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Home / Equine Research Bank / Front Genet / Expression Quantitative Trait Loci in Equine Skeletal Muscle...
Frontiers in genetics2019; 10; 1215; doi: 10.3389/fgene.2019.01215

Expression Quantitative Trait Loci in Equine Skeletal Muscle Reveals Heritable Variation in Metabolism and the Training Responsive Transcriptome.

Abstract: 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 ( = 92) and four hours after high-intensity exercise ( = 77); = 60 were sampled at both time points. Genotypes were generated from the Illumina Equine SNP70 BeadChip. Applying a False Discovery Rate (FDR) corrected -value threshold ( < 0.05), association tests identified 3,583 -eQTL associated with expression of 1,456 genes at rest; 4,992 -eQTL associated with the expression of 1,922 genes post-exercise; 1,703 -eQTL associated with 563 genes at rest; and 1,219 -eQTL associated with 425 genes post-exercise. The gene with the highest -eQTL association at both time-points was the endosome-associated-trafficking regulator 1 gene (; Rest: = 3.81 × 10, Post-exercise: = 1.66 × 10), 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.
Copyright © 2019 Farries, Bryan, McGivney, McGettigan, Gough, Browne, MacHugh, Katz and Hill.
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Publication Date: 2019-11-26 PubMed ID: 31850069PubMed Central: PMC6902038DOI: 10.3389/fgene.2019.01215Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study delves into the genetic determinants of traits and inherited diseases in Thoroughbred horses. By examining gene expression in skeletal muscle before and after high-intensity exercise, the research identified certain genetic loci that influence gene expression and may have an impact on metabolic function and athletic performance.

Understanding the Research Objective

  • The goal of this research was to better understand the genetic basis behind phenotypic traits and inherited diseases. Phenotypic traits are discernible characteristics that are the outcome of interactions between genes and environment.
  • Considering how gene expression influences these traits can reveal biologically significant genetic loci linked to the traits’ manifestation.
  • The study centered on Thoroughbred horses, specifically looking at skeletal muscle gene expression at rest and after intense exercise. The researchers hoped that these examinations could unveil functional links between genomic variants, gene expression, and phenotypes, like elite racing performance and training adaptation.

The Methodology Employed

  • The researchers measured gene expression in the gluteus medius (a muscle) of 111 Thoroughbreds through RNA sequencing. These horses were all in race training, and samples were taken both at rest and four hours after high-intensity exercise.
  • Traits of interest were identified using False Discovery Rate (FDR) corrected p-value threshold, locating eQTL (expression quantitative trait loci) which influence gene expression and may reveal biologically relevant variants.

Research Findings

  • The study identified thousands of eQTL associated with gene expression at rest and post-exercise. These eQTL corresponded with a significant number of genes.
  • The gene with the most significant association was the endosome-associated-trafficking regulator 1 gene, which may play a role in regulating the glucose transporter protein SLC2A1.
  • Functional analysis revealed that genes with significant eQTL are enriched for cofactor metabolic processes, suggesting heritable variation in elements like gene regulatory sequences, microRNA, and transcription factor genes that could be associated with metabolic function.

Implications of the Findings

  • The findings suggest that heritable variations in certain genomic elements may influence muscle and athletic performance phenotypes in Thoroughbred horses.
  • Linking these identified eQTL with wider genomic and transcriptomic databases could reveal biologically significant correlations between genetic variations and their impact on notable traits such as racing performance and training adaptation.

Cite This Article

APA
Farries G, Bryan K, McGivney CL, McGettigan PA, Gough KF, Browne JA, MacHugh DE, Katz LM, Hill EW. (2019). Expression Quantitative Trait Loci in Equine Skeletal Muscle Reveals Heritable Variation in Metabolism and the Training Responsive Transcriptome. Front Genet, 10, 1215. https://doi.org/10.3389/fgene.2019.01215

Publication

Frontiers in genetics
ISSN: 1664-8021
NlmUniqueID: 101560621
Country: Switzerland
Language: English
Volume: 10
Pages: 1215
PII: 1215

Researcher Affiliations

Farries, Gabriella
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Bryan, Kenneth
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
McGivney, Charlotte L
  • UCD School of Veterinary Medicine, University College Dublin, Dublin, Ireland.
McGettigan, Paul A
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Gough, Katie F
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Browne, John A
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
MacHugh, David E
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
  • UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.
Katz, Lisa Michelle
  • UCD School of Veterinary Medicine, University College Dublin, Dublin, Ireland.
Hill, Emmeline W
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
  • Research and Development, Plusvital Ltd., Dublin, Ireland.

