FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
iScience2026; 29(8); 116830; doi: 10.1016/j.isci.2026.116830

Demographic change and selection patterns in thoroughbred racing horses over the 20th century.

Abstract: The Thoroughbred horse is a legacy of centuries of selective breeding for speed, emerging from the admixture of British mares and imported Oriental stallions. While previous work explored its genetic diversity, inbreeding, and selection signatures, the breed demographic and selection dynamics during the 20-century remain poorly understood. Here, we sequenced the genome of Phar Lap, a late-1920s/early-1930s racing champion, and analyzed the largest time-series of Thoroughbred whole-genome sequences, within a global panel of 850 horse genomes. Demographic modeling reveals a marked decline in genetic diversity since 1932, driven by a reduced effective population size, particularly until 1960. This aligns with a modest yet significant increase in long runs-of-homozygosity (ROH), suggesting that breeding practices only partially mitigated ROH accumulation and inbreeding depression. Temporal shifts in genetic variation identify candidate loci linked to racing performance, offering insights into past and ongoing selection, and new loci of major potential for the global racing industry.
© 2026 The Authors.
Get Full Text
Publication Date: 2026-07-17 PubMed ID: 42502398PubMed Central: PMC13400855DOI: 10.1016/j.isci.2026.116830Google 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 investigates the genetic changes and breeding patterns in Thoroughbred racing horses throughout the 20th century by sequencing genomes and analyzing genetic diversity, inbreeding, and selection dynamics.
  • It provides insights into how genetic diversity has declined, how breeding practices influenced inbreeding, and identifies key genetic regions linked to racing performance.

Background and Motivation

  • The Thoroughbred breed was developed over centuries by selective breeding, primarily mixing British mares with imported Oriental stallions to emphasize speed.
  • Previous research examined genetic diversity, inbreeding, and signatures of selection but lacked comprehensive analysis across the entire 20th century.
  • Understanding the demographic changes and selection patterns is crucial because these factors impact the health, performance, and sustainability of the breed in the racing industry.

Research Objectives

  • Sequence the genome of Phar Lap, a famous champion racehorse from the late 1920s and early 1930s, serving as an important historical reference.
  • Analyze a broad dataset of Thoroughbred whole-genome sequences spanning multiple decades to capture temporal genomic trends.
  • Assess genetic diversity and inbreeding over time, identifying demographic shifts and the impact of breeding practices.
  • Detect candidate genes and loci associated with racing performance and understand how selection shaped these genomic regions during the 20th century.

Methodology

  • Sequenced the genome of Phar Lap to obtain a historical genetic snapshot from around 1930.
  • Compiled and analyzed the largest time-series dataset available of Thoroughbred whole-genome sequences.
  • Included a global panel of 850 horse genomes for context, allowing comparison with other horse populations.
  • Applied demographic modeling techniques to infer changes in effective population size and genetic diversity over time.
  • Analyzed runs-of-homozygosity (ROH) to detect inbreeding levels and patterns across decades.
  • Identified genetic variants showing temporal shifts in frequency, pointing to loci under selection related to racing performance.

Key Findings

  • There has been a marked decline in genetic diversity since 1932 in Thoroughbreds, mainly due to a reduction in effective population size.
  • This reduction in genetic diversity was particularly pronounced until around 1960, indicating a population bottleneck or intensified selective breeding.
  • Long runs-of-homozygosity increased modestly but significantly, indicating some accumulation of inbreeding despite breeding strategies aimed at limiting it.
  • Breeding practices helped mitigate but did not fully prevent increases in homozygosity and potential inbreeding depression.
  • Temporal genetic shifts reveal candidate genomic loci linked to racing ability, highlighting ongoing artificial selection for performance traits.
  • New loci identified could have major potential benefits if incorporated into breeding programs aimed at enhancing racing performance worldwide.

Implications and Importance

  • Provides the most detailed temporal genomic portrait of Thoroughbred horses over the 20th century, filling a gap in understanding of breed evolution and health.
  • Helps breeders and geneticists understand the consequences of past breeding practices on genetic diversity and inbreeding, guiding future strategies.
  • Offers practical genetic targets for improving racing performance, contributing to the global horse racing industry’s productivity and sustainability.
  • Improved knowledge of the breed’s demographic history supports efforts to preserve genetic diversity and reduce risks associated with excessive inbreeding.

