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Abstract Background The composition and distribution of fatty acids (FA) are important factors determining the quality, flavor, and nutrient value of meat. In addition, FAs synthesized in the body participate in energy metabolism and are involved in different regulatory pathways in the form of signaling molecules or by acting as agonist or antagonist ligands of different nuclear receptors. Finally, synthesis and catabolism of FAs affect adaptive immunity by regulating lymphocyte metabolism. The present study performed genome-wide association studies using FA profiles of blood, liver, backfat and muscle from 432 commercial Duroc pigs. Results Twenty-five genomic regions located on 15 Sus scrofa chromosomes (SSC) were detected. Annotation of the quantitative trait locus (QTL) regions identified 49 lipid metabolism-related candidate genes. Among these QTLs, four were identified in more than one tissue. The ratio of C20:4n-6/C20:3n-6 was associated with the region on SSC2 at 7.56–14.26 Mb for backfat, liver, and muscle. Members of the fatty acid desaturase gene cluster (FADS1, FADS2, and FADS3) are the most promising candidate genes in this region. Two QTL regions on SSC14 (103.81–115.64 Mb and 100.91–128.14 Mb) were identified for FA desaturation in backfat and muscle. In addition, two separate regions on SSC9 at 0 – 14.55 Mb and on SSC12 at 0–1.91 Mb were both associated with the same multiple FA traits for backfat, with candidate genes involved in de novo FA synthesis and triacylglycerol (TAG) metabolism, such as DGAT2 and FASN. The ratio C20:0/C18:0 was associated with the region on SSC5 at 64.84–78.32 Mb for backfat. Furthermore, the association of the C16:0 content with the region at 118.92–123.95 Mb on SSC4 was blood specific. Finally, candidate genes involved in de novo lipogenesis regulate T cell differentiation and promote the generation of palmitoleate, an adipokine that alleviates inflammation. Conclusions Several SNPs and candidate genes were associated with lipid metabolism in blood, liver, backfat, and muscle. These results contribute to elucidating the molecular mechanisms implicated in the determination of the FA profile in different pig tissues and can be useful in selection programs that aim to improve health and energy metabolism in pigs.

Abstract Background To date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. Results from different genome-wide association studies and gene expression analyses are not always consistent. The aim of this research was to use machine learning to identify genes associated with feed efficiency (FE) using transcriptomic (RNA-Seq) data from pigs that are phenotypically extreme for RFI. Methods RFI was computed by considering within-sex regression on mean metabolic body weight, average daily gain, and average backfat gain. RNA-Seq analyses were performed on liver and duodenum tissue from 32 high and 33 low RFI pigs collected at 153 d of age. Machine-learning algorithms were used to predict RFI class based on gene expression levels in liver and duodenum after adjusting for batch effects. Genes were ranked according to their contribution to the classification using the permutation accuracy importance score in an unbiased random forest (RF) algorithm based on conditional inference. Support vector machine, RF, elastic net (ENET) and nearest shrunken centroid algorithms were tested using different subsets of the top rank genes. Nested resampling for hyperparameter tuning was implemented with tenfold cross-validation in the outer and inner loops. Results The best classification was obtained with ENET using the expression of 200 genes in liver [area under the receiver operating characteristic curve (AUROC): 0.85; accuracy: 0.78] and 100 genes in duodenum (AUROC: 0.76; accuracy: 0.69). Canonical pathways and candidate genes that were previously reported as associated with FE in several species were identified. The most remarkable pathways and genes identified were NRF2-mediated oxidative stress response and aldosterone signalling in epithelial cells, the DNAJC6, DNAJC1, MAPK8, PRKD3 genes in duodenum, and melatonin degradation II, PPARα/RXRα activation, and GPCR-mediated nutrient sensing in enteroendocrine cells and SMOX, IL4I1, PRKAR2B, CLOCK and CCK genes in liver. Conclusions ML algorithms and RNA-Seq expression data were found to provide good performance for classifying pigs into high or low RFI groups. Classification was better with gene expression data from liver than from duodenum. Genes associated with FE in liver and duodenum tissue that can be used as predictive biomarkers for this trait were identified.