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Frontiers in genetics2017; 8; 89; doi: 10.3389/fgene.2017.00089

Endurance Exercise Ability in the Horse: A Trait with Complex Polygenic Determinism.

Abstract: Endurance horses are able to run at more than 20 km/h for 160 km (in bouts of 30-40 km). This level of performance is based on intense aerobic metabolism, effective body heat dissipation and the ability to endure painful exercise. The known heritabilities of endurance performance and exercise-related physiological traits in Arabian horses suggest that adaptation to extreme endurance exercise is influenced by genetic factors. The objective of the present genome-wide association study (GWAS) was to identify single nucleotide polymorphisms (SNPs) related to endurance racing performance in 597 Arabian horses. The performance traits studied were the total race distance, average race speed and finishing status (qualified, eliminated or retired). We used three mixed models that included a fixed allele or genotype effect and a random, polygenic effect. Quantile-quantile plots were acceptable, and the regression coefficients for actual vs. expected log-values ranged from 0.865 to 1.055. The GWAS revealed five significant quantitative trait loci (QTL) corresponding to 6 SNPs on chromosomes 6, 1, 7, 16, and 29 (two SNPs) with corrected -values from 1.7 × 10 to 1.8 × 10. Annotation of these 5 QTL revealed two genes: sortilin-related VPS10-domain-containing receptor 3 () on chromosome 1 is involved in protein trafficking, and solute carrier family 39 member 12 () on chromosome 29 is active in zinc transport and cell homeostasis. These two coding genes could be involved in neuronal tissues (CNS). The other QTL on chromosomes 6, 7, and 16 may be involved in the regulation of the gene expression through non-coding RNAs, CpG islands and transcription factor binding sites. On chromosome 6, a new candidate equine long non-coding RNA ( ortholog: opposite antisense transcript 1 of potassium voltage-gated channel subfamily Q member 1 gene) was predicted and validated by RT-qPCR in primary cultures of equine myoblasts and fibroblasts. This lncRNA could be one element of the cardiac rhythm regulation. Our GWAS revealed that equine performance during endurance races is a complex polygenic trait, and is partially governed by at least 5 QTL: two coding genes involved in neuronal tissues and three other loci with many regulatory functions such as slowing down heart rate.
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Publication Date: 2017-06-28 PubMed ID: 28702049PubMed Central: PMC5488500DOI: 10.3389/fgene.2017.00089Google Scholar: Lookup
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  • Journal Article

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.

The research paper investigates the genetic factors influencing the physical endurance of Arabian horses. The researchers conducted a genome-wide association study to identify the specific single nucleotide polymorphisms (SNPs) that contribute to traits like racing speed, total race distance, and finishing status in endurance racing.

Methodology

  • The research team employed a genome-wide association study (GWAS), with the goal of identifying specific single nucleotide polymorphisms (SNPs) that are associated with endurance racing performance in Arabian horses.
  • They studied 597 Arabian horses and analyzed traits like total race distance, racing speed, and finishing status–whether the horse qualified, was eliminated or retired.
  • Three mixed models were used for the study. These models comprised a fixed allele or genotype effect, and a fluctuating polygenic effect.

Results

  • The GWAS revealed five significant quantitative trait loci (QTL) pertaining to 6 SNPs on five different chromosomes (6, 1, 7, 16, and 29).
  • Of these, two genes responsible for protein trafficking and zinc transport and cell homeostasis were identified on chromosome 1 and chromosome 29 respectively.
  • These identified genes, sortilin-related VPS10-domain-containing receptor 3 (SORCS3) and solute carrier family 39 member 12 (SLC39A12), both likely have roles in the central nervous system(CNS).
  • The QTLs on chromosomes 6, 7, and 16 could potentially be involved in regulating gene expression through non-coding RNAs, CpG islands, and transcription factor binding sites.
  • A new candidate equine long non-coding RNA (lncRNA) was predicted on chromosome 6, which might have a role in regulating cardiac rhythm.

Conclusion

  • The researchers concluded that the physical endurance of Arabian horses during a race is a complex trait determined by many genetic factors.
  • They identified at least 5 QTLs that play a significant role: two coding genes that function in the nervous tissues and three other locations that perform regulatory roles, such as controlling heart rate.

Implications

  • This research has potential implications for the selective breeding of Arabian horses. By understanding the genetic markers that influence endurance, breeders could potentially optimize these traits in future generations of racing horses.

