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Animal genetics2010; 41 Suppl 2; 8-15; doi: 10.1111/j.1365-2052.2010.02092.x

Linkage disequilibrium and historical effective population size in the Thoroughbred horse.

Abstract: Many genomic methodologies rely on the presence and extent of linkage disequilibrium (LD) between markers and genetic variants underlying traits of interest, but the extent of LD in the horse has yet to be comprehensively characterized. In this study, we evaluate the extent and decay of LD in a sample of 817 Thoroughbreds. Horses were genotyped for over 50,000 single nucleotide polymorphism (SNP) markers across the genome, with 34,848 autosomal SNPs used in the final analysis. Linkage disequilibrium, as measured by the squared correlation coefficient (r(2)), was found to be relatively high between closely linked markers (>0.6 at 5 kb) and to extend over long distances, with average r(2) maintained above non-syntenic levels for single nucleotide polymorphisms (SNPs) up to 20 Mb apart. Using formulae which relate expected LD to effective population size (N(e)), and assuming a constant actual population size, N(e) was estimated to be 100 in our population. Values of historical N(e), calculated assuming linear population growth, suggested a decrease in N(e) since the distant past, reaching a minimum twenty generations ago, followed by a subsequent increase until the present time. The qualitative trends observed in N(e) can be rationalized by current knowledge of the history of the Thoroughbred breed, and inbreeding statistics obtained from published pedigree analyses are in agreement with observed values of N(e). Given the high LD observed and the small estimated N(e), genomic methodologies such as genomic selection could feasibly be applied to this population using the existing SNP marker set.
© 2010 The Authors, Journal compilation © 2010 Stichting International Foundation for Animal Genetics.
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Publication Date: 2010-11-26 PubMed ID: 21070270DOI: 10.1111/j.1365-2052.2010.02092.xGoogle 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 research article reveals the high extent of linkage disequilibrium (LD) in the Thoroughbred horse population and estimates its historical effective population size using over 50,000 single nucleotide polymorphism (SNP) markers. This comprehensive analysis informs of the potential for genomic methodologies such as genomic selection in this population.

Understanding the Research Focus

  • The key focus of this research is the concept of linkage disequilibrium (LD), which essentially is a non-random association of alleles at different locations in a population’s genome. In simpler terms, it studies how variations in one part of the DNA sequence are associated with variations in another part.
  • Besides, the research also emphasizes on the effective population size (Ne), which is a vital measure in population genetics which reflects the number of individuals in a population who contribute to the next generation.
  • By exploring LD and Ne in Thoroughbred horses, the study aims to understand genetic variations better and inform genomic methodologies like genomic selection.

Methodology and Data Analysis

  • The study involves 817 Thoroughbred horses, genotyped using over 50,000 single nucleotide polymorphism (SNP) markers. The final analysis incorporated 34,848 autosomal SNPs.
  • Linkage disequilibrium was measured through the squared correlation coefficient (r(2)). Observations reveal high LD between closely linked markers and extended over long distances.
  • The effective population size (Ne) was estimated considering a constant actual population size. Assessment of historical Ne was also done assuming linear population growth.

Insights and Conclusions

  • The estimated Ne in the Thoroughbred population was found to be 100. The historical Ne suggested a decrease since the distant past, reaching a minimum twenty generations ago, followed by an increase until the present.
  • The patterns observed in Ne align well with the known history of the Thoroughbred breed.
  • Given the achieved levels of measured high LD and the small estimated Ne, the researchers affirm that genomic methodologies could feasibly be applied to this population using the existing SNP marker set.

Implications

  • This research provides deep insights into the Thoroughbred horse’s genetic structure, contributing to genomic methodologies’ potential applications.
  • Understanding the LD and Ne in this population can assist in more targeted breeding and conservation strategies for the Thoroughbred breed.

