FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Genes2023; 14(8); doi: 10.3390/genes14081623

Genome-Wide Single-Nucleotide Polymorphism-Based Genomic Diversity and Runs of Homozygosity for Selection Signatures in Equine Breeds.

Abstract: The horse, one of the most domesticated animals, has been used for several purposes, like transportation, hunting, in sport, or for agriculture-related works. Kathiawari, Marwari, Manipuri, Zanskari, Bhutia, Spiti, and Thoroughbred are the main breeds of horses, particularly due to their agroclimatic adaptation and role in any kind of strong physical activity, and these characteristics are majorly governed by genetic factors. The genetic diversity and phylogenetic relationship of these Indian equine breeds using microsatellite markers have been reported, but further studies exploring the SNP diversity and runs of homozygosity revealing the selection signature of breeds are still warranted. In our study, the identification of genes that play a vital role in muscle development is performed through SNP detection via the whole-genome sequencing approach. A total of 96 samples, categorized under seven breeds, and 620,721 SNPs were considered to ascertain the ROH patterns amongst all the seven breeds. Over 5444 ROH islands were mined, and the maximum number of ROHs was found to be present in Zanskari, while Thoroughbred was confined to the lowest number of ROHs. Gene enrichment of these ROH islands produced 6757 functional genes, with AGPAT1, CLEC4, and CFAP20 as important gene families. However, QTL annotation revealed that the maximum QTLs were associated with Wither's height trait ontology that falls under the growth trait in all seven breeds. An Equine SNP marker database (EqSNPDb) was developed to catalogue ROHs for all these equine breeds for the flexible and easy chromosome-wise retrieval of ROH along with the genotype details of all the SNPs. Such a study can reveal breed divergence in different climatic and ecological conditions.
Get Full Text
Publication Date: 2023-08-14 PubMed ID: 37628674PubMed Central: PMC10454598DOI: 10.3390/genes14081623Google 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
  • 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.

The research article describes a genomic study that investigates the genetic diversity and selection signatures of seven key horse breeds. Using single-nucleotide polymorphism (SNP) detection from a whole-genome sequencing approach, important breed-specific genes relating to muscle development were identified, and an equine SNP marker database was created for cataloguing and retrieval of genomic information.

Objective and Methodology

  • The study aimed to understand the genetic diversity, phylogenetic relationship, and selection signatures of seven Indian horse breeds: Kathiawari, Marwari, Manipuri, Zanskari, Bhutia, Spiti, and Thoroughbred.
  • A total of 96 samples from these breeds were sequenced, unveiling 620,721 SNPs. The analysis focused on looking at runs of homozygosity (ROH) – stretching sequences of identical genomic blocks that shed light on the inheritance and selection patterns in the genomes.

Key Findings

  • Zanskari breed has the highest number of ROHs, while Thoroughbred has the lowest. This indicates a difference in the degree of selection and inbreeding these breeds have gone through, with Zanskari likely having a higher degree of selection or inbreeding.
  • A total of 5444 ROH islands were discovered across breeds, leading to the identification of 6757 functional genes. Among these genes, AGPAT1, CLEC4, and CFAP20 were noted as important gene families. These genes significantly contribute to the unique characteristics and capabilities of these horse breeds.
  • Wither’s height trait ontology, which falls under the growth trait, was revealed to be associated with the maximum Quantitative Trait Loci (QTLs) in all seven breeds. Quantitative Trait Loci are regions of the genome associated with particular phenotypic traits.

Equine SNP Marker Database (EqSNPDb)

  • The study also developed an Equine SNP marker database that catalogs ROHs for all the studied breeds. It allows easy chromosome-wise retrieval of ROH along with the genotype details of all the SNPs.
  • This tool will be instrumental in future studies of horse genetics, potentially aiding in breed development, conservation, and preservation of equine genetic diversity.

Conclusions and Implications

  • The study provides a deepened understanding of the genetic diversity and selection signatures across seven breed types of horses.
  • The unique features and capabilities of these breeds are primarily governed by their genetic composition and selective breeding, which has resulted in variations in terms of physical strength, endurance, and adaptability to agroclimatic conditions.
  • The findings could lead to improvements in breed conservation efforts, and could contribute to more precise genetic selection in breeding for desired traits in horses.