References

This article includes 106 references
  1. Abe S, Chamnan C, Miyamoto K, Minamino Y, Nouda M. Isolation and identification of 3-methylcrotonyl coenzyme A carboxylase cDNAs and pyruvate carboxylase, and their expression in red seabream (Pagrus major) organs.. Mar Biotechnol (NY) 2004 Nov-Dec;6(6):527-40.
    doi: 10.1007/s10126-004-4203-xpubmed: 15690100google scholar: lookup
  2. Achim K, Peltopuro P, Lahti L, Tsai HH, Zachariah A, Astrand M, Salminen M, Rowitch D, Partanen J. The role of Tal2 and Tal1 in the differentiation of midbrain GABAergic neuron precursors.. Biol Open 2013;2(10):990-7.
    doi: 10.1242/bio.20135041pmc: PMC3798194pubmed: 24167708google scholar: lookup
  3. Acosta MJ, Vazquez Fonseca L, Desbats MA, Cerqua C, Zordan R, Trevisson E, Salviati L. Coenzyme Q biosynthesis in health and disease.. Biochim Biophys Acta 2016 Aug;1857(8):1079-1085.
    doi: 10.1016/j.bbabio.2016.03.036pubmed: 27060254google scholar: lookup
  4. Andrews S. FastQC: a quality control tool for high throughput sequence data. Ref. Source .
  5. Arany Z. PGC-1 coactivators and skeletal muscle adaptations in health and disease.. Curr Opin Genet Dev 2008 Oct;18(5):426-34.
    doi: 10.1016/j.gde.2008.07.018pmc: PMC2629557pubmed: 18782618google scholar: lookup
  6. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.. Nat Genet 2000 May;25(1):25-9.
    doi: 10.1038/75556pmc: PMC3037419pubmed: 10802651google scholar: lookup
  7. Avellini L, Chiaradia E, Gaiti A. Effect of exercise training, selenium and vitamin E on some free radical scavengers in horses (Equus caballus).. Comp Biochem Physiol B Biochem Mol Biol 1999 Jun;123(2):147-54.
    doi: 10.1016/s0305-0491(99)00045-0pubmed: 10425718google scholar: lookup
  8. Barnes KM, Evenson JK, Raines AM, Sunde RA. Transcript analysis of the selenoproteome indicates that dietary selenium requirements of rats based on selenium-regulated selenoprotein mRNA levels are uniformly less than those based on glutathione peroxidase activity.. J Nutr 2009 Feb;139(2):199-206.
    doi: 10.3945/jn.108.098624pmc: PMC2635526pubmed: 19106321google scholar: lookup
  9. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57 (1), 289–300.
    doi: 10.2307/2346101google scholar: lookup
  10. nBolstad B. PreprocessCore: a collection of pre-processing functions in R package version. .
  11. Bouchard C, Sarzynski MA, Rice TK, Kraus WE, Church TS, Sung YJ, Rao DC, Rankinen T. Genomic predictors of the maximal O₂ uptake response to standardized exercise training programs.. J Appl Physiol (1985) 2011 May;110(5):1160-70.
    doi: 10.1152/japplphysiol.00973.2010pmc: PMC3098655pubmed: 21183627google scholar: lookup
  12. Brady PS, Ku PK, Ullrey DE. Lack of effect of selenium supplementation on the response of the equine erythrocyte glutathione system and plasma enzymes to exercise.. J Anim Sci 1978 Aug;47(2):492-6.
    doi: 10.2527/jas1978.472492xpubmed: 730624google scholar: lookup
  13. Brown CD, Mangravite LM, Engelhardt BE. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.. PLoS Genet 2013;9(8):e1003649.
    doi: 10.1371/journal.pgen.1003649pmc: PMC3731231pubmed: 23935528google scholar: lookup
  14. Bryan K, McGivney BA, Farries G, McGettigan PA, McGivney CL, Gough KF, MacHugh DE, Katz LM, Hill EW. Equine skeletal muscle adaptations to exercise and training: evidence of differential regulation of autophagosomal and mitochondrial components.. BMC Genomics 2017 Aug 9;18(1):595.
    doi: 10.1186/s12864-017-4007-9pmc: PMC5551008pubmed: 28793853google scholar: lookup
  15. Bucher K, Sofroniew MV, Pannell R, Impey H, Smith AJ, Torres EM, Dunnett SB, Jin Y, Baer R, Rabbitts TH. The T cell oncogene Tal2 is necessary for normal development of the mouse brain.. Dev Biol 2000 Nov 15;227(2):533-44.
    doi: 10.1006/dbio.2000.9920pubmed: 11071772google scholar: lookup
  16. Campolo A, de Laat MA, Keith L, Gruntmeir KJ, Lacombe VA. Prolonged hyperinsulinemia affects metabolic signal transduction markers in a tissue specific manner.. Domest Anim Endocrinol 2016 Apr;55:41-5.
    doi: 10.1016/j.domaniend.2015.11.001pubmed: 26773366google scholar: lookup
  17. Carlson M. org.Hs.eg.db: Genome wide annotation for Human. R package version 3.8.2. .
  18. Chamberlain AJ, Vander Jagt CJ, Hayes BJ, Khansefid M, Marett LC, Millen CA, Nguyen TT, Goddard ME. Extensive variation between tissues in allele specific expression in an outbred mammal.. BMC Genomics 2015 Nov 23;16:993.
