Abstract
BackgroundFrench beef producers suffer from the decrease in profitability of their farms mainly because of the continuous increase in feed costs. Selection for feed efficiency in beef cattle represents a relevant solution to face this problem. However, feed efficiency is a complex trait that can be assessed by three major criteria: residual feed intake (RFI), residual gain (RG) and feed efficiency ratio (FE), which involve different genetic determinisms. An analysis that combines phenotype and whole-genome sequence data provides a unique framework for genomic studies. The aim of our study was to identify the gene networks and the biological processes that are responsible for the genetic determinism that is shared between these three feed efficiency criteria.ResultsA population of 1477 French Charolais young bulls was phenotyped for feed intake (FI), average daily gain (ADG) and final weight (FW) to estimate RFI, RG and FE. A subset of 789 young bulls was genotyped on the BovineSNP50 single nucleotide polymorphism (SNP) array and imputed at the sequence level using RUN6 of the 1000 Bull Genomes Project. We conducted a genome-wide association study (GWAS) to estimate the individual effect of 8.5 million SNPs and applied an association weight matrix (AWM) approach to analyse the results, one for each feed efficiency criterion. The results highlighted co-association networks including 626 genes for RFI, 426 for RG and 564 for FE. Enrichment assessment revealed the biological processes that show the strongest association with RFI, RG and FE, i.e. digestive tract (salivary, gastric and mucin secretion) and metabolic processes (cellular and cardiovascular). Energetic functions were more associated with RFI and FE and cardio-vascular and cellular processes with RG. Several hormones such as apelin, glucagon, insulin, aldosterone, the gonadotrophin releasing hormone and the thyroid hormone were also identified, and these should be tested in future studies as candidate biomarkers for feed efficiency.ConclusionsThe combination of network and pathway analyses at the sequence level led to the identification of both common and specific mechanisms that are involved in RFI, RG and FE, and to a better understanding of the genetic determinism underlying these three criteria. The effects of the genes involved in each of the identified processes need to be tested in genomic evaluations to confirm the potential gain in reliability of using functional variants to select animals for feed efficiency.
Highlights
French beef producers suffer from the decrease in profitability of their farms mainly because of the continuous increase in feed costs
In [7] 75 single nucleotide polymorphism (SNP) were associated with residual feed intake (RFI) with a P-value of 0.001; in [8] 31 SNPs were associated with RFI with the same P-value but none exceeded the false discovery rate (FDR) threshold; in [10] only two SNPs were detected that exceeded the Bonferroni threshold; and in [15] only three quantitative trait loci (QTL) for RFI were reported when using a Bonferroni threshold for high-density SNPs, of which only one reached the
Several studies [35, 55, 64, 65] have revealed mitogen-activated protein kinase (MAP-K) processes related to feed efficiency traits, which suggest the important role of these processes in these phenotypes. Our results revealed both common and specific biological processes associated to RFI, residual gain (RG) and feed efficiency ratio (FE) and allow a better understanding of the genetic determinism of these feed efficiency criteria
Summary
French beef producers suffer from the decrease in profitability of their farms mainly because of the continuous increase in feed costs. For growing animals, RFI is calculated as the residual of the regression of intake on metabolic body weight and daily gain This criterion is not correlated with its components, and animals that have negative RFI values, i.e. that eat less than expected, are considered as efficient. Another feed efficiency criterion that has been suggested to identify differences in feed use among growing animals is residual gain (RG) [5], which is the difference between observed and predicted ADG and is calculated as the residual from a multiple regression of ADG on metabolic body weight and feed intake.
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