Cite This Article

APA
Ricard A, Robert C, Blouin C, Baste F, Torquet G, Morgenthaler C, Rivière J, Mach N, Mata X, Schibler L, Barrey E. (2017). Endurance Exercise Ability in the Horse: A Trait with Complex Polygenic Determinism. Front Genet, 8, 89. https://doi.org/10.3389/fgene.2017.00089

Publication

Frontiers in genetics
ISSN: 1664-8021
NlmUniqueID: 101560621
Country: Switzerland
Language: English
Volume: 8
Pages: 89
PII: 89

Researcher Affiliations

Ricard, Anne
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
  • Institut Français du Cheval et de l'Equitation, Département Recherche et InnovationExmes, France.
Robert, Céline
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
  • Ecole Nationale Vétérinaire d'AlfortMaisons Alfort, France.
Blouin, Christine
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
Baste, Fanny
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
Torquet, Gwendoline
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
Morgenthaler, Caroline
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
Rivière, Julie
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
Mach, Nuria
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
Mata, Xavier
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
Schibler, Laurent
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.
Barrey, Eric
  • Institut National de la Recherche Agronomique, AgroParisTech, Université Paris Saclay, Département Sciences du Vivant, UMR 1313 Génétique Animale et Biologie IntégrativeJouy-en-Josas, France.