Cite This Article

APA
Corbin LJ, Blott SC, Swinburne JE, Vaudin M, Bishop SC, Woolliams JA. (2010). Linkage disequilibrium and historical effective population size in the Thoroughbred horse. Anim Genet, 41 Suppl 2, 8-15. https://doi.org/10.1111/j.1365-2052.2010.02092.x

Publication

Animal genetics
ISSN: 1365-2052
NlmUniqueID: 8605704
Country: England
Language: English
Volume: 41 Suppl 2
Pages: 8-15

Researcher Affiliations

Corbin, L J
  • Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin Biocentre, EH25 9PS, UK. laura.corbin@roslin.ed.ac.uk
Blott, S C
    Swinburne, J E
      Vaudin, M
        Bishop, S C
          Woolliams, J A

            MeSH Terms

            • Animals
            • Genetic Markers
            • Genome-Wide Association Study
            • Horses / genetics
            • Linkage Disequilibrium
            • Pedigree
            • Population Density

            Grant Funding

            • BBS/E/D/05191133 / Biotechnology and Biological Sciences Research Council

            Citations

            This article has been cited 51 times.
            1. 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
            2. Mousavi SF, Razmkabir M, Rostamzadeh J, Seyedabadi HR, Naboulsi R, Petersen JL, Lindgren G. Genetic diversity and signatures of selection in four indigenous horse breeds of Iran. Heredity (Edinb) 2023 Aug;131(2):96-108.
              doi: 10.1038/s41437-023-00624-7pubmed: 37308718google scholar: lookup
            3. Zhang CL, Zhang J, Tuersuntuoheti M, Chang Q, Liu S. Population structure, genetic diversity and prolificacy in pishan red sheep under an extreme desert environment. Front Genet 2023;14:1092066.
              doi: 10.3389/fgene.2023.1092066pubmed: 37113996google scholar: lookup
            4. Nolte W, Alkhoder H, Wobbe M, Stock KF, Kalm E, Vosgerau S, Krattenmacher N, Thaller G, Tetens J, Kühn C. Replacement of microsatellite markers by imputed medium-density SNP arrays for parentage control in German warmblood horses. J Appl Genet 2022 Dec;63(4):783-792.
              doi: 10.1007/s13353-022-00725-9pubmed: 36173533google scholar: lookup
            5. Campos GS, Cardoso FF, Gomes CCG, Domingues R, de Almeida Regitano LC, de Sena Oliveira MC, de Oliveira HN, Carvalheiro R, Albuquerque LG, Miller S, Misztal I, Lourenco D. Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires. J Anim Sci 2022 Feb 1;100(2).
              doi: 10.1093/jas/skac009pubmed: 35031806google scholar: lookup
            6. Rahimmadar S, Ghaffari M, Mokhber M, Williams JL. Linkage Disequilibrium and Effective Population Size of Buffalo Populations of Iran, Turkey, Pakistan, and Egypt Using a Medium Density SNP Array. Front Genet 2021;12:608186.
              doi: 10.3389/fgene.2021.608186pubmed: 34950186google scholar: lookup
            7. Oyelami FO, Zhao Q, Xu Z, Zhang Z, Sun H, Zhang Z, Ma P, Wang Q, Pan Y. Haplotype Block Analysis Reveals Candidate Genes and QTLs for Meat Quality and Disease Resistance in Chinese Jiangquhai Pig Breed. Front Genet 2020;11:752.
              doi: 10.3389/fgene.2020.00752pubmed: 33101353google scholar: lookup
            8. Todd ET, Thomson PC, Hamilton NA, Ang RA, Lindgren G, Viklund Å, Eriksson S, Mikko S, Strand E, Velie BD. A genome-wide scan for candidate lethal variants in Thoroughbred horses. Sci Rep 2020 Aug 4;10(1):13153.
              doi: 10.1038/s41598-020-68946-8pubmed: 32753654google scholar: lookup
            9. Bitaraf Sani M, Zare Harofte J, Bitaraf A, Esmaeilkhanian S, Banabazi MH, Salim N, Teimoori A, Shafei Naderi A, Faghihi MA, Burger PA, Silawi M, Taghipour Sheshdeh A. Genome-Wide Diversity, Population Structure and Demographic History of Dromedaries in the Central Desert of Iran. Genes (Basel) 2020 May 29;11(6).
              doi: 10.3390/genes11060599pubmed: 32485848google scholar: lookup
            10. Zhan H, Zhang S, Zhang K, Peng X, Xie S, Li X, Zhao S, Ma Y. Genome-Wide Patterns of Homozygosity and Relevant Characterizations on the Population Structure in Piétrain Pigs. Genes (Basel) 2020 May 21;11(5).
              doi: 10.3390/genes11050577pubmed: 32455573google scholar: lookup
            11. Todd ET, Hamilton NA, Velie BD, Thomson PC. The effects of inbreeding on covering success, gestation length and foal sex ratio in Australian thoroughbred horses. BMC Genet 2020 Apr 8;21(1):41.
              doi: 10.1186/s12863-020-00847-1pubmed: 32268877google scholar: lookup
            12. Deniskova T, Dotsev A, Lushihina E, Shakhin A, Kunz E, Medugorac I, Reyer H, Wimmers K, Khayatzadeh N, Sölkner J, Sermyagin A, Zhunushev A, Brem G, Zinovieva N. Population Structure and Genetic Diversity of Sheep Breeds in the Kyrgyzstan. Front Genet 2019;10:1311.
              doi: 10.3389/fgene.2019.01311pubmed: 31921318google scholar: lookup
            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 (Basel) 2019 Nov 6;10(11).
              doi: 10.3390/genes10110893pubmed: 31698676google scholar: lookup
            14. Beeson SK, Mickelson JR, McCue ME. Exploration of fine-scale recombination rate variation in the domestic horse. Genome Res 2019 Oct;29(10):1744-1752.
              doi: 10.1101/gr.243311.118pubmed: 31434677google scholar: lookup
            15. Fages A, Hanghøj K, Khan N, Gaunitz C, Seguin-Orlando A, Leonardi M, McCrory Constantz C, Gamba C, Al-Rasheid KAS, Albizuri S, Alfarhan AH, Allentoft M, Alquraishi S, Anthony D, Baimukhanov N, Barrett JH, Bayarsaikhan J, Benecke N, Bernáldez-Sánchez E, Berrocal-Rangel L, Biglari F, Boessenkool S, Boldgiv B, Brem G, Brown D, Burger J, Crubézy E, Daugnora L, Davoudi H, de Barros Damgaard P, de Los Ángeles de Chorro Y de Villa-Ceballos M, Deschler-Erb S, Detry C, Dill N, do Mar Oom M, Dohr A, Ellingvåg S, Erdenebaatar D, Fathi H, Felkel S, Fernández-Rodríguez C, García-Viñas E, Germonpré M, Granado JD, Hallsson JH, Hemmer H, Hofreiter M, Kasparov A, Khasanov M, Khazaeli R, Kosintsev P, Kristiansen K, Kubatbek T, Kuderna L, Kuznetsov P, Laleh H, Leonard JA, Lhuillier J, Liesau von Lettow-Vorbeck C, Logvin A, Lõugas L, Ludwig A, Luis C, Arruda AM, Marques-Bonet T, Matoso Silva R, Merz V, Mijiddorj E, Miller BK, Monchalov O, Mohaseb FA, Morales A, Nieto-Espinet A, Nistelberger H, Onar V, Pálsdóttir AH, Pitulko V, Pitskhelauri K, Pruvost M, Rajic Sikanjic P, Rapan Papeša A, Roslyakova N, Sardari A, Sauer E, Schafberg R, Scheu A, Schibler J, Schlumbaum A, Serrand N, Serres-Armero A, Shapiro B, Sheikhi Seno S, Shevnina I, Shidrang S, Southon J, Star B, Sykes N, Taheri K, Taylor W, Teegen WR, Trbojević Vukičević T, Trixl S, Tumen D, Undrakhbold S, Usmanova E, Vahdati A, Valenzuela-Lamas S, Viegas C, Wallner B, Weinstock J, Zaibert V, Clavel B, Lepetz S, Mashkour M, Helgason A, Stefánsson K, Barrey E, Willerslev E, Outram AK, Librado P, Orlando L. Tracking Five Millennia of Horse Management with Extensive Ancient Genome Time Series. Cell 2019 May 30;177(6):1419-1435.e31.
              doi: 10.1016/j.cell.2019.03.049pubmed: 31056281google scholar: lookup
            16. Deng T, Liang A, Liu J, Hua G, Ye T, Liu S, Campanile G, Plastow G, Zhang C, Wang Z, Salzano A, Gasparrini B, Cassandro M, Riaz H, Liang X, Yang L. Genome-Wide SNP Data Revealed the Extent of Linkage Disequilibrium, Persistence of Phase and Effective Population Size in Purebred and Crossbred Buffalo Populations. Front Genet 2018;9:688.
