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Asian-Australasian journal of animal sciences2015; 28(11); 1525-1531; doi: 10.5713/ajas.14.0696

A Genome-wide Scan for Selective Sweeps in Racing Horses.

Abstract: Using next-generation sequencing, we conducted a genome-wide scan of selective sweeps associated with selection toward genetic improvement in Thoroughbreds. We investigated potential phenotypic consequence of putative candidate loci by candidate gene association mapping for the finishing time in 240 Thoroughbred horses. We found a significant association with the trait for Ral GApase alpha 2 (RALGAP2) that regulates a variety of cellular processes of signal trafficking. Neighboring genes around RALGAP2 included insulinoma-associated 1 (INSM1), pallid (PLDN), and Ras and Rab interactor 2 (RIN2) genes have similar roles in signal trafficking, suggesting that a co-evolving gene cluster located on the chromosome 22 is under strong artificial selection in racehorses.
Publication Date: 2015-09-04 PubMed ID: 26333666PubMed Central: PMC4647090DOI: 10.5713/ajas.14.0696Google 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 examines the genetic aspects that contribute to the improved physical ability and finishing times in racehorses, discovering a significant association with the RALGAP2 gene and a cluster of similar genes on chromosome 22.

Research Objective and Methodology

  • This study aimed to understand the genetic factors associated with selection toward genetic improvement in Thoroughbred horses, specifically targeting their racing performance.
  • The researchers used next-generation sequencing, a method that provides rapid, cost-effective, accurate, and detailed genome information. This allowed them to conduct a genome-wide scan for instances of selective sweeps, which are reductions in genetic variation due to selective breeding.
  • The study involved 240 Thoroughbred horses and mapped potential phenotypic consequences (physical characteristics) associated with selective sweeps by focusing on candidate genes and their association with finishing times in these horses.

Findings and Significance

  • The research revealed a significant correlation between the trait of fast finishing time and the gene known as Ral GApase alpha 2 (RALGAP2). This gene regulates a variety of cellular processes involving signal trafficking, which plays a crucial role in the cell’s response to external stimuli and overall functioning.
  • Other genes found in the vicinity of RALGAP2 – insulinoma-associated 1 (INSM1), pallid (PLDN), and Ras and Rab interactor 2 (RIN2) – were discovered to have similar roles in signal trafficking. This suggests the presence of a gene cluster located on chromosome 22 that could be under strong artificial selection in racehorses.
  • Artificial selection here refers to the human-driven selection of specific desirable traits in the breeding process. The selection of these traits leads to genetic changes within the population – in this case, faster racehorses.
  • This study’s findings can contribute to a greater understanding of horse genetics, potentially enhancing Thoroughbred horse breeding, performance, and care strategies. Moreover, it also provides further insights into the field of genomics by demonstrating the impact of selective breeding on the genetic composition of a species over time.

Cite This Article

APA
Moon S, Lee JW, Shin D, Shin KY, Kim J, Choi IY, Kim J, Kim H. (2015). A Genome-wide Scan for Selective Sweeps in Racing Horses. Asian-Australas J Anim Sci, 28(11), 1525-1531. https://doi.org/10.5713/ajas.14.0696

Publication

ISSN: 1011-2367
NlmUniqueID: 9884245
Country: Korea (South)
Language: English
Volume: 28
Issue: 11
Pages: 1525-1531

Researcher Affiliations

Moon, Sunjin
  • Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Korea.
Lee, Jin Woo
  • Horse Registry, Korea Racing Authority (KRA), Gwacheon 427-711, Korea.
Shin, Donghyun
  • Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Korea.
Shin, Kwang-Yun
  • Institute for Livestock Promotion, Jeju 690-802, Korea.
Kim, Jun
  • Institute for Livestock Promotion, Jeju 690-802, Korea.
Choi, Ik-Young
  • Genome analysis center, National Instrumentation and Environmental Management (NICEM), Seoul National University, Seoul 151-921, Korea.
Kim, Jaemin
  • Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Korea.
Kim, Heebal
  • Department of Agricultural Biotechnology and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 151-742, Korea ; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Korea ; C&K Genomics, Seoul 151-742, Korea.

