Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs
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Author
Fernandez‑Lozano, Carlos
Velasco‑Galilea, María
González‑Rodríguez, Olga
Sánchez, Juan Pablo
Ballester, Maria
Publication date
2019-03-13ISSN
0999-193X
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.
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
636 - Animal husbandry and breeding in general. Livestock rearing. Breeding of domestic animals
Pages
15
Publisher
BMC
Is part of
Genetics Selection Evolution
Citation
Piles, Miriam, Carlos Fernandez-Lozano, María Velasco-Galilea, Olga González-Rodríguez, Juan Pablo Sánchez, David Torrallardona, Maria Ballester, and Raquel Quintanilla. 2019. "Machine Learning Applied To Transcriptomic Data To Identify Genes Associated With Feed Efficiency In Pigs". Genetics Selection Evolution 51 (1). Springer Nature. doi:10.1186/s12711-019-0453-y.
Grant agreement number
EC/FP7/311794/EU/A whole-systems approach to optimising feed efficiency and reducing the ecological footprint of monogastrics/ECO-FCE
MINECO/Programa Estatal de promoción del talento y su empleabilidad en I+D+I/FJCI-2015-26071/ES/ /
MINECO/Programa Estatal de promoción del talento y su empleabilidad en I+D+I/RYC‑2013‑12573/ES/ /
Program
Genètica i Millora Animal
Nutrició Animal
This item appears in the following Collection(s)
- ARTICLES CIENTÍFICS [2811]
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/