    doi: 10.1186/s12864-015-2174-0pmc: PMC4657355pubmed: 26596891google scholar: lookup
  19. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets.. Gigascience 2015;4:7.
    doi: 10.1186/s13742-015-0047-8pmc: PMC4342193pubmed: 25722852google scholar: lookup
  20. . UniProt: the universal protein knowledgebase.. Nucleic Acids Res 2017 Jan 4;45(D1):D158-D169.
    doi: 10.1093/nar/gkw1099pmc: PMC5210571pubmed: 27899622google scholar: lookup
  21. Corbin LJ, Kranis A, Blott SC, Swinburne JE, Vaudin M, Bishop SC, Woolliams JA. The utility of low-density genotyping for imputation in the Thoroughbred horse.. Genet Sel Evol 2014 Feb 4;46(1):9.
    doi: 10.1186/1297-9686-46-9pmc: PMC3930001pubmed: 24495673google scholar: lookup
  22. Delesalle C, de Bruijn M, Wilmink S, Vandendriessche H, Mol G, Boshuizen B, Plancke L, Grinwis G. White muscle disease in foals: focus on selenium soil content. A case series.. BMC Vet Res 2017 May 3;13(1):121.
    doi: 10.1186/s12917-017-1040-5pmc: PMC5415829pubmed: 28468621google scholar: lookup
  23. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner.. Bioinformatics 2013 Jan 1;29(1):15-21.
    doi: 10.1093/bioinformatics/bts635pmc: PMC3530905pubmed: 23104886google scholar: lookup
  24. Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt.. Nat Protoc 2009;4(8):1184-91.
    doi: 10.1038/nprot.2009.97pmc: PMC3159387pubmed: 19617889google scholar: lookup
  25. Durussel J, Haile DW, Mooses K, Daskalaki E, Beattie W, Mooses M, Mekonen W, Ongaro N, Anjila E, Patel RK, Padmanabhan N, McBride MW, McClure JD, Pitsiladis YP. Blood transcriptional signature of recombinant human erythropoietin administration and implications for antidoping strategies.. Physiol Genomics 2016 Mar;48(3):202-9.
    doi: 10.1152/physiolgenomics.00108.2015pubmed: 26757800google scholar: lookup
  26. Eivers SS, McGivney BA, Fonseca RG, MacHugh DE, Menson K, Park SD, Rivero JL, Taylor CT, Katz LM, Hill EW. Alterations in oxidative gene expression in equine skeletal muscle following exercise and training.. Physiol Genomics 2010 Jan 8;40(2):83-93.
    doi: 10.1152/physiolgenomics.00041.2009pubmed: 19861432google scholar: lookup
  27. Eivers SS, McGivney BA, Gu J, MacHugh DE, Katz LM, Hill EW. PGC-1α encoded by the PPARGC1A gene regulates oxidative energy metabolism in equine skeletal muscle during exercise.. Anim Genet 2012 Apr;43(2):153-62.
    doi: 10.1111/j.1365-2052.2011.02238.xpubmed: 22404351google scholar: lookup
  28. Farries G, Gough KF, Parnell AC, McGivney BA, McGivney CL, McGettigan PA, MacHugh DE, Katz LM, Hill EW. Analysis of genetic variation contributing to measured speed in Thoroughbreds identifies genomic regions involved in the transcriptional response to exercise.. Anim Genet 2019 Dec;50(6):670-685.
    doi: 10.1111/age.12848pubmed: 31508842google scholar: lookup
  29. Feng T, Yamamoto A, Wilkins SE, Sokolova E, Yates LA, Münzel M, Singh P, Hopkinson RJ, Fischer R, Cockman ME, Shelley J, Trudgian DC, Schödel J, McCullagh JS, Ge W, Kessler BM, Gilbert RJ, Frolova LY, Alkalaeva E, Ratcliffe PJ, Schofield CJ, Coleman ML. Optimal translational termination requires C4 lysyl hydroxylation of eRF1.. Mol Cell 2014 Feb 20;53(4):645-54.
    doi: 10.1016/j.molcel.2013.12.028pmc: PMC3991326pubmed: 24486019google scholar: lookup
  30. Fonseca H, Azevedo L, Serrano C, Sousa C, Marcão A, Vilarinho L. 3-Methylcrotonyl-CoA carboxylase deficiency: Mutational spectrum derived from comprehensive newborn screening.. Gene 2016 Dec 15;594(2):203-210.
    doi: 10.1016/j.gene.2016.09.003pubmed: 27601257google scholar: lookup
  31. Gallardo ME, Desviat LR, Rodríguez JM, Esparza-Gordillo J, Pérez-Cerdá C, Pérez B, Rodríguez-Pombo P, Criado O, Sanz R, Morton DH, Gibson KM, Le TP, Ribes A, de Córdoba SR, Ugarte M, Peñalva MA. The molecular basis of 3-methylcrotonylglycinuria, a disorder of leucine catabolism.. Am J Hum Genet 2001 Feb;68(2):334-46.
    doi: 10.1086/318202pmc: PMC1235267pubmed: 11170888google scholar: lookup
  32. Gencheva M, Lin TY, Wu X, Yang L, Richard C, Jones M, Lin SB, Lin RJ. Nuclear retention of unspliced pre-mRNAs by mutant DHX16/hPRP2, a spliceosomal DEAH-box protein.. J Biol Chem 2010 Nov 12;285(46):35624-32.
    doi: 10.1074/jbc.M110.122309pmc: PMC2975187pubmed: 20841358google scholar: lookup
  33. González-Prendes R, Quintanilla R, Amills M. Investigating the genetic regulation of the expression of 63 lipid metabolism genes in the pig skeletal muscle.. Anim Genet 2017 Oct;48(5):606-610.