References

This article includes 58 references
  1. Andersson LS, Larhammar M, Memic F, Wootz H, Schwochow D, Rubin CJ, Patra K, Arnason T, Wellbring L, Hjälm G, Imsland F, Petersen JL, McCue ME, Mickelson JR, Cothran G, Ahituv N, Roepstorff L, Mikko S, Vallstedt A, Lindgren G, Andersson L, Kullander K. Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice.. Nature 2012 Aug 30;488(7413):642-6.
    doi: 10.1038/nature11399pmc: PMC3523687pubmed: 22932389google scholar: lookup
  2. Barrey E. Reviewe: Genetics and genomics in equine exercise physiology: an overview of the new applications of molecular biology as positive and negative markers of performance and health.. Equine Vet J Suppl 2010 Nov;(38):561-8.
    doi: 10.1111/j.2042-3306.2010.00299.xpubmed: 21059061google scholar: lookup
  3. Barrey E, Mucher E, Robert C, Amiot F, Gidrol X. Gene expression profiling in blood cells of endurance horses completing competition or disqualified due to metabolic disorder.. Equine Vet J Suppl 2006 Aug;(36):43-9.
    doi: 10.1111/j.2042-3306.2006.tb05511.xpubmed: 17402390google scholar: lookup
  4. Bayly WM, Grant BD, Breeze RG. The effects of maximal exercise on acid-base balance and arterial blood gas tension in Thoroughbred horses. 1983, in Equine Exercise Physiology, eds Snow D. H., Persson S. G. B., Rose R. J., Cambridge: Granta Editions, 400–407.
  5. Bishwajit G, Tang S, Yaya S, He Z, Feng Z. Lifestyle Behaviors, Subjective Health, and Quality of Life Among Chinese Men Living With Type 2 Diabetes.. Am J Mens Health 2017 Mar;11(2):357-364.
    doi: 10.1177/1557988316681128pmc: PMC5675285pubmed: 27923972google scholar: lookup
  6. Breiderhoff T, Christiansen GB, Pallesen LT, Vaegter C, Nykjaer A, Holm MM, Glerup S, Willnow TE. Sortilin-related receptor SORCS3 is a postsynaptic modulator of synaptic depression and fear extinction.. PLoS One 2013;8(9):e75006.
    doi: 10.1371/journal.pone.0075006pmc: PMC3777878pubmed: 24069373google scholar: lookup
  7. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering.. Am J Hum Genet 2007 Nov;81(5):1084-97.
    doi: 10.1086/521987pmc: PMC2265661pubmed: 17924348google scholar: lookup
  8. Capomaccio S, Cappelli K, Barrey E, Felicetti M, Silvestrelli M, Verini-Supplizi A. Microarray analysis after strenuous exercise in peripheral blood mononuclear cells of endurance horses.. Anim Genet 2010 Dec;41 Suppl 2:166-75.
    doi: 10.1111/j.1365-2052.2010.02129.xpubmed: 21070292google scholar: lookup
  9. Chen G, Wang Z, Wang D, Qiu C, Liu M, Chen X, Zhang Q, Yan G, Cui Q. LncRNADisease: a database for long-non-coding RNA-associated diseases.. Nucleic Acids Res 2013 Jan;41(Database issue):D983-6.
    doi: 10.1093/nar/gks1099pmc: PMC3531173pubmed: 23175614google scholar: lookup
  10. Chowanadisai W, Graham DM, Keen CL, Rucker RB, Messerli MA. Neurulation and neurite extension require the zinc transporter ZIP12 (slc39a12).. Proc Natl Acad Sci U S A 2013 Jun 11;110(24):9903-8.
    doi: 10.1073/pnas.1222142110pmc: PMC3683776pubmed: 23716681google scholar: lookup
  11. Clee SM, Yandell BS, Schueler KM, Rabaglia ME, Richards OC, Raines SM, Kabara EA, Klass DM, Mui ET, Stapleton DS, Gray-Keller MP, Young MB, Stoehr JP, Lan H, Boronenkov I, Raess PW, Flowers MT, Attie AD. Positional cloning of Sorcs1, a type 2 diabetes quantitative trait locus.. Nat Genet 2006 Jun;38(6):688-93.
    doi: 10.1038/ng1796pubmed: 16682971google scholar: lookup
  12. Druet T, Georges M. A hidden markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping.. Genetics 2010 Mar;184(3):789-98.
    doi: 10.1534/genetics.109.108431pmc: PMC2845346pubmed: 20008575google scholar: lookup
  13. Ghosh S, Qu Z, Das PJ, Fang E, Juras R, Cothran EG, McDonell S, Kenney DG, Lear TL, Adelson DL, Chowdhary BP, Raudsepp T. Copy number variation in the horse genome.. PLoS Genet 2014 Oct;10(10):e1004712.
    doi: 10.1371/journal.pgen.1004712pmc: PMC4207638pubmed: 25340504google scholar: lookup
  14. Goddard M. Genomic selection: prediction of accuracy and maximisation of long term response.. Genetica 2009 Jun;136(2):245-57.
    doi: 10.1007/s10709-008-9308-0pubmed: 18704696google scholar: lookup
  15. Goddard ME, Hayes BJ, Meuwissen TH. Using the genomic relationship matrix to predict the accuracy of genomic selection.. J Anim Breed Genet 2011 Dec;128(6):409-21.
    doi: 10.1111/j.1439-0388.2011.00964.xpubmed: 22059574google scholar: lookup