              doi: 10.3389/fgene.2018.00688pubmed: 30671082google scholar: lookup
            17. Kim NY, Seong HS, Kim DC, Park NG, Yang BC, Son JK, Shin SM, Woo JH, Shin MC, Yoo JH, Choi JW. Genome-wide analyses of the Jeju, Thoroughbred, and Jeju crossbred horse populations using the high density SNP array. Genes Genomics 2018 Nov;40(11):1249-1258.
              doi: 10.1007/s13258-018-0722-0pubmed: 30099720google scholar: lookup
            18. Shin D, Won KH, Kim SH, Kim YM. Extent of linkage disequilibrium and effective population size of Korean Yorkshire swine. Asian-Australas J Anim Sci 2018 Dec;31(12):1843-1851.
              doi: 10.5713/ajas.17.0258pubmed: 30056677google scholar: lookup
            19. Todd ET, Ho SYW, Thomson PC, Ang RA, Velie BD, Hamilton NA. Founder-specific inbreeding depression affects racing performance in Thoroughbred horses. Sci Rep 2018 Apr 18;8(1):6167.
              doi: 10.1038/s41598-018-24663-xpubmed: 29670190google scholar: lookup
            20. Schultz AJ, Cristescu RH, Littleford-Colquhoun BL, Jaccoud D, Frère CH. Fresh is best: Accurate SNP genotyping from koala scats. Ecol Evol 2018 Mar;8(6):3139-3151.
              doi: 10.1002/ece3.3765pubmed: 29607013google scholar: lookup
            21. Shin D, Kim SH, Park J, Lee HK, Song KD. Extent of linkage disequilibrium and effective population size of the Landrace population in Korea. Asian-Australas J Anim Sci 2018 Aug;31(8):1078-1087.
              doi: 10.5713/ajas.17.0237pubmed: 29531193google scholar: lookup
            22. Liu S, He S, Chen L, Li W, Di J, Liu M. Estimates of linkage disequilibrium and effective population sizes in Chinese Merino (Xinjiang type) sheep by genome-wide SNPs. Genes Genomics 2017;39(7):733-745.
              doi: 10.1007/s13258-017-0539-2pubmed: 28706593google scholar: lookup
            23. Menéndez J, Álvarez I, Fernandez I, Menéndez-Arias NA, Goyache F. Assessing performance of single-sample molecular genetic methods to estimate effective population size: empirical evidence from the endangered Gochu Asturcelta pig breed. Ecol Evol 2016 Jul;6(14):4971-80.
              doi: 10.1002/ece3.2240pubmed: 27547327google scholar: lookup
            24. Russell J, Matika O, Russell T, Reardon RJ. Heritability and prevalence of selected osteochondrosis lesions in yearling Thoroughbred horses. Equine Vet J 2017 May;49(3):282-287.
              doi: 10.1111/evj.12613pubmed: 27448988google scholar: lookup
            25. Hollenbeck CM, Portnoy DS, Gold JR. A method for detecting recent changes in contemporary effective population size from linkage disequilibrium at linked and unlinked loci. Heredity (Edinb) 2016 Oct;117(4):207-16.
              doi: 10.1038/hdy.2016.30pubmed: 27165767google scholar: lookup
            26. Zanella R, Peixoto JO, Cardoso FF, Cardoso LL, Biegelmeyer P, Cantão ME, Otaviano A, Freitas MS, Caetano AR, Ledur MC. Genetic diversity analysis of two commercial breeds of pigs using genomic and pedigree data. Genet Sel Evol 2016 Mar 30;48:24.
              doi: 10.1186/s12711-016-0203-3pubmed: 27029213google scholar: lookup
            27. Biegelmeyer P, Gulias-Gomes CC, Caetano AR, Steibel JP, Cardoso FF. Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle. BMC Genet 2016 Feb 1;17:32.
              doi: 10.1186/s12863-016-0339-8pubmed: 26832943google scholar: lookup