    doi: 10.1111/age.12586pubmed: 28737243google scholar: lookup
  34. González-Prendes R, Mármol-Sánchez E, Quintanilla R, Castelló A, Zidi A, Ramayo-Caldas Y, Cardoso TF, Manunza A, Cánovas Á, Amills M. About the existence of common determinants of gene expression in the porcine liver and skeletal muscle.. BMC Genomics 2019 Jun 24;20(1):518.
    doi: 10.1186/s12864-019-5889-5pmc: PMC6591854pubmed: 31234802google scholar: lookup
  35. Gunn H M. Muscle, bone and fat proportions and muscle distribution of thoroughbreds and other horses in Equine exercise physiology 2: Proceedings of the second international conference on equine exercise physiology. 253–264. August 7-11, 1986.
  36. Hagemann N, Ackermann N, Christmann J, Brier S, Yu F, Erdmann KS. The serologically defined colon cancer antigen-3 interacts with the protein tyrosine phosphatase PTPN13 and is involved in the regulation of cytokinesis.. Oncogene 2013 Sep 26;32(39):4602-13.
    doi: 10.1038/onc.2012.485pubmed: 23108400google scholar: lookup
  37. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.. Proc Natl Acad Sci U S A 2009 Jun 9;106(23):9362-7.
    doi: 10.1073/pnas.0903103106pmc: PMC2687147pubmed: 19474294google scholar: lookup
  38. Ishii Y, Hatsumura M, Ishida T, Ariyoshi N, Oguri K. Significant induction of a 54-kDa protein in rat liver with homologous alignment to mouse selenium binding protein by a coplanar polychlorinated biphenyl, 3,4,5,3',4'-pentachlorobiphenyl and 3-methylcholanthrene.. Toxicol Lett 1996 Sep;87(1):1-9.
    doi: 10.1016/0378-4274(96)03668-5pubmed: 8701438google scholar: lookup
  39. Jacobsen JC, Whitford W, Swan B, Taylor J, Love DR, Hill R, Molyneux S, George PM, Mackay R, Robertson SP, Snell RG, Lehnert K. Compound Heterozygous Inheritance of Mutations in Coenzyme Q8A Results in Autosomal Recessive Cerebellar Ataxia and Coenzyme Q(10) Deficiency in a Female Sib-Pair.. JIMD Rep 2018;42:31-36.
    doi: 10.1007/8904_2017_73pmc: PMC6226395pubmed: 29159460google scholar: lookup
  40. Jamba L, Nehru B, Bansal MP. Redox modulation of selenium binding proteins by cadmium exposures in mice.. Mol Cell Biochem 1997 Dec;177(1-2):169-75.
    doi: 10.1023/a:1006869623864pubmed: 9450659google scholar: lookup
  41. Jansen RC, Nap JP. Genetical genomics: the added value from segregation.. Trends Genet 2001 Jul;17(7):388-91.
    doi: 10.1016/s0168-9525(01)02310-1pubmed: 11418218google scholar: lookup
  42. Kasprzyk A. BioMart: driving a paradigm change in biological data management.. Database (Oxford) 2011;2011:bar049.
    doi: 10.1093/database/bar049pmc: PMC3215098pubmed: 22083790google scholar: lookup
  43. Kayar SR, Hoppeler H, Lindstedt SL, Claassen H, Jones JH, Essen-Gustavsson B, Taylor CR. Total muscle mitochondrial volume in relation to aerobic capacity of horses and steers.. Pflugers Arch 1989 Feb;413(4):343-7.
    doi: 10.1007/bf00584481pubmed: 2928085google scholar: lookup
  44. Kearns CF, McKeever KH, Abe T. Overview of horse body composition and muscle architecture: implications for performance.. Vet J 2002 Nov;164(3):224-34.
    doi: 10.1053/tvjl.2001.0702pubmed: 12505395google scholar: lookup
  45. Keildson S, Fadista J, Ladenvall C, Hedman ÅK, Elgzyri T, Small KS, Grundberg E, Nica AC, Glass D, Richards JB, Barrett A, Nisbet J, Zheng HF, Rönn T, Ström K, Eriksson KF, Prokopenko I, Spector TD, Dermitzakis ET, Deloukas P, McCarthy MI, Rung J, Groop L, Franks PW, Lindgren CM, Hansson O. Expression of phosphofructokinase in skeletal muscle is influenced by genetic variation and associated with insulin sensitivity.. Diabetes 2014 Mar;63(3):1154-65.
    doi: 10.2337/db13-1301pmc: PMC3931395pubmed: 24306210google scholar: lookup
  46. Keildson S, Fadista J, Ladenvall C, Hedman ÅK, Elgzyri T, Small KS, Grundberg E, Nica AC, Glass D, Richards JB, Barrett A, Nisbet J, Zheng HF, Rönn T, Ström K, Eriksson KF, Prokopenko I, Spector TD, Dermitzakis ET, Deloukas P, McCarthy MI, Rung J, Groop L, Franks PW, Lindgren CM, Hansson O. Expression of phosphofructokinase in skeletal muscle is influenced by genetic variation and associated with insulin sensitivity.. Diabetes 2014 Mar;63(3):1154-65.
    pmc: PMC3931395pubmed: 24306210doi: 10.2337/db13-1301google scholar: lookup
  47. Kelly SA, Nehrenberg DL, Hua K, Garland T Jr, Pomp D. Quantitative genomics of voluntary exercise in mice: transcriptional analysis and mapping of expression QTL in muscle.. Physiol Genomics 2014 Aug 15;46(16):593-601.
