**5. Conclusions**

We utilized metabolic and genomic datasets from a total of 108 pigs that were made available for this study from our own previously published studies [6,11] in publicly available data repositories. 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). Utilizing these datasets, our main aim was to identify genetic variants (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, as measured by RFI values. Genome-wide significant genetic variants could be then used as potential genetic or biomarkers in breeding programs for feed efficiency. In order to achieve this main objective, we performed GWAS in the mixed linear model-based association analysis and found 152 genome-wide significant snps (*p*-value < 1.06 × <sup>10</sup>−6) in association with 17 metabolites that included 90 significant SNPs annotated to 52 genes. On chromosome one alone, we found SNPs in strong LD that could be annotated to *FBXL4* and *CCNC*; it consisted of two haplotype blocks, where three SNPs (ALGA0004000, ALGA0004041, and ALGA0004042) were in the intron regions of *FBXL4* and *CCNC*. The interaction network analyses revealed that *CCNC* and *FBXL4* were linked to each other by *N6AMT1* gene and were associated with compounds isovalerylcarnitine and propionylcarnitine. The identified genetic variants and genes affecting important metabolites in high versus low feed efficient pigs could be considered as potential genetic or biomarkers, but we recommend that these results are validated in much higher sample size.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2218-1989/10/5/201/s1, Supplementary Table S1. All the significant SNPs of genome-wide association with chromosome, position, and p-value information for metabolites from first, second, and combined two sampling times. Supplementary Table S2. All the metabolites in association with significant SNPs from first, second, and combined two sampling times. Supplementary Table S3. Top ten SNPs associated with residual feed intake (RFI). Supplementary Figure S1. Manhattan plots of genome-wide association for 43 metabolites. Supplementary Figure S2. Linkage disequilibrium (LD) pattern for all significant SNPs.

**Author Contributions:** H.N.K. was a gran<sup>t</sup> holder and lead PI for the FeedOMICS project who conceived and designed the experiments and made the related datasets available in public repositories. X.W. analyzed the data under the supervision of H.N.K., X.W. and H.N.K. wrote the first draft of this paper, which was improved by H.N.K. All authors have read and agree to the published version of the manuscript.

**Funding:** Xiao Wang was funded by the FeedOMICS research project, headed by Haja Kadarmideen at DTU Denmark. FeedOMICS project was funded by the Independent Research Fund Denmark (DFF)—Technology and Production (FTP) gran<sup>t</sup> (grant number: 4184-00268B).

**Acknowledgments:** Authors thank open access platforms MetaboLights and NCBI-GEO from which we downloaded the datasets for research reported in this study and cited under the section "availability of data and materials". The authors thank Claus Thorn Ekstrøm from Faculty of Health and Medical Sciences, University of Copenhagen for his advice on statistical modelling.

**Conflicts of Interest:** The authors declare that there are no conflict of interest.

**Availability of Data and Materials:** All datasets used in this paper are from public repositories. Metabolite data were accessed using MetaboLights accession ID MTBLS1384 with a link: https://www.ebi.ac.uk/metabolights/ MTBLS1384. Genotype data were accessed from NCBI GEO accession number GSE144064 with the following link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144064). The details of these datasets can be found in Carmelo et al. (2020) [6] and Banerjee et al. (2020) [11].
