1 SNPs (chromosome and position in base pair) along with their annotated gene names; b Group 2 SNPs; and c Group 3 SNPs. Lead SNPs within each group are highlighted with blue stars; Genes (in brackets) are the alternative most likely putative candidates within the regions

Fig. 7

The –log₁₀ (P-values) of SNP effects from the multi-trait test results for OAR18_64.5 Mb, where not all genes in this region are shown: The lead SNP is shown by a purple diamond in each plot (labelled with chromosome and position, Mb) and the LD between this variant and all others is colour coded