Summary

  • The study used genome sequencing and a large temporal dataset to examine Thoroughbred horses through the 20th century.
  • It found a decline in genetic diversity driven by smaller effective population size and noted moderate increases in inbreeding markers.
  • Selection has targeted specific loci associated with racing performance, with new candidate genes identified.
  • This research advances understanding of how breeding practices shape horse genetics and assists in optimizing racing horse populations going forward.

Cite This Article

APA
Asadollahpour Nanaei H, Seguin-Orlando A, Negara K, MacLeod JN, Kalbfleish T, Orlando L. (2026). Demographic change and selection patterns in thoroughbred racing horses over the 20th century. iScience, 29(8), 116830. https://doi.org/10.1016/j.isci.2026.116830

Publication

iScience
ISSN: 2589-0042
NlmUniqueID: 101724038
Country: United States
Language: English
Volume: 29
Issue: 8
Pages: 116830
PII: 116830

Researcher Affiliations

Asadollahpour Nanaei, Hojjat
  • Centre d'Anthropobiologie et de Génomique de Toulouse, CNRS/University of Toulouse, 37 Allées Jules Guesde, Toulouse 31000, France.
Seguin-Orlando, Andaine
  • Centre d'Anthropobiologie et de Génomique de Toulouse, CNRS/University of Toulouse, 37 Allées Jules Guesde, Toulouse 31000, France.
Negara, Kerry
  • Negara Film TV Media, Melbourne, VIC, Australia.
MacLeod, James N
  • Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
Kalbfleish, Ted
  • Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
Orlando, Ludovic
  • Centre d'Anthropobiologie et de Génomique de Toulouse, CNRS/University of Toulouse, 37 Allées Jules Guesde, Toulouse 31000, France.

Conflict of Interest Statement

The authors declare no competing interests.