  16. Granhall C, Park HB, Fakhrai-Rad H, Luthman H. High-resolution quantitative trait locus analysis reveals multiple diabetes susceptibility loci mapped to intervals<800 kb in the species-conserved Niddm1i of the GK rat.. Genetics 2006 Nov;174(3):1565-72.
    doi: 10.1534/genetics.106.062208pmc: PMC1667097pubmed: 16951059google scholar: lookup
  17. Guttman M, Amit I, Garber M, French C, Lin MF, Feldser D, Huarte M, Zuk O, Carey BW, Cassady JP, Cabili MN, Jaenisch R, Mikkelsen TS, Jacks T, Hacohen N, Bernstein BE, Kellis M, Regev A, Rinn JL, Lander ES. Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals.. Nature 2009 Mar 12;458(7235):223-7.
    doi: 10.1038/nature07672pmc: PMC2754849pubmed: 19182780google scholar: lookup
  18. Hermey G, Mahlke C, Gutzmann JJ, Schreiber J, Blüthgen N, Kuhl D. Genome-wide profiling of the activity-dependent hippocampal transcriptome.. PLoS One 2013;8(10):e76903.
    doi: 10.1371/journal.pone.0076903pmc: PMC3798291pubmed: 24146943google scholar: lookup
  19. Hermey G, Plath N, Hübner CA, Kuhl D, Schaller HC, Hermans-Borgmeyer I. The three sorCS genes are differentially expressed and regulated by synaptic activity.. J Neurochem 2004 Mar;88(6):1470-6.
    doi: 10.1046/j.1471-4159.2004.02286.xpubmed: 15009648google scholar: lookup
  20. Hill EW, McGivney BA, Gu J, Whiston R, Machugh DE. A genome-wide SNP-association study confirms a sequence variant (g.66493737C>T) in the equine myostatin (MSTN) gene as the most powerful predictor of optimum racing distance for Thoroughbred racehorses.. BMC Genomics 2010 Oct 11;11:552.
    doi: 10.1186/1471-2164-11-552pmc: PMC3091701pubmed: 20932346google scholar: lookup
  21. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.. Nat Protoc 2009;4(1):44-57.
    doi: 10.1038/nprot.2008.211pubmed: 19131956google scholar: lookup
  22. Jedrychowski MP, Gartner CA, Gygi SP, Zhou L, Herz J, Kandror KV, Pilch PF. Proteomic analysis of GLUT4 storage vesicles reveals LRP1 to be an important vesicle component and target of insulin signaling.. J Biol Chem 2010 Jan 1;285(1):104-14.
    doi: 10.1074/jbc.M109.040428pmc: PMC2804154pubmed: 19864425google scholar: lookup
  23. Kanagasabai T, Riddell MC, Ardern CI. Physical Activity Contributes to Several Sleep-Cardiometabolic Health Relationships.. Metab Syndr Relat Disord 2017 Feb;15(1):44-51.
    doi: 10.1089/met.2016.0103pubmed: 27925858google scholar: lookup
  24. Kemper KE, Emery DL, Bishop SC, Oddy H, Hayes BJ, Dominik S, Henshall JM, Goddard ME. The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections.. Genet Res (Camb) 2011 Jun;93(3):203-19.
    doi: 10.1017/S0016672311000097pubmed: 24725775google scholar: lookup
  25. Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data.. Nucleic Acids Res 2014 Jan;42(Database issue):D68-73.
    doi: 10.1093/nar/gkt1181pmc: PMC3965103pubmed: 24275495google scholar: lookup
  26. Lane RF, St George-Hyslop P, Hempstead BL, Small SA, Strittmatter SM, Gandy S. Vps10 family proteins and the retromer complex in aging-related neurodegeneration and diabetes.. J Neurosci 2012 Oct 10;32(41):14080-6.
    doi: 10.1523/JNEUROSCI.3359-12.2012pmc: PMC3576841pubmed: 23055476google scholar: lookup
  27. Mach N, Plancade S, Pacholewska A, Lecardonnel J, Rivière J, Moroldo M, Vaiman A, Morgenthaler C, Beinat M, Nevot A, Robert C, Barrey E. Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse.. Sci Rep 2016 Mar 10;6:22932.
    doi: 10.1038/srep22932pmc: PMC4785432pubmed: 26960911google scholar: lookup
  28. Maltese PE, Orlova N, Krasikova E, Emelyanchik E, Cheremisina A, Kuscaeva A, Salmina A, Miotto R, Bonizzato A, Guerri G, Zuntini M, Nicoulina S, Bertelli M. Gene-Targeted Analysis of Clinically Diagnosed Long QT Russian Families.. Int Heart J 2017 Feb 7;58(1):81-87.
    doi: 10.1536/ihj.16-133pubmed: 28003625google scholar: lookup
  29. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TF, McCarroll SA, Visscher PM. Finding the missing heritability of complex diseases.. Nature 2009 Oct 8;461(7265):747-53.
    doi: 10.1038/nature08494pmc: PMC2831613pubmed: 19812666google scholar: lookup
  30. McCarthy MI, Hirschhorn JN. Genome-wide association studies: potential next steps on a genetic journey.. Hum Mol Genet 2008 Oct 15;17(R2):R156-65.
    doi: 10.1093/hmg/ddn289pmc: PMC2782356pubmed: 18852205google scholar: lookup
