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Asian-Australasian journal of animal sciences2017; 31(8); 1110-1118; doi: 10.5713/ajas.17.0460

Analysis of cross-population differentiation between Thoroughbred and Jeju horses.

Abstract: This study was intended to identify genes positively selected in Thoroughbred horses (THBs) that potentially contribute to their running performances. Methods: The genomes of THB and Jeju horses (JH, Korean native horse) were compared to identify genes positively selected in THB. We performed cross-population extended haplotype homozygosity (XP-EHH) and cross-population composite likelihood ratio test (XP-CLR) statistical methods for our analysis using whole genome resequencing data of 14 THB and 6 JH. Results: We identified 98 (XP-EHH) and 200 (XP-CLR) genes that are under positive selection in THB. Gene enrichment analysis identified 72 gene ontology biological process (GO BP) terms. The genes and GO BP terms explained some of THB's characteristics such as immunity, energy metabolism and eye size and function related to running performances. GO BP terms that play key roles in several cell signaling mechanisms, which affected ocular size and visual functions were identified. GO BP term Eye photoreceptor cell differentiation is among the terms annotated presumed to affect eye size. Conclusions: Our analysis revealed some positively selected candidate genes in THB related to their racing performances. The genes detected are related to the immunity, ocular size and function, and energy metabolism.
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Publication Date: 2017-12-19 PubMed ID: 29268585PubMed Central: PMC6043458DOI: 10.5713/ajas.17.0460Google 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.

The abstract is about a research that compared the genomes of Thoroughbred and Jeju horses in an effort to identify genes that contribute to Thoroughbred horses’ superior running performance. Using two statistical methods, the study found a number of genes positively selected in Thoroughbred horses that correlate with characteristics such as immunity, energy metabolism, and eye size and function.

Research Methodology

  • The researchers examined and compared the genomes of Thoroughbred and Jeju horses. Thoroughbred horses are well-known for their running capabilities, whilst Jeju horses are a native breed from Korea.
  • The genetic comparison was conducted using two statistical methods: cross-population extended haplotype homozygosity (XP-EHH) and cross-population composite likelihood ratio test (XP-CLR). Both of these methods utilise whole genome resequencing data.
  • The sample for this study comprised of 14 Thoroughbred horses and 6 Jeju horses.

Results of the Study

  • The XP-EHH method identified 98 genes, while the XP-CLR method found 200 genes that are under positive selection in Thoroughbred horses. The term ‘positive selection’ refers to genetic variations that offer some sort of advantage, hence are more likely to be passed down to future generations.
  • Using gene enrichment analysis, the researchers identified 72 gene ontology biological process (GO BP) terms. These are terms used to annotate genes and gene products according to their associated biological processes.
  • The identified genes and the associated GO BP terms shed light on some of the Thoroughbred’s unique characteristics related to running performance. These include immunity, energy metabolism, and eye size and function.
  • The genes related to eye size and function were found to play a key role in cell signalling mechanisms affecting ocular size and visual functions. The GO BP term “Eye photoreceptor cell differentiation” was also identified and is thought to affect eye size.

Research Conclusions

  • The study concluded by revealing several positively selected candidate genes in Thoroughbred horses that potentially contribute to their racing performances.
  • The detected genes are most notably related to immunity, energy metabolism, and ocular size and function.

Cite This Article

APA
Lee W, Park KD, Taye M, Lee C, Kim H, Lee HK, Shin D. (2017). Analysis of cross-population differentiation between Thoroughbred and Jeju horses. Asian-Australas J Anim Sci, 31(8), 1110-1118. https://doi.org/10.5713/ajas.17.0460

Publication

Asian-Australasian journal of animal sciences
ISSN: 1011-2367
NlmUniqueID: 9884245
Country: Korea (South)
Language: English
Volume: 31
Issue: 8
Pages: 1110-1118