References

This article includes 74 references
  1. Orlando L. The Evolutionary and Historical Foundation of the Modern Horse: Lessons from Ancient Genomics. Annu. Rev. Genet. 2020;54:563–581.
    doi: 10.1146/annurev-genet-021920-011805pubmed: 32960653google scholar: lookup
  2. Bailey E, Petersen J.L, Kalbfleisch T.S. Genetics of Thoroughbred racehorse performance. Annu. Rev. Anim. Biosci. 2022;10:131–150.
    doi: 10.1146/annurev-animal-020420-035235pubmed: 34780248google scholar: lookup
  3. Mercier Q, Aftalion A. Optimal speed in Thoroughbred horse racing. PLoS One 2020;15.
    doi: 10.1371/journal.pone.0235024pmc: PMC7710124pubmed: 33264298google scholar: lookup
  4. Bower M.A, McGivney B.A, Campana M.G, Gu J, Andersson L.S, Barrett E, Davis C.R, Mikko S, Stock F, Voronkova V. The genetic origin and history of speed in the Thoroughbred racehorse. Nat. Commun. 2012;3:643.
    doi: 10.1038/ncomms1644pubmed: 22273681google scholar: lookup
  5. Gaffney B, Cunningham E.P. Estimation of genetic trend in racing performance of thoroughbred horses. Nature 1988;332:722–724.
    doi: 10.1038/332722a0pubmed: 3357536google scholar: lookup
  6. McGivney B.A, Han H, Corduff L.R, Katz L.M, Tozaki T, MacHugh D.E, Hill E.W. Genomic inbreeding trends, influential sire lines and selection in the global Thoroughbred horse population. Sci. Rep. 2020;10:466.
    doi: 10.1038/s41598-019-57389-5pmc: PMC6965197pubmed: 31949252google scholar: lookup
  7. Yoon S.H, Lee W, Ahn H, Caetano-Anolles K, Park K.D, Kim H. Origin and spread of Thoroughbred racehorses inferred from complete mitochondrial genome sequences: Phylogenomic and Bayesian coalescent perspectives. PLoS One 2018;13.
    doi: 10.1371/journal.pone.0203917pmc: PMC6138400pubmed: 30216366google scholar: lookup
  8. Bailey E, Finno C.J, Cullen J.N, Kalbfleisch T, Petersen J.L. Analyses of whole-genome sequences from 185 North American Thoroughbred horses, spanning 5 generations. Sci. Rep. 2024;14.
    doi: 10.1038/s41598-024-73645-9pmc: PMC11447028pubmed: 39358442google scholar: lookup
  9. Orlando L, Librado P. Origin and evolution of deleterious mutations in horses. Genes 2019;10:649.
    doi: 10.3390/genes10090649pmc: PMC6769756pubmed: 31466279google scholar: lookup
  10. Hill E.W, Stoffel M.A, McGivney B.A, MacHugh D.E, Pemberton J.M. Inbreeding depression and the probability of racing in the Thoroughbred horse. Proc. Biol. Sci. 2022;289.
    doi: 10.1098/rspb.2022.0487pmc: PMC9240673pubmed: 35765835google scholar: lookup
  11. Todd E.T, Fromentier A, Sutcliffe R, Running Horse Collin Y, Perdereau A, Aury J.-M, Èche C, Bouchez O, Donnadieu C, Wincker P. Imputed genomes of historical horses provide insights into modern breeding. iScience 2023;26.
    doi: 10.1016/j.isci.2023.107104pmc: PMC10319840pubmed: 37416458google scholar: lookup
  12. Asadollahpour Nanaei H, Ayatollahi Mehrgardi A, Esmailizadeh A. Whole-genome sequence analysis reveals candidate genomic footprints and genes associated with reproductive traits in Thoroughbred horse. Reprod. Domest. Anim. 2020;55:200–208.
    doi: 10.1111/rda.13608pubmed: 31858623google scholar: lookup
  13. 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.
    doi: 10.1371/journal.pone.0215913pmc: PMC6483353pubmed: 31022261google scholar: lookup
  14. Hill E.W, Gu J, Eivers S.S, Fonseca R.G, McGivney B.A, Govindarajan P, Orr N, Katz L.M, MacHugh D.E. A Sequence Polymorphism in MSTN Predicts Sprinting Ability and Racing Stamina in Thoroughbred Horses. PLoS One 2010;5.
    doi: 10.1371/journal.pone.0008645pmc: PMC2808334pubmed: 20098749google scholar: lookup
  15. Hill E.W, McGivney B.A, Gu J, Whiston R, Machugh D.E. A genome-wide SNP-association study confirms a sequence variant (g.66493737C>T) in the equine myostatin (MSTN) gene as the most powerful predictor of optimum racing distance for Thoroughbred racehorses. BMC Genom. 2010;11:552.
    doi: 10.1186/1471-2164-11-552pmc: PMC3091701pubmed: 20932346google scholar: lookup
  16. Rooney M.F., Hill E.W., Kelly V.P., Porter R.K. The “speed gene” effect of myostatin arises in Thoroughbred horses due to a promoter proximal SINE insertion. PLoS One. 2018;13 doi: 10.1371/journal.pone.0205664.
    doi: 10.1371/journal.pone.0205664pmc: PMC6209199pubmed: 30379863google scholar: lookup
  17. Fages A., Hanghøj K., Khan N., Gaunitz C., Seguin-Orlando A., Leonardi M., McCrory Constantz C., Gamba C., Al-Rasheid K.A.S., Albizuri S., et al. Tracking five millennia of horse management with extensive ancient genome time series. Cell. 2019;177:1419–1435.e31. doi: 10.1016/j.cell.2019.03.049.
    doi: 10.1016/j.cell.2019.03.049pmc: PMC6547883pubmed: 31056281google scholar: lookup
  18. Orlando L., Allaby R., Skoglund P., Der Sarkissian C., Stockhammer P.W., Ávila-Arcos M.C., Fu Q., Krause J., Willerslev E., Stone A.C., Warinner C. Ancient DNA analysis. Nat. Rev. Methods Primers. 2021;1:14. doi: 10.1038/s43586-020-00011-0.
    doi: 10.1038/s43586-020-00011-0google scholar: lookup
  19. Barnard J., Willis E. The legend you could come and see: Celebrating Phar Lap. J. Aust. Stud. 1997;21:194–199. doi: 10.1080/14443059709387350.
    doi: 10.1080/14443059709387350google scholar: lookup
  20. Reason M. Museums Victoria Publishing; 2014. Phar Lap: A True Legend.
  21. White M. E.H. Gibson, taxidermist, and the assembly of Phar Lap’s skeleton. TUHINGA. 2017;28:80–89.
  22. Librado P., Tressières G., Chauvey L., Fages A., Khan N., Schiavinato S., Calvière-Tonasso L., Kusliy M.A., Gaunitz C., Liu X., et al. Widespread horse-based mobility arose around 2200 bce in Eurasia. Nature. 2024;631:819–825. doi: 10.1038/s41586-024-07597-5.
    doi: 10.1038/s41586-024-07597-5pmc: PMC11269178pubmed: 38843826google scholar: lookup
  23. Eggertsson H.P., Jonsson H., Kristmundsdottir S., Hjartarson E., Kehr B., Masson G., Zink F., Hjorleifsson K.E., Jonasdottir A., Jonasdottir A., et al. Graphtyper enables population-scale genotyping using pangenome graphs. Nat. Genet. 2017;49:1654–1660. doi: 10.1038/ng.3964.
    doi: 10.1038/ng.3964pubmed: 28945251google scholar: lookup
  24. Browning B.L., Tian X., Zhou Y., Browning S.R. Fast two-stage phasing of large-scale sequence data. Am. J. Hum. Genet. 2021;108:1880–1890. doi: 10.1016/j.ajhg.2021.08.005.
    doi: 10.1016/j.ajhg.2021.08.005pmc: PMC8551421pubmed: 34478634google scholar: lookup
  25. Rubinacci S., Hofmeister R.J., Sousa da Mota B., Delaneau O. Imputation of low-coverage sequencing data from 150,119 UK Biobank genomes. Nat. Genet. 2023;55:1088–1090. doi: 10.1038/s41588-023-01438-3.
    doi: 10.1038/s41588-023-01438-3pmc: PMC10335927pubmed: 37386250google scholar: lookup
  26. Alexander D.H., Novembre J., Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19:1655–1664. doi: 10.1101/gr.094052.109.
    doi: 10.1101/gr.094052.109pmc: PMC2752134pubmed: 19648217google scholar: lookup
  27. Liu X., Jia Y., Pan J., Zhang Y., Gong Y., Wang X., Ma Y., Alvarez N., Jiang L., Orlando L. Selection at the GSDMC locus in horses and its implications for human mobility. Science. 2025;389:925–930. doi: 10.1126/science.adp4581.
    doi: 10.1126/science.adp4581pubmed: 40875841google scholar: lookup
  28. Librado P., Khan N., Fages A., Kusliy M.A., Suchan T., Tonasso-Calvière L., Schiavinato S., Alioglu D., Fromentier A., Perdereau A., et al. The origins and spread of domestic horses from the Western Eurasian steppes. Nature. 2021;598:634–640. doi: 10.1038/s41586-021-04018-9.
    doi: 10.1038/s41586-021-04018-9pmc: PMC8550961pubmed: 34671162google scholar: lookup
  29. Coombs J.A., Letcher B.H., Nislow K.H. GONe: software for estimating effective population size in species with generational overlap. Mol. Ecol. Resour. 2012;12:160–163. doi: 10.1111/j.1755-0998.2011.03057.x.
    doi: 10.1111/j.1755-0998.2011.03057.xpubmed: 21827640google scholar: lookup
  30. Petersen J.L., Mickelson J.R., Rendahl A.K., Valberg S.J., Andersson L.S., Axelsson J., Bailey E., Bannasch D., Binns M.M., Borges A.S., et al. Genome-wide analysis reveals selection for important traits in domestic horse breeds. PLoS Genet. 2013;9 doi: 10.1371/journal.pgen.1003211.
    doi: 10.1371/journal.pgen.1003211pmc: PMC3547851pubmed: 23349635google scholar: lookup
  31. Silva G.A.A., Harder A.M., Kirksey K.B., Mathur S., Willoughby J.R. Detectability of runs of homozygosity is influenced by analysis parameters and population-specific demographic history. PLoS Comput. Biol. 2024;20 doi: 10.1371/journal.pcbi.1012566.
    doi: 10.1371/journal.pcbi.1012566pmc: PMC11556709pubmed: 39480880google scholar: lookup
  32. Kirin M., McQuillan R., Franklin C.S., Campbell H., McKeigue P.M., Wilson J.F. Genomic runs of homozygosity record population history and consanguinity. PLoS One. 2010;5 doi: 10.1371/journal.pone.0013996.
    doi: 10.1371/journal.pone.0013996pmc: PMC2981575pubmed: 21085596google scholar: lookup
  33. Szpiech Z.A., Xu J., Pemberton T.J., Peng W., Zöllner S., Rosenberg N.A., Li J.Z. Long runs of homozygosity are enriched for deleterious variation. Am. J. Hum. Genet. 2013;93:90–102. doi: 10.1016/j.ajhg.2013.05.003.
    doi: 10.1016/j.ajhg.2013.05.003pmc: PMC3710769pubmed: 23746547google scholar: lookup
  34. Norton E.M., Mickelson J.R., Binns M.M., Blott S.C., Caputo P., Isgren C.M., McCoy A.M., Moore A., Piercy R.J., Swinburne J.E., et al. Heritability of recurrent exertional rhabdomyolysis in standardbred and Thoroughbred racehorses derived from SNP genotyping data. J. Hered. 2016;107:537–543. doi: 10.1093/jhered/esw042.
    doi: 10.1093/jhered/esw042pmc: PMC5006745pubmed: 27489252google scholar: lookup
  35. Corbin L.J., Blott S.C., Swinburne J.E., Sibbons C., Fox-Clipsham L.Y., Helwegen M., Parkin T.D.H., Newton J.R., Bramlage L.R., McIlwraith C.W., et al. A genome-wide association study of osteochondritis dissecans in the Thoroughbred. Mamm. Genome. 2012;23:294–303. doi: 10.1007/s00335-011-9363-1.
    doi: 10.1007/s00335-011-9363-1pubmed: 22052004google scholar: lookup
  36. Blott S., Cunningham H., Malkowski L., Brown A., Rauch C. A mechanogenetic model of exercise-induced pulmonary haemorrhage in the Thoroughbred horse. Genes. 2019;10:880. doi: 10.3390/genes10110880.
    doi: 10.3390/genes10110880pmc: PMC6895809pubmed: 31683933google scholar: lookup
  37. Arango-Sabogal J.C., Mouncey R., de Mestre A.M., Verheyen K. Retrospective analysis of the population dynamics and racing outcomes of the 2014 and 2015 UK and Ireland Thoroughbred foal crops. Vet. Rec. 2021;189 doi: 10.1002/vetr.298.
    doi: 10.1002/vetr.298pubmed: 33870524google scholar: lookup
  38. Yi X., Liang Y., Huerta-Sanchez E., Jin X., Cuo Z.X.P., Pool J.E., Xu X., Jiang H., Vinckenbosch N., Korneliussen T.S., et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science. 2010;329:75–78. doi: 10.1126/science.1190371.
    doi: 10.1126/science.1190371pmc: PMC3711608pubmed: 20595611google scholar: lookup
