Genome-wide association study for residual concentrate intake using different approaches in Italian Brown Swiss. Issue 1 (1st January 2021) - Manca, E.; Cesarani, A.; Falchi, L.; Atzori, A. S.; Gaspa, G.; Rossoni, A.; Macciotta, N. P. P.; Dimauro, C.
Abstract: Residual feed intake (RFI) is the most used measure of feed efficiency. However, considering the importance of concentrates in the ration, a new index, the residual concentrate intake (RCI), was here defined. RCI aims to measure the individual efficiency in converting the concentrate into animal products. Brown Swiss young bulls ( N  = 736) were genotyped at 41, 183 loci. Animals were housed in pens equipped with an automatic feeding system able to recognise the animal and record the concentrate intake. The diet consisted of concentrate and hay ( ad libitum ). The new RCI index was calculated as the residuals of the linear regression of concentrate intake on metabolic live weight and average daily gain. Animals were ranked according to their corrected RCI and divided into low (LRCI) and high phenotypes (HRCI). A low heritability (0.06 ± 0.03) was estimated using only genomics for this new index. Results from multivariate (M-GWAS) and Bayesian (B-GWAS) approaches were combined to identify SNP associated with RCI. The M-GWAS selected 698 SNPs potentially associated, whereas no significant markers were obtained in B-GWAS. Markers in the last approach were ranked according to their posterior inclusion probability and the first 698 were retained. Only SNPs in common between sorted B-GWAS and M-GWAS ( N  = 11) were considered associated with RCI. A total of 48 candidate genes were retrieved near these SNPs. Most of them were previously reported to be associated with feed efficiency and RFI. The combined use of multivariate and Bayesian techniques allow to identify SNPs associated with the investigated trait. Highlights: RCI could be promising to select animals 48 candidate genes were found associated with RCI Multivariate technique allowed to identify significant SNPs