  31. McKay SD, Schnabel RD, Murdoch BM, Matukumalli LK, Aerts J, Coppieters W, Crews D, Dias Neto E, Gill CA, Gao C, Mannen H, Stothard P, Wang Z, Van Tassell CP, Williams JL, Taylor JF, Moore SS. Whole genome linkage disequilibrium maps in cattle.. BMC Genet 2007 Oct 25;8:74.
    doi: 10.1186/1471-2156-8-74pmc: PMC2174945pubmed: 17961247google scholar: lookup
  32. Misztal I, Tsuruta S, Strabel T, Auvray B, Druet T, Lee DH. ÒBLUPF90 and related programs (BGF90),Ó. 2002, in Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, vol. 28, 19th August 2002, Communication No. 28–27, Montpellier, 21–22.
  33. Mohammad F, Pandey RR, Nagano T, Chakalova L, Mondal T, Fraser P, Kanduri C. Kcnq1ot1/Lit1 noncoding RNA mediates transcriptional silencing by targeting to the perinucleolar region.. Mol Cell Biol 2008 Jun;28(11):3713-28.
    doi: 10.1128/MCB.02263-07pmc: PMC2423283pubmed: 18299392google scholar: lookup
  34. Neilson HK, Friedenreich CM, Brockton NT, Millikan RC. Physical activity and postmenopausal breast cancer: proposed biologic mechanisms and areas for future research.. Cancer Epidemiol Biomarkers Prev 2009 Jan;18(1):11-27.
    doi: 10.1158/1055-9965.EPI-08-0756pubmed: 19124476google scholar: lookup
  35. Noakes TD. Fatigue is a Brain-Derived Emotion that Regulates the Exercise Behavior to Ensure the Protection of Whole Body Homeostasis.. Front Physiol 2012;3:82.
    doi: 10.3389/fphys.2012.00082pmc: PMC3323922pubmed: 22514538google scholar: lookup
  36. Nyman G, Bjšrk M, Funkquist P. Ventilation-perfusion relationships during graded exercise in the Standardbred trotter. Equine Vet. J. Suppl. 1995;18:63–69.
    doi: 10.1111/j.2042-3306.1995.tb04892.xgoogle scholar: lookup
  37. Pandey RR, Mondal T, Mohammad F, Enroth S, Redrup L, Komorowski J, Nagano T, Mancini-Dinardo D, Kanduri C. Kcnq1ot1 antisense noncoding RNA mediates lineage-specific transcriptional silencing through chromatin-level regulation.. Mol Cell 2008 Oct 24;32(2):232-46.
    doi: 10.1016/j.molcel.2008.08.022pubmed: 18951091google scholar: lookup
  38. Paterson AD, Waggott D, Boright AP, Hosseini SM, Shen E, Sylvestre MP, Wong I, Bharaj B, Cleary PA, Lachin JM, Below JE, Nicolae D, Cox NJ, Canty AJ, Sun L, Bull SB. A genome-wide association study identifies a novel major locus for glycemic control in type 1 diabetes, as measured by both A1C and glucose.. Diabetes 2010 Feb;59(2):539-49.
    doi: 10.2337/db09-0653pmc: PMC2809960pubmed: 19875614google scholar: lookup
  39. Pérusse L, Rankinen T, Hagberg JM, Loos RJ, Roth SM, Sarzynski MA, Wolfarth B, Bouchard C. Advances in exercise, fitness, and performance genomics in 2012.. Med Sci Sports Exerc 2013 May;45(5):824-31.
    doi: 10.1249/MSS.0b013e31828b28a3pmc: PMC3640622pubmed: 23470294google scholar: lookup
  40. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Capomaccio S, Cappelli K, Cothran EG, Distl O, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Swinburne J, Tozaki T, Vaudin M, Wade CM, McCue ME. Genome-wide analysis reveals selection for important traits in domestic horse breeds.. PLoS Genet 2013;9(1):e1003211.
    doi: 10.1371/journal.pgen.1003211pmc: PMC3547851pubmed: 23349635google scholar: lookup
  41. Quek XC, Thomson DW, Maag JL, Bartonicek N, Signal B, Clark MB, Gloss BS, Dinger ME. lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs.. Nucleic Acids Res 2015 Jan;43(Database issue):D168-73.
    doi: 10.1093/nar/gku988pmc: PMC4384040pubmed: 25332394google scholar: lookup
  42. Rankinen T, Sung YJ, Sarzynski MA, Rice TK, Rao DC, Bouchard C. Heritability of submaximal exercise heart rate response to exercise training is accounted for by nine SNPs.. J Appl Physiol (1985) 2012 Mar;112(5):892-7.
    doi: 10.1152/japplphysiol.01287.2011pmc: PMC3311659pubmed: 22174390google scholar: lookup
  43. Reitz C, Tosto G, Vardarajan B, Rogaeva E, Ghani M, Rogers RS, Conrad C, Haines JL, Pericak-Vance MA, Fallin MD, Foroud T, Farrer LA, Schellenberg GD, George-Hyslop PS, Mayeux R. Independent and epistatic effects of variants in VPS10-d receptors on Alzheimer disease risk and processing of the amyloid precursor protein (APP).. Transl Psychiatry 2013 May 14;3(5):e256.
    doi: 10.1038/tp.2013.13pmc: PMC3669917pubmed: 23673467google scholar: lookup
  44. Ricard A. Does heterozygosity at the DMRT3 gene make French trotters better racers?. Genet Sel Evol 2015 Feb 26;47(1):10.
    doi: 10.1186/s12711-015-0095-7pmc: PMC4340234pubmed: 25886871google scholar: lookup