  39. Han H., McGivney B.A., Allen L., Bai D., Corduff L.R., Davaakhuu G., Davaasambuu J., Dorjgotov D., Hall T.J., Hemmings A.J., et al. Common protein-coding variants influence the racing phenotype in galloping racehorse breeds. Commun. Biol. 2022;5:1320. doi: 10.1038/s42003-022-04206-x.
    doi: 10.1038/s42003-022-04206-xpmc: PMC9748125pubmed: 36513809google scholar: lookup
  40. Han H., McGivney B.A., Farries G., Katz L.M., MacHugh D.E., Randhawa I.A.S., Hill E.W. Selection in Australian Thoroughbred horses acts on a locus associated with early two-year old speed. PLoS One. 2020;15 doi: 10.1371/journal.pone.0227212.
    doi: 10.1371/journal.pone.0227212pmc: PMC7015314pubmed: 32049967google scholar: lookup
  41. Ding W., Gong W., Bou T., Shi L., Lin Y., Shi X., Li Z., Wu H., Dugarjaviin M., Bai D. whole-genome resequencing analysis of athletic traits in Grassland-Thoroughbred. Animals. 2025;15:2323. doi: 10.3390/ani15152323.
    doi: 10.3390/ani15152323pmc: PMC12346297pubmed: 40805113google scholar: lookup
  42. Fawcett J.A., Sato F., Sakamoto T., Iwasaki W.M., Tozaki T., Innan H. Genome-wide SNP analysis of Japanese Thoroughbred racehorses. PLoS One. 2019;14 doi: 10.1371/journal.pone.0218407.
    doi: 10.1371/journal.pone.0218407pmc: PMC6655603pubmed: 31339891google scholar: lookup
  43. Gu J., Orr N., Park S.D., Katz L.M., Sulimova G., MacHugh D.E., Hill E.W. A genome scan for positive selection in thoroughbred horses. PLoS One. 2009;4 doi: 10.1371/journal.pone.0005767.
    doi: 10.1371/journal.pone.0005767pmc: PMC2685479pubmed: 19503617google scholar: lookup
  44. Hill E.W., Fonseca R.G., McGivney B.A., Gu J., MacHugh D.E., Katz L.M. MSTN genotype (g. 66493737C/T) association with speed indices in Thoroughbred racehorses. J. Appl. Physiol. 2012;112:86–90. doi: 10.1152/japplphysiol.00793.2011.
    doi: 10.1152/japplphysiol.00793.2011pubmed: 22016373google scholar: lookup
  45. Szpiech Z.A., Hernandez R.D. Selscan: an efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 2014;31:2824–2827. doi: 10.1093/molbev/msu211.
    doi: 10.1093/molbev/msu211pmc: PMC4166924pubmed: 25015648google scholar: lookup
  46. Shin D.H., Lee J.W., Park J.E., Choi I.Y., Oh H.S., Kim H.J., Kim H. Multiple genes related to muscle identified through a joint analysis of a two-stage genome-wide association study for racing performance of 1,156 Thoroughbreds. Asian-Australas. J. Anim. Sci. 2015;28:771–781. doi: 10.5713/ajas.14.0008.
    doi: 10.5713/ajas.14.0008pmc: PMC4412973pubmed: 25925054google scholar: lookup
  47. Littiere T.O., Castro G.H.F., Rodriguez M.d.P.R., Bonafé C.M., Magalhães A.F.B., Faleiros R.R., Vieira J.I.G., Santos C.G., Verardo L.L. Identification and functional annotation of genes related to horses’ performance: from GWAS to post-GWAS. Animals. 2020;10:1173. doi: 10.3390/ani10071173.
    doi: 10.3390/ani10071173pmc: PMC7401650pubmed: 32664293google scholar: lookup
  48. Pereira G.L., Chardulo L.A., Silva J.A.I., Faria R., Curi R.A. Genomic regions associated with performance in racing line of Quarter Horses. Livest. Sci. 2018;211:42–51. doi: 10.1016/j.livsci.2018.02.015.
    doi: 10.1016/j.livsci.2018.02.015google scholar: lookup
  49. Sharman P., Wilson A.J. Genetic improvement of speed across distance categories in Thoroughbred racehorses in Great Britain. Heredity. 2023;131:79–85. doi: 10.1038/s41437-023-00623-8.
    doi: 10.1038/s41437-023-00623-8pmc: PMC10313675pubmed: 37244934google scholar: lookup
  50. Moon K.L., Huson H.J., Morrill K., Wang M.S., Li X., Srikanth K., Zoonomia Consortium. Lindblad-Toh K., Svenson G.J., Karlsson E.K., Shapiro B. Comparative genomics of Balto, a famous historic dog, captures lost diversity of 1920s sled dogs. Science. 2023;380 doi: 10.1126/science.abn5887.