Researcher Affiliations

Lee, Wonseok
  • Department of Agricultural Biotechnology, Animal Biotechnology, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea.
Park, Kyung-Do
  • Department of Animal Biotechnology, College of Agricultural and Life Sciences, Chonbuk National University, Jeonju 54896, Korea.
Taye, Mengistie
  • Department of Agricultural Biotechnology, Animal Biotechnology, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea.
  • College of Agriculture and Environmental Sciences, Bahir Dar University, PO Box 79, Bahir Dar, Ethiopia.
Lee, Chul
  • Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Kim, Heebal
  • Department of Agricultural Biotechnology, Animal Biotechnology, and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea.
  • Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
  • Institute for Biomedical Sciences, Shinshu University, Nagano 8304, Japan.
Lee, Hak-Kyo
  • Department of Animal Biotechnology, College of Agricultural and Life Sciences, Chonbuk National University, Jeonju 54896, Korea.
Shin, Donghyun
  • Department of Animal Biotechnology, College of Agricultural and Life Sciences, Chonbuk National University, Jeonju 54896, Korea.

Conflict of Interest Statement

. We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.

References

This article includes 37 references
  1. 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.
    pmc: PMC2685479pubmed: 19503617doi: 10.1371/journal.pone.0005767google scholar: lookup
  2. Hinchcliff KW, Kaneps AJ, Geor RJ. Equine exercise physiology: the science of exercise in the athletic horse.. Melbourne, Australia: Elsevier Health Sciences; 2008.
  3. Montgomery ES. The Thoroughbred.. London, UK: Thomas Yoseloff Ltd; 1971.
  4. Howland HC, Merola S, Basarab JR. The allometry and scaling of the size of vertebrate eyes.. Vision Res 2004;44(17):2043-65.
    pubmed: 15149837doi: 10.1016/j.visres.2004.03.023google scholar: lookup
  5. Heard-Booth AN, Kirk EC. The influence of maximum running speed on eye size: a test of Leuckart's Law in mammals.. Anat Rec (Hoboken) 2012 Jun;295(6):1053-62.
    pubmed: 22539450doi: 10.1002/ar.22480google scholar: lookup
  6. Chen H, Patterson N, Reich D. Population differentiation as a test for selective sweeps.. Genome Res 2010 Mar;20(3):393-402.
    pmc: PMC2840981pubmed: 20086244doi: 10.1101/gr.100545.109google scholar: lookup
  7. Ma Y, Zhang H, Zhang Q, Ding X. Identification of selection footprints on the X chromosome in pig.. PLoS One 2014;9(4):e94911.
    pmc: PMC3989256pubmed: 24740293doi: 10.1371/journal.pone.0094911google scholar: lookup
  8. Park W, Kim J, Kim HJ, Choi J, Park JW, Cho HW, Kim BW, Park MH, Shin TS, Cho SK, Park JK, Kim H, Hwang JY, Lee CK, Lee HK, Cho S, Cho BW. Investigation of de novo unique differentially expressed genes related to evolution in exercise response during domestication in Thoroughbred race horses.. PLoS One 2014;9(3):e91418.
    pmc: PMC3962374pubmed: 24658125doi: 10.1371/journal.pone.0091418google scholar: lookup
  9. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, Xie X, Byrne EH, McCarroll SA, Gaudet R, Schaffner SF, Lander ES, Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA, Belmont JW, Boudreau A, Hardenbol P, Leal SM, Pasternak S, Wheeler DA, Willis TD, Yu F, Yang H, Zeng C, Gao Y, Hu H, Hu W, Li C, Lin W, Liu S, Pan H, Tang X, Wang J, Wang W, Yu J, Zhang B, Zhang Q, Zhao H, Zhao H, Zhou J, Gabriel SB, Barry R, Blumenstiel B, Camargo A, Defelice M, Faggart M, Goyette M, Gupta S, Moore J, Nguyen H, Onofrio RC, Parkin M, Roy J, Stahl E, Winchester E, Ziaugra L, Altshuler D, Shen Y, Yao Z, Huang W, Chu X, He Y, Jin L, Liu Y, Shen Y, Sun W, Wang H, Wang Y, Wang Y, Xiong X, Xu L, Waye MM, Tsui SK, Xue H, Wong JT, Galver LM, Fan JB, Gunderson K, Murray SS, Oliphant AR, Chee MS, Montpetit A, Chagnon F, Ferretti V, Leboeuf M, Olivier JF, Phillips MS, Roumy S, Sallée C, Verner A, Hudson TJ, Kwok PY, Cai D, Koboldt DC, Miller RD, Pawlikowska L, Taillon-Miller P, Xiao M, Tsui LC, Mak W, Song YQ, Tam PK, Nakamura Y, Kawaguchi T, Kitamoto T, Morizono T, Nagashima A, Ohnishi Y, Sekine A, Tanaka T, Tsunoda T, Deloukas P, Bird CP, Delgado M, Dermitzakis ET, Gwilliam R, Hunt S, Morrison J, Powell D, Stranger BE, Whittaker P, Bentley DR, Daly MJ, de Bakker PI, Barrett J, Chretien YR, Maller J, McCarroll S, Patterson N, Pe'er I, Price A, Purcell S, Richter DJ, Sabeti P, Saxena R, Schaffner SF, Sham PC, Varilly P, Altshuler D, Stein LD, Krishnan L, Smith AV, Tello-Ruiz MK, Thorisson GA, Chakravarti A, Chen PE, Cutler DJ, Kashuk CS, Lin S, Abecasis GR, Guan W, Li Y, Munro HM, Qin ZS, Thomas DJ, McVean G, Auton A, Bottolo L, Cardin N, Eyheramendy S, Freeman C, Marchini J, Myers S, Spencer C, Stephens M, Donnelly P, Cardon LR, Clarke G, Evans DM, Morris AP, Weir BS, Tsunoda T, Johnson TA, Mullikin JC, Sherry ST, Feolo M, Skol A, Zhang H, Zeng C, Zhao H, Matsuda I, Fukushima Y, Macer DR, Suda E, Rotimi CN, Adebamowo CA, Ajayi I, Aniagwu T, Marshall PA, Nkwodimmah C, Royal CD, Leppert MF, Dixon M, Peiffer A, Qiu R, Kent A, Kato K, Niikawa N, Adewole IF, Knoppers BM, Foster MW, Clayton EW, Watkin J, Gibbs RA, Belmont JW, Muzny D, Nazareth L, Sodergren E, Weinstock GM, Wheeler DA, Yakub I, Gabriel SB, Onofrio RC, Richter DJ, Ziaugra L, Birren BW, Daly MJ, Altshuler D, Wilson RK, Fulton LL, Rogers J, Burton J, Carter NP, Clee CM, Griffiths M, Jones MC, McLay K, Plumb RW, Ross MT, Sims SK, Willey DL, Chen Z, Han H, Kang L, Godbout M, Wallenburg JC, L'Archevêque P, Bellemare G, Saeki K, Wang H, An D, Fu H, Li Q, Wang Z, Wang R, Holden AL, Brooks LD, McEwen JE, Guyer MS, Wang VO, Peterson JL, Shi M, Spiegel J, Sung LM, Zacharia LF, Collins FS, Kennedy K, Jamieson R, Stewart J. Genome-wide detection and characterization of positive selection in human populations.. Nature 2007 Oct 18;449(7164):913-8.
    pmc: PMC2687721pubmed: 17943131doi: 10.1038/nature06250google scholar: lookup
  10. 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.
    pubmed: 10376300doi: 10.1046/j.1365-2052.1999.00419.xgoogle scholar: lookup
  11. Choi SK, Cho CY, Yeon SH, Cho BW, Cho GJ. Genetic characterization and polymorphisms for parentage testing of the Jeju horse using 20 microsatellite loci.. J Vet Med Sci 2008 Oct;70(10):1111-5.
    pubmed: 18981670doi: 10.1292/jvms.70.1111google scholar: lookup
  12. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2.. Nat Methods 2012 Mar 4;9(4):357-9.
    pmc: PMC3322381pubmed: 22388286doi: 10.1038/nmeth.1923google scholar: lookup
  13. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The Sequence Alignment/Map format and SAMtools.. Bioinformatics 2009 Aug 15;25(16):2078-9.
    pmc: PMC2723002pubmed: 19505943doi: 10.1093/bioinformatics/btp352google scholar: lookup
  14. 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
  15. 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
  16. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis.. Am J Hum Genet 2011 Jan 7;88(1):76-82.
    pmc: PMC3014363pubmed: 21167468doi: 10.1016/j.ajhg.2010.11.011google scholar: lookup
  17. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies.. Nat Genet 2006 Aug;38(8):904-9.
    pubmed: 16862161doi: 10.1038/ng1847google scholar: lookup
  18. 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
  19. Pickrell JK, Pritchard JK. Inference of population splits and mixtures from genome-wide allele frequency data.. PLoS Genet 2012;8(11):e1002967.
    pmc: PMC3499260pubmed: 23166502doi: 10.1371/journal.pgen.1002967google scholar: lookup
  20. Lee HJ, Kim J, Lee T, Son JK, Yoon HB, Baek KS, Jeong JY, Cho YM, Lee KT, Yang BC, Lim HJ, Cho K, Kim TH, Kwon EG, Nam J, Kwak W, Cho S, Kim H. Deciphering the genetic blueprint behind Holstein milk proteins and production.. Genome Biol Evol 2014 May 14;6(6):1366-74.
    pmc: PMC4079194pubmed: 24920005doi: 10.1093/gbe/evu102google scholar: lookup
  21. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z, Galon J. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks.. Bioinformatics 2009 Apr 15;25(8):1091-3.
    pmc: PMC2666812pubmed: 19237447doi: 10.1093/bioinformatics/btp101google scholar: lookup
  22. Cho G-J. Genetic relationship and characteristics using microsatellite DNA loci in horse breeds.. J Life Sci 2007;17:699–705.
  23. Ricard A, Bruns E, Cunningham E. The genetics of the horse.. Wallingford, UK: CABI Publishing; 2000. Genetics of performance traits; pp. 411–538.
  24. Kelley JL, Madeoy J, Calhoun JC, Swanson W, Akey JM. Genomic signatures of positive selection in humans and the limits of outlier approaches.. Genome Res 2006 Aug;16(8):980-9.
    pmc: PMC1524870pubmed: 16825663doi: 10.1101/gr.5157306google scholar: lookup
  25. Evans DT, Serra-Moreno R, Singh RK, Guatelli JC. BST-2/tetherin: a new component of the innate immune response to enveloped viruses.. Trends Microbiol 2010 Sep;18(9):388-96.
    pmc: PMC2956607pubmed: 20688520doi: 10.1016/j.tim.2010.06.010google scholar: lookup
  26. Yin X, Guo M, Gu Q, Wu X, Wei P, Wang X. Antiviral potency and functional analysis of tetherin orthologues encoded by horse and donkey.. Virol J 2014 Aug 27;11:151.
    pmc: PMC4152588pubmed: 25158826doi: 10.1186/1743-422x-11-151google scholar: lookup
  27. O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, Astashyn A, Badretdin A, Bao Y, Blinkova O, Brover V, Chetvernin V, Choi J, Cox E, Ermolaeva O, Farrell CM, Goldfarb T, Gupta T, Haft D, Hatcher E, Hlavina W, Joardar VS, Kodali VK, Li W, Maglott D, Masterson P, McGarvey KM, Murphy MR, O'Neill K, Pujar S, Rangwala SH, Rausch D, Riddick LD, Schoch C, Shkeda A, Storz SS, Sun H, Thibaud-Nissen F, Tolstoy I, Tully RE, Vatsan AR, Wallin C, Webb D, Wu W, Landrum MJ, Kimchi A, Tatusova T, DiCuccio M, Kitts P, Murphy TD, Pruitt KD. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.. Nucleic Acids Res 2016 Jan 4;44(D1):D733-45.
    pmc: PMC4702849pubmed: 26553804doi: 10.1093/nar/gkv1189google scholar: lookup
  28. Moon S, Lee JW, Shin D, Shin KY, Kim J, Choi IY, Kim J, Kim H. A Genome-wide Scan for Selective Sweeps in Racing Horses.. Asian-Australas J Anim Sci 2015 Nov;28(11):1525-31.
    pmc: PMC4647090pubmed: 26333666doi: 10.5713/ajas.14.0696google scholar: lookup
  29. Jan YN, Jan LY. The control of dendrite development.. Neuron 2003 Oct 9;40(2):229-42.
    pubmed: 14556706doi: 10.1016/s0896-6273(03)00631-7google scholar: lookup
  30. Ohshima T. Neuronal migration and protein kinases.. Front Neurosci 2014;8:458.
    pmc: PMC4292441pubmed: 25628530doi: 10.3389/fnins.2014.00458google scholar: lookup
  31. Quach TT, Wilson SM, Rogemond V, Chounlamountri N, Kolattukudy PE, Martinez S, Khanna M, Belin MF, Khanna R, Honnorat J, Duchemin AM. Mapping CRMP3 domains involved in dendrite morphogenesis and voltage-gated calcium channel regulation.. J Cell Sci 2013 Sep 15;126(Pt 18):4262-73.
    pubmed: 23868973doi: 10.1242/jcs.131409google scholar: lookup
  32. Dalva MB, Takasu MA, Lin MZ, Shamah SM, Hu L, Gale NW, Greenberg ME. EphB receptors interact with NMDA receptors and regulate excitatory synapse formation.. Cell 2000 Dec 8;103(6):945-56.
    pubmed: 11136979doi: 10.1016/s0092-8674(00)00197-5google scholar: lookup
  33. Zerial M, McBride H. Rab proteins as membrane organizers.. Nat Rev Mol Cell Biol 2001 Feb;2(2):107-17.
    pubmed: 11252952doi: 10.1038/35052055google scholar: lookup
  34. Waites CL, Craig AM, Garner CC. Mechanisms of vertebrate synaptogenesis.. Annu Rev Neurosci 2005;28:251-74.
    pubmed: 16022596doi: 10.1146/annurev.neuro.27.070203.144336google scholar: lookup
  35. Puigserver P, Spiegelman BM. Peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC-1 alpha): transcriptional coactivator and metabolic regulator.. Endocr Rev 2003 Feb;24(1):78-90.
    pubmed: 12588810doi: 10.1210/er.2002-0012google scholar: lookup
  36. Sharma A, Huard C, Vernochet C, Ziemek D, Knowlton KM, Tyminski E, Paradis T, Zhang Y, Jones JE, von Schack D, Brown CT, Milos PM, Coyle AJ, Tremblay F, Martinez RV. Brown fat determination and development from muscle precursor cells by novel action of bone morphogenetic protein 6.. PLoS One 2014;9(3):e92608.
    pmc: PMC3962431pubmed: 24658703doi: 10.1371/journal.pone.0092608google scholar: lookup
  37. Laurie GW, Olsakovsky LA, Conway BP, McKown RL, Kitagawa K, Nichols JJ. Dry eye and designer ophthalmics.. Optom Vis Sci 2008 Aug;85(8):643-52.
    pmc: PMC2815326pubmed: 18677231doi: 10.1097/opx.0b013e318181ae73google scholar: lookup

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  1. Wang H, Wen J, Li H, Zhu T, Zhao X, Zhang J, Zhang X, Tang C, Qu L, Gemingguli M. Candidate pigmentation genes related to feather color variation in an indigenous chicken breed revealed by whole genome data. Front Genet 2022;13:985228.
    doi: 10.3389/fgene.2022.985228pubmed: 36479242google scholar: lookup
  2. 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
  3. 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
  4. Seong PN, Kang GH, Cho SH, Park BY, Park NG, Kim JH, Ba HV. Comparative study of nutritional composition and color traits of meats obtained from the horses and Korean native black pigs raised in Jeju Island. Asian-Australas J Anim Sci 2019 Feb;32(2):249-256.
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    doi: 10.1007/s44154-025-00274-1pubmed: 41521281google scholar: lookup
  6. Ding W, Gong W, Bou T, Shi L, Lin Y, Shi X, Li Z, Wu H, Dugarjaviin M, Bai D, Zhao Y. Preliminary Study on the Genetic Structure and Functional Candidate Genes of Grassland-Thoroughbreds Based on Whole-Genome Resequencing. Animals (Basel) 2025 May 19;15(10).
    doi: 10.3390/ani15101462pubmed: 40427339google scholar: lookup
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