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iScience2023; 26(7); 107104; doi: 10.1016/j.isci.2023.107104

Imputed genomes of historical horses provide insights into modern breeding.

Abstract: Historical genomes can provide important insights into recent genomic changes in horses, especially the development of modern breeds. In this study, we characterized 8.7 million genomic variants from a panel of 430 horses from 73 breeds, including newly sequenced genomes from 20 Clydesdales and 10 Shire horses. We used this modern genomic variation to impute the genomes of four historically important horses, consisting of publicly available genomes from 2 Przewalski's horses, 1 Thoroughbred, and a newly sequenced Clydesdale. Using these historical genomes, we identified modern horses with higher genetic similarity to those in the past and unveiled increased inbreeding in recent times. We genotyped variants associated with appearance and behavior to uncover previously unknown characteristics of these important historical horses. Overall, we provide insights into the history of Thoroughbred and Clydesdale breeds and highlight genomic changes in the endangered Przewalski's horse following a century of captive breeding.
Publication Date: 2023-06-14 PubMed ID: 37416458PubMed Central: PMC10319840DOI: 10.1016/j.isci.2023.107104Google Scholar: Lookup
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  • Journal Article

Summary

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This research investigates the historical genomes of horses to better understand the genetic changes that have led to the development of modern breeds. It provides insights into the history and evolution of Clydesdale, Thoroughbred, and the endangered Przewalski’s horse breeds, as well as highlighting the increased inbreeding in recent times.

Genomic Variations Analysis Context

  • The study embarks on the process of characterizing 8.7 million genomic variants. This information was obtained from a panel of 430 horses belonging to 73 breeds. Distinct in the panel, genomes from 20 Clydesdales and 10 Shire horses were newly sequenced.
  • The modern genomic variants thus obtained were used to impute, or predict, the genomes of four historically important horse breeds. These include 2 Przewalski’s horses, a Thoroughbred, and a newly sequenced Clydesdale. This essentially entails using known genetic data to estimate unknown genetic data.

Connecting Historical and Modern Genomes

  • Using the imputed historical genomes, the researchers identified modern horses that showed higher genetic similarity to past horses. This information is valuable in breed preservation or phenotype prediction efforts, thereby helping breeders in making informed decisions.
  • The team also found evidence of increased inbreeding in recent times. Inbreeding could lead to decreased genetic diversity, which can have detrimental effects on a breed’s health and resilience.

Discovering Unknown Characteristics

  • The researchers genotyped variants associated with appearance and behavior. Genotyping refers to determining the genetic constitution of an individual by examining their DNA sequence. This helped to reveal previously unknown characteristics of these historically significant horses.
  • These insights could help in managing breeding choices for specific traits and ensuring the health and vitality of future horse populations.

Conclusion of the Study

  • The study provides valuable insights into the history and development of the Thoroughbred and Clydesdale horse breeds. These could be utilized in efforts aimed at preserving or recreating specific breed traits.
  • The study also throws light on genomic changes in the endangered Przewalski’s horse following a century of captive breeding. This knowledge could contribute to future conservation efforts.

Cite This Article

APA
Todd ET, Fromentier A, Sutcliffe R, Running Horse Collin Y, Perdereau A, Aury JM, Èche C, Bouchez O, Donnadieu C, Wincker P, Kalbfleisch T, Petersen JL, Orlando L. (2023). Imputed genomes of historical horses provide insights into modern breeding. iScience, 26(7), 107104. https://doi.org/10.1016/j.isci.2023.107104

Publication

ISSN: 2589-0042
NlmUniqueID: 101724038
Country: United States
Language: English
Volume: 26
Issue: 7
Pages: 107104
PII: 107104