    doi: 10.1152/physiolgenomics.00023.2014pmc: PMC4137147pubmed: 24939925google scholar: lookup
  48. Kraniou GN, Cameron-Smith D, Hargreaves M. Acute exercise and GLUT4 expression in human skeletal muscle: influence of exercise intensity.. J Appl Physiol (1985) 2006 Sep;101(3):934-7.
    doi: 10.1152/japplphysiol.01489.2005pubmed: 16763099google scholar: lookup
  49. Krook A, Wallberg-Henriksson H, Zierath JR. Sending the signal: molecular mechanisms regulating glucose uptake.. Med Sci Sports Exerc 2004 Jul;36(7):1212-7.
    doi: 10.1249/01.mss.0000132387.25853.3bpubmed: 15235328google scholar: lookup
  50. Langefeld CD, Comeau ME, Sharma NK, Bowden DW, Freedman BI, Das SK. Transcriptional Regulatory Mechanisms in Adipose and Muscle Tissue Associated with Composite Glucometabolic Phenotypes.. Obesity (Silver Spring) 2018 Mar;26(3):559-569.
    doi: 10.1002/oby.22113pmc: PMC5821540pubmed: 29377571google scholar: lookup
  51. Langlands K, Yin X, Anand G, Prochownik EV. Differential interactions of Id proteins with basic-helix-loop-helix transcription factors.. J Biol Chem 1997 Aug 8;272(32):19785-93.
    doi: 10.1074/jbc.272.32.19785pubmed: 9242638google scholar: lookup
  52. Lenaz G. Coenzyme Q: Biochemistry, Bioenergetics, and Clinical Applications of Ubiquinone. .
  53. Levine M. The origins of horse husbandry on the eurasian steppe in Late prehistoric exploitation of the eurasian steppe. 5–58.
  54. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.. Bioinformatics 2014 Apr 1;30(7):923-30.
    doi: 10.1093/bioinformatics/btt656pubmed: 24227677google scholar: lookup
  55. Löfstedt J. White muscle disease of foals.. Vet Clin North Am Equine Pract 1997 Apr;13(1):169-85.
    doi: 10.1016/s0749-0739(17)30262-6pubmed: 9106350google scholar: lookup
  56. . The Genotype-Tissue Expression (GTEx) project.. Nat Genet 2013 Jun;45(6):580-5.
    doi: 10.1038/ng.2653pmc: PMC4010069pubmed: 23715323google scholar: lookup
  57. Mason CC, Hanson RL, Ossowski V, Bian L, Baier LJ, Krakoff J, Bogardus C. Bimodal distribution of RNA expression levels in human skeletal muscle tissue.. BMC Genomics 2011 Feb 7;12:98.
    doi: 10.1186/1471-2164-12-98pmc: PMC3044673pubmed: 21299892google scholar: lookup
  58. McGivney BA, Eivers SS, MacHugh DE, MacLeod JN, O'Gorman GM, Park SD, Katz LM, Hill EW. Transcriptional adaptations following exercise in thoroughbred horse skeletal muscle highlights molecular mechanisms that lead to muscle hypertrophy.. BMC Genomics 2009 Dec 30;10:638.
    doi: 10.1186/1471-2164-10-638pmc: PMC2812474pubmed: 20042072google scholar: lookup
  59. McGivney BA, McGettigan PA, Browne JA, Evans AC, Fonseca RG, Loftus BJ, Lohan A, MacHugh DE, Murphy BA, Katz LM, Hill EW. Characterization of the equine skeletal muscle transcriptome identifies novel functional responses to exercise training.. BMC Genomics 2010 Jun 23;11:398.
    doi: 10.1186/1471-2164-11-398pmc: PMC2900271pubmed: 20573200google scholar: lookup
  60. McGough IJ, Steinberg F, Gallon M, Yatsu A, Ohbayashi N, Heesom KJ, Fukuda M, Cullen PJ. Identification of molecular heterogeneity in SNX27-retromer-mediated endosome-to-plasma-membrane recycling.. J Cell Sci 2014 Nov 15;127(Pt 22):4940-53.
    doi: 10.1242/jcs.156299pmc: PMC4231307pubmed: 25278552google scholar: lookup
  61. Miyaguchi K. Localization of selenium-binding protein at the tips of rapidly extending protrusions.. Histochem Cell Biol 2004 May;121(5):371-6.
    doi: 10.1007/s00418-004-0623-ypubmed: 15108003google scholar: lookup
  62. Mollet J, Delahodde A, Serre V, Chretien D, Schlemmer D, Lombes A, Boddaert N, Desguerre I, de Lonlay P, de Baulny HO, Munnich A, Rötig A. CABC1 gene mutations cause ubiquinone deficiency with cerebellar ataxia and seizures.. Am J Hum Genet 2008 Mar;82(3):623-30.
    doi: 10.1016/j.ajhg.2007.12.022pmc: PMC2427298pubmed: 18319072google scholar: lookup
  63. Monks SA, Leonardson A, Zhu H, Cundiff P, Pietrusiak P, Edwards S, Phillips JW, Sachs A, Schadt EE. Genetic inheritance of gene expression in human cell lines.. Am J Hum Genet 2004 Dec;75(6):1094-105.
    doi: 10.1086/426461pmc: PMC1182144pubmed: 15514893google scholar: lookup
  64. Pampouille E, Berri C, Boitard S, Hennequet-Antier C, Beauclercq SA, Godet E, Praud C, Jégo Y, Le Bihan-Duval E. Mapping QTL for white striping in relation to breast muscle yield and meat quality traits in broiler chickens.. BMC Genomics 2018 Mar 20;19(1):202.
