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BMC genomics2010; 11; 552; doi: 10.1186/1471-2164-11-552

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.

Abstract: Thoroughbred horses have been selected for traits contributing to speed and stamina for centuries. It is widely recognized that inherited variation in physical and physiological characteristics is responsible for variation in individual aptitude for race distance, and that muscle phenotypes in particular are important. Results: A genome-wide SNP-association study for optimum racing distance was performed using the EquineSNP50 Bead Chip genotyping array in a cohort of n = 118 elite Thoroughbred racehorses divergent for race distance aptitude. In a cohort-based association test we evaluated genotypic variation at 40,977 SNPs between horses suited to short distance (≤ 8 f) and middle-long distance (> 8 f) races. The most significant SNP was located on chromosome 18: BIEC2-417495 ~690 kb from the gene encoding myostatin (MSTN) [P(unadj.) = 6.96 x 10⁻⁶]. Considering best race distance as a quantitative phenotype, a peak of association on chromosome 18 (chr18:65809482-67545806) comprising eight SNPs encompassing a 1.7 Mb region was observed. Again, similar to the cohort-based analysis, the most significant SNP was BIEC2-417495 (P(unadj.) = 1.61 x 10⁻⁹; P(Bonf.) = 6.58 x 10⁻⁵). In a candidate gene study we have previously reported a SNP (g.66493737C>T) in MSTN associated with best race distance in Thoroughbreds; however, its functional and genome-wide relevance were uncertain. Additional re-sequencing in the flanking regions of the MSTN gene revealed four novel 3' UTR SNPs and a 227 bp SINE insertion polymorphism in the 5' UTR promoter sequence. Linkage disequilibrium was highest between g.66493737C>T and BIEC2-417495 (r² = 0.86). Conclusions: Comparative association tests consistently demonstrated the g.66493737C>T SNP as the superior variant in the prediction of distance aptitude in racehorses (g.66493737C>T, P = 1.02 x 10⁻¹⁰; BIEC2-417495, P(unadj.) = 1.61 x 10⁻⁹). Functional investigations will be required to determine whether this polymorphism affects putative transcription-factor binding and gives rise to variation in gene and protein expression. Nonetheless, this study demonstrates that the g.66493737C>T SNP provides the most powerful genetic marker for prediction of race distance aptitude in Thoroughbreds.
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Publication Date: 2010-10-11 PubMed ID: 20932346PubMed Central: PMC3091701DOI: 10.1186/1471-2164-11-552Google Scholar: Lookup
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Summary

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This research explored the genetic basis for optimum racing distance in Thoroughbred racehorses, identifying a variant in the equine myostatin gene as the strongest indicator.

Study Overview

  • The researchers conducted a genome-wide SNP (single nucleotide polymorphism) association study on a group of 118 elite Thoroughbred racehorses. Their main area of interest was to identify genetic variations linked to optimum racing distance for different horses.
  • The horses under study were divided into two categories: those better suited for short distance races (less than or equal to 8 furlongs), and those for middle-long distance races (greater than 8 furlongs). The researchers then analyzed close to 41,000 SNPs across the genomes of these horses to identify any significant genetic variations between the two groups.

Main Findings

  • The study found that the most significant SNP was located on chromosome 18, approximately 690 kb from the gene encoding myostatin (MSTN), a protein that limits muscle growth and differentiation.
  • The same region of chromosome 18 also featured a “peak of association” with best racing distance, as indicated by eight SNPs within a 1.7 Mb section. This implies a strong connection between this chromosomal region and the ability to race over certain distances.
  • Among these SNPs, the one known as BIEC2-417495 stood out as the most significant in both analyses.

Validation of Prior Findings

  • The researchers previously reported another SNP (g.66493737C>T) in the MSTN gene related to race distance in Thoroughbreds. The current study confirmed that this SNP is indeed important.
  • Additional sequencing revealed other polymorphisms in the vicinity of the MSTN gene, including a SINE (short interspersed nuclear element) insertion and four novel 3′ UTR (untranslated region) SNPs. However, the original SNP (g.66493737C>T) had the highest linkage disequilibrium with BIEC2-417495, reinforcing its relevance.

Conclusion and Future Directions

  • The g.66493737C>T SNP in the MSTN gene emerged as the most powerful genetic marker for predicting race distance aptitude in Thoroughbred horses. However, functional studies are necessary to elucidate whether and how this polymorphism affects gene and protein expression, and in turn, racing capacities.
  • Such research could have significant implications not just for racehorse breeding, but also for our understanding of genetic determinants of athletic performance in other species.

Cite This Article

APA
Hill EW, McGivney BA, Gu J, Whiston R, Machugh DE. (2010). 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, 11, 552. https://doi.org/10.1186/1471-2164-11-552

Publication

BMC genomics
ISSN: 1471-2164
NlmUniqueID: 100965258
Country: England
Language: English
Volume: 11
Pages: 552

Researcher Affiliations

Hill, Emmeline W
  • Animal Genomics Laboratory, School of Agriculture, Food Science and Veterinary Medicine, University College Dublin, Belfield, Dublin 4, Ireland. emmeline.hill@ucd.ie
McGivney, Beatrice A
    Gu, Jingjing
      Whiston, Ronan
        Machugh, David E

          MeSH Terms

          • Animals
          • Base Pairing / genetics
          • Base Sequence
          • Breeding
          • Chromosomes, Mammalian / genetics
          • Cohort Studies
          • DNA, Intergenic / genetics
          • Genome-Wide Association Study / methods
          • Haplotypes / genetics
          • Horses / genetics
          • Linkage Disequilibrium / genetics
          • Myostatin / genetics
          • Phenotype
          • Polymerase Chain Reaction
          • Polymorphism, Single Nucleotide / genetics
          • Sequence Analysis, DNA
          • Sports