References

This article includes 39 references
  1. Anisimova M, Kosiol C. Investigating protein-coding sequence evolution with probabilistic codon substitution models.. Mol Biol Evol 2009 Feb;26(2):255-71.
    pubmed: 18922761doi: 10.1093/molbev/msn232google scholar: lookup
  2. Binns MM, Boehler DA, Lambert DH. Identification of the myostatin locus (MSTN) as having a major effect on optimum racing distance in the Thoroughbred horse in the USA.. Anim Genet 2010 Dec;41 Suppl 2:154-8.
  3. 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.
    pmc: PMC2265661pubmed: 17924348doi: 10.1086/521987google scholar: lookup
  4. Cunningham EP, Dooley JJ, Splan RK, Bradley DG. Microsatellite diversity, pedigree relatedness and the contributions of founder lineages to thoroughbred horses.. Anim Genet 2001 Dec;32(6):360-4.
  5. Duggal P, Gillanders EM, Holmes TN, Bailey-Wilson JE. Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies.. BMC Genomics 2008 Oct 31;9:516.
    pmc: PMC2621212pubmed: 18976480doi: 10.1186/1471-2164-9-516google scholar: lookup
  6. Excoffier L, Hofer T, Foll M. Detecting loci under selection in a hierarchically structured population.. Heredity (Edinb) 2009 Oct;103(4):285-98.
    pubmed: 19623208doi: 10.1038/hdy.2009.74google scholar: lookup
  7. Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows.. Mol Ecol Resour 2010 May;10(3):564-7.
  8. Gilmour AR, Gogel BJ, Cullis BR, Thompson R, Butler D, Cherry M, Collins D, Dutkowski G, Harding SA, Haskard K. ASReml User Guide Release 3.0. .
  9. Grobet L, Pirottin D, Farnir F, Poncelet D, Royo LJ, Brouwers B, Christians E, Desmecht D, Coignoul F, Kahn R, Georges M. Modulating skeletal muscle mass by postnatal, muscle-specific inactivation of the myostatin gene.. Genesis 2003 Apr;35(4):227-38.
    pubmed: 12717734doi: 10.1002/gene.10188google scholar: lookup
  10. Gu J, Orr N, Park SD, Katz LM, Sulimova G, MacHugh DE, Hill EW. A genome scan for positive selection in thoroughbred horses.. PLoS One 2009 Jun 2;4(6):e5767.
  11. Hill EW, Bradley DG, Al-Barody M, Ertugrul O, Splan RK, Zakharov I, Cunningham EP. History and integrity of thoroughbred dam lines revealed in equine mtDNA variation.. Anim Genet 2002 Aug;33(4):287-94.
  12. 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.
  13. Innan H, Kim Y. Detecting local adaptation using the joint sampling of polymorphism data in the parental and derived populations.. Genetics 2008 Jul;179(3):1713-20.
    pmc: PMC2475763pubmed: 18562650doi: 10.1534/genetics.108.086835google scholar: lookup
  14. Jacob J, Storm R, Castro DS, Milton C, Pla P, Guillemot F, Birchmeier C, Briscoe J. Insm1 (IA-1) is an essential component of the regulatory network that specifies monoaminergic neuronal phenotypes in the vertebrate hindbrain.. Development 2009 Jul;136(14):2477-85.
    pmc: PMC2729354pubmed: 19542360doi: 10.1242/dev.034546google scholar: lookup
  15. Jansen T, Forster P, Levine MA, Oelke H, Hurles M, Renfrew C, Weber J, Olek K. Mitochondrial DNA and the origins of the domestic horse.. Proc Natl Acad Sci U S A 2002 Aug 6;99(16):10905-10.
    pmc: PMC125071pubmed: 12130666doi: 10.1073/pnas.152330099google scholar: lookup
  16. Kalashnikova E, Lorca RA, Kaur I, Barisone GA, Li B, Ishimaru T, Trimmer JS, Mohapatra DP, Díaz E. SynDIG1: an activity-regulated, AMPA- receptor-interacting transmembrane protein that regulates excitatory synapse development.. Neuron 2010 Jan 14;65(1):80-93.
  17. Kambadur R, Sharma M, Smith TP, Bass JJ. Mutations in myostatin (GDF8) in double-muscled Belgian Blue and Piedmontese cattle.. Genome Res 1997 Sep;7(9):910-6.
    pubmed: 9314496doi: 10.1101/gr.7.9.910google scholar: lookup
  18. Kim KI, Yang YH, Lee SS, Park C, Ma R, Bouzat JL, Lewin HA. Phylogenetic relationships of Cheju horses to other horse breeds as determined by mtDNA D-loop sequence polymorphism.. Anim Genet 1999 Apr;30(2):102-8.
  19. Lee SJ. Sprinting without myostatin: a genetic determinant of athletic prowess.. Trends Genet 2007 Oct;23(10):475-7.
    pubmed: 17884234doi: 10.1016/j.tig.2007.08.008google scholar: lookup
  20. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.. Bioinformatics 2009 Jul 15;25(14):1754-60.
  21. Lindgren G, Backström N, Swinburne J, Hellborg L, Einarsson A, Sandberg K, Cothran G, Vilà C, Binns M, Ellegren H. Limited number of patrilines in horse domestication.. Nat Genet 2004 Apr;36(4):335-6.
    pubmed: 15034578doi: 10.1038/ng1326google scholar: lookup
  22. McGivney BA, Browne JA, Fonseca RG, Katz LM, Machugh DE, Whiston R, Hill EW. MSTN genotypes in Thoroughbred horses influence skeletal muscle gene expression and racetrack performance.. Anim Genet 2012 Dec;43(6):810-2.
  23. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.. Genome Res 2010 Sep;20(9):1297-303.
    pmc: PMC2928508pubmed: 20644199doi: 10.1101/gr.107524.110google scholar: lookup
  24. 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.
    pmc: PMC24998pubmed: 9356471doi: 10.1073/pnas.94.23.12457google scholar: lookup
  25. Nam DY. Horse production in Cheju during Lee dynasty. Studies on Korean History 1969;4:131–131.
  26. Nei M. Molecular evolutionary genetics. .
  27. Nei M, Li WH. Mathematical model for studying genetic variation in terms of restriction endonucleases.. Proc Natl Acad Sci U S A 1979 Oct;76(10):5269-73.
    pmc: PMC413122pubmed: 291943doi: 10.1073/pnas.76.10.5269google scholar: lookup
  28. Orlando L, Ginolhac A, Zhang G, Froese D, Albrechtsen A, Stiller M, Schubert M, Cappellini E, Petersen B, Moltke I, Johnson PL, Fumagalli M, Vilstrup JT, Raghavan M, Korneliussen T, Malaspinas AS, Vogt J, Szklarczyk D, Kelstrup CD, Vinther J, Dolocan A, Stenderup J, Velazquez AM, Cahill J, Rasmussen M, Wang X, Min J, Zazula GD, Seguin-Orlando A, Mortensen C, Magnussen K, Thompson JF, Weinstock J, Gregersen K, Røed KH, Eisenmann V, Rubin CJ, Miller DC, Antczak DF, Bertelsen MF, Brunak S, Al-Rasheid KA, Ryder O, Andersson L, Mundy J, Krogh A, Gilbert MT, Kjær K, Sicheritz-Ponten T, Jensen LJ, Olsen JV, Hofreiter M, Nielsen R, Shapiro B, Wang J, Willerslev E. Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse.. Nature 2013 Jul 4;499(7456):74-8.
    pubmed: 23803765doi: 10.1038/nature12323google scholar: lookup
  29. Park KD, Kim H, Hwang JY, Lee CK, Do KT, Kim HS, Yang YM, Kwon YJ, Kim J, Kim HJ, Song KD, Oh JD, Kim H, Cho BW, Cho S, Lee HK. Copy number deletion has little impact on gene expression levels in racehorses.. Asian-Australas J Anim Sci 2014 Sep;27(9):1345-54.
    pmc: PMC4150202pubmed: 25178379doi: 10.5713/ajas.2013.13857google scholar: lookup
  30. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Distl O, Felicetti M, 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, Silvestrelli M, Swinburne J, Tozaki T, Vaudin M, M Wade C, McCue ME. Genetic diversity in the modern horse illustrated from genome-wide SNP data.. PLoS One 2013;8(1):e54997.
  31. 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.
    pmc: PMC1950838pubmed: 17701901doi: 10.1086/519795google scholar: lookup
  32. Qanbari S, Pausch H, Jansen S, Somel M, Strom TM, Fries R, Nielsen R, Simianer H. Classic selective sweeps revealed by massive sequencing in cattle.. PLoS Genet 2014 Feb;10(2):e1004148.
  33. 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.
    pubmed: 9288100doi: 10.1038/ng0997-71google scholar: lookup
  34. Suzuki J, Yamazaki Y, Li G, Kaziro Y, Koide H. Involvement of Ras and Ral in chemotactic migration of skeletal myoblasts.. Mol Cell Biol 2000 Jul;20(13):4658-65.
  35. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A. PANTHER: a library of protein families and subfamilies indexed by function.. Genome Res 2003 Sep;13(9):2129-41.
    pmc: PMC403709pubmed: 12952881doi: 10.1101/gr.772403google scholar: lookup
  36. Tozaki T, Miyake T, Kakoi H, Gawahara H, Sugita S, Hasegawa T, Ishida N, Hirota K, Nakano Y. A genome-wide association study for racing performances in Thoroughbreds clarifies a candidate region near the MSTN gene.. Anim Genet 2010 Dec;41 Suppl 2:28-35.
  37. Vaysse A, Ratnakumar A, Derrien T, Axelsson E, Rosengren Pielberg G, Sigurdsson S, Fall T, Seppälä EH, Hansen MS, Lawley CT, Karlsson EK, Bannasch D, Vilà C, Lohi H, Galibert F, Fredholm M, Häggström J, Hedhammar A, André C, Lindblad-Toh K, Hitte C, Webster MT. Identification of genomic regions associated with phenotypic variation between dog breeds using selection mapping.. PLoS Genet 2011 Oct;7(10):e1002316.
  38. Williamson SA, Beilharz RG. The inheritance of speed, stamina and other racing performance characters in the Australian Thoroughbred. J Anim Breed Genet 1998;115:1–16.
  39. Willing EM, Dreyer C, van Oosterhout C. Estimates of genetic differentiation measured by F(ST) do not necessarily require large sample sizes when using many SNP markers.. PLoS One 2012;7(8):e42649.

Citations

This article has been cited 13 times.
  1. Lin W, Ren T, Li W, Liu M, He D, Liang S, Luo W, Zhang X. Novel 61-bp Indel of RIN2 Is Associated With Fat and Hatching Weight Traits in Chickens.. Front Genet 2021;12:672888.
    doi: 10.3389/fgene.2021.672888pubmed: 34276778google scholar: lookup
  2. Asadollahpour Nanaei H, Esmailizadeh A, Ayatollahi Mehrgardi A, Han J, Wu DD, Li Y, Zhang YP. Comparative population genomic analysis uncovers novel genomic footprints and genes associated with small body size in Chinese pony.. BMC Genomics 2020 Jul 20;21(1):496.
    doi: 10.1186/s12864-020-06887-2pubmed: 32689947google scholar: lookup
  3. Salek Ardestani S, Aminafshar M, Zandi Baghche Maryam MB, Banabazi MH, Sargolzaei M, Miar Y. Whole-Genome Signatures of Selection in Sport Horses Revealed Selection Footprints Related to Musculoskeletal System Development Processes.. Animals (Basel) 2019 Dec 26;10(1).
    doi: 10.3390/ani10010053pubmed: 31888018google scholar: lookup
  4. Ablondi M, Eriksson S, Tetu S, Sabbioni A, Viklund Å, Mikko S. Genomic Divergence in Swedish Warmblood Horses Selected for Equestrian Disciplines.. Genes (Basel) 2019 Nov 27;10(12).
    doi: 10.3390/genes10120976pubmed: 31783652google scholar: lookup
  5. Srikanth K, Kim NY, Park W, Kim JM, Kim KD, Lee KT, Son JH, Chai HH, Choi JW, Jang GW, Kim H, Ryu YC, Nam JW, Park JE, Kim JM, Lim D. Comprehensive genome and transcriptome analyses reveal genetic relationship, selection signature, and transcriptome landscape of small-sized Korean native Jeju horse.. Sci Rep 2019 Nov 13;9(1):16672.
    doi: 10.1038/s41598-019-53102-8pubmed: 31723199google scholar: lookup
  6. 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
  7. Ablondi M, Viklund Å, Lindgren G, Eriksson S, Mikko S. Signatures of selection in the genome of Swedish warmblood horses selected for sport performance.. BMC Genomics 2019 Sep 18;20(1):717.
    doi: 10.1186/s12864-019-6079-1pubmed: 31533613google scholar: lookup
  8. 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
  9. Nolte W, Thaller G, Kuehn C. Selection signatures in four German warmblood horse breeds: Tracing breeding history in the modern sport horse.. PLoS One 2019;14(4):e0215913.
    doi: 10.1371/journal.pone.0215913pubmed: 31022261google scholar: lookup
  10. 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
  11. 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
  12. Avila F, Mickelson JR, Schaefer RJ, McCue ME. Genome-Wide Signatures of Selection Reveal Genes Associated With Performance in American Quarter Horse Subpopulations.. Front Genet 2018;9:249.
    doi: 10.3389/fgene.2018.00249pubmed: 30105047google scholar: lookup
  13. Lee W, Park KD, Taye M, Lee C, Kim H, Lee HK, Shin D. Analysis of cross-population differentiation between Thoroughbred and Jeju horses.. Asian-Australas J Anim Sci 2018 Aug;31(8):1110-1118.
    doi: 10.5713/ajas.17.0460pubmed: 29268585google scholar: lookup