Metabolite Genome-Wide Association Study (mGWAS) and Gene-Metabolite Interaction Network Analysis Reveal Potential Biomarkers for Feed Efficiency in Pigs

Abstract

Metabolites represent the ultimate response of biological systems, so metabolomics is considered the link between genotypes and phenotypes. Feed efficiency is one of the most important phenotypes in sustainable pig production and is the main breeding goal trait. We utilized metabolic and genomic datasets from a total of 108 pigs from our own previously published studies that involved 59 Duroc and 49 Landrace pigs with data on feed efficiency (residual feed intake (RFI)), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from LC-MS system). Utilizing these datasets, our main aim was to identify genetic variants (single-nucleotide polymorphisms (SNPs)) that affect 45 different metabolite concentrations in plasma collected at the start and end of the performance testing of pigs categorized as high or low in their feed efficiency (based on RFI values). Genome-wide significant genetic variants could be then used as potential genetic or biomarkers in breeding programs for feed efficiency. The other objective was to reveal the biochemical mechanisms underlying genetic variation for pigs’ feed efficiency. In order to achieve these objectives, we firstly conducted a metabolite genome-wide association study (mGWAS) based on mixed linear models and found 152 genome-wide significant SNPs (p-value < 1.06 × 10−6) in association with 17 metabolites that included 90 significant SNPs annotated to 52 genes. On chromosome one alone, 51 significant SNPs associated with isovalerylcarnitine and propionylcarnitine were found to be in strong linkage disequilibrium (LD). SNPs in strong LD annotated to FBXL4, and CCNC consisted of two haplotype blocks where three SNPs (ALGA0004000, ALGA0004041, and ALGA0004042) were in the intron regions of FBXL4 and CCNC. The interaction network revealed that CCNC and FBXL4 were linked by the hub gene N6AMT1 that was associated with isovalerylcarnitine and propionylcarnitine. Moreover, three metabolites (i.e., isovalerylcarnitine, propionylcarnitine, and pyruvic acid) were clustered in one group based on the low-high RFI pigs. This study performed a comprehensive metabolite-based genome-wide association study (GWAS) analysis for pigs with differences in feed efficiency and provided significant metabolites for which there is significant genetic variation as well as biological interaction networks. The identified metabolite genetic variants, genes, and networks in high versus low feed efficient pigs could be considered as potential genetic or biomarkers for feed efficiency.

Effect of host breeds on gut microbiome and serum metabolome in meat rabbits

“…For example, Liao et al found that significant differences in levels of glutamine, cysteine, and glutamic acid among different beef cattle breeds led to alterations in the metabolism of cysteine, methionine, and glutamate, which are related to heat stress adaptability [8]. Wang et al indicated that plasma alanine and proline were potential biomarkers for feed efficiency in Duroc and Landrace pigs due to their important roles in the metabolism of alanine, arginine, and proline [51]. Other studies have demonstrated that serum concentrations of glycine, histidine, lysine, and serine were associated with diverging RFI of different broiler chicken lines which may be attributed to their contributions to protein biosynthesis and ammonia recycling [52,53].…”

Applications of Omics Technology for Livestock Selection and Improvement

“…The latter is further substantiated by the fact that metabolites represent cells' ultimate physiological response and thereby represent a link between genotype and phenotype (Wang and Kadarmideen 2020). GWAS-based studies, using SNP chips and LC-MS metabolomics, the identified mechanism underlying the genetic variation in pigs for feed efficiency (Banerjee et al, 2020;Wang and Kadarmideen 2020). Integrating high-density SNP data and metabolite information with predictive value was also found to help improve the accuracy of genetic selection in cattle (Ehret et al, 2015).…”

“…Given that genomic prediction to predict breeding values based on phenotypic, pedigree, and genomic data is insufficient to describe the genetic potential of animals, incorporating the whole-metabolomic data in the genomic prediction equation may play a crucial role in increasing the genetic gain by increasing the accuracy of selection. The latter is further substantiated by the fact that metabolites represent cells' ultimate physiological response and thereby represent a link between genotype and phenotype (Wang and Kadarmideen 2020). GWAS-based studies, using SNP chips and LC-MS metabolomics, the identified mechanism underlying the genetic variation in pigs for feed efficiency (Banerjee et al, 2020;Wang and Kadarmideen 2020).…”

Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency

“…However, identifying biological pathways affecting FE is challenging, and GWAS studies are limited in finding the chromosomal regions or preselected genes that might be related to FE ( Singer, 2009 ). Instead, a growing number of studies have applied omics methods to explore the mechanisms affecting FE in pigs, including transcriptomics ( Vigors et al, 2019 ; Xu et al, 2020a ), 16S rRNA gene sequencing ( Quan et al, 2018 ; Si et al, 2020 ), proteomics ( Wu et al, 2020 ), and metabolomics ( Carmelo et al, 2020 ; Wang and Kadarmideen, 2020 ). By screening and analyzing the differentially expressed genes (DEGs) and related biological pathways derived from transcriptomics studies, candidate genes and pathways affecting FE can be identified ( Nagalakshmi et al, 2008 ; Wilhelm and Landry, 2009 ).…”