          References

          This article includes 34 references
          1. Willett P. The classic racehorse. London: Stanley Paul; 1981.
          2. Williamson SA, Beilharz RG. The inheritance of speed, stamina and other racing performance characters in the Australian Thoroughbred. J Anim Breed Genet 1998;115(1):1–16.
          3. Barrey E, Valette JP, Jouglin M, Blouin C, Langlois B. Heritability of percentage of fast myosin heavy chains in skeletal muscles and relationship with performance.. Equine Vet J Suppl 1999 Jul;(30):289-92.
            pubmed: 10659270doi: 10.1111/j.2042-3306.1999.tb05236.xgoogle scholar: lookup
          4. Rivero JL, Serrano AL, Henckel P, Agüera E. Muscle fiber type composition and fiber size in successfully and unsuccessfully endurance-raced horses.. J Appl Physiol (1985) 1993 Oct;75(4):1758-66.
            pubmed: 8282629doi: 10.1152/jappl.1993.75.4.1758google scholar: lookup
          5. Rivero J-L, Piercy RJ. Muscle physiology: responses to exercise and training. In: Equine exercise physiology: the science of exercise in the athletic horse. Hinchcliff KW, Kaneps AJ, Geor RJ, editor. ix. Edinburgh: Elsevier Saunders; 2008; p. 463.
          6. Hill EW, Gu J, Eivers SS, Fonseca RG, McGivney BA, Govindarajan P, Orr N, Katz LM, MacHugh DE. A sequence polymorphism in MSTN predicts sprinting ability and racing stamina in thoroughbred horses.. PLoS One 2010 Jan 20;5(1):e8645.
            doi: 10.1371/journal.pone.0008645pmc: PMC2808334pubmed: 20098749google scholar: lookup
          7. Grobet L, Martin LJ, Poncelet D, Pirottin D, Brouwers B, Riquet J, Schoeberlein A, Dunner S, Ménissier F, Massabanda J, Fries R, Hanset R, Georges M. A deletion in the bovine myostatin gene causes the double-muscled phenotype in cattle.. Nat Genet 1997 Sep;17(1):71-4.
            doi: 10.1038/ng0997-71pubmed: 9288100google scholar: lookup
          8. McPherron AC, Lawler AM, Lee SJ. Regulation of skeletal muscle mass in mice by a new TGF-beta superfamily member.. Nature 1997 May 1;387(6628):83-90.
            doi: 10.1038/387083a0pubmed: 9139826google scholar: lookup
          9. McPherron AC, Lee SJ. Double muscling in cattle due to mutations in the myostatin gene.. Proc Natl Acad Sci U S A 1997 Nov 11;94(23):12457-61.
            doi: 10.1073/pnas.94.23.12457pmc: PMC24998pubmed: 9356471google scholar: lookup
          10. Mosher DS, Quignon P, Bustamante CD, Sutter NB, Mellersh CS, Parker HG, Ostrander EA. A mutation in the myostatin gene increases muscle mass and enhances racing performance in heterozygote dogs.. PLoS Genet 2007 May 25;3(5):e79.
            doi: 10.1371/journal.pgen.0030079pmc: PMC1877876pubmed: 17530926google scholar: lookup
          11. Schuelke M, Wagner KR, Stolz LE, Hübner C, Riebel T, Kömen W, Braun T, Tobin JF, Lee SJ. Myostatin mutation associated with gross muscle hypertrophy in a child.. N Engl J Med 2004 Jun 24;350(26):2682-8.
            doi: 10.1056/NEJMoa040933pubmed: 15215484google scholar: lookup
          12. Sambrook J, Russell DW. Molecular cloning: a laboratory manual. 3. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press; 2001.
          13. 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
          14. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses.. Am J Hum Genet 2007 Sep;81(3):559-75.
            doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
          15. Barrett JC. Haploview: Visualization and analysis of SNP genotype data.. Cold Spring Harb Protoc 2009 Oct;2009(10):pdb.ip71.
            pubmed: 20147036doi: 10.1101/pdb.ip71google scholar: lookup
          16. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps.. Bioinformatics 2005 Jan 15;21(2):263-5.
            doi: 10.1093/bioinformatics/bth457pubmed: 15297300google scholar: lookup
          17. Rozen S, Skaletsky H. Primer3 on the WWW for general users and for biologist programmers.. Methods Mol Biol 2000;132:365-86.
            pubmed: 10547847doi: 10.1385/1-59259-192-2:365google scholar: lookup
          18. van Baren MJ, Heutink P. The PCR suite.. Bioinformatics 2004 Mar 1;20(4):591-3.
            doi: 10.1093/bioinformatics/btg473pubmed: 14751986google scholar: lookup
          19. Gordon D, Abajian C, Green P. Consed: a graphical tool for sequence finishing.. Genome Res 1998 Mar;8(3):195-202.