Researcher Affiliations

Todd, Evelyn T
  • Centre d'Anthropobiologie et de Génomique de Toulouse (CAGT), CNRS UMR 5288, Université Paul Sabatier, 37 Allées Jules Guesde, Bâtiment A, 31000 Toulouse, France.
Fromentier, Aurore
  • Centre d'Anthropobiologie et de Génomique de Toulouse (CAGT), CNRS UMR 5288, Université Paul Sabatier, 37 Allées Jules Guesde, Bâtiment A, 31000 Toulouse, France.
Sutcliffe, Richard
  • Glasgow Museums Resource Centre, 200 Woodhead Road, Nitshill, G53 7NN Glasgow, UK.
Running Horse Collin, Yvette
  • Centre d'Anthropobiologie et de Génomique de Toulouse (CAGT), CNRS UMR 5288, Université Paul Sabatier, 37 Allées Jules Guesde, Bâtiment A, 31000 Toulouse, France.
Perdereau, Aude
  • Genoscope, Institut de biologie François Jacob, CEA, Université d'Evry, Université Paris-Saclay, 91042 Evry, France.
Aury, Jean-Marc
  • Genoscope, Institut de biologie François Jacob, CEA, Université d'Evry, Université Paris-Saclay, 91042 Evry, France.
Èche, Camille
  • GeT-PlaGe - Génome et Transcriptome - Plateforme Génomique, GET - Plateforme Génome & Transcriptome, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, 31326 Castanet-Tolosan Cedex, France.
Bouchez, Olivier
  • GeT-PlaGe - Génome et Transcriptome - Plateforme Génomique, GET - Plateforme Génome & Transcriptome, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, 31326 Castanet-Tolosan Cedex, France.
Donnadieu, Cécile
  • GeT-PlaGe - Génome et Transcriptome - Plateforme Génomique, GET - Plateforme Génome & Transcriptome, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, 31326 Castanet-Tolosan Cedex, France.
Wincker, Patrick
  • Genoscope, Institut de biologie François Jacob, CEA, Université d'Evry, Université Paris-Saclay, 91042 Evry, France.
Kalbfleisch, Ted
  • MH Gluck Equine Research Center, University of Kentucky, Lexington, KY 40546-0091, USA.
Petersen, Jessica L
  • Department of Animal Science, University of Nebraska-Lincoln, 3940 Fair St, Lincoln, NE 68583-0908, USA.
Orlando, Ludovic
  • Centre d'Anthropobiologie et de Génomique de Toulouse (CAGT), CNRS UMR 5288, Université Paul Sabatier, 37 Allées Jules Guesde, Bâtiment A, 31000 Toulouse, France.

Conflict of Interest Statement

The authors declare no competing interests.

References

This article includes 69 references
  1. Librado P, Khan N, Fages A, Kusliy MA, Suchan T, Tonasso-Calvière L, Schiavinato S, Alioglu D, Fromentier A, Perdereau A, Aury JM, Gaunitz C, Chauvey L, Seguin-Orlando A, Der Sarkissian C, Southon J, Shapiro B, Tishkin AA, Kovalev AA, Alquraishi S, Alfarhan AH, Al-Rasheid KAS, Seregély T, Klassen L, Iversen R, Bignon-Lau O, Bodu P, Olive M, Castel JC, Boudadi-Maligne M, Alvarez N, Germonpré M, Moskal-Del Hoyo M, Wilczyński J, Pospuła S, Lasota-Kuś A, Tunia K, Nowak M, Rannamäe E, Saarma U, Boeskorov G, Lōugas L, Kyselý R, Peške L, Bălășescu A, Dumitrașcu V, Dobrescu R, Gerber D, Kiss V, Szécsényi-Nagy A, Mende BG, Gallina Z, Somogyi K, Kulcsár G, Gál E, Bendrey R, Allentoft ME, Sirbu G, Dergachev V, Shephard H, Tomadini N, Grouard S, Kasparov A, Basilyan AE, Anisimov MA, Nikolskiy PA, Pavlova EY, Pitulko V, Brem G, Wallner B, Schwall C, Keller M, Kitagawa K, Bessudnov AN, Bessudnov A, Taylor W, Magail J, Gantulga JO, Bayarsaikhan J, Erdenebaatar D, Tabaldiev K, Mijiddorj E, Boldgiv B, Tsagaan T, Pruvost M, Olsen S, Makarewicz CA, Valenzuela Lamas S, Albizuri Canadell S, Nieto Espinet A, Iborra MP, Lira Garrido J, Rodríguez González E, Celestino S, Olària C, Arsuaga JL, Kotova N, Pryor A, Crabtree P, Zhumatayev R, Toleubaev A, Morgunova NL, Kuznetsova T, Lordkipanize D, Marzullo M, Prato O, Bagnasco Gianni G, Tecchiati U, Clavel B, Lepetz S, Davoudi H, Mashkour M, Berezina NY, Stockhammer PW, Krause J, Haak W, Morales-Muñiz A, Benecke N, Hofreiter M, Ludwig A, Graphodatsky AS, Peters J, Kiryushin KY, Iderkhangai TO, Bokovenko NA, Vasiliev SK, Seregin NN, Chugunov KV, Plasteeva NA, Baryshnikov GF, Petrova E, Sablin M, Ananyevskaya E, Logvin A, Shevnina I, Logvin V, Kalieva S, Loman V, Kukushkin I, Merz I, Merz V, Sakenov S, Varfolomeyev V, Usmanova E, Zaibert V, Arbuckle B, Belinskiy AB, Kalmykov A, Reinhold S, Hansen S, Yudin AI, Vybornov AA, Epimakhov A, Berezina NS, Roslyakova N, Kosintsev PA, Kuznetsov PF, Anthony D, Kroonen GJ, Kristiansen K, Wincker P, