Genome-wide association and systems genetic analyses of residual feed intake, daily feed consumption, backfat and weight gain in pigs

Abstract

BackgroundFeed efficiency is one of the major components determining costs of animal production. Residual feed intake (RFI) is defined as the difference between the observed and the expected feed intake given a certain production. Residual feed intake 1 (RFI1) was calculated based on regression of individual daily feed intake (DFI) on initial test weight and average daily gain. Residual feed intake 2 (RFI2) was as RFI1 except it was also regressed with respect to backfat (BF). It has been shown to be a sensitive and accurate measure for feed efficiency in livestock but knowledge of the genomic regions and mechanisms affecting RFI in pigs is lacking. The study aimed to identify genetic markers and candidate genes for RFI and its component traits as well as pathways associated with RFI in Danish Duroc boars by genome-wide associations and systems genetic analyses.ResultsPhenotypic and genotypic records (using the Illumina Porcine SNP60 BeadChip) were available on 1,272 boars. Fifteen and 12 loci were significantly associated (p < 1.52 × 10-6) with RFI1 and RFI2, respectively. Among them, 10 SNPs were significantly associated with both RFI1 and RFI2 implying the existence of common mechanisms controlling the two RFI measures. Significant QTL regions for component traits of RFI (DFI and BF) were detected on pig chromosome (SSC) 1 (for DFI) and 2 for (BF). The SNPs within MAP3K5 and PEX7 on SSC 1, ENSSSCG00000022338 on SSC 9, and DSCAM on SSC 13 might be interesting markers for both RFI measures. Functional annotation of genes in 0.5 Mb size flanking significant SNPs indicated regulation of protein and lipid metabolic process, gap junction, inositol phosphate metabolism and insulin signaling pathway are significant biological processes and pathways for RFI, respectively.ConclusionsThe study detected novel genetic variants and QTLs on SSC 1, 8, 9, 13 and 18 for RFI and indicated significant biological processes and metabolic pathways involved in RFI. The study also detected novel QTLs for component traits of RFI. These results improve our knowledge of the genetic architecture and potential biological pathways underlying RFI; which would be useful for further investigations of key candidate genes for RFI and for development of biomarkers.

Pigs that are divergent in feed efficiency, differ in intestinal enzyme and nutrient transporter gene expression, nutrient digestibility and microbial activity

“…In contrast to previous RFI studies (Young et al, 2011;Mani et al, 2013;Montagne et al, 2014) backfat was not included in the model in this study. However, Do et al (2014) established a correlation coefficient of 0.96 between models with or without backfat, suggesting that the non-inclusion of backfat in this study will not unduly influence the results.…”

Systems genetics and genome-wide association approaches for analysis of feed intake, feed efficiency, and performance in beef cattle

“…A recent study on LD structure by Villa-Angulo et al (2009) revealed that there were regions of high LD extending up to 100 kb and the size of haplotype blocks ranged between 30 bases and 75 kb (the average being 10.3 kb). Previous studies also suggested that a similar distance could be used in the SG approach to capture the causal genes/SNPs affecting a quantitative trait like feed efficiency (Do et al, 2014). The over-representation analyses identified five KEGG pathways associated with feed efficiency-related traits (Table 3).…”

Genome-wide association studies for small intestine length in an F2population of chickens

“…Compared with traditional QTL mapping strategies, GWAS confers major advantages in the power to detect variants and is a powerful tool to analyse the genetic architecture of important animal characteristics (Hirschhorn and Daly 2005;Kronenberg 2008). So far, many QTL, genomic regions, and SNPs have been identified in various farm animals, such as cattle (Bolormaa et al 2010;Sherman et al 2010), pigs (Do et al 2014;Zhang et al 2016) and poultry Yuan et al 2015). So, it is necessary to use GWAS to study the small intestine length in chickens.…”