Cite This Article

APA
Bhardwaj A, Tandon G, Pal Y, Sharma NK, Nayan V, Soni S, Iquebal MA, Jaiswal S, Legha RA, Talluri TR, Bhattacharya TK, Kumar D, Rai A, Tripathi BN. (2023). Genome-Wide Single-Nucleotide Polymorphism-Based Genomic Diversity and Runs of Homozygosity for Selection Signatures in Equine Breeds. Genes (Basel), 14(8). https://doi.org/10.3390/genes14081623

Publication

Genes
ISSN: 2073-4425
NlmUniqueID: 101551097
Country: Switzerland
Language: English
Volume: 14
Issue: 8

Researcher Affiliations

Bhardwaj, Anuradha
  • ICAR-National Research Centre on Equines, Sirsa Road, Hisar 125001, India.
Tandon, Gitanjali
  • Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
Pal, Yash
  • ICAR-National Research Centre on Equines, Sirsa Road, Hisar 125001, India.
Sharma, Nitesh Kumar
  • Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
Nayan, Varij
  • ICAR-Central Institute for Research on Buffaloes, Hisar 125001, India.
Soni, Sonali
  • ICAR-National Research Centre on Equines, Sirsa Road, Hisar 125001, India.
Iquebal, Mir Asif
  • Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
Jaiswal, Sarika
  • Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
Legha, Ram Avatar
  • ICAR-National Research Centre on Equines, Sirsa Road, Hisar 125001, India.
Talluri, Thirumala Rao
  • ICAR-National Research Centre on Equines, Sirsa Road, Hisar 125001, India.
Bhattacharya, Tarun Kumar
  • ICAR-National Research Centre on Equines, Sirsa Road, Hisar 125001, India.
Kumar, Dinesh
  • Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
Rai, Anil
  • Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110012, India.
Tripathi, B N
  • ICAR-National Research Centre on Equines, Sirsa Road, Hisar 125001, India.
  • Indian Council of Agricultural Research, Krishi Bhawan, New Delhi 110001, India.

MeSH Terms

  • Animals
  • Horses / genetics
  • Polymorphism, Single Nucleotide / genetics
  • Phylogeny
  • Homozygote
  • Genotype
  • Genomics

Conflict of Interest Statement

The authors declare that there are no conflict of interest.

References

This article includes 45 references
  1. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lindblad-Toh K. Genome sequence, comparative analysis, and population genetics of the domestic horse.. Science 2009;326:865–867.
    pmc: PMC3785132pubmed: 19892987
  2. Jun J, Cho YS, Hu H, Kim HM, Jho S, Gadhvi P, Bhak J. Whole genome sequence and analysis of the Marwari horse breed and its genetic origin.. BMC Genom 2014;15:S4.
    pmc: PMC4290615pubmed: 25521865
  3. Gupta AK, Tandon SN, Pal Y, Bhardwaj A, Chauhan M. Phenotypic characterization of Indian equine breeds: A comparative study.. Anim. Genet. Resour. 2012;50:49–58.
  4. Gupta AK, Chauhan M, Bhardwaj A, Gupta N, Gupta SC, Pal Y, Vijh RK. Comparative genetic diversity analysis among six Indian breeds and English Thoroughbred horses.. Livest. Sci. 2014;163:1–11.
  5. Pal Y, Legha RA, Bhardwaj A, Tripathi BN. Status and conservation of equine biodiversity in India.. Indian J. Comp. Microbiol. Immunol. Infect. Dis. 2020;41:174–184.
  6. Behl R, Behl J, Gupta N, Gupta SC. Genetic relationships of five Indian horse breeds using microsatellite markers.. Animal 2007;1:483–488.
    pubmed: 22444405
  7. McCue ME, Bannasch DL, Petersen JL, Gurr J, Bailey E, Binns MM, Mickelson JR. A high density SNP array for the domestic horse and extant Perissodactyla: Utility for association mapping, genetic diversity, and phylogeny studies.. PLoS Genet. 2012;8:e1002451.
    pmc: PMC3257288pubmed: 22253606
  8. Schaefer RJ, Schubert M, Bailey E, Bannasch DL, Barrey E, Bar-Gal GK, McCue ME. Developing a 670k genotyping array to tag~ 2M SNPs across 24 horse breeds.. BMC Genom 2017;18:565.
    pmc: PMC5530493pubmed: 28750625
  9. Gupta A, Bhardwaj A, Sharma P, Pal Y. Mitochondrial DNA-a tool for phylogenetic and biodiversity search in equines.. J. Biodivers. Endanger. Species. 2015;1:1–8.
  10. Rafati N, Andersson LS, Mikko S, Feng C, Raudsepp T, Pettersson J, Rubin CJ. Large deletions at the SHOX locus in the pseudoautosomal region are associated with skeletal atavism in Shetland Ponies.. G3 Genes Genomes Genet. 2016;6:2213–2223.
    pmc: PMC4938674pubmed: 27207956
  11. Grilz-Seger G, Neuditschko M, Ricard A, Velie B, Lindgren G, Mesarič M, Druml T. Genome-wide homozygosity patterns and evidence for selection in a set of European and near eastern horse breeds.. Genes 2019;10:491.
    pmc: PMC6679042pubmed: 31261764
  12. Solé M, Ablondi M, Binzer-Panchal A, Velie BD, Hollfelder N, Buys N, Ducro BJ, François L, Janssens S, Schurink A. Inter-and intra-breed genome-wide copy number diversity in a large cohort of European equine breeds.. BMC Genom 2019;20:759.
    pmc: PMC6805398pubmed: 31640551
  13. Norton E, Schultz N, Geor R, McFarlane D, Mickelson J, McCue M. Genome-wide association analyses of equine metabolic syndrome phenotypes in Welsh ponies and Morgan horses.. Genes 2019;10:893.
    pmc: PMC6895807pubmed: 31698676
  14. Raudsepp T, McCue ME, Das PJ, Dobson L, Vishnoi M, Fritz KL, Schaefer R, Rendahl AK, Derr JN, Love CC. Genome-wide association study implicates testis-sperm specific FKBP6 as a susceptibility locus for impaired acrosome reaction in stallions.. PLoS Genet. 2012;8:1003139.
    pmc: PMC3527208pubmed: 23284302
  15. Fawcett JA, Sato F, Sakamoto T, Iwasaki WM, Tozaki T, Innan H. Genome-wide SNP analysis of Japanese Thoroughbred racehorses.. PLoS ONE 2019;14:0218407.
    pmc: PMC6655603pubmed: 31339891
  16. Cosgrove EJ, Sadeghi R, Schlamp F, Holl HM, Moradi-Shahrbabak M, Miraei-Ashtiani SR, Brooks SA. Genome diversity and the origin of the Arabian horse.. Sci. Rep. 2020;10:9702.
    pmc: PMC7298027pubmed: 32546689
  17. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: Rising to the challenge of larger and richer datasets.. Gigascience 2015;4:s13742-015.
    pmc: PMC4342193pubmed: 25722852
  18. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, De Bakker PI, Daly MJ. PLINK: A tool set for whole-genome association and population-based linkage analyses.. Am. J. Hum. Genet. 2007;81:559–575.
    pmc: PMC1950838pubmed: 17701901
  19. Fang Y, Hao X, Xu Z, Sun H, Zhao Q, Cao R, Pan Y. Genome-wide detection of runs of homozygosity in Laiwu pigs revealed by sequencing data.. Front. Genet. 2021;12:629966.
    pmc: PMC8116706pubmed: 33995477
  20. Kirin M, McQuillan R, Franklin CS, Campbell H, McKeigue PM, Wilson JF. Genomic runs of homozygosity record population history and consanguinity.. PLoS ONE 2010;5:e13996.
    pmc: PMC2981575pubmed: 21085596
  21. Ferenčaković M, Hamzić E, Gredler B, Solberg TR, Klemetsdal G, Curik I, Sölkner J. Estimates of autozygosity derived from runs of homozygosity: Empirical evidence from selected cattle populations.. J. Anim. Breed. Genet. 2013;130:286–293.
    pubmed: 23855630
  22. Ferenčaković M, Sölkner J, Curik I. Estimating autozygosity from high-throughput information: Effects of SNP density and genotyping errors.. Genet. Sel. Evol. 2013;45:42.
    pmc: PMC4176748pubmed: 24168655
  23. McQuillan R, Leutenegger AL, Abdel-Rahman R, Franklin CS, Pericic M, Barac-Lauc L, Smolej-Narancic N, Janicijevic B, Polasek O, Tenesa A. Runs of homozygosity in European populations.. Am. J. Hum. Genet. 2008;83:359–372.
    doi: 10.1016/j.ajhg.2008.08.007pmc: PMC2556426pubmed: 18760389google scholar: lookup
  24. Kaeuffer R, Réale D, Coltman DW, Pontier D. Detecting population structure using STRUCTURE software: Effect of background linkage disequilibrium.. Heredity 2007;99:374–380.
    pubmed: 17622269
  25. Cingolani P. Variant Calling: Methods and Protocols.. Springer; New York, NY, USA: 2022. Variant annotation and functional prediction: SnpEff; pp. 289–314.
    pubmed: 35751823
  26. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC. DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists.. Nucleic Acids Res. 2007;35((Suppl. 2)):W169–W175.
    pmc: PMC1933169pubmed: 17576678
  27. Willing EM, Dreyer C, van Oosterhout C. Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers.. PLoS ONE 2012;7:e42649.
    pmc: PMC3419229pubmed: 22905157
  28. Colli L, Milanesi M, Vajana E, Iamartino D, Bomba L, Puglisi F. New insights on water buffalo genomic diversity and postdomestication migration routes from medium density SNP chip data.. Front. Genet. 2018;9:53.
    doi: 10.3389/fgene.2018.00053pmc: PMC5841121pubmed: 29552025google scholar: lookup
  29. Utsunomiya YT, Milanesi M, Fortes MRS, Porto-Neto LR, Utsunomiya ATH, Silva MVGB, Garcia JF, Ajmone-Marsan P. Genomic clues of the evolutionary history of Bos indicus cattle.. Anim. Genet. 2019;50:557–568.
    pubmed: 31475748
  30. Dzomba EF, Chimonyo M, Pierneef R, Muchadeyi FC. Runs of homozygosity analysis of South African sheep breeds from various production systems investigated using OvineSNP50k data.. BMC Genom. 2021;22:7.
    pmc: PMC7788743pubmed: 33407115
  31. Xu Z, Mei S, Zhou J, Zhang Y, Qiao M, Sun H, Peng X. Genome-wide assessment of runs of homozygosity and estimates of genomic inbreeding in a chinese composite pig breed.. Front. Genet. 2021;12:720081.
    pmc: PMC8440853pubmed: 34539748
  32. Howrigan DP, Simonson MA, Keller MC. Detecting autozygosity through runs of homozygosity: A comparison of three autozygosity detection algorithms.. BMC Genom. 2011;12:460.
    pmc: PMC3188534pubmed: 21943305
  33. Mastrangelo S, Tolone M, Sardina MT, Sottile G, Sutera AM, Di Gerlando R, Portolano B. Genome-wide scan for runs of homozygosity identifies potential candidate genes associated with local adaptation in Valle del Belice sheep.. Genet. Sel. Evol. 2017;49:84.
    pmc: PMC5684758pubmed: 29137622
  34. Peripolli E, Stafuzza NB, Munari DP, Lima ALF, Irgang R, Machado MA, do Carmo Panetto JC, Vieira Ventura R, Baldi F, da Silva MVGB. Assessment of runs of homozygosity islands and estimates of genomic inbreeding in Gyr (Bos indicus) dairy cattle.. BMC Genom. 2018;19:34.
