FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Iranian journal of biotechnology2026; 24(3); e4002; doi: 10.30498/ijb.2026.482549.4002

Identification of Genomic Differences Resulting from Selection in Iranian Kurdish and Turkmen Horse Breeds Using Single Nucleotide Polymorphisms.

Abstract: Breakthroughs in genomic technologies have enabled the formulation of methods to identify genomic variation in domestic animal species. Detection of the variation is necessary because it is associated with breed characteristics and economic importance in domestic animal genetics. Unassigned: This study aimed to identify the genomic regions in Turkmen and Kurdish horse breeds that have been the target of different selections over many years. It also sought to locate the genes and Quantitative trait loci (QTL) in these regions that contributed to the differentiation of these two breeds or the distinguishing traits within them breeds. Unassigned: In this research, the genomic differences between the Iranian Turkmen and Kurdish horses were identified using 70kb SNP markers. For this purpose, each breed's 68 Turkmen and 32 Kurdish horses were selected and genotyped. After data quality control and population structure investigation, 82 horses with 49,578 single nucleotide markers were used to search for the identification of genomic differences. Two statistical tests, fixation index (Fst) and cross-population extended haplotype homozygosity (XP-EHH), were used to identify genomic differences between populations. Unassigned: Using the Fst test, 4 genomic regions on chromosomes 5, 12, 29, and 30, whose theta is in the top 99.9% percentile of theta value of the experimental distribution of theta, were identified as population differentiation regions in both breeds. To identify differences caused by selection in these regions, the extended haplotype homozygosity test and the length of gene linkage disequilibrium were used. The analysis showed that chromosomes 5, 12, and 29 in Kurdish horses and chromosome 30 in Turkmen horses had differences caused by selection. The genes that played a role in the nervous system, cell metabolism, immune system, cell division, and gene regulation and expression were identified in these regions. The XP-EHH test identified 5 genomic regions on chromosomes 4, 5,9,10 and 11 as candidate regions for genomic differentiation in both populations. In each genomic region, several genes with different functions were identified. Several QTLs were identified on chromosome 4 in Turkmen horses, which are associated with reproductive traits in horses. A QTL with susceptibility to insect bites was identified on chromosome 11 of Kurdish horses. Using both mentioned statistical tests, regions on chromosome 5 were identified as candidate regions for selection in both breeds. Unassigned: Selection signatures in horse genomes indicate that natural and artificial selection have significantly influenced their genetic diversity. This data can enhance the understanding of horses' evolutionary past and refine breeding initiatives focused on enhancing their economic, functional, and biological traits. Examining selection signals offers essential resources for preserving genetic variety and improving productivity in equine breeding.
Copyright: © 2021 The Author(s); Published by Iranian Journal of Biotechnology.
Get Full Text
Publication Date: 2026-07-01 PubMed ID: 42471853PubMed Central: PMC13379593DOI: 10.30498/ijb.2026.482549.4002Google 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.
LinkedInCopy
  • 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.

Overview

  • This study investigated the genetic differences between Iranian Kurdish and Turkmen horse breeds caused by natural and artificial selection.
  • Using SNP markers and statistical tests, the researchers identified specific genomic regions and genes associated with breed differentiation and traits relevant to function and reproduction.

Background and Purpose

  • Advances in genomic technology allow precise detection of genetic variation within domestic animals, helping link genetics with breed characteristics and economic traits.
  • The research aimed to find genomic regions in Kurdish and Turkmen horses influenced by selection pressures over many years.
  • The goal was also to identify genes and quantitative trait loci (QTLs) in these regions that contribute to differences between the breeds or to distinct traits within them.

Materials and Methods

  • Sample: 68 Turkmen and 32 Kurdish horses were selected and genotyped using 70K SNP markers.
  • Data quality control and population structure assessment narrowed the data to 82 horses and 49,578 SNP markers for analysis.
  • Two statistical tests were applied to detect genomic differentiation:
    • Fixation index (Fst): Measures population differentiation based on allele frequencies.
    • Cross-population extended haplotype homozygosity (XP-EHH): Detects recent selection by comparing haplotype lengths between populations.

