Integrating genome-wide co-association and gene expression to identify putative regulators and predictors of feed efficiency in pigs

Abstract

Background Feed efficiency (FE) has a major impact on the economic sustainability of pig production. We used a systems-based approach that integrates single nucleotide polymorphism (SNP) co-association and gene-expression data to identify candidate genes, biological pathways, and potential predictors of FE in a Duroc pig population. Results We applied an association weight matrix (AWM) approach to analyse the results from genome-wide association studies (GWAS) for nine FE associated and production traits using 31K SNPs by defining residual feed intake (RFI) as the target phenotype. The resulting co-association network was formed by 829 SNPs. Additive effects of this SNP panel explained 61% of the phenotypic variance of RFI, and the resulting phenotype prediction accuracy estimated by cross-validation was 0.65 (vs. 0.20 using pedigree-based best linear unbiased prediction and 0.12 using the 31K SNPs). Sixty-eight transcription factor (TF) genes were identified in the co-association network; based on the lossless approach, the putative main regulators were COPS5 , GTF2H5 , RUNX1 , HDAC4 , ESR1 , USP16 , SMARCA2 and GTF2F2 . Furthermore, gene expression data of the gluteus medius muscle was explored through differential expression and multivariate analyses. A list of candidate genes showing functional and/or structural associations with FE was elaborated based on results from both AWM and gene expression analyses, and included the aforementioned TF genes and other ones that have key roles in metabolism, e.g. ESRRG , RXRG , PPARGC1A , TCF7L2 , LHX4 , MAML2 , NFATC3 , NFKBIZ , TCEA1 , CDCA7L , LZTFL1 or CBFB . The most enriched biological pathways in this list were associated with behaviour, immunity, nervous system, and neurotransmitters, including melatonin, glutamate receptor, and gustation pathways. Finally, an expression GWAS allowed identifying 269 SNPs associated with the candidate genes’ expression (eSNPs). Addition of these eSNPs to the AWM panel of 829 SNPs did not improve the accuracy of genomic predictions. Conclusions Candidate genes that have a direct or indirect effect on FE-related traits belong to various biological processes that are mainly related to immunity, behaviour, energy metabolism, and the nervous system. The pituitary gland, hypothalamus and thyroid axis, and estrogen signalling play fundamental roles in the regulation of FE in pigs. The 829 selected SNPs exp...

Gene networks for three feed efficiency criteria reveal shared and specific biological processes

“…The AWM method was proposed as an alternative approach to retain more variants than the P-value approach while minimising the number of false positive variants by in-silico validation [ 21 ]. Only a few studies [ 37 , 38 ] have used this approach for feed efficiency traits. The AWM networks highlighted more genes associated with RFI than with FE and RG.…”

“…The genomic correlation between RFI and ADG, although quite low (ranging from − 0.29 to − 0.11), was surprising because, by definition, these two traits are phenotypically independent and were genetically independent (− 0.04). In a study on pigs, a very low genomic correlation (0.011) was found between RFI and ADG [ 38 ]. Interestingly, the strongest genomic correlations found in this study were between RFI and FE.…”

“…Interestingly, the strongest genomic correlations found in this study were between RFI and FE. In the literature, genomic correlations of 0.63 and 0.763 were estimated between RFI and FCR using a bovine 50 K SNP array [ 37 ] and in pig [ 38 ], respectively.…”

“…2 ). Several other studies also highlighted cardio-vascular system processes associated with feed efficiency [ 37 , 38 , 55 ].…”

An integrative systems biology approach to identify the molecular basis of sperm quality in swine

“…Obviously, transcriptomics data can contribute additional valuable information in the description of these genes and pathways. The integration of both sources of information can also be used to improve the accuracy of genomic predictions (Ramayo-Caldas et al, 2019 (Figure 2.B; Additional file 6). CARF acts as a transcriptional activator promoted by calcium influx (Tao et al, 2002).…”