    doi: 10.1186/s12864-018-4598-9pmc: PMC5859760pubmed: 29554873google scholar: lookup
  65. Peansukmanee S, Vaughan-Thomas A, Carter SD, Clegg PD, Taylor S, Redmond C, Mobasheri A. Effects of hypoxia on glucose transport in primary equine chondrocytes in vitro and evidence of reduced GLUT1 gene expression in pathologic cartilage in vivo.. J Orthop Res 2009 Apr;27(4):529-35.
    doi: 10.1002/jor.20772pubmed: 18973239google scholar: lookup
  66. Ponsuksili S, Siengdee P, Du Y, Trakooljul N, Murani E, Schwerin M, Wimmers K. Identification of common regulators of genes in co-expression networks affecting muscle and meat properties.. PLoS One 2015;10(4):e0123678.
    doi: 10.1371/journal.pone.0123678pmc: PMC4397042pubmed: 25875247google scholar: lookup
  67. Putiri E, Pelegri F. The zebrafish maternal-effect gene mission impossible encodes the DEAH-box helicase Dhx16 and is essential for the expression of downstream endodermal genes.. Dev Biol 2011 May 15;353(2):275-89.
    doi: 10.1016/j.ydbio.2011.03.001pmc: PMC3088167pubmed: 21396359google scholar: lookup
  68. R Core Team. R: A language and environment for statistical computing. Vienna, Austria:R Foundation for Statistical Computing.
  69. Rederstorff M, Krol A, Lescure A. Understanding the importance of selenium and selenoproteins in muscle function.. Cell Mol Life Sci 2006 Jan;63(1):52-9.
    doi: 10.1007/s00018-005-5313-ypmc: PMC2792354pubmed: 16314926google scholar: lookup
  70. Renault AL, Mebirouk N, Cavaciuti E, Le Gal D, Lecarpentier J, d'Enghien CD, Laugé A, Dondon MG, Labbé M, Lesca G, Leroux D, Gladieff L, Adenis C, Faivre L, Gilbert-Dussardier B, Lortholary A, Fricker JP, Dahan K, Bay JO, Longy M, Buecher B, Janin N, Zattara H, Berthet P, Combès A, Coupier I, Hall J, Stoppa-Lyonnet D, Andrieu N, Lesueur F. Telomere length, ATM mutation status and cancer risk in Ataxia-Telangiectasia families.. Carcinogenesis 2017 Oct 1;38(10):994-1003.
    doi: 10.1093/carcin/bgx074pmc: PMC5862273pubmed: 28981872google scholar: lookup
  71. Rivero JL, Talmadge RJ, Edgerton VR. Correlation between myofibrillar ATPase activity and myosin heavy chain composition in equine skeletal muscle and the influence of training.. Anat Rec 1996 Oct;246(2):195-207.
    doi: 10.1002/(sici)1097-0185(199610)246:2<195::aid-ar6>3.0.co;2-0pubmed: 8888961google scholar: lookup
  72. Rivero JL, Sporleder HP, Quiroz-Rothe E, Vervuert I, Coenen M, Harmeyer J. Oral L-carnitine combined with training promotes changes in skeletal muscle.. Equine Vet J Suppl 2002 Sep;(34):269-74.
    doi: 10.1111/j.2042-3306.2002.tb05431.xpubmed: 12405699google scholar: lookup
  73. Rivero J L. Muscle morphology heterogeneity: control, significance for performance and responses to training, in Emerging Equine Science. 11–27.
    doi: 10.1017/S0263967X00041197google scholar: lookup
  74. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.. Bioinformatics 2010 Jan 1;26(1):139-40.
    doi: 10.1093/bioinformatics/btp616pmc: PMC2796818pubmed: 19910308google scholar: lookup
  75. Rooney MF, Porter RK, Katz LM, Hill EW. Skeletal muscle mitochondrial bioenergetics and associations with myostatin genotypes in the Thoroughbred horse.. PLoS One 2017;12(11):e0186247.
    doi: 10.1371/journal.pone.0186247pmc: PMC5708611pubmed: 29190290google scholar: lookup
  76. Rudich A, Konrad D, Török D, Ben-Romano R, Huang C, Niu W, Garg RR, Wijesekara N, Germinario RJ, Bilan PJ, Klip A. Indinavir uncovers different contributions of GLUT4 and GLUT1 towards glucose uptake in muscle and fat cells and tissues.. Diabetologia 2003 May;46(5):649-58.
    doi: 10.1007/s00125-003-1080-1pubmed: 12712244google scholar: lookup
  77. Sajuthi SP, Sharma NK, Chou JW, Palmer ND, McWilliams DR, Beal J, Comeau ME, Ma L, Calles-Escandon J, Demons J, Rogers S, Cherry K, Menon L, Kouba E, Davis D, Burris M, Byerly SJ, Ng MC, Maruthur NM, Patel SR, Bielak LF, Lange LA, Guo X, Sale MM, Chan KH, Monda KL, Chen GK, Taylor K, Palmer C, Edwards TL, North KE, Haiman CA, Bowden DW, Freedman BI, Langefeld CD, Das SK. Mapping adipose and muscle tissue expression quantitative trait loci in African Americans to identify genes for type 2 diabetes and obesity.. Hum Genet 2016 Aug;135(8):869-80.
    doi: 10.1007/s00439-016-1680-8pmc: PMC4947558pubmed: 27193597google scholar: lookup
