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

Miriam Piles,Carlos Fernandez-Lozano,Maria Ballester,Juan Pablo Sánchez,María Velasco-Galilea,David Torrallardona,Raquel Quintanilla,Olga González-Rodríguez

doi:10.1186/s12711-019-0453-y

Abstract

BackgroundTo 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.MethodsRFI 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.ResultsThe 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.ConclusionsML 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.

Highlights

To date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown
Since the feeding strategy had no significant effect on RFI, it was not included in the final model of analysis
Differences, in absolute value, between low and high RFI groups were equal to 0.56 ± 0.04 kg in females and 0.31 ± 0.04 kg (1.9 standard deviation units) in males

Summary

Introduction

The molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. 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. Several genomewide association studies [7] and gene expression analyses based on microarrays [8,9,10] or RNA sequencing (RNASeq) [11] have been performed, in an attempt to unravel the genetic architecture of this complex trait. Results from such studies are not always consistent. ML algorithms have been used for the analysis of high-throughput deep sequencing data due to their computational efficiency in finding generalizable patterns from high-dimensional data obtained from a small number of samples

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