    doi: 10.1126/science.abn5887pmc: PMC10184777pubmed: 37104591google scholar: lookup
  51. Scarsbrook L., Cairns K.M., Mitchell K.J., Bougiouri K., Evin A., Harris A.C., Wood A.E., Zhang Z., Lawson D.J., Alves J.M., et al. The impacts of European arrival on Australian dingoes. Proc. Natl. Acad. Sci. USA. 2025;122 doi: 10.1073/pnas.2421749122.
    doi: 10.1073/pnas.2421749122pmc: PMC12684890pubmed: 41284893google scholar: lookup
  52. Rasmussen M., Li Y., Lindgreen S., Pedersen J.S., Albrechtsen A., Moltke I., Metspalu M., Metspalu E., Kivisild T., Gupta R., et al. Ancient human genome sequence of an extinct Palaeo-Eskimo. Nature. 2010;463:757–762. doi: 10.1038/nature08835.
    doi: 10.1038/nature08835pmc: PMC3951495pubmed: 20148029google scholar: lookup
  53. Taylor W.T.T., Librado P., Hunska Tašunke Icu M., Shield Chief Gover C., Arterberry J., Luta Wiƞ A., Left Heron H., Yellow Hair R.M., Gonzalez M., Means B., et al. Early dispersal of domestic horses into the Great Plains and northern Rockies. Science. 2023;379:1316–1323. doi: 10.1126/science.adc9691.
    doi: 10.1126/science.adc9691pubmed: 36996225google scholar: lookup
  54. Lira-Garrido J., Tressières G., Chauvey L., Schiavinato S., Calvière-Tonasso L., Seguin-Orlando A., Southon J., Shapiro B., Bataille C., Birgel J., et al. The genomic history of Iberian horses since the last Ice Age. Nat. Commun. 2025;16:7098. doi: 10.1038/s41467-025-62266-z.
    doi: 10.1038/s41467-025-62266-zpmc: PMC12317975pubmed: 40753154google scholar: lookup
  55. Meyer M., Kircher M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 2010;2010 doi: 10.1101/pdb.prot5448.
    doi: 10.1101/pdb.prot5448pubmed: 20516186google scholar: lookup
  56. Rohland N., Harney E., Mallick S., Nordenfelt S., Reich D. Partial uracil–DNA–glycosylase treatment for screening of ancient DNA. Phil. Trans. R. Soc. B. 2015;370 doi: 10.1098/rstb.2013.0624.
    doi: 10.1098/rstb.2013.0624pmc: PMC4275898pubmed: 25487342google scholar: lookup
  57. Kalbfleisch T.S., Rice E.S., DePriest M.S., Jr., Walenz B.P., Hestand M.S., Vermeesch J.R., O′ Connell B.L., Fiddes I.T., Vershinina A.O., Saremi N.F., et al. Improved reference genome for the domestic horse increases assembly contiguity and composition. Commun. Biol. 2018;1:197. doi: 10.1038/s42003-018-0199-z.
    doi: 10.1038/s42003-018-0199-zpmc: PMC6240028pubmed: 30456315google scholar: lookup
  58. Schubert M., Lindgreen S., Orlando L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res. Notes. 2016;9:88. doi: 10.1186/s13104-016-1900-2.
    doi: 10.1186/s13104-016-1900-2pmc: PMC4751634pubmed: 26868221google scholar: lookup
  59. Schubert M., Ermini L., Sarkissian C.D., Jónsson H., Ginolhac A., Schaefer R., Martin M.D., Fernández R., Kircher M., McCue M., et al. Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX. Nat. Protoc. 2014;9:1056–1082. doi: 10.1038/nprot.2014.063.
    doi: 10.1038/nprot.2014.063pubmed: 24722405google scholar: lookup
  60. Langmead B., Salzberg S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods. 2012;9:357–359. doi: 10.1038/nmeth.1923.
    doi: 10.1038/nmeth.1923pmc: PMC3322381pubmed: 22388286google scholar: lookup
  61. Poullet M., Orlando L. Assessing DNA sequence alignment methods for characterizing ancient genomes and methylomes. Front. Ecol. Evol. 2020;8:105. doi: 10.3389/fevo.2020.00105.
    doi: 10.3389/fevo.2020.00105google scholar: lookup
  62. McKenna A., Hanna M., Banks E., Sivachenko A., Cibulskis K., Kernytsky A., Garimella K., Altshuler D., Gabriel S., Daly M., DePristo M.A. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–1303. doi: 10.1101/gr.107524.110.
