**1. Introduction**

Large populations are generally essential for genome-wide association study (GWAS) to obtain sufficient statistical power for the identification of genetic polymorphisms [1]. However, some intermediate phenotypes like metabolites could potentially avoid this problem, as they are directly involved in metabolite conversion modification [2,3]. 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]. Additionally, metabolites could provide details of physiological state, so genetic variant-associated metabolites are expected to display larger e ffect sizes [9]. Gieger et al. (2008) firstly used metabolite concentrations as quantitative traits in association with genotypes and found their available applications in GWAS [9]. 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 e fficiency 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 e fficiency [8].

In this study, we aimed to identify genetic variants (SNP markers) a ffecting concentrations of metabolites and to reveal the biochemical mechanisms underlying genetic variation for pigs' feed efficiency. Our study is based on two of our previously published papers and datasets used therein [6,11]. Briefly, the experiment consisted of 59 Duroc and 49 Landrace pigs with data on feed e fficiency (RFI), genotype (PorcineSNP80 BeadChip) data, and metabolomic data (45 final metabolite datasets derived from liquid chromatography-mass spectrometry (LC-MS) system). While our previous studies only looked at metabolome-phenotype associations [6], we report an integrated systems genomics approach to identify quantitative trait loci (QTLs) or SNPs a ffecting metabolite concentration via metabolite GWAS methods (mGWAS), where each metabolite is itself a phenotype. To the best of our knowledge, this is the first study to link the genomics with metabolomics to identify significant genetic variants associated with known metabolites that di ffer in pigs with di fferent levels of feed e fficiency. Main aims of our study are as follows:

