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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

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

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. More... »

PAGES

10

References to SciGraph publications

  • 2018-12. Integrative approach using liver and duodenum RNA-Seq data identifies candidate genes and pathways associated with feed efficiency in pigs in SCIENTIFIC REPORTS
  • 2010-10. Differential expression analysis for sequence count data in GENOME BIOLOGY
  • 2017-12. A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs in BMC GENOMICS
  • 2018-12. Expression analysis of candidate genes for fatty acid composition in adipose tissue and identification of regulatory regions in SCIENTIFIC REPORTS
  • 2008-12. Conditional variable importance for random forests in BMC BIOINFORMATICS
  • 2013. Applied Predictive Modeling in NONE
  • 2015-12. Messenger RNA sequencing and pathway analysis provide novel insights into the biological basis of chickens’ feed efficiency in BMC GENOMICS
  • 1996-08. Genetically lean mice result from targeted disruption of the RIIβ subunit of protein kinase A in NATURE
  • 2001-10. Random Forests in MACHINE LEARNING
  • 1998-06. A Tutorial on Support Vector Machines for Pattern Recognition in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2015-12. Transcriptome analysis of mRNA and miRNA in skeletal muscle indicates an important network for differential Residual Feed Intake in pigs in SCIENTIFIC REPORTS
  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 2018-12. RNA-seq of muscle from pigs divergent in feed efficiency and product quality identifies differences in immune response, growth, and macronutrient and connective tissue metabolism in BMC GENOMICS
  • 2015-12. Global liver gene expression differences in Nelore steers with divergent residual feed intake phenotypes in BMC GENOMICS
  • 2010-10. Tackling the widespread and critical impact of batch effects in high-throughput data in NATURE REVIEWS GENETICS
  • 2007-12. Bias in random forest variable importance measures: Illustrations, sources and a solution in BMC BIOINFORMATICS
  • 2016-10. Expression-based GWAS identifies variants, gene interactions and key regulators affecting intramuscular fatty acid content and composition in porcine meat in SCIENTIFIC REPORTS
  • 2014-12. Sexually dimorphic characteristics of the small intestine and colon of prepubescent C57BL/6 mice in BIOLOGY OF SEX DIFFERENCES
  • 2017-12. Integration of liver gene co-expression networks and eGWAs analyses highlighted candidate regulators implicated in lipid metabolism in pigs in SCIENTIFIC REPORTS
  • 2016-12. Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq in BMC GENOMICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s12711-019-0453-y

    DOI

    http://dx.doi.org/10.1186/s12711-019-0453-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112738966

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30866799


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