            28. Al-Mamun HA, Clark SA, Kwan P, Gondro C. Genome-wide linkage disequilibrium and genetic diversity in five populations of Australian domestic sheep. Genet Sel Evol 2015 Nov 24;47:90.
              doi: 10.1186/s12711-015-0169-6pubmed: 26602211google scholar: lookup
            29. Saura M, Tenesa A, Woolliams JA, Fernández A, Villanueva B. Evaluation of the linkage-disequilibrium method for the estimation of effective population size when generations overlap: an empirical case. BMC Genomics 2015 Nov 11;16:922.
              doi: 10.1186/s12864-015-2167-zpubmed: 26559809google scholar: lookup
            30. Do KT, Lee JH, Lee HK, Kim J, Park KD. Estimation of effective population size using single-nucleotide polymorphism (SNP) data in Jeju horse. J Anim Sci Technol 2014;56:28.
              doi: 10.1186/2055-0391-56-28pubmed: 26290717google scholar: lookup
            31. Brito LF, Jafarikia M, Grossi DA, Kijas JW, Porto-Neto LR, Ventura RV, Salgorzaei M, Schenkel FS. Characterization of linkage disequilibrium, consistency of gametic phase and admixture in Australian and Canadian goats. BMC Genet 2015 Jun 25;16:67.
              doi: 10.1186/s12863-015-0220-1pubmed: 26108536google scholar: lookup
            32. Mastrangelo S, Di Gerlando R, Tolone M, Tortorici L, Sardina MT, Portolano B. Genome wide linkage disequilibrium and genetic structure in Sicilian dairy sheep breeds. BMC Genet 2014 Oct 10;15:108.
              doi: 10.1186/s12863-014-0108-5pubmed: 25928374google scholar: lookup
            33. Barbato M, Orozco-terWengel P, Tapio M, Bruford MW. SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data. Front Genet 2015;6:109.
              doi: 10.3389/fgene.2015.00109pubmed: 25852748google scholar: lookup
            34. Khanyile KS, Dzomba EF, Muchadeyi FC. Population genetic structure, linkage disequilibrium and effective population size of conserved and extensively raised village chicken populations of Southern Africa. Front Genet 2015;6:13.
              doi: 10.3389/fgene.2015.00013pubmed: 25691890google scholar: lookup
            35. Yang J, Shikano T, Li MH, Merilä J. Genome-wide linkage disequilibrium in nine-spined stickleback populations. G3 (Bethesda) 2014 Aug 12;4(10):1919-29.
              doi: 10.1534/g3.114.013334pubmed: 25122668google scholar: lookup
            36. Shin DH, Cho KH, Park KD, Lee HJ, Kim H. Accurate estimation of effective population size in the korean dairy cattle based on linkage disequilibrium corrected by genomic relationship matrix. Asian-Australas J Anim Sci 2013 Dec;26(12):1672-9.
              doi: 10.5713/ajas.2013.13320pubmed: 25049757google scholar: lookup
            37. Gurgul A, Semik E, Pawlina K, Szmatoła T, Jasielczuk I, Bugno-Poniewierska M. The application of genome-wide SNP genotyping methods in studies on livestock genomes. J Appl Genet 2014 May;55(2):197-208.
              doi: 10.1007/s13353-014-0202-4pubmed: 24566962google scholar: lookup
            38. Corbin LJ, Kranis A, Blott SC, Swinburne JE, Vaudin M, Bishop SC, Woolliams JA. The utility of low-density genotyping for imputation in the Thoroughbred horse. Genet Sel Evol 2014 Feb 4;46(1):9.
              doi: 10.1186/1297-9686-46-9pubmed: 24495673google scholar: lookup
            39. Wang L, Sørensen P, Janss L, Ostersen T, Edwards D. Genome-wide and local pattern of linkage disequilibrium and persistence of phase for 3 Danish pig breeds. BMC Genet 2013 Dec 5;14:115.
              doi: 10.1186/1471-2156-14-115pubmed: 24304600google scholar: lookup
            40. Pimentel EC, Wensch-Dorendorf M, König S, Swalve HH. Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture. Genet Sel Evol 2013 Apr 26;45(1):12.