    pmc: PMC5759835pubmed: 29316879
  35. Subauste AR, Elliott B, Das AK, Burant CF. A role for 1-acylglycerol-3-phosphate-O-acyltransferase-1 in myoblast differentiation.. Differentiation 2010;80:140–146.
    pmc: PMC3449212pubmed: 20561744
  36. Zhu Q, Huo H, Fu Q, Yang N, Xue T, Zhuang C, Liu X, Wang B, Su B, Li C. Identification and characterization of a C-type lectin in turbot (Scophthalmus maximus) which functioning as a pattern recognition receptor that binds and agglutinates various bacteria.. Fish Shellfish Immunol. 2021;115:104–111.
    pubmed: 34062237
  37. Hestand MS, Kalbfleisch TS, Coleman SJ, Zeng Z, Liu J, Orlando L, MacLeod JN. Annotation of the protein coding regions of the equine genome.. PLoS ONE 2015;10:e0124375.
    pmc: PMC4481266pubmed: 26107351
  38. Ząbek T, Semik E, Wnuk M, Fornal A, Gurgul A, Bugno-Poniewierska M. Epigenetic structure and the role of polymorphism in the shaping of DNA methylation patterns of equine OAS1 locus.. J. Appl. Genet. 2015;56:231–238.
    pubmed: 25195205
  39. Braiman A, Isakov N. The role of Crk adaptor proteins in T-cell adhesion and migration.. Front. Immunol. 2015;6:509.
    doi: 10.3389/fimmu.2015.00509pmc: PMC4593252pubmed: 26500649google scholar: lookup
  40. Nosrati M, Nanaei HA, Javanmard A, Esmailizadeh A. The pattern of runs of homozygosity and genomic inbreeding in world-wide sheep populations.. Genomics 2021;113:1407–1415.
    pubmed: 33705888
  41. Addo S, Klingel S, Thaller G, Hinrichs D. Genetic diversity and the application of runs of homozygosity-based methods for inbreeding estimation in German White-headed Mutton sheep.. PLoS ONE 2021;16:e0250608.
    pmc: PMC8101715pubmed: 33956807
  42. Barani S, Nejati-Javaremi A, Moradi MH, Moradi-Sharbabak M, Gholizadeh M, Esfandyari H. Genome-wide study of linkage disequilibrium, population structure, and inbreeding in Iranian indigenous sheep breeds.. PLoS ONE 2023;18:e0286463.
    pmc: PMC10237446pubmed: 37267244
  43. Peripolli E, Munari DP, Silva MVGB, Lima ALF, Irgang R, Baldi F. Runs of homozygosity: Current knowledge and applications in livestock.. Anim. Genet. 2017;48:255–271.
    pubmed: 27910110
  44. Selli A, Ventura RV, Fonseca PA, Buzanskas ME, Andrietta LT, Balieiro JC, Brito LF. Detection and visualization of heterozygosity-rich regions and runs of homozygosity in worldwide sheep populations.. Animals 2021;11:2696.
    pmc: PMC8472390pubmed: 34573664
  45. Sanglard LP, Huang Y, Gray KA, Linhares DC, Dekkers J, Niederwerder MC, Serão NV. Further host-genomic characterization of total antibody response to PRRSV vaccination and its relationship with reproductive performance in commercial sows: Genome-wide haplotype and zygosity analyses.. Genet. Sel. Evol. 2021;53:1–17.
    pmc: PMC8650375pubmed: 34875996

Citations

This article has been cited 4 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. Luštrek B, Šimon M, Turk K, Bogičević S, Potočnik K. Comparing Genomic and Pedigree Inbreeding Coefficients in the Slovenian Lipizzan Horse as a Case Study for Small Closed Populations. Animals (Basel) 2025 Sep 23;15(19).
    doi: 10.3390/ani15192774pubmed: 41096369google scholar: lookup
  3. Van Buren SL, Brown CT, Szmatoła T, Finno CJ. A comparative review of short genetic variant databases across humans and animal species. Brief Bioinform 2025 Jul 2;26(4).
    doi: 10.1093/bib/bbaf356pubmed: 40736747google scholar: lookup
  4. Ren H, Wen X, He Q, Yi M, Dugarjaviin M, Bou G. Comparative Study on the Sperm Proteomes of Horses and Donkeys. Animals (Basel) 2024 Jul 31;14(15).
    doi: 10.3390/ani14152237pubmed: 39123763google scholar: lookup
Subscribe to our newsletter
Join 100,127 horse owners like you who receive our email updates on horse health and nutrition.