            pubmed: 9521923doi: 10.1101/gr.8.3.195google scholar: lookup
          20. Cartharius K, Frech K, Grote K, Klocke B, Haltmeier M, Klingenhoff A, Frisch M, Bayerlein M, Werner T. MatInspector and beyond: promoter analysis based on transcription factor binding sites.. Bioinformatics 2005 Jul 1;21(13):2933-42.
            doi: 10.1093/bioinformatics/bti473pubmed: 15860560google scholar: lookup
          21. Zhou M, Wang Q, Sun J, Li X, Xu L, Yang H, Shi H, Ning S, Chen L, Li Y, He T, Zheng Y. In silico detection and characteristics of novel microRNA genes in the Equus caballus genome using an integrated ab initio and comparative genomic approach.. Genomics 2009 Aug;94(2):125-31.
            doi: 10.1016/j.ygeno.2009.04.006pubmed: 19406225google scholar: lookup
          22. Clop A, Marcq F, Takeda H, Pirottin D, Tordoir X, Bibé B, Bouix J, Caiment F, Elsen JM, Eychenne F, Larzul C, Laville E, Meish F, Milenkovic D, Tobin J, Charlier C, Georges M. A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep.. Nat Genet 2006 Jul;38(7):813-8.
            doi: 10.1038/ng1810pubmed: 16751773google scholar: lookup
          23. Sakagami M, Ohshima K, Mukoyama H, Yasue H, Okada N. A novel tRNA species as an origin of short interspersed repetitive elements (SINEs). Equine SINEs may have originated from tRNA(Ser).. J Mol Biol 1994 Jun 24;239(5):731-5.
            doi: 10.1006/jmbi.1994.1410pubmed: 8014993google scholar: lookup
          24. Jorgensen TJ, Ruczinski I, Kessing B, Smith MW, Shugart YY, Alberg AJ. Hypothesis-driven candidate gene association studies: practical design and analytical considerations.. Am J Epidemiol 2009 Oct 15;170(8):986-93.
            doi: 10.1093/aje/kwp242pmc: PMC2765367pubmed: 19762372google scholar: lookup
          25. Tabor HK, Risch NJ, Myers RM. Candidate-gene approaches for studying complex genetic traits: practical considerations.. Nat Rev Genet 2002 May;3(5):391-7.
            doi: 10.1038/nrg796pubmed: 11988764google scholar: lookup
          26. Buitrago M, Lorenz K, Maass AH, Oberdorf-Maass S, Keller U, Schmitteckert EM, Ivashchenko Y, Lohse MJ, Engelhardt S. The transcriptional repressor Nab1 is a specific regulator of pathological cardiac hypertrophy.. Nat Med 2005 Aug;11(8):837-44.
            doi: 10.1038/nm1272pubmed: 16025126google scholar: lookup
          27. Thiel G, Kaufmann K, Magin A, Lietz M, Bach K, Cramer M. The human transcriptional repressor protein NAB1: expression and biological activity.. Biochim Biophys Acta 2000 Oct 2;1493(3):289-301.
            pubmed: 11018254doi: 10.1016/s0167-4781(00)00207-4google scholar: lookup
          28. 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
          29. 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
          30. Joulia D, Bernardi H, Garandel V, Rabenoelina F, Vernus B, Cabello G. Mechanisms involved in the inhibition of myoblast proliferation and differentiation by myostatin.. Exp Cell Res 2003 Jun 10;286(2):263-75.
            doi: 10.1016/S0014-4827(03)00074-0pubmed: 12749855google scholar: lookup
          31. Langley B, Thomas M, Bishop A, Sharma M, Gilmour S, Kambadur R. Myostatin inhibits myoblast differentiation by down-regulating MyoD expression.. J Biol Chem 2002 Dec 20;277(51):49831-40.
            doi: 10.1074/jbc.M204291200pubmed: 12244043google scholar: lookup
          32. Thomas M, Langley B, Berry C, Sharma M, Kirk S, Bass J, Kambadur R. Myostatin, a negative regulator of muscle growth, functions by inhibiting myoblast proliferation.. J Biol Chem 2000 Dec 22;275(51):40235-43.
            doi: 10.1074/jbc.M004356200pubmed: 10976104google scholar: lookup
          33. Polager S, Ginsberg D. p53 and E2f: partners in life and death.. Nat Rev Cancer 2009 Oct;9(10):738-48.
            doi: 10.1038/nrc2718pubmed: 19776743google scholar: lookup
          34. Hallstrom TC, Nevins JR. Balancing the decision of cell proliferation and cell fate.. Cell Cycle 2009 Feb 15;8(4):532-5.
            pmc: PMC3018328pubmed: 19182518doi: 10.4161/cc.8.4.7609google scholar: lookup

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          This article has been cited 71 times.
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