  78. Schaefer RJ, Schubert M, Bailey E, Bannasch DL, Barrey E, Bar-Gal GK, Brem G, Brooks SA, Distl O, Fries R, Finno CJ, Gerber V, Haase B, Jagannathan V, Kalbfleisch T, Leeb T, Lindgren G, Lopes MS, Mach N, da Câmara Machado A, MacLeod JN, McCoy A, Metzger J, Penedo C, Polani S, Rieder S, Tammen I, Tetens J, Thaller G, Verini-Supplizi A, Wade CM, Wallner B, Orlando L, Mickelson JR, McCue ME. Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds.. BMC Genomics 2017 Jul 27;18(1):565.
    doi: 10.1186/s12864-017-3943-8pmc: PMC5530493pubmed: 28750625google scholar: lookup
  79. Serrano AL, Quiroz-Rothe E, Rivero JL. Early and long-term changes of equine skeletal muscle in response to endurance training and detraining.. Pflugers Arch 2000 Dec;441(2-3):263-74.
    doi: 10.1007/s004240000408pubmed: 11211112google scholar: lookup
  80. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations.. Bioinformatics 2012 May 15;28(10):1353-8.
    doi: 10.1093/bioinformatics/bts163pmc: PMC3348564pubmed: 22492648google scholar: lookup
  81. Sharma NK, Langberg KA, Mondal AK, Elbein SC, Das SK. Type 2 diabetes (T2D) associated polymorphisms regulate expression of adjacent transcripts in transformed lymphocytes, adipose, and muscle from Caucasian and African-American subjects.. J Clin Endocrinol Metab 2011 Feb;96(2):E394-403.
    doi: 10.1210/jc.2010-1754pmc: PMC3048324pubmed: 21084393google scholar: lookup
  82. Snow DH, Harris RC, Gash SP. Metabolic response of equine muscle to intermittent maximal exercise.. J Appl Physiol (1985) 1985 May;58(5):1689-97.
    doi: 10.1152/jappl.1985.58.5.1689pubmed: 3997731google scholar: lookup
  83. Snow D H, a V SJ. Muscle anatomy, physiology and adaptations to exercise and training in The athletic horse: principles and practice of equine sports medicine. 145–179.
  84. Stadler SC, Polanetz R, Meier S, Mayerhofer PU, Herrmann JM, Anslinger K, Roscher AA, Röschinger W, Holzinger A. Mitochondrial targeting signals and mature peptides of 3-methylcrotonyl-CoA carboxylase.. Biochem Biophys Res Commun 2005 Sep 2;334(3):939-46.
    doi: 10.1016/j.bbrc.2005.06.190pubmed: 16023992google scholar: lookup
  85. Stadler SC, Polanetz R, Maier EM, Heidenreich SC, Niederer B, Mayerhofer PU, Lagler F, Koch HG, Santer R, Fletcher JM, Ranieri E, Das AM, Spiekerkötter U, Schwab KO, Pötzsch S, Marquardt I, Hennermann JB, Knerr I, Mercimek-Mahmutoglu S, Kohlschmidt N, Liebl B, Fingerhut R, Olgemöller B, Muntau AC, Roscher AA, Röschinger W. Newborn screening for 3-methylcrotonyl-CoA carboxylase deficiency: population heterogeneity of MCCA and MCCB mutations and impact on risk assessment.. Hum Mutat 2006 Aug;27(8):748-59.
    doi: 10.1002/humu.20349pubmed: 16835865google scholar: lookup
  86. Stefely JA, Pagliarini DJ. Biochemistry of Mitochondrial Coenzyme Q Biosynthesis.. Trends Biochem Sci 2017 Oct;42(10):824-843.
    doi: 10.1016/j.tibs.2017.06.008pmc: PMC5731490pubmed: 28927698google scholar: lookup
  87. Stefely JA, Licitra F, Laredj L, Reidenbach AG, Kemmerer ZA, Grangeray A, Jaeg-Ehret T, Minogue CE, Ulbrich A, Hutchins PD, Wilkerson EM, Ruan Z, Aydin D, Hebert AS, Guo X, Freiberger EC, Reutenauer L, Jochem A, Chergova M, Johnson IE, Lohman DC, Rush MJP, Kwiecien NW, Singh PK, Schlagowski AI, Floyd BJ, Forsman U, Sindelar PJ, Westphall MS, Pierrel F, Zoll J, Dal Peraro M, Kannan N, Bingman CA, Coon JJ, Isope P, Puccio H, Pagliarini DJ. Cerebellar Ataxia and Coenzyme Q Deficiency through Loss of Unorthodox Kinase Activity.. Mol Cell 2016 Aug 18;63(4):608-620.
    doi: 10.1016/j.molcel.2016.06.030pmc: PMC5012427pubmed: 27499294google scholar: lookup
  88. Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, Ingle CE, Dunning M, Flicek P, Koller D, Montgomery S, Tavaré S, Deloukas P, Dermitzakis ET. Population genomics of human gene expression.. Nat Genet 2007 Oct;39(10):1217-24.
    doi: 10.1038/ng2142pmc: PMC2683249pubmed: 17873874google scholar: lookup
  89. Taylor RM, Sunde RA. Selenium requirements based on muscle and kidney selenoprotein enzyme activity and transcript expression in the turkey poult (Meleagris gallopavo).. PLoS One 2017;12(11):e0189001.
    doi: 10.1371/journal.pone.0189001pmc: PMC5708738pubmed: 29190764google scholar: lookup
  90. Timmons JA, Knudsen S, Rankinen T, Koch LG, Sarzynski M, Jensen T, Keller P, Scheele C, Vollaard NB, Nielsen S, Akerström T, MacDougald OA, Jansson E, Greenhaff PL, Tarnopolsky MA, van Loon LJ, Pedersen BK, Sundberg CJ, Wahlestedt C, Britton SL, Bouchard C. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans.. J Appl Physiol (1985) 2010 Jun;108(6):1487-96.
    doi: 10.1152/japplphysiol.01295.2009pmc: PMC2886694pubmed: 20133430google scholar: lookup
  91. Todd ET, Ho SYW, Thomson PC, Ang RA, Velie BD, Hamilton NA. Founder-specific inbreeding depression affects racing performance in Thoroughbred horses.. Sci Rep 2018 Apr 18;8(1):6167.
    doi: 10.1038/s41598-018-24663-xpmc: PMC5906619pubmed: 29670190google scholar: lookup
  92. Turunen M, Olsson J, Dallner G. Metabolism and function of coenzyme Q.. Biochim Biophys Acta 2004 Jan 28;1660(1-2):171-99.
    doi: 10.1016/j.bbamem.2003.11.012pubmed: 14757233google scholar: lookup
  93. Tyler CM, Golland LC, Evans DL, Hodgson DR, Rose RJ. Skeletal muscle adaptations to prolonged training, overtraining and detraining in horses.. Pflugers Arch 1998 Aug;436(3):391-7.
    doi: 10.1007/s004240050648pubmed: 9644221google scholar: lookup
  94. Valette JP, Barrey E, Jouglin M, Courouce A, Auvinet B, Flaux B. Standardisation of muscular biopsy of gluteus medius in French trotters.. Equine Vet J Suppl 1999 Jul;(30):342-4.
    doi: 10.1111/j.2042-3306.1999.tb05246.xpubmed: 10659280google scholar: lookup
  95. Velez-Irizarry D, Casiro S, Daza KR, Bates RO, Raney NE, Steibel JP, Ernst CW. Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs.. BMC Genomics 2019 Jan 3;20(1):3.
    doi: 10.1186/s12864-018-5386-2pmc: PMC6319002pubmed: 30606113google scholar: lookup
  96. Votion DM, Gnaiger E, Lemieux H, Mouithys-Mickalad A, Serteyn D. Physical fitness and mitochondrial respiratory capacity in horse skeletal muscle.. PLoS One 2012;7(4):e34890.
    doi: 10.1371/journal.pone.0034890pmc: PMC3329552pubmed: 22529950google scholar: lookup
  97. Wang G, Durussel J, Shurlock J, Mooses M, Fuku N, Bruinvels G, Pedlar C, Burden R, Murray A, Yee B, Keenan A, McClure JD, Sottas PE, Pitsiladis YP. Validation of whole-blood transcriptome signature during microdose recombinant human erythropoietin (rHuEpo) administration.. BMC Genomics 2017 Nov 14;18(Suppl 8):817.
    doi: 10.1186/s12864-017-4191-7pmc: PMC5688496pubmed: 29143667google scholar: lookup
  98. Westra HJ, Franke L. From genome to function by studying eQTLs.. Biochim Biophys Acta 2014 Oct;1842(10):1896-1902.
    doi: 10.1016/j.bbadis.2014.04.024pubmed: 24798236google scholar: lookup
  99. Wickham H. Ggplot2: elegant graphics for data analysis. New York:Springer.
  100. Willett P. An introduction to the thoroughbred. London:Paul.
  101. Wittkopp PJ. Genomic sources of regulatory variation in cis and in trans.. Cell Mol Life Sci 2005 Aug;62(16):1779-83.
    doi: 10.1007/s00018-005-5064-9pubmed: 15968467google scholar: lookup
  102. Wright FA, Sullivan PF, Brooks AI, Zou F, Sun W, Xia K, Madar V, Jansen R, Chung W, Zhou YH, Abdellaoui A, Batista S, Butler C, Chen G, Chen TH, D'Ambrosio D, Gallins P, Ha MJ, Hottenga JJ, Huang S, Kattenberg M, Kochar J, Middeldorp CM, Qu A, Shabalin A, Tischfield J, Todd L, Tzeng JY, van Grootheest G, Vink JM, Wang Q, Wang W, Wang W, Willemsen G, Smit JH, de Geus EJ, Yin Z, Penninx BW, Boomsma DI. Heritability and genomics of gene expression in peripheral blood.. Nat Genet 2014 May;46(5):430-7.
    doi: 10.1038/ng.2951pmc: PMC4012342pubmed: 24728292google scholar: lookup
  103. Xia Y, Brown L, Yang CY, Tsan JT, Siciliano MJ, Espinosa R 3rd, Le Beau MM, Baer RJ. TAL2, a helix-loop-helix gene activated by the (7;9)(q34;q32) translocation in human T-cell leukemia.. Proc Natl Acad Sci U S A 1991 Dec 15;88(24):11416-20.
    doi: 10.1073/pnas.88.24.11416pmc: PMC53146pubmed: 1763056google scholar: lookup
  104. Yang M, Sytkowski AJ. Differential expression and androgen regulation of the human selenium-binding protein gene hSP56 in prostate cancer cells.. Cancer Res 1998 Jul 15;58(14):3150-3.
    pubmed: 9679983
  105. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters.. OMICS 2012 May;16(5):284-7.
    doi: 10.1089/omi.2011.0118pmc: PMC3339379pubmed: 22455463google scholar: lookup
  106. Zisman A, Peroni OD, Abel ED, Michael MD, Mauvais-Jarvis F, Lowell BB, Wojtaszewski JF, Hirshman MF, Virkamaki A, Goodyear LJ, Kahn CR, Kahn BB. Targeted disruption of the glucose transporter 4 selectively in muscle causes insulin resistance and glucose intolerance.. Nat Med 2000 Aug;6(8):924-8.
    doi: 10.1038/78693pubmed: 10932232google scholar: lookup

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