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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.01215

Expression quantitative trait loci in equine skeletal muscle reveal heritable variation in metabolism and the training responsive transcriptome

 Gabriella Farries1, Kenneth Bryan1,  Charlotte L. McGivney2, Paul A. McGettigan1, Katie F. Gough1,  John A. Browne1,  David E. MacHugh1, 3,  Lisa M. Katz2 and  Emmeline W. Hill1, 4*
  • 1School of Agriculture and Food Science, College of Health and Agricultural Sciences, University College Dublin, Ireland
  • 2School of Veterinary Medicine, College of Health and Agricultural Sciences, University College Dublin, Ireland
  • 3Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Ireland
  • 4Other, Ireland

While over ten thousand genetic loci have been associated with phenotypic traits and inherited diseases in genome-wide association studies, in most cases only a relatively small proportion of the trait heritability is explained and biological mechanisms underpinning these traits have not been clearly identified. Expression quantitative trait loci (eQTL) are subsets of genomic loci shown experimentally to influence gene expression. Since gene expression is one of the primary determinants of phenotype, the identification of eQTL may reveal biologically relevant loci and provide functional links between genomic variants, gene expression and ultimately phenotype. Skeletal muscle (gluteus medius) gene expression was quantified by RNA-seq for 111 Thoroughbreds (47 male, 64 female) in race training at a single training establishment sampled at two time-points: at rest (n = 92) and four hours after high-intensity exercise (n = 77); n = 60 were sampled at both time points. Genotypes were generated from the Illumina Equine SNP70 BeadChip. Applying a False Discovery Rate (FDR) corrected P-value threshold (PFDR < 0.05), association tests identified 3,583 cis-eQTL associated with expression of 1,456 genes at rest; 4,992 cis-eQTL associated with the expression of 1,922 genes post-exercise; 1,703 trans-eQTL associated with 563 genes at rest; and 1,219 trans-eQTL associated with 425 genes post-exercise. The gene with the highest cis-eQTL association at both time-points was the endosome-associated-trafficking regulator 1 gene (ENTR1; Rest: PFDR = 3.81 × 10-27, Post-exercise: PFDR = 1.66 × 10-24), which has a potential role in the transcriptional regulation of the solute carrier family 2 member 1 glucose transporter protein (SLC2A1). Functional analysis of genes with significant eQTL revealed significant enrichment for cofactor metabolic processes. These results suggest heritable variation in genomic elements such as regulatory sequences (e.g. gene promoters, enhancers, silencers), microRNA and transcription factor genes, which are associated with metabolic function and may have roles in determining end-point muscle and athletic performance phenotypes in Thoroughbred horses. The incorporation of the eQTL identified with genome and transcriptome-wide association may reveal useful biological links between genetic variants and their impact on traits of interest, such as elite racing performance and adaptation to training.

Keywords: eQTL, Gene Expression, RNA-Seq, Horse (Equus caballus), Exercise, Aerobic metabolism

Received: 09 Jul 2019; Accepted: 04 Nov 2019.

Copyright: © 2019 Farries, Bryan, McGivney, McGettigan, Gough, Browne, MacHugh, Katz and Hill. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Emmeline W. Hill, School of Agriculture and Food Science, College of Health and Agricultural Sciences, University College Dublin, Belfield, County Dublin, Ireland, emmeline.hill@ucd.ie