Results from Fst Analysis

  • Four genomic regions were identified with high population differentiation on chromosomes 5, 12, 29, and 30, in the top 0.1% of differentiation values.
  • Extended haplotype homozygosity and linkage disequilibrium analyses within these regions showed:
    • Chromosomes 5, 12, and 29 had selection-related differences primarily in Kurdish horses.
    • Chromosome 30 showed selection differences in Turkmen horses.
  • Genes in these regions were involved in:
    • The nervous system
    • Cell metabolism
    • Immune system function
    • Cell division
    • Gene regulation and expression

Results from XP-EHH Analysis

  • Five additional genomic regions with signs of selection were identified on chromosomes 4, 5, 9, 10, and 11.
  • In Turkmen horses, several QTLs related to reproductive traits were found on chromosome 4.
  • In Kurdish horses, a QTL on chromosome 11 was linked to susceptibility to insect bites.
  • Chromosome 5 emerged as a key candidate for selection in both breeds, confirmed by both statistical tests.

Conclusions and Implications

  • The study revealed that both natural and artificial selection have shaped the genetic diversity of Kurdish and Turkmen horse breeds.
  • Identification of specific genomic regions and genes supports a better understanding of evolutionary history and breed-specific traits.
  • This knowledge can inform and improve horse breeding strategies targeting enhanced economic, functional, and biological performance.
  • Recognizing selection signatures is critical for maintaining genetic diversity and improving productivity in equine breeding programs.

Cite This Article

APA
Khanahmadi A, Rahimi Mianji G, Moradi Shahrebabak H, Hafezian SH, Zandi MB. (2026). Identification of Genomic Differences Resulting from Selection in Iranian Kurdish and Turkmen Horse Breeds Using Single Nucleotide Polymorphisms. Iran J Biotechnol, 24(3), e4002. https://doi.org/10.30498/ijb.2026.482549.4002

Publication

Iranian journal of biotechnology
ISSN: 1728-3043
NlmUniqueID: 101276796
Country: Iran
Language: English
Volume: 24
Issue: 3
PII: e4002

Researcher Affiliations

Khanahmadi, Alireza
  • Department of Animal Science, College of Agriculture and Natural Resources, Gonbad Kavous University, Gonbad Kavous, Iran.
Rahimi Mianji, Ghodrat
  • Department of Animal Science, College of Animal Science and Fisheries, Sari Agricultural Science and Natural Resources University. Sari. Iran.
Moradi Shahrebabak, Hossein
  • Department of Animal Science, College of Agriculture, University of Tehran. Tehran. Iran.
Hafezian, Seyyed Hassan
  • Department of Animal Science, College of Animal Science and Fisheries, Sari Agricultural Science and Natural Resources University. Sari. Iran.
Zandi, Mohammad Bagher
  • Department of Animal Science, College of Agriculture and Natural Resources, University of Zanjan. Zanjan. Iran.

References

This article includes 52 references
  1. Barendse W, Harrison BE, Bunch RJ, Thomas MB, Turner LB. Genome wide signatures of positive selection: the comparison of independent samples and the identification of regions associated to traits.. BMC Genom 2009;10:1–15.
    doi: 10.1186/1471-2164-10-178pmc: PMC2681478pubmed: 19393047google scholar: lookup
  2. Firouz L. Conservation of a domestic breed.. 1969.
    doi: 10.1016/0006-3207(69)90117-7google scholar: lookup
  3. Behroozinia S, Hosseini Moghaddam SH, Afraz F, Sohrabi A, Mohammadi SA, Shahbazi S. Genetic characterization of two Iranian Turkoman horse populations from Turkoman Sahra and Turkoman Jergelan regions.. Iran J Animal Sci Res 2011;3(1).
    doi: 10.3923/pjbs.2006.155.159google scholar: lookup
  4. Ala-Amjadi M, Yeganeh H, Sadeghi M. Study of Genetic variation in Iranian Kurdish horse using microsatellite marker.. Iran J Anim Sci 2017;48(3):335–342.
    doi: 10.22059/ijas.2017.237799.653544google scholar: lookup
  5. Diamond J. Evolution, consequences and future of plant and animal domestication.. Nature 2002;418(6898):700–707.
    doi: 10.1038/nature01019pubmed: 12167878google scholar: lookup
  6. Harris EE, Meyer D. The molecular signature of selection underlying human adaptations.. Am J Phys Anthropol 2006;43:89–130.
    doi: 10.1002/ajpa.20518pubmed: 17103426google scholar: lookup
  7. Akey JM. Constructing genomic maps of positive selection in humans: where do we go from here?. Genome Res 2009;19(5):711–722.
    doi: 10.1101/gr.086652.108pmc: PMC3647533pubmed: 19411596google scholar: lookup
  8. Biswas S, Akey JM. Genomic insights into positive selection.. TRENDS in Gen 2006;22(8):437–446.
    doi: 10.1016/j.tig.2006.06.005pubmed: 16808986google scholar: lookup
  9. Qanbari S, Strom TM, Haberer G, Weigend S, Gheyas AA, Turner F. A high resolution genome-wide scan for significant selective sweeps: an application to pooled sequence data in laying chickens.. Plos One 2012;7(11):e49525.
    doi: 10.1371/journal.pone.0049525pmc: PMC3510216pubmed: 23209582google scholar: lookup
  10. Qanbari S, Pausch H, Jansen S, Somel M, Strom TM, Fries R. Classic selective sweeps revealed by massive sequencing in cattle.. Plos Gen 2014;10(2):e1004148.
    doi: 10.1371/journal.pgen.1004148pmc: PMC3937232pubmed: 24586189google scholar: lookup
  11. Gu J, Orr N, Park SD, Katz LM, Sulimova G, MacHugh DE. A genome scan for positive selection in thoroughbred horses.. Plos One 2009;4(6):e5767.
    doi: 10.1371/journal.pone.0005767pmc: PMC2685479pubmed: 19503617google scholar: lookup
  12. Kim Y, Stephan W. Detecting a local signature of genetic hitchhiking along a recombining chromosome.. Genetics 2002;160(2):765–777.
    pmc: PMC1461968pubmed: 11861577
  13. Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive selection in the human genome.. PLoS Biology 2006;4(3):e72.
    doi: 10.1093/genetics/160.2.765pmc: PMC1382018pubmed: 16494531google scholar: lookup
  14. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure.. Evolution 1984:1358–1370.
    doi: 10.2307/2408641pubmed: 28563791google scholar: lookup
  15. Nasirpour M, moradi shahr babak m, Moradi Shahr Babak H, mehrabani yeganeh H, doosti Y. Analysis of SNP Chip 70k Genomic Data to Identify Loci Related to Differentiation of Caspian and Kurdish Horses using Selection Sweep.. Res Animal Product 2023;14(1):154–162.
    doi: 10.61186/rap.14.39.154google scholar: lookup
  16. 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 2013;8(1):e54997.
    doi: 10.1371/journal.pone.0054997pmc: PMC3559798pubmed: 23383025google scholar: lookup
  17. 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 Genom 2019;20(1):717.
    doi: 10.1186/s12864-019-6079-1pmc: PMC6751828pubmed: 31533613google scholar: lookup
  18. Sabeti PC, Reich DE, Higgins JM, Levine HZ, Richter DJ, Schaffner SF. Detecting recent positive selection in the human genome from haplotype structure. Nature 2002;419(6909):832–837.
    doi: 10.1038/nature01140pubmed: 12397357google scholar: lookup
  19. Maiorano AM, Lourenco DL, Tsuruta S, Ospina AMT, Stafuzza NB, Masuda Y. Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information. PLoS One 2018;13(8):e0200694.
    doi: 10.1371/journal.pone.0200694pmc: PMC6071998pubmed: 30071036google scholar: lookup
  20. Bomba L, Nicolazzi EL, Milanesi M, Negrini R, Mancini G, Biscarini F. Relative extended haplotype homozygosity signals across breeds reveal dairy and beef specific signatures of selection. Gen Select Evolution 2015;47(1):25.
    doi: 10.1186/s12711-015-0113-9pmc: PMC4383072pubmed: 25888030google scholar: lookup
  21. Randhawa IAS, Khatkar MS, Thomson PC, Raadsma HW. Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep. BMC Gen 2014;15(1):34.
    doi: 10.1186/1471-2156-15-34pmc: PMC4101850pubmed: 24636660google scholar: lookup
  22. Frischknecht M, Flury C, Leeb T, Rieder S, Neuditschko M. Selection signatures in Shetland ponies. Animal Gen 2016;47(3):370–372.
    doi: 10.1111/age.12416pubmed: 26857482google scholar: lookup
  23. 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(1):764.
    doi: 10.1186/s12864-015-1977-3pmc: PMC4600213pubmed: 26452642google scholar: lookup
  24. Moon S, Lee JW, Shin D, Shin K-Y, Kim J, Choi I-Y. A genome-wide scan for selective sweeps in racing horses. Asian-Australasian J Animal Sci 2015;28(11):1525.
    doi: 10.5713/ajas.14.0696pmc: PMC4647090pubmed: 26333666google scholar: lookup
  25. MWer S, Dykes D, Polesky H. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 1988;16(3):1215.
    doi: 10.1093/nar/16.3.1215pmc: PMC334765pubmed: 3344216google scholar: lookup
  26. Teo YY, Fry AE, Clark TG, Tai E, Seielstad M. On the usage of HWE for identifying genotyping errors. Annals Human Gen 2007;71(5).
    doi: 10.1111/j.1469-1809.2007.00356.xpubmed: 17388941google scholar: lookup
  27. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D. PLINK: a tool set for whole-genome association and population-based linkage analyses. American J Human Gen 2007;81(3):559–575.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  28. Pérez O’Brien AM, Utsunomiya YT, Mészáros G, Bickhart DM, Liu GE, Van Tassell CP. Assessing signatures of selection through variation in linkage disequilibrium between taurine and indicine cattle. Gen Select Evolu 2014;46:1–14.
    doi: 10.1186/1297-9686-46-19pmc: PMC4014805pubmed: 24592996google scholar: lookup
  29. MacEachern S, Hayes B, McEwan J, Goddard M. An examination of positive selection and changing effective population size in Angus and Holstein cattle populations (Bos taurus) using a high density SNP genotyping platform and the contribution of ancient polymorphism to genomic diversity in Domestic cattle. BMC Genom 2009;10:1–19.
    doi: 10.1186/1471-2164-10-181pmc: PMC2681480pubmed: 19393053google scholar: lookup
  30. Moradi MH, Nejati-Javaremi A, Moradi-Shahrbabak M, Dodds KG, McEwan JC. Genomic scan of selective sweeps in thin and fat tail sheep breeds for identifying of candidate regions associated with fat deposition. BMC Gen 2012;13:1–15.
    doi: 10.1186/1471-2156-13-10pmc: PMC3351017pubmed: 22364287google scholar: lookup
  31. Kijas JW, Lenstra JA, Hayes B, Boitard S, Porto Neto LR, San Cristobal M, et al. Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. Plos Biology. 2012;10(2):e1001258. doi: 10.1371/journal.pbio.1001258.
    doi: 10.1371/journal.pbio.1001258pmc: PMC3274507pubmed: 22346734google scholar: lookup
  32. Jarvis JP, Scheinfeldt LB, Soi S, Lambert C, Omberg L, Ferwerda B, et al. Patterns of ancestry, signatures of natural selection, and genetic association with stature in Western African pygmies. Plos Gen. 2012;8(4):e1002641. doi: 10.1371/journal.pgen.1002641.
    doi: 10.1371/journal.pgen.1002641pmc: PMC3343053pubmed: 22570615google scholar: lookup
  33. Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive selection in the human genome. Plos Biology. 2006;4(3):13. doi: 10.1371/journal.pbio.0040072.
    doi: 10.1371/journal.pbio.0040072pmc: PMC1382018pubmed: 16494531google scholar: lookup
  34. Aken BL, Ayling S, Barrell D, Clarke L, Curwen V, Fairley S, et al. The Ensembl gene annotation system. Database. 2016;2016:baw093. doi: 10.1093/database/baw093.
    doi: 10.1093/database/baw093pmc: PMC4919035pubmed: 27337980google scholar: lookup
  35. Karolchik D, Hinrichs AS, Furey TS, Roskin KM, Sugnet CW, Haussler D, et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 2004;32(suppl_1):D493–D496. doi: 10.1093/nar/gkh103.
    doi: 10.1093/nar/gkh103pmc: PMC308837pubmed: 14681465google scholar: lookup
  36. Hu ZL, Fritz ER, Reecy JM. AnimalQTLdb: a livestock QTL database tool set for positional QTL information mining and beyond. Nucleic Acids Res. 2007;35(1,1):5. doi: 10.1093/nar/gkl946.
    doi: 10.1093/nar/gkl946pmc: PMC1781224pubmed: 17135205google scholar: lookup
  37. Schurink A, Wolc A, Ducro BJ, Frankena K, Garrick DJ, Dekkers JC, et al. Genome-wide association study of insect bite hypersensitivity in two horse populations in the Netherlands. Gen Selec Evolu. 2012;44:1–12. doi: 10.1186/1297-9686-44-31.
    doi: 10.1186/1297-9686-44-31pmc: PMC3524047pubmed: 23110538google scholar: lookup
  38. Charlesworth D. Balancing selection and its effects on sequences in nearby genome regions. Plos Gen. 2006;2(4):e64. doi: 10.1371/journal.pgen.0020064.
    doi: 10.1371/journal.pgen.0020064pmc: PMC1449905pubmed: 16683038google scholar: lookup
  39. Grasso AN, Goldberg V, Navajas EA, Iriarte W, Gimeno D, Aguilar I, et al. Genomic variation and population structure detected by single nucleotide polymorphism arrays in Corriedale, Merino and Creole sheep. Gen Mol Biol. 2014;37:389–395. doi: 10.1590/s1415-47572014000300011.
    doi: 10.1590/s1415-47572014000300011pmc: PMC4094612pubmed: 25071404google scholar: lookup
  40. Mousavi SF RM, Rostamzadeh J, Seyedabadi HR, Naboulsi R, Petersen JL, Lindgren G. Genetic diversity and signatures of selection in four indigenous horse breeds of Iran. Heredity. 2023;131(2):96–108. doi: 10.1038/s41437-023-00624-7.
    doi: 10.1038/s41437-023-00624-7pmc: PMC10382556pubmed: 37308718google scholar: lookup
  41. Feng X, Diao S, Liu Y, Xu Z, Li G, Ma Y, et al. Exploring the mechanism of artificial selection signature in Chinese indigenous pigs by leveraging multiple bioinformatics database tools. BMC Genomics. 2023;24(1):743. doi: 10.1186/s12864-023-09848-7.
    doi: 10.1186/s12864-023-09848-7pmc: PMC10699062pubmed: 38053015google scholar: lookup
  42. Weckman MJ, Karikoski N, Raekallio M, Box JR, Kvist L. Genome-wide association study suggests genetic candidate loci of insulin dysregulation in Finnhorses. Veterinary J. 2024;303:106063. doi: 10.1016/j.tvjl.2024.106063.
    doi: 10.1016/j.tvjl.2024.106063pubmed: 38232813google scholar: lookup
  43. Sabeti PC, Schaffner SF, Fry B, Lohmueller J, Varilly P, Shamovsky O, et al. Positive natural selection in the human lineage. Science. 2006;312(5780):1614–1620. doi: 10.1126/science.1124309.
    doi: 10.1126/science.1124309pubmed: 16778047google scholar: lookup
  44. Bazvand B, Rashidi A, Zandi M, Moradi M, Rostamzadeh J. Genome-wide analysis of population structure, effective population size and inbreeding in Iranian and exotic horses. Plos One. 2024;19(3):e0299109. doi: 10.1371/journal.pone.0299109.
    doi: 10.1371/journal.pone.0299109pmc: PMC10914290pubmed: 38442089google scholar: lookup
  45. Hancock AM, Di Rienzo A. Detecting the genetic signature of natural selection in human populations: models, methods, and data. Ann Rev Anthropolog. 2008;37(1):197–217. doi: 10.1146/annurev.anthro.37.081407.085141.
    doi: 10.1146/annurev.anthro.37.081407.085141pmc: PMC2901121pubmed: 20622977google scholar: lookup
  46. Qanbari S, Gianola D, Hayes B, Schenkel F, Miller S, Moore S, et al. Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle. BMC Genomics. 2011;12:1–12. doi: 10.1186/1471-2164-12-318.
    doi: 10.1186/1471-2164-12-318pmc: PMC3146955pubmed: 21679429google scholar: lookup
  47. Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME. Invited review: Genomic selection in dairy cattle: Progress and challenges. J Dairy Sci. 2009;92(2):433–443. doi: 10.3168/jds.2008-1646.
    doi: 10.3168/jds.2008-1646pubmed: 19164653google scholar: lookup
  48. Huttley GA, Smith MW, Carrington M, O’Brien SJ. A scan for linkage disequilibrium across the human genome. Genetics. 1999;152(4):1711–1722. doi: 10.1093/genetics/152.4.1711.
    doi: 10.1093/genetics/152.4.1711pmc: PMC1460700pubmed: 10430595google scholar: lookup
  49. Scharrenberg A, Gerber V, Swinburne J, Wilson A, Klukowska-Rötzler J, Laumen E, et al. IgE, IgGa, IgGb and IgG (T) serum antibody levels in offspring of two sires affected with equine recurrent airway obstruction. Animal Gen. 2010;41:131–137. doi: 10.1111/j.1365-2052.2010.02122.x.
    doi: 10.1111/j.1365-2052.2010.02122.xpubmed: 21070286google scholar: lookup
  50. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, et al. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007;449(7164):913–918. doi: 10.1038/nature06250.
    doi: 10.1038/nature06250pmc: PMC2687721pubmed: 17943131google scholar: lookup
  51. Zhang H, Wang S-Z, Wang Z-P, Da Y, Wang N, Hu X-X, et al. A genome-wide scan of selective sweeps in two broiler chicken lines divergently selected for abdominal fat content. BMC Genomics. 2012;13:1–16. doi: 10.1186/1471-2164-13-704.
    doi: 10.1186/1471-2164-13-704pmc: PMC3557156pubmed: 23241142google scholar: lookup
  52. Laseca N, Demyda-Peyrás S, Valera M, Ramón M, Escribano B, Perdomo-González DI, et al. A genome-wide association study of mare fertility in the Pura Raza Español horse. Animal. 2022;16(3):100476. doi: 10.1016/j.animal.2022.100476.
    doi: 10.1016/j.animal.2022.100476pubmed: 35247706google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 106,350 horse owners like you who receive our email updates on horse health and nutrition.