    doi: 10.1101/gr.107524.110pmc: PMC2928508pubmed: 20644199google scholar: lookup
  63. Jónsson H., Ginolhac A., Schubert M., Johnson P.L.F., Orlando L. mapDamage2. 0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics. 2013;29:1682–1684. doi: 10.1093/bioinformatics/btt193.
    doi: 10.1093/bioinformatics/btt193pmc: PMC3694634pubmed: 23613487google scholar: lookup
  64. Skoglund P., Northoff B.H., Shunkov M.V., Derevianko A.P., Pääbo S., Krause J., Jakobsson M. Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal. Proc. Natl. Acad. Sci. USA. 2014;111:2229–2234. doi: 10.1073/pnas.1318934111.
    doi: 10.1073/pnas.1318934111pmc: PMC3926038pubmed: 24469802google scholar: lookup
  65. Garrison E., Kronenberg Z.N., Dawson E.T., Pedersen B.S., Prins P. A spectrum of free software tools for processing the VCF variant call format: vcflib, bio-vcf, cyvcf2, hts-nim and slivar. PLoS Comput. Biol. 2022;18 doi: 10.1371/journal.pcbi.1009123.
    doi: 10.1371/journal.pcbi.1009123pmc: PMC9286226pubmed: 35639788google scholar: lookup
  66. Danecek P., Bonfield J.K., Liddle J., Marshall J., Ohan V., Pollard M.O., Whitwham A., Keane T., McCarthy S.A., Davies R.M., Li H. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10 doi: 10.1093/gigascience/giab008.
    doi: 10.1093/gigascience/giab008pmc: PMC7931819pubmed: 33590861google scholar: lookup
  67. Browning B.L., Zhou Y., Browning S.R. A one-penny imputed genome from next-generation reference panels. Am. J. Hum. Genet. 2018;103:338–348. doi: 10.1016/j.ajhg.2018.07.015.
    doi: 10.1016/j.ajhg.2018.07.015pmc: PMC6128308pubmed: 30100085google scholar: lookup
  68. Beeson S.K., Mickelson J.R., McCue M.E. Exploration of fine-scale recombination rate variation in the domestic horse. Genome Res. 2019;29:1744–1752. doi: 10.1101/gr.243311.118.
    doi: 10.1101/gr.243311.118pmc: PMC6771410pubmed: 31434677google scholar: lookup
  69. Chang C.C., Chow C.C., Tellier L.C., Vattikuti S., Purcell S.M., Lee J.J. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience. 2015;4:7. doi: 10.1186/s13742-015-0047-8.
    doi: 10.1186/s13742-015-0047-8pmc: PMC4342193pubmed: 25722852google scholar: lookup
  70. Lefort V., Desper R., Gascuel O. FastME 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program. Mol. Biol. Evol. 2015;32:2798–2800. doi: 10.1093/molbev/msv150.
    doi: 10.1093/molbev/msv150pmc: PMC4576710pubmed: 26130081google scholar: lookup
  71. Patterson N., Price A.L., Reich D. Population structure and eigenanalysis. PLoS Genet. 2006;2 doi: 10.1371/journal.pgen.0020190.
    doi: 10.1371/journal.pgen.0020190pmc: PMC1713260pubmed: 17194218google scholar: lookup
  72. Patterson N., Moorjani P., Luo Y., Mallick S., Rohland N., Zhan Y., Genschoreck T., Webster T., Reich D. Ancient admixture in human history. Genetics. 2012;192:1065–1093. doi: 10.1534/genetics.112.145037.
    doi: 10.1534/genetics.112.145037pmc: PMC3522152pubmed: 22960212google scholar: lookup
  73. Manichaikul A., Mychaleckyj J.C., Rich S.S., Daly K., Sale M., Chen W.M. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26:2867–2873. doi: 10.1093/bioinformatics/btq559.
    doi: 10.1093/bioinformatics/btq559pmc: PMC3025716pubmed: 20926424google scholar: lookup
  74. Korneliussen T.S., Albrechtsen A., Nielsen R. ANGSD: analysis of next generation sequencing data. BMC Bioinf. 2014;15:356. doi: 10.1186/s12859-014-0356-4.
    doi: 10.1186/s12859-014-0356-4pmc: PMC4248462pubmed: 25420514google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 106,559 horse owners like you who receive our email updates on horse health and nutrition.