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

Xiao Wang,Haja N Kadarmideen

doi:10.3390/metabo10050201

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.

Highlights

Large populations are generally essential for genome-wide association study (GWAS) to obtain sufficient statistical power for the identification of genetic polymorphisms [1]
Values of aspartic acid, isovalerylcarnitine, propionylcarnitine, and pyruvic acid for which the propionylcarnitine from the low residual feed intake (RFI) group had higher values while other three metabolite values in associated significant single-nucleotide polymorphisms (SNPs) were in the introns of MTRF1, FBXL4/CCNC, SFXN1 (Table 3)
These studies involved 59 Duroc and 49 Landrace pigs and consisted of data on feed efficiency (RFI), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from LC-MS system)

Summary

Introduction

Large populations are generally essential for genome-wide association study (GWAS) to obtain sufficient statistical power for the identification of genetic polymorphisms [1]. As the end products of cellular regulatory processes, metabolites represent the ultimate response of biological systems associated with genetic changes, so metabolomics is considered the link between genotypes and phenotypes [4]. Metabolomics refers to the measurements of all endogenous metabolites, intermediates, and products of metabolism and has been applied to measure the dynamic metabolic responses in pigs [5,6] and dairy cows [7,8]. Do et al (2014) [10] conducted GWAS using residual feed intake (RFI) phenotypes to identify single-nucleotide polymorphisms (SNPs) that explain significant variation in feed efficiency for pigs. Our previous study found two metabolites (i.e., α-ketoglutarate and succinic acid) in a RFI-related network of dairy cows which could represent biochemical mechanisms underlying variation for phenotypes of feed efficiency [8]

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