  45. Ricard A, Touvais M. Genetic parameters of performance traits in horse endurance races. Livest Sci 2007;110:118–125.
    doi: 10.1016/j.livsci.2006.10.008google scholar: lookup
  46. Rice P, Longden I, Bleasby A. EMBOSS: the European Molecular Biology Open Software Suite.. Trends Genet 2000 Jun;16(6):276-7.
    doi: 10.1016/S0168-9525(00)02024-2pubmed: 10827456google scholar: lookup
  47. Rice TK, Sarzynski MA, Sung YJ, Argyropoulos G, Stütz AM, Teran-Garcia M, Rao DC, Bouchard C, Rankinen T. Fine mapping of a QTL on chromosome 13 for submaximal exercise capacity training response: the HERITAGE Family Study.. Eur J Appl Physiol 2012 Aug;112(8):2969-78.
    doi: 10.1007/s00421-011-2274-8pmc: PMC4109813pubmed: 22170014google scholar: lookup
  48. Self SG, Liang KY. Asymptotic properties of maximum-likelihood estimators and likelihood ratio tests under nonstandard conditions. J. Amer. Stat. Ass. 1987;82:605–610.
    doi: 10.1080/01621459.1987.10478472google scholar: lookup
  49. Signer-Hasler H, Flury C, Haase B, Burger D, Simianer H, Leeb T, Rieder S. A genome-wide association study reveals loci influencing height and other conformation traits in horses.. PLoS One 2012;7(5):e37282.
    doi: 10.1371/journal.pone.0037282pmc: PMC3353922pubmed: 22615965google scholar: lookup
  50. Sullivan S, Hinchcliff K. Update on exercise-induced pulmonary hemorrhage.. Vet Clin North Am Equine Pract 2015 Apr;31(1):187-98.
    doi: 10.1016/j.cveq.2014.11.011pubmed: 25770069google scholar: lookup
  51. Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, Paraskevopoulou MD, Prionidis K, Dalamagas T, Hatzigeorgiou AG. DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways.. Nucleic Acids Res 2012 Jul;40(Web Server issue):W498-504.
    doi: 10.1093/nar/gks494pmc: PMC3394305pubmed: 22649059google scholar: lookup
  52. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR, Blöcker H, Distl O, Edgar RC, Garber M, Leeb T, Mauceli E, MacLeod JN, Penedo MC, Raison JM, Sharpe T, Vogel J, Andersson L, Antczak DF, Biagi T, Binns MM, Chowdhary BP, Coleman SJ, Della Valle G, Fryc S, Guérin G, Hasegawa T, Hill EW, Jurka J, Kiialainen A, Lindgren G, Liu J, Magnani E, Mickelson JR, Murray J, Nergadze SG, Onofrio R, Pedroni S, Piras MF, Raudsepp T, Rocchi M, Røed KH, Ryder OA, Searle S, Skow L, Swinburne JE, Syvänen AC, Tozaki T, Valberg SJ, Vaudin M, White JR, Zody MC, Lander ES, Lindblad-Toh K. Genome sequence, comparative analysis, and population genetics of the domestic horse.. Science 2009 Nov 6;326(5954):865-7.
    doi: 10.1126/science.1178158pmc: PMC3785132pubmed: 19892987google scholar: lookup
  53. Wagner PD, Gillespie JR, Landgren GL, Fedde MR, Jones BW, DeBowes RM, Pieschl RL, Erickson HH. Mechanism of exercise-induced hypoxemia in horses.. J Appl Physiol (1985) 1989 Mar;66(3):1227-33.
    pubmed: 2496088doi: 10.1152/jappl.1989.66.3.1227google scholar: lookup
  54. Wang X, El Naqa IM. Prediction of both conserved and nonconserved microRNA targets in animals.. Bioinformatics 2008 Feb 1;24(3):325-32.
    doi: 10.1093/bioinformatics/btm595pubmed: 18048393google scholar: lookup
  55. Westergaard UB, Kirkegaard K, Sørensen ES, Jacobsen C, Nielsen MS, Petersen CM, Madsen P. SorCS3 does not require propeptide cleavage to bind nerve growth factor.. FEBS Lett 2005 Feb 14;579(5):1172-6.
    doi: 10.1016/j.febslet.2004.12.088pubmed: 15710408google scholar: lookup
  56. Smith JL, Anderson CL, Burgess DE, Elayi CS, January CT, Delisle BP. Molecular pathogenesis of long QT syndrome type 2.. J Arrhythm 2016 Oct;32(5):373-380.
    doi: 10.1016/j.joa.2015.11.009pmc: PMC5063260pubmed: 27761161google scholar: lookup
  57. Younes M, Robert C, Cottin F, Barrey E. Speed and Cardiac Recovery Variables Predict the Probability of Elimination in Equine Endurance Events.. PLoS One 2015;10(8):e0137013.
    doi: 10.1371/journal.pone.0137013pmc: PMC4556447pubmed: 26322506google scholar: lookup
  58. Zhao L, Oliver E, Maratou K, Atanur SS, Dubois OD, Cotroneo E, Chen CN, Wang L, Arce C, Chabosseau PL, Ponsa-Cobas J, Frid MG, Moyon B, Webster Z, Aldashev A, Ferrer J, Rutter GA, Stenmark KR, Aitman TJ, Wilkins MR. The zinc transporter ZIP12 regulates the pulmonary vascular response to chronic hypoxia.. Nature 2015 Aug 20;524(7565):356-60.
    doi: 10.1038/nature14620pmc: PMC6091855pubmed: 26258299google scholar: lookup

