Genome-wide association studies and genetic architecture of carcass traits in Angus beef cattle using imputed whole-genome sequences data - Hasan Baneh; Nikolay Elatkin; Laurent Gentzbittel (Genetics Selection Evolution)
Abstract Background Carcass related traits are economically important traits for the beef industry, which affect quantity, quality and pricing of meat and farmers profitability. The current study was carried out to identify genomic regions associated with carcass traits including carcass weight (CW), marbling score (MS), rib-eye area (REA), and back fat thickness (BFT). Genome-wide association studies (GWAS) were performed using linear mixed models on 6,511,978 imputed whole genome sequence (WGS) variants in a population of 13,241 Angus beef cattle. The genetic architecture of the traits was evaluated based on the GWAS results. Results With a threshold of p-value < 3.96 × 10–7, 842, 745, 340, and 101 SNPs located in 13 genomic regions were significantly associated with CW, MS, REA, and BFT, respectively. While the majority of the identified quantitative trait loci (QTL) were trait-specific, two QTLs with pleiotropic effect were identified, including a QTL on BTA7 (88.25–91.96 Mb) affecting CW, MS and REA, and a QTL on BTA20 (4.55–5.01 Mb) affecting CW and BFT. Several important genes are harbored by the detected QTLs, which can be considered potential candidate genes for carcass traits in Angus beef cattle. Our findings also showed that higher density panels are more powerful in GWAS, such that the signals on BTA6 affecting CW, and two signals on BTA17 and BTA18 affecting MS were not detectable using medium SNP array genotypes. The allele substitution effects and additive genetic variances of the imputed variants followed a bell-shaped and a scaled inverse chi-squared distribution, respectively. Among functional categories, missense variants had the highest allele substitution effects for CW, MS and BFT, while 3′ UTR variants had higher effects for REA, compared to other functional classes. Conclusions Our findings highlight the power of using imputation to perform GWAS and provide some valuable information for a better understanding of the underlying genetic background and architecture of carcass traits in beef cattle.