              doi: 10.1186/1297-9686-45-12pubmed: 23621897google scholar: lookup
            41. Lewis TW, Blott SC, Woolliams JA. Comparative analyses of genetic trends and prospects for selection against hip and elbow dysplasia in 15 UK dog breeds. BMC Genet 2013 Mar 2;14:16.
              doi: 10.1186/1471-2156-14-16pubmed: 23452300google scholar: lookup
            42. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Distl O, Felicetti M, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Silvestrelli M, Swinburne J, Tozaki T, Vaudin M, M Wade C, McCue ME. Genetic diversity in the modern horse illustrated from genome-wide SNP data. PLoS One 2013;8(1):e54997.
              doi: 10.1371/journal.pone.0054997pubmed: 23383025google scholar: lookup
            43. Alhaddad H, Khan R, Grahn RA, Gandolfi B, Mullikin JC, Cole SA, Gruffydd-Jones TJ, Häggström J, Lohi H, Longeri M, Lyons LA. Extent of linkage disequilibrium in the domestic cat, Felis silvestris catus, and its breeds. PLoS One 2013;8(1):e53537.
              doi: 10.1371/journal.pone.0053537pubmed: 23308248google scholar: lookup
            44. Eaglen SA, Coffey MP, Woolliams JA, Wall E. Evaluating alternate models to estimate genetic parameters of calving traits in United Kingdom Holstein-Friesian dairy cattle. Genet Sel Evol 2012 Jul 28;44(1):23.
              doi: 10.1186/1297-9686-44-23pubmed: 22839757google scholar: lookup
            45. García-Gámez E, Sahana G, Gutiérrez-Gil B, Arranz JJ. Linkage disequilibrium and inbreeding estimation in Spanish Churra sheep. BMC Genet 2012 Jun 12;13:43.
              doi: 10.1186/1471-2156-13-43pubmed: 22691044google scholar: lookup
            46. Badke YM, Bates RO, Ernst CW, Schwab C, Steibel JP. Estimation of linkage disequilibrium in four US pig breeds. BMC Genomics 2012 Jan 17;13:24.
              doi: 10.1186/1471-2164-13-24pubmed: 22252454google scholar: lookup
            47. Corbin LJ, Blott SC, Swinburne JE, Sibbons C, Fox-Clipsham LY, Helwegen M, Parkin TD, Newton JR, Bramlage LR, McIlwraith CW, Bishop SC, Woolliams JA, Vaudin M. A genome-wide association study of osteochondritis dissecans in the Thoroughbred. Mamm Genome 2012 Apr;23(3-4):294-303.
              doi: 10.1007/s00335-011-9363-1pubmed: 22052004google scholar: lookup
            48. Machado FB, de Vasconcellos Machado L, Bydlowski CR, Bydlowski SP, Medina-Acosta E. Gametic phase disequilibrium between the syntenic multiallelic HTG4 and HMS3 markers widely used for parentage testing in Thoroughbred horses. Mol Biol Rep 2012 Feb;39(2):1447-52.
              doi: 10.1007/s11033-011-0881-4pubmed: 21607619google scholar: lookup
            49. Talenti A, Wilkinson T, Morrison LJ, Prendergast JGD. The evolution and convergence of mutation spectra across mammals. Commun Biol 2025 May 17;8(1):763.
              doi: 10.1038/s42003-025-08181-xpubmed: 40379828google scholar: lookup
            50. Ahlawat S, Niranjan SK, Arora R, Vijh RK, Kumar A, Sharma U, Raheja M, Popli K, Yadav S, Mehta SC. Advancing equine genomics: the development of a high density Axiom_Ashwa SNP chip for Indian horses and ponies. Funct Integr Genomics 2024 Oct 23;24(6):195.
              doi: 10.1007/s10142-024-01482-0pubmed: 39441226google scholar: lookup
            51. Bahbahani H. Long-range linkage disequilibrium events on the genome of dromedary camels as a signal of epistatic and directional positive selection. Heliyon 2024 Jul 30;10(14):e34343.
              doi: 10.1016/j.heliyon.2024.e34343pubmed: 39100441google scholar: lookup
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