Citations

This article has been cited 27 times.
  1. Barłowska J, Polak G, Janczarek I, Tkaczyk E. The Influence of Selected Factors on the Nutritional Value of the Milk of Cold-Blooded Mares: The Example of the Sokólski Breed. Animals (Basel) 2023 Mar 24;13(7).
    doi: 10.3390/ani13071152pubmed: 37048410google scholar: lookup
  2. Han H, McGivney BA, Allen L, Bai D, Corduff LR, Davaakhuu G, Davaasambuu J, Dorjgotov D, Hall TJ, Hemmings AJ, Holtby AR, Jambal T, Jargalsaikhan B, Jargalsaikhan U, Kadri NK, MacHugh DE, Pausch H, Readhead C, Warburton D, Dugarjaviin M, Hill EW. Common protein-coding variants influence the racing phenotype in galloping racehorse breeds. Commun Biol 2022 Dec 13;5(1):1320.
    doi: 10.1038/s42003-022-04206-xpubmed: 36513809google scholar: lookup
  3. Mach N, Midoux C, Leclercq S, Pennarun S, Le Moyec L, Rué O, Robert C, Sallé G, Barrey E. Mining the equine gut metagenome: poorly-characterized taxa associated with cardiovascular fitness in endurance athletes. Commun Biol 2022 Oct 3;5(1):1032.
    doi: 10.1038/s42003-022-03977-7pubmed: 36192523google scholar: lookup
  4. Davis DN, Strong MD, Chambers E, Hart MD, Bettaieb A, Clarke SL, Smith BJ, Stoecker BJ, Lucas EA, Lin D, Chowanadisai W. A role for zinc transporter gene SLC39A12 in the nervous system and beyond. Gene 2021 Oct 5;799:145824.
    doi: 10.1016/j.gene.2021.145824pubmed: 34252531google scholar: lookup
  5. Ducret V, Richards AJ, Videlier M, Scalvenzi T, Moore KA, Paszkiewicz K, Bonneaud C, Pollet N, Herrel A. Transcriptomic analysis of the trade-off between endurance and burst-performance in the frog Xenopus allofraseri. BMC Genomics 2021 Mar 23;22(1):204.
    doi: 10.1186/s12864-021-07517-1pubmed: 33757428google scholar: lookup
  6. Dhorne-Pollet S, Barrey E, Pollet N. A new method for long-read sequencing of animal mitochondrial genomes: application to the identification of equine mitochondrial DNA variants. BMC Genomics 2020 Nov 11;21(1):785.
    doi: 10.1186/s12864-020-07183-9pubmed: 33176683google scholar: lookup
  7. Littiere TO, Castro GHF, Rodriguez MDPR, Bonafé CM, Magalhães AFB, Faleiros RR, Vieira JIG, Santos CG, Verardo LL. Identification and Functional Annotation of Genes Related to Horses' Performance: From GWAS to Post-GWAS. Animals (Basel) 2020 Jul 10;10(7).
    doi: 10.3390/ani10071173pubmed: 32664293google scholar: lookup
  8. Cosgrove EJ, Sadeghi R, Schlamp F, Holl HM, Moradi-Shahrbabak M, Miraei-Ashtiani SR, Abdalla S, Shykind B, Troedsson M, Stefaniuk-Szmukier M, Prabhu A, Bucca S, Bugno-Poniewierska M, Wallner B, Malek J, Miller DC, Clark AG, Antczak DF, Brooks SA. Genome Diversity and the Origin of the Arabian Horse. Sci Rep 2020 Jun 16;10(1):9702.
    doi: 10.1038/s41598-020-66232-1pubmed: 32546689google scholar: lookup
  9. Raudsepp T, Finno CJ, Bellone RR, Petersen JL. Ten years of the horse reference genome: insights into equine biology, domestication and population dynamics in the post-genome era. Anim Genet 2019 Dec;50(6):569-597.
    doi: 10.1111/age.12857pubmed: 31568563google scholar: lookup
  10. Ropka-Molik K, Stefaniuk-Szmukier M, Musiał AD, Velie BD. The Genetics of Racing Performance in Arabian Horses. Int J Genomics 2019;2019:9013239.
    doi: 10.1155/2019/9013239pubmed: 31565654google scholar: lookup
  11. Ropka-Molik K, Stefaniuk-Szmukier M, Szmatoła T, Piórkowska K, Bugno-Poniewierska M. The use of the SLC16A1 gene as a potential marker to predict race performance in Arabian horses. BMC Genet 2019 Sep 11;20(1):73.
    doi: 10.1186/s12863-019-0774-4pubmed: 31510920google scholar: lookup
  12. van den Berg S, Vandenplas J, van Eeuwijk FA, Lopes MS, Veerkamp RF. Significance testing and genomic inflation factor using high-density genotypes or whole-genome sequence data. J Anim Breed Genet 2019 Nov;136(6):418-429.
    doi: 10.1111/jbg.12419pubmed: 31215703google scholar: lookup
  13. Abri MAA, Faye B. Genetic Improvement in Dromedary Camels: Challenges and Opportunities. Front Genet 2019;10:167.
    doi: 10.3389/fgene.2019.00167pubmed: 30915101google scholar: lookup
  14. Velie BD, Lillie M, Fegraeus KJ, Rosengren MK, Solé M, Wiklund M, Ihler CF, Strand E, Lindgren G. Exploring the genetics of trotting racing ability in horses using a unique Nordic horse model. BMC Genomics 2019 Feb 4;20(1):104.
    doi: 10.1186/s12864-019-5484-9pubmed: 30717660google scholar: lookup
  15. Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Szmatoła T, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. A genome-wide scan for diversifying selection signatures in selected horse breeds. PLoS One 2019;14(1):e0210751.
    doi: 10.1371/journal.pone.0210751pubmed: 30699152google scholar: lookup
  16. van den Berg S, Vandenplas J, van Eeuwijk FA, Bouwman AC, Lopes MS, Veerkamp RF. Imputation to whole-genome sequence using multiple pig populations and its use in genome-wide association studies. Genet Sel Evol 2019 Jan 24;51(1):2.
    doi: 10.1186/s12711-019-0445-ypubmed: 30678638google scholar: lookup
  17. Velie BD, Fegraeus KJ, Solé M, Rosengren MK, Røed KH, Ihler CF, Strand E, Lindgren G. A genome-wide association study for harness racing success in the Norwegian-Swedish coldblooded trotter reveals genes for learning and energy metabolism. BMC Genet 2018 Aug 29;19(1):80.
    doi: 10.1186/s12863-018-0670-3pubmed: 30157760google scholar: lookup
  18. Yuan X, Yao X, Zeng Y, Wang J, Ren W, Wang T, Li X, Yang L, Yang X, Meng J. The Effect of Training on the Expression of Protein and Metabolites in the Plasma Exosomes of the Yili Horse. Animals (Basel) 2026 Jan 6;16(2).
    doi: 10.3390/ani16020158pubmed: 41594348google scholar: lookup
  19. Jafari H, Abebe BK, Cong L, Ahmed Z, Zhaofei W, Sun M, Muhatai G, Chuzhao L, Dang R. Review: Genomic insights into the adaptive traits and stress resistance in modern horses. Stress Biol 2026 Jan 12;6(1):5.
    doi: 10.1007/s44154-025-00274-1pubmed: 41521281google scholar: lookup
  20. Sharma M, Singh A, Kumar V, Olla N, Arora R, Sharma R, Mohan NH, Ahlawat S. Advances in Equine Genomics: Decoding the Genetic Architecture of Morphology, Performance, Behavior, and Adaptation. Mol Biotechnol 2025 Dec 19;.
    doi: 10.1007/s12033-025-01544-zpubmed: 41417456google scholar: lookup
  21. Stefaniuk-Szmukier M, Szmatoła T, Ropka-Molik K. Molecular Signatures of Exercise Adaptation in Arabian Racing Horses: Transcriptomic Insights from Blood and Muscle. Genes (Basel) 2025 Apr 4;16(4).
    doi: 10.3390/genes16040431pubmed: 40282391google scholar: lookup
  22. Ding W, Gong W, Bou T, Shi L, Lin Y, Wu H, Dugarjaviin M, Bai D. Pilot Study on the Profiling and Functional Analysis of mRNA, miRNA, and lncRNA in the Skeletal Muscle of Mongolian Horses, Xilingol Horses, and Grassland-Thoroughbreds. Animals (Basel) 2025 Apr 13;15(8).
    doi: 10.3390/ani15081123pubmed: 40281957google scholar: lookup
  23. Boozarjomehri Amnieh S, Hassanpour A, Moghaddam S, Sakhaee F, Ropka-Molik K. Study of Variation of ACOX1 Gene Among Different Horse Breeds Maintained in Iran. Animals (Basel) 2024 Dec 10;14(24).
    doi: 10.3390/ani14243566pubmed: 39765470google scholar: lookup
  24. Wang C, Zeng Y, Wang J, Wang T, Li X, Shen Z, Meng J, Yao X. A genome-wide association study of the racing performance traits in Yili horses based on Blink and FarmCPU models. Sci Rep 2024 Nov 12;14(1):27648.
    doi: 10.1038/s41598-024-79014-wpubmed: 39532956google scholar: lookup
  25. Sousa Junior LPB, Pinto LFB, Cruz VAR, Oliveira Junior GA, Oliveira HR, Chud TS, Pedrosa VB, Miglior F, Schenkel FS, Brito LF. Genome-wide association and functional genomic analyses for body conformation traits in North American Holstein cattle. Front Genet 2024;15:1478788.
    doi: 10.3389/fgene.2024.1478788pubmed: 39512801google scholar: lookup
  26. Reißmann M, Rajavel A, Kokov ZA, Schmitt AO. Identification of Differentially Expressed Genes after Endurance Runs in Karbadian Horses to Determine Candidates for Stress Indicators and Performance Capability. Genes (Basel) 2023 Oct 24;14(11).
    doi: 10.3390/genes14111982pubmed: 38002925google scholar: lookup
  27. Massányi M, Halo M, Mlyneková E, Kováčiková E, Tokárová K, Greń A, Massányi P, Halo M. The effect of training load stress on salivary cortisol concentrations, health parameters and hematological parameters in horses. Heliyon 2023 Aug;9(8):e19037.
    doi: 10.1016/j.heliyon.2023.e19037pubmed: 37636408google scholar: lookup
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