Analyze Diet
PloS one2019; 14(1); e0210751; doi: 10.1371/journal.pone.0210751

A genome-wide scan for diversifying selection signatures in selected horse breeds.

Abstract: The genetic differentiation of the current horse population was evolutionarily created by natural or artificial selection which shaped the genomes of individual breeds in several unique ways. The availability of high throughput genotyping methods created the opportunity to study this genetic variation on a genome-wide level allowing detection of genome regions divergently selected between separate breeds as well as among different horse types sharing similar phenotypic features. In this study, we used the population differentiation index (FST) that is generally used for measuring locus-specific allele frequencies variation between populations, to detect selection signatures among six horse breeds maintained in Poland. These breeds can be classified into three major categories, including light, draft and primitive horses, selected mainly in terms of type (utility), exterior, performance, size, coat color and appearance. The analysis of the most pronounced selection signals found in this study allowed us to detect several genomic regions and genes connected with processes potentially important for breed phenotypic differentiation and associated with energy homeostasis during physical effort, heart functioning, fertility, disease resistance and motor coordination. Our results also confirmed previously described association of loci on ECA3 (spanning LCORL and NCAPG genes) and ECA11 (spanning LASP1 gene) with the regulation of body size in our draft and primitive (small size) horses. The efficiency of the applied FST-based approach was also confirmed by the identification of a robust selection signal in the blue dun colored Polish Konik horses at the locus of TBX3 gene, which was previously shown to be responsible for dun coat color dilution in other horse breeds. FST-based method showed to be efficient in detection of diversifying selection signatures in the analyzed horse breeds. Especially pronounced signals were observed at the loci responsible for fixed breed-specific features. Several candidate genes under selection were proposed in this study for traits selected in separate breeds and horse types, however, further functional and comparative studies are needed to confirm and explain their effect on the observed genetic diversity of the horse breeds.
Publication Date: 2019-01-30 PubMed ID: 30699152PubMed Central: PMC6353161DOI: 10.1371/journal.pone.0210751Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

Summary

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

This study investigates the level of genetic divergence among different horse breeds in Poland, using high throughput genotyping methods and the population differentiation index (FST). The results highlight various gene regions connected with traits such as energy balance during exercise, heart function, fertility, disease resistance, and motor coordination, which may contribute to the distinct characteristics of individual breeds.

Methodology

  • The research used techno-scientific advancements in genotyping methods to analyze the genetic variation in horse breeds. This approach allows more comprehensive studies of genetic differentiation at a genome-wide level.
  • The researchers used a measurable standard known as the population differentiation index (FST) to measure differences in allele frequencies between the different horse breeds.
  • The study focuses on six horse breeds, each representing a distinct category: light, draft, and primitive. These categories were selected based on factors such as utilization, exterior aesthetics, performance, size, coat color, and general appearance.

Findings

  • The results showed several gene regions that appeared to be associated with various traits. These traits include energy expenditure during physical effort, heart functioning, fertility, disease resistance, and motor coordination — all important factors in a horse’s phenotype or observable characteristics.
  • Two locations in the genome, known as loci (ECA3, which includes LCORL and NCAPG genes, and ECA11, which includes the LASP1 gene), were confirmed to be associated with regulation of body size in draft and primitive horses. These findings align with previous research.
  • The study also identified a potent selection signal in the blue dun colored Polish Konik horses at the locus of the TBX3 gene, previously associated with the dun coat color dilution in horses from other breeds.

Conclusion

  • The study demonstrated the efficacy of the FST-based approach, finding strong selection signals at loci responsible for breed-specific features.
  • The results contribute to the broader understanding of the genetic diversity in horse breeds. However, researchers highlight that further research is required to fully understand and explain the observed genetic diversity of distinct horse breeds and individual traits.

Cite This Article

APA
Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Szmatoła T, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. (2019). A genome-wide scan for diversifying selection signatures in selected horse breeds. PLoS One, 14(1), e0210751. https://doi.org/10.1371/journal.pone.0210751

Publication

ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 14
Issue: 1
Pages: e0210751

Researcher Affiliations

Gurgul, Artur
  • Department of Animal Molecular Biology, National Research Institute of Animal Production, Balice, Poland.
Jasielczuk, Igor
  • Department of Animal Molecular Biology, National Research Institute of Animal Production, Balice, Poland.
Semik-Gurgul, Ewelina
  • Department of Animal Molecular Biology, National Research Institute of Animal Production, Balice, Poland.
Pawlina-Tyszko, Klaudia
  • Department of Animal Molecular Biology, National Research Institute of Animal Production, Balice, Poland.
Stefaniuk-Szmukier, Monika
  • Department of Horse Breeding, University of Agriculture in Kraków, al. Kraków, Poland.
Szmatoła, Tomasz
  • Department of Animal Molecular Biology, National Research Institute of Animal Production, Balice, Poland.
Polak, Grażyna
  • Department of Horse Breeding, National Research Institute of Animal Production, Balice, Poland.
Tomczyk-Wrona, Iwona
  • Department of Horse Breeding, National Research Institute of Animal Production, Balice, Poland.
Bugno-Poniewierska, Monika
  • Department of Animal Molecular Biology, National Research Institute of Animal Production, Balice, Poland.
  • Institute of Veterinary Sciences, University of Agriculture in Krakow, al. Kraków, Poland.

MeSH Terms

  • Animals
  • Chromosome Mapping
  • Female
  • Gene Frequency
  • Genetic Variation
  • Genetics, Population
  • Genome
  • Hair Color / genetics
  • Horses / anatomy & histology
  • Horses / genetics
  • Horses / physiology
  • Male
  • Phenotype
  • Poland
  • Polymorphism, Single Nucleotide
  • Selective Breeding

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 76 references
  1. Lippold S, Matzke NJ, Reissmann M, Hofreiter M. Whole mitochondrial genome sequencing of domestic horses reveals incorporation of extensive wild horse diversity during domestication.. BMC Evol Biol 2011;11:328.
    doi: 10.1186/1471-2148-11-328pmc: PMC3247663pubmed: 22082251google scholar: lookup
  2. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J. Genome-Wide Analysis Reveals Selection for Important Traits in Domestic Horse Breeds.. PLoS Genet 2013;9(1):e1003211.
  3. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E. Genetic Diversity in the Modern Horse Illustrated from Genome-Wide SNP Data.. PLoS ONE 2013a. 8(1):e54997.
    pmc: PMC3559798pubmed: 23383025
  4. Metzger J, Karwath M, Tonda R, Beltran S, Águeda L, Gut M. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses.. BMC Genomics 2015;16:764.
    doi: 10.1186/s12864-015-1977-3pmc: PMC4600213pubmed: 26452642google scholar: lookup
  5. Ma Y, Ding X, Qanbari S, Weigend S, Zhang Q, Simianer H. Properties of different selection signature statistics and a new strategy for combining them.. Heredity 2015;115:426–36.
    doi: 10.1038/hdy.2015.42pmc: PMC4611237pubmed: 25990878google scholar: lookup
  6. Fariello MI, Boitard S, Naya H, SanCristobal M, Servin B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations.. Genetics 2013;193(3):929–41.
    doi: 10.1534/genetics.112.147231pmc: PMC3584007pubmed: 23307896google scholar: lookup
  7. Lewontin RC, Krakauer J. Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms.. Genetics 1973;74:175–95.
    pmc: PMC1212935pubmed: 4711903
  8. Akey JM, Zhang G, Zhang K, Jin L, Shriver MD. Interrogating a high-density SNP map for signatures of natural selection.. Genome Res 2002;12:1805–14.
    doi: 10.1101/gr.631202pmc: PMC187574pubmed: 12466284google scholar: lookup
  9. Wright S. Evolution in Mendelian populations.. Genetics 1931;16:97–159.
    pmc: PMC1201091pubmed: 17246615
  10. Akey JM, Ruhe AL, Akey DT, Wong AK, Connelly CF, Madeoy J. Tracking footprints of artificial selection in the dog genome.. PNAS 2010;107:1160–1165.
    doi: 10.1073/pnas.0909918107pmc: PMC2824266pubmed: 20080661google scholar: lookup
  11. Rubin CJ, Megens HJ, Barrio AM, Maqbool K, Sayyab S, Schwochow D. Strong signatures of selection in the domestic pig genome.. PNAS 2012;109:19529–19536.
    doi: 10.1073/pnas.1217149109pmc: PMC3511700pubmed: 23151514google scholar: lookup
  12. Wilkinson S, Lu ZH, Megens HJ, Archibald AL, Haley C, Jackson IJ. Signatures of diversifying selection in European pig breeds.. PLoS Genetics 2013;9: e1003453.
  13. De Simoni Gouveia JJ, da Silva MVGB, Paiva SR, de Oliveira SMP. Identification of selection signatures in livestock species.. Genet. Mol. Biol. 2014;37(2):330–342.
    pmc: PMC4094609pubmed: 25071397
  14. Zhao F, McParland S, Kearney F, Du L, Berry DP. Detection of selection signatures in dairy and beef cattle using high-density genomic information.. Genet Sel Evol 2015;47:49.
    doi: 10.1186/s12711-015-0127-3pmc: PMC4472243pubmed: 26089079google scholar: lookup
  15. Moon S, Lee JW, Shin D, Shin KY, Kim J, Choi IY. A Genome-wide Scan for Selective Sweeps in Racing Horses.. Asian-Australas J Anim Sci 2015;28(11):1525–31.
    doi: 10.5713/ajas.14.0696pmc: PMC4647090pubmed: 26333666google scholar: lookup
  16. Frischknecht M, Flury C, Leeb T, Rieder S, Neuditschko M. Selection signatures in Shetland ponies.. Anim Genet 2016;47(3):370–2.
    doi: 10.1111/age.12416pubmed: 26857482google scholar: lookup
  17. Stachurska A, Nogaj A, Brodacki A, Nogaj J, Batkowska J. Genetic distances between horse breeds in Poland estimated according to blood protein polymorphism.. Czech J Anim Sci 2014;59(6):257–267.
  18. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps.. Bioinformatics 2005;21(2):263–5.
    doi: 10.1093/bioinformatics/bth457pubmed: 15297300google scholar: lookup
  19. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B. The structure of haplotype blocks in the human genome.. Science 2002;296(5576):2225–9.
    doi: 10.1126/science.1069424pubmed: 12029063google scholar: lookup
  20. Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM. Four Loci explain 83% of size variation in the horse.. PLoS ONE 2012;7:e39929.
  21. Imsland F, McGowan K, Rubin CJ, Henegar C, Sundström E, Berglund J. Regulatory mutations in TBX3 disrupt asymmetric hair pigmentation that underlies Dun camouflage color in horses.. Nat Genet 2016;48(2):152–8.
    doi: 10.1038/ng.3475pmc: PMC4731265pubmed: 26691985google scholar: lookup
  22. Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees.. Mol Biol Evol 1987;4:406–25.
  23. Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases.. Nucleic Acids Res 2011;39:W316–22.
    doi: 10.1093/nar/gkr483pmc: PMC3125809pubmed: 21715386google scholar: lookup
  24. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements.. Nucl. Acids Res 2017;45:183–9.
    pmc: PMC5210595pubmed: 27899595
  25. Heberle H, Meirelles GV, da Silva FR, Telles GP, Minghim R. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams.. BMC Bioinform 2015;16:169.
    pmc: PMC4455604pubmed: 25994840
  26. Golynski M, Krumrych W, Lutnicki K. The role of beta-endorphin in horses: a review.. Vet. Med. 2011;56(9):423–9.
  27. Stefaniuk-Szmukier M, Ropka-Molik K, Piórkowska K, Szmatoła T, Długosz B, Pisarczyk W. Variation in TBX3 Gene Region in Dun Coat Color Polish Konik Horses.. 2017;49:60–2.
  28. Mackowski M, Mucha S, Cholewinski G, Cieslak J. Genetic diversity in Hucul and Polish primitive horse breeds.. Arch. Anim. Breed. 2015;58:23–31.
  29. Kim ES, Kirkpatrick BW. Linkage disequilibrium in the North American Holstein population.. Anim Genet 2009;40(3):279–88.
  30. Zhong M, Lange K, Papp JC, Fan R. A powerful score test to detect positive selection in genome-wide scans.. Eur J Hum Genet 2010;18(10):1148–59.
    doi: 10.1038/ejhg.2010.60pmc: PMC2987455pubmed: 20461112google scholar: lookup
  31. Cagan A, Blass T. Identification of genomic variants putatively targeted by selection during dog domestication.. BMC Evol Biol 2016;16:10.
    doi: 10.1186/s12862-015-0579-7pmc: PMC4710014pubmed: 26754411google scholar: lookup
  32. Pan Y, Wang KS, Aragam N. NTM and NR3C2 polymorphisms influencing intelligence: family-based association studies.. PROG NEURO-PSYCHOPH 2011;35(1):154–60.
    pubmed: 21036197
  33. Liu F, Arias-Vasquez A, Sleegers K, Aulchenko YS, Kayser M. A genomewide screen for late-onset Alzheimer disease in a genetically isolated Dutch population.. Am J Hum Genet 2007;81:17–31.
    doi: 10.1086/518720pmc: PMC1950931pubmed: 17564960google scholar: lookup
  34. . The UniProt Consortium https://www.uniprot.org/uniprot/P10916. Accessed 20 May 2018.. .
  35. Sheikh F, Lyon RC, Chen J. Functions of myosin light chain-2 (MYL2) in cardiac muscle and disease.. Gene 2015;569(1):14–20.
    doi: 10.1016/j.gene.2015.06.027pmc: PMC4496279pubmed: 26074085google scholar: lookup
  36. Claes GR, van Tienen FH, Lindsey P, Krapels IP, Helderman-van den Enden AT, Hoos MB. Hypertrophic remodelling in cardiac regulatory myosin light chain (MYL2) founder mutation carriers.. Eur Heart J 2016;37(23):1815–22.
    doi: 10.1093/eurheartj/ehv522pubmed: 26497160google scholar: lookup
  37. Raub RH, Warren J, Pool-Anderson K. The effect of forced exercise on development of heart, lung and diaphragm muscle in weanling horses.. J Equine Vet Sci 1992;12(2):106–8.
  38. Schröder W, Klostermann A, Stock KF, Distl O. A genome-wide association study for quantitative trait loci of show-jumping in Hanoverian warmblood horses.. Anim Genet 2012;43(4):392–400.
  39. . GeneCards HUMAN GENE DATABASE. Weizmann Institute of Science. https://www.genecards.org/cgi-bin/carddisp.pl?gene=SLC16A1. Accessed 20 May 2018.. .
  40. Ropka-Molik K, Stefaniuk-Szmukier M, Żukowski K, Piórkowska K, Bugno-Poniewierska M. Exercise-induced modification of the skeletal muscle transcriptome in Arabian horses.. Physiol Genomics 2017;49(6):318–26.
  41. Ropka-Molik K, Stefaniuk-Szmukier M, Szmatoła T, Piórkowska K, Bugno-Poniewierska M. Patent protection application no. P.423426, 2017. “A method for identification of the SLC16A1 gene polymorphisms as a genetic marker”. Patent Office of the Republic of Poland. .
  42. Grunewald TG, Butt E. The LIM and SH3 domain protein family: structural proteins or signal transducers or both?. Mol Cancer 2008;7:31.
    doi: 10.1186/1476-4598-7-31pmc: PMC2359764pubmed: 18419822google scholar: lookup
  43. Durstine JL, Grandjean PW, Cox CA, Thompson PD. Lipids, lipoproteins, and exercise.. J Cardiopulm Rehabil 2002;22:385–98.
    pubmed: 12464825
  44. Ramachandran S, Penumetcha M, Merchant NK, Santanam N, Rong R, Parthasarathy S. Exercise reduces preexisting atherosclerotic lesions in LDL receptor knock out mice.. Atherosclerosis 2005;178:33–8.
  45. Halverstadt A, Phares DA, Wilund KR, Goldberg AP, Hagberg JM. Endurance exercise training raises high-density lipoprotein cholesterol and lowers small low-density lipoprotein and very low-density lipoprotein independent of body fat phenotypes in older men and women.. Metabolism 2007;56:444–50.
    doi: 10.1016/j.metabol.2006.10.019pubmed: 17378998google scholar: lookup
  46. Meissner M, Lombardo E, Havinga R, Tietge UJ, Kuipers F, Groen AK. Voluntary wheel running increases bile acid as well as cholesterol excretion and decreases atherosclerosis in hypercholesterolemic mice.. Atherosclerosis 2011;218(2):323–9.
  47. Kim YB, Inoue T, Nakajima R, Shirai-Morishita Y, Tokuyama K, Suzuki M. Effect of long-term exercise on gene expression of insulin signaling pathway intermediates in skeletal muscle.. Biochem Biophys Res Commun 1999;254(3):720–7.
    doi: 10.1006/bbrc.1998.9940pubmed: 9920808google scholar: lookup
  48. Ropka-Molik K, Stefaniuk-Szmukier M, Żukowski K, Piórkowska K, Gurgul A, Bugno-Poniewierska M. Transcriptome profiling of Arabian horse blood during training regimens.. BMC Genetics 2017a;18:31.
    doi: 10.1186/s12863-017-0499-1pmc: PMC5382464pubmed: 28381206google scholar: lookup
  49. Zhang C, Ni P, Ahmad HI, Gemingguli M, Baizilaitibei A, Gulibaheti D. Detecting the Population Structure and Scanning for Signatures of Selection in Horses (Equus caballus) From Whole-Genome Sequencing Data.. Evol Bioinform Online 2018;14:1176934318775106.
    doi: 10.1177/1176934318775106pmc: PMC5990873pubmed: 29899660google scholar: lookup
  50. Ricard A, Robert C, Blouin C, Baste F, Torquet G, Morgenthaler C. Endurance Exercise Ability in the Horse: A Trait with Complex Polygenic Determinism.. Front Genet 2017;8:89.
    doi: 10.3389/fgene.2017.00089pmc: PMC5488500pubmed: 28702049google scholar: lookup
  51. Laughlin MH, Korthuis RJ, Duncker DJ, Bache RJ. Control of blood flow to cardiac and skeletal muscle during exercise In: Rowell LB, Shepherd JT, editors. Handbook of Pathophysiology. Regulation and Integration of Multiple Systems. Section 12.. Oxford University Press; New York: Oxford: Published for the American Physiological Society 1996. p. 705–769.
  52. Boushel R, Langberg H, Green S, Skovgaard D, Bulow J, Kjaer M. Blood flow and oxygenation in peritendinous tissue and calf muscle during dynamic exercise in humans.. J Physiol 2000;524(1):305–13.
    pmc: PMC2269862pubmed: 10747200
  53. Sarelius I, Pohl U. Control of muscle blood flow during exercise: local factors and integrative mechanisms.. Acta Physiol 2010;199(4):349–65.
    pmc: PMC3157959pubmed: 20353492
  54. Warskulat U, Flögel U, Jacoby C, Hartwig HG, Thewissen M, Merx MW. Taurine transporter knockout depletes muscle taurine levels and results in severe skeletal muscle impairment but leaves cardiac function uncompromised.. FASEB J 2004;18(3):577–9.
    doi: 10.1096/fj.03-0496fjepubmed: 14734644google scholar: lookup
  55. Ito T, Kimura Y, Uozumi Y, Takai M, Muraoka S, Matsuda T. Taurine depletion caused by knocking out the taurine transporter gene leads to cardiomyopathy with cardiac atrophy.. J Mol Cell Cardiol 2008;44(5):927–37.
    doi: 10.1016/j.yjmcc.2008.03.001pubmed: 18407290google scholar: lookup
  56. Dawson R Jr, Biasetti M, Messina S, Dominy J. The cytoprotective role of taurine in exercise-induced muscle injury.. Amino Acids 2002;22(4):309–24.
    doi: 10.1007/s007260200017pubmed: 12107759google scholar: lookup
  57. Miyazaki T, Matsuzaki Y, Ikegami T, Miyakawa S, Doy M, Tanaka N. The harmful effect of exercise on reducing taurine concentration in the tissues of rats treated with CC14 administration.. J Gastroenterol 2004;39(6):557–62.
    doi: 10.1007/s00535-003-1342-1pubmed: 15235873google scholar: lookup
  58. Korzeniewski B, Zoladz JA. Training-induced adaptation of oxidative phosphorylation in skeletal muscles.. Biochem J 2003;374(1):37–40.
    pmc: PMC1223571pubmed: 12741955
  59. Buck L, Axel R. A novel multigene family may encode odorant receptors: a molecular basis for odor recognition.. Cell 1991;65:175–87.
    pubmed: 1840504
  60. Nei M, Niimura Y, Nozawa M. The evolution of animal chemosensory receptor gene repertoires: roles of chance and necessity.. Nat Rev Genet 2008;9:951–63.
    doi: 10.1038/nrg2480pubmed: 19002141google scholar: lookup
  61. Touhara K, Vosshall LB. Sensing odorants and pheromones with chemosensory receptors.. Annu Rev Physiol 2009;71:307–32.
  62. Niimura Y. Olfactory receptor multigene family in vertebrates: from the viewpoint of evolutionary genomics.. Curr. Genomics 2012;13(2): 103–14.
    doi: 10.2174/138920212799860706pmc: PMC3308321pubmed: 23024602google scholar: lookup
  63. Niimura Y, Matsui A, Touhara K. Extreme expansion of the olfactory receptor gene repertoire in African elephants and evolutionary dynamics of orthologous gene groups in 13 placental mammals.. Genome Res 2014;24(9):1485–96.
    doi: 10.1101/gr.169532.113pmc: PMC4158756pubmed: 25053675google scholar: lookup
  64. Bollag WB. Regulation of aldosterone synthesis and secretion.. Compreh Physiol 2014;4(3):1017–55.
    doi: 10.1002/cphy.c130037pubmed: 24944029google scholar: lookup
  65. Blaine J, Chonchol M, Levi M. Renal Control of Calcium, Phosphate, and Magnesium Homeostasis.. Clin J Am Soc Nephrol 2015;10(7):1257–72.
    doi: 10.2215/CJN.09750913pmc: PMC4491294pubmed: 25287933google scholar: lookup
  66. Al Abri MA, Posbergh C, Palermo K, Sutter NB, Eberth J, Hoffman GE. Genome-Wide Scans Reveal a Quantitative Trait Locus for Withers Height in Horses Near the ANKRD1 Gene.. J Equine Vet Sci 2018;60:67–73.e1.
  67. Zhang W, Li J, Guo Y, Zhang L, Xu L, Gao X. Multi-strategy genome-wide association studies identify the DCAF16-NCAPG region as a susceptibility locus for average daily gain in cattle.. Sci Rep 2016;6:38073.
    doi: 10.1038/srep38073pmc: PMC5125095pubmed: 27892541google scholar: lookup
  68. Eberlein A, Takasuga A, Setoguchi K, Pfuhl R, Flisikowski K, Fries R. Dissection of genetic factors modulating fetal growth in cattle indicates a substantial role of the non-SMC condensin I complex, subunit G (NCAPG) Gene.. Genetics 2009;183:951–64.
    doi: 10.1534/genetics.109.106476pmc: PMC2778990pubmed: 19720859google scholar: lookup
  69. Setoguchi K, Furuta M, Hirano T, Nagao T, Watanabe T, Sugimoto Y. Cross-breed comparisons identified a critical 591 kb region for bovine carcass weight QTL (CW-2) on chromosome 6 and the Ile-442-Met substitution in NCAPG as a positional candidate.. BMC Genetics 2009;10:43.
    doi: 10.1186/1471-2156-10-43pmc: PMC2736976pubmed: 19653884google scholar: lookup
  70. Snelling WM, Allan MF, Keele JW, Kuehn LA, McDaneld T. Genome-wide association study of growth in crossbred beef cattle.. J. Anim. Sci. 2010;88(3):837–48.
    doi: 10.2527/jas.2009-2257pubmed: 19966163google scholar: lookup
  71. Weikard R, Altmaier E, Suhre K, Weinberger KM, Hammon HM, Albrecht E. Metabolomic profiles indicate distinct physiological pathways affected by two loci with major divergent effect on Bos taurus growth and lipid deposition.. Physiol Genomics 2010;42(2):79–88.
    pubmed: 20647382
  72. Setoguchi K, Watanabe T, Weikard R, Albrecht E, Kühn C, Kinoshita A. The SNP c.1326T>G in the non-SMC condensin I complex, subunit G (NCAPG) gene encoding a p.Ile442Met variant is associated with an increase in body frame size at puberty in cattle.. Anim Genet 2011;42:650–5.
  73. Signer-Hasler H, Flury C, Haase B, Burger D, Simianer H, Leeb T. A genome-wide association study reveals loci influencing height and other conformation traits in horses.. PLoS ONE 2012;7:e37282.
  74. Tetens J, Widmann P, Kühn C, Thaller G. A genome-wide association study indicates LCORL/NCAPG as a candidate locus for withers height in German Warmblood horses.. Anim Genet 2013;44:467–71.
    doi: 10.1111/age.12031pubmed: 23418885google scholar: lookup
  75. Metzger J, Schrimpf R, Philipp U, Distl O. Expression levels of LCORL are associated with body size in horses.. PLoS ONE 2013;8:e56497.
  76. Boyko AR, Brooks SA, Behan-Braman A, Castelhano M, Corey E, Oliveira KC. Genomic analysis establishes correlation between growth and laryngeal neuropathy in Thoroughbreds.. BMC Genomics 2014;15:259.
    doi: 10.1186/1471-2164-15-259pmc: PMC4051171pubmed: 24707981google scholar: lookup

Citations

This article has been cited 44 times.
  1. 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
  2. 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
  3. Stachurska A, Wnuk E, Łuszczyński J, Donderowicz W. Preliminary Biometric Study on Symmetry of Hoof Solear Aspect in Forelimbs in Four Horse Breeds. Animals (Basel) 2025 Nov 21;15(23).
    doi: 10.3390/ani15233369pubmed: 41375428google scholar: lookup
  4. Rao Z, Han R, Sun T, Li M, Wang Y, Xu J, Li Z, Cao L. Whole genome sequencing and resequencing provide insight into the genetic structure of wild Ophiocordyceps sinensis. BMC Genomics 2025 Oct 30;26(1):976.
    doi: 10.1186/s12864-025-12177-6pubmed: 41168753google scholar: lookup
  5. Lewczuk D, Wypchło M, Hecold M, Buczkowska R, Korwin-Kossakowska A. Connections Between Gene Polymorphism and Fetlock and Hock Measurements in Polish Sport Horses. Int J Mol Sci 2025 Oct 2;26(19).
    doi: 10.3390/ijms26199645pubmed: 41096909google scholar: lookup
  6. Steensma MJ, Doekes HP, Derks MFL, Ducro BJ. Genome-wide association study reveals candidate loci on ECA1 and ECA9 for withers height in Friesian horses. Anim Genet 2025 Oct;56(5):e70049.
    doi: 10.1111/age.70049pubmed: 41090464google scholar: lookup
  7. Duderstadt S, Distl O. Insights into Genomic Patterns of Homozygosity in the Endangered Dülmen Wild Horse Population. Genes (Basel) 2025 Sep 8;16(9).
    doi: 10.3390/genes16091054pubmed: 41009997google scholar: lookup
  8. Hassanine NNAM, Saleh AA, Essa MOA, Adam SY, Mohai Ud Din R, Rehman SU, Ali R, Husien HM, Wang M. Candidate Genes, Markers, Signatures of Selection, and Quantitative Trait Loci (QTLs) and Their Association with Economic Traits in Livestock: Genomic Insights and Selection. Int J Mol Sci 2025 Aug 8;26(16).
    doi: 10.3390/ijms26167688pubmed: 40869008google scholar: lookup
  9. Asti V, Summer A, Ablondi M, Sartori C, Giontella A, Pilastro V, Mecocci S, Cappelli K, Mancin E, Oian A, Mantovani R, Capomaccio S, Sabbioni A. Selection signatures and inbreeding: exploring genetic diversity in five native horse breeds. BMC Vet Res 2025 May 16;21(1):346.
    doi: 10.1186/s12917-025-04794-wpubmed: 40380299google scholar: lookup
  10. 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
  11. Li X, Wang Z, Zhu M, Wang B, Teng S, Yan J, Wang H, Yuan P, Cao S, Qu X, Wang Z, Zhan K, Choudhury MP, Yang X, Bao Q, He S, Liu L, Zhao P, Jiang J, Xiang H, Fang L, Tang Z, Liao Y, Yi G. Genomic Insights into Post-Domestication Expansion and Selection of Body Size in Ponies. Adv Sci (Weinh) 2025 Apr;12(16):e2413023.
    doi: 10.1002/advs.202413023pubmed: 40009528google scholar: lookup
  12. Azcona F, Molina A, Demyda-Peyrás S. Genomic-Inbreeding Landscape and Selection Signatures in the Polo Argentino Horse Breed. Int J Mol Sci 2024 Dec 24;26(1).
    doi: 10.3390/ijms26010026pubmed: 39795883google scholar: lookup
  13. Husien HM, Saleh AA, Hassanine NNAM, Rashad AMA, Sharaby MA, Mohamed AZ, Abdelhalim H, Hafez EE, Essa MOA, Adam SY, Chen N, Wang M. The Evolution and Role of Molecular Tools in Measuring Diversity and Genomic Selection in Livestock Populations (Traditional and Up-to-Date Insights): A Comprehensive Exploration. Vet Sci 2024 Dec 6;11(12).
    doi: 10.3390/vetsci11120627pubmed: 39728967google scholar: lookup
  14. Duderstadt S, Distl O. Genetic Diversity and Population Structure of Dülmen Wild, Liebenthal and Polish Konik Horses in Comparison with Przewalski, Sorraia, German Draught and Riding Horses. Animals (Basel) 2024 Jul 31;14(15).
    doi: 10.3390/ani14152221pubmed: 39123746google scholar: lookup
  15. Gmel AI, Mikko S, Ricard A, Velie BD, Gerber V, Hamilton NA, Neuditschko M. Using high-density SNP data to unravel the origin of the Franches-Montagnes horse breed. Genet Sel Evol 2024 Jul 10;56(1):53.
    doi: 10.1186/s12711-024-00922-6pubmed: 38987703google scholar: lookup
  16. Reich P, Möller S, Stock KF, Nolte W, von Depka Prondzinski M, Reents R, Kalm E, Kühn C, Thaller G, Falker-Gieske C, Tetens J. Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes. Genet Sel Evol 2024 Jun 13;56(1):45.
    doi: 10.1186/s12711-024-00914-6pubmed: 38872118google scholar: lookup
  17. Thompson MA, McCann BE, Rhen T, Simmons R. Population genomics provide insight into ancestral relationships and diversity of the feral horses of Theodore Roosevelt National Park. Ecol Evol 2024 Apr;14(4):e11197.
    doi: 10.1002/ece3.11197pubmed: 38571790google scholar: lookup
  18. Saif R, Mahmood T, Zia S, Henkel J, Ejaz A. Genomic selection pressure discovery using site-frequency spectrum and reduced local variability statistics in Pakistani Dera-Din-Panah goat. Trop Anim Health Prod 2023 Sep 26;55(5):331.
    doi: 10.1007/s11250-023-03758-2pubmed: 37750990google scholar: lookup
  19. Lindsay-McGee V, Sanchez-Molano E, Banos G, Clark EL, Piercy RJ, Psifidi A. Genetic characterisation of the Connemara pony and the Warmblood horse using a within-breed clustering approach. Genet Sel Evol 2023 Aug 17;55(1):60.
    doi: 10.1186/s12711-023-00827-wpubmed: 37592264google scholar: lookup
  20. Zhi Y, Wang D, Zhang K, Wang Y, Geng W, Chen B, Li H, Li Z, Tian Y, Kang X, Liu X. Genome-Wide Genetic Structure of Henan Indigenous Chicken Breeds. Animals (Basel) 2023 Feb 19;13(4).
    doi: 10.3390/ani13040753pubmed: 36830540google scholar: lookup
  21. Szmatoła T, Gurgul A, Jasielczuk I, Oclon E, Ropka-Molik K, Stefaniuk-Szmukier M, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. Assessment and Distribution of Runs of Homozygosity in Horse Breeds Representing Different Utility Types. Animals (Basel) 2022 Nov 25;12(23).
    doi: 10.3390/ani12233293pubmed: 36496815google scholar: lookup
  22. Momen M, Brounts SH, Binversie EE, Sample SJ, Rosa GJM, Davis BW, Muir P. Selection signature analyses and genome-wide association reveal genomic hotspot regions that reflect differences between breeds of horse with contrasting risk of degenerative suspensory ligament desmitis. G3 (Bethesda) 2022 Sep 30;12(10).
    doi: 10.1093/g3journal/jkac179pubmed: 35866615google scholar: lookup
  23. Colpitts J, McLoughlin PD, Poissant J. Runs of homozygosity in Sable Island feral horses reveal the genomic consequences of inbreeding and divergence from domestic breeds. BMC Genomics 2022 Jul 12;23(1):501.
    doi: 10.1186/s12864-022-08729-9pubmed: 35820826google scholar: lookup
  24. Moradi MH, Nejati-Javaremi A, Moradi-Shahrbabak M, Dodds KG, Brauning R, McEwan JC. Hitchhiking Mapping of Candidate Regions Associated with Fat Deposition in Iranian Thin and Fat Tail Sheep Breeds Suggests New Insights into Molecular Aspects of Fat Tail Selection. Animals (Basel) 2022 May 31;12(11).
    doi: 10.3390/ani12111423pubmed: 35681887google scholar: lookup
  25. Hall SJG. Genetic Differentiation among Livestock Breeds-Values for F(st). Animals (Basel) 2022 Apr 26;12(9).
    doi: 10.3390/ani12091115pubmed: 35565543google scholar: lookup
  26. de Faria DA, do Prado Paim T, Dos Santos CA, Paiva SR, Nogueira MB, McManus C. Selection signatures for heat tolerance in Brazilian horse breeds. Mol Genet Genomics 2022 Mar;297(2):449-462.
    doi: 10.1007/s00438-022-01862-wpubmed: 35150300google scholar: lookup
  27. Perrett J, Harris IT, Maddock C, Farnworth M, Pyatt AZ, Sumner RN. Systematic Analysis of Breed, Methodological, and Geographical Impact on Equine Sperm Progressive Motility. Animals (Basel) 2021 Oct 29;11(11).
    doi: 10.3390/ani11113088pubmed: 34827820google scholar: lookup
  28. Orbán L, Shen X, Phua N, Varga L. Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?. Front Genet 2021;12:506754.
    doi: 10.3389/fgene.2021.506754pubmed: 33968125google scholar: lookup
  29. Polak G, Gurgul A, Jasielczuk I, Szmatoła T, Krupiński J, Bugno-Poniewierska M. Suitability of Pedigree Information and Genomic Methods for Analyzing Inbreeding of Polish Cold-Blooded Horses Covered by Conservation Programs. Genes (Basel) 2021 Mar 17;12(3).
    doi: 10.3390/genes12030429pubmed: 33802830google scholar: lookup
  30. Laseca N, Anaya G, Peña Z, Pirosanto Y, Molina A, Demyda Peyrás S. Impaired Reproductive Function in Equines: From Genetics to Genomics. Animals (Basel) 2021 Feb 3;11(2).
    doi: 10.3390/ani11020393pubmed: 33546520google scholar: lookup
  31. Kvist L, Honka J, Niskanen M, Liedes O, Aspi J. Selection in the Finnhorse, a native all-around horse breed. J Anim Breed Genet 2021 Mar;138(2):188-203.
    doi: 10.1111/jbg.12524pubmed: 33226152google scholar: lookup
  32. Nguyen TB, Paul RC, Okuda Y, LE TNA, Pham PTK, Kaissar KJ, Kazhmurat A, Bibigul S, Bakhtin M, Kazymbet P, Maratbek SZ, Meldebekov A, Nishibori M, Ibi T, Tsuji T, Kunieda T. Genetic characterization of Kushum horses in Kazakhstan based on haplotypes of mtDNA and Y chromosome, and genes associated with important traits of the horses. J Equine Sci 2020 Oct;31(3):35-43.
    doi: 10.1294/jes.31.35pubmed: 33061782google scholar: lookup
  33. Fornal A, Kowalska K, Zabek T, Piestrzynska-Kajtoch A, Musiał AD, Ropka-Molik K. Genetic Diversity and Population Structure of Polish Konik Horse Based on Individuals from All the Male Founder Lines and Microsatellite Markers. Animals (Basel) 2020 Sep 3;10(9).
    doi: 10.3390/ani10091569pubmed: 32899310google scholar: lookup
  34. Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Szmatoła T, Polak G, Bugno-Poniewierska M. Genetic Differentiation of the Two Types of Polish Cold-blooded Horses Included in the National Conservation Program. Animals (Basel) 2020 Mar 24;10(3).
    doi: 10.3390/ani10030542pubmed: 32214005google scholar: lookup
  35. Boccardo A, Marelli SP, Pravettoni D, Bagnato A, Busca GA, Strillacci MG. The German Shorthair Pointer Dog Breed (Canis lupus familiaris): Genomic Inbreeding and Variability. Animals (Basel) 2020 Mar 17;10(3).
    doi: 10.3390/ani10030498pubmed: 32192001google scholar: lookup
  36. Martin LM, Johnson PJ, Amorim JR, DeClue AE. Effects of Orally Administered Resveratrol on TNF, IL-1β, Leukocyte Phagocytic Activity and Oxidative Burst Function in Horses: A Prospective, Randomized, Double-Blinded, Placebo-Controlled Study. Int J Mol Sci 2020 Feb 20;21(4).
    doi: 10.3390/ijms21041453pubmed: 32093379google scholar: lookup
  37. Han H, McGivney BA, Farries G, Katz LM, MacHugh DE, Randhawa IAS, Hill EW. Selection in Australian Thoroughbred horses acts on a locus associated with early two-year old speed. PLoS One 2020;15(2):e0227212.
    doi: 10.1371/journal.pone.0227212pubmed: 32049967google scholar: lookup
  38. 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
  39. 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
  40. 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
  41. Castaneda C, Juras R, Khanshour A, Randlaht I, Wallner B, Rigler D, Lindgren G, Raudsepp T, Cothran EG. Population Genetic Analysis of the Estonian Native Horse Suggests Diverse and Distinct Genetics, Ancient Origin and Contribution from Unique Patrilines. Genes (Basel) 2019 Aug 20;10(8).
    doi: 10.3390/genes10080629pubmed: 31434327google scholar: lookup
  42. Stefaniuk-Szmukier M, Szmatoła T, Łątka J, Długosz B, Ropka-Molik K. The Blood and Muscle Expression Pattern of the Equine TCAP Gene during the Race Track Training of Arabian Horses. Animals (Basel) 2019 Aug 18;9(8).
    doi: 10.3390/ani9080574pubmed: 31426609google scholar: lookup
  43. Grilz-Seger G, Neuditschko M, Ricard A, Velie B, Lindgren G, Mesarič M, Cotman M, Horna M, Dobretsberger M, Brem G, Druml T. Genome-Wide Homozygosity Patterns and Evidence for Selection in a Set of European and Near Eastern Horse Breeds. Genes (Basel) 2019 Jun 28;10(7).
    doi: 10.3390/genes10070491pubmed: 31261764google scholar: lookup
  44. 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