Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs

Abstract

Background: To date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. Results from diferent 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 efciency (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 efects. Genes were ranked according to their contribution to the classifcation 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 diferent 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 classifcation 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 identifed. The most remarkable pathways and genes identifed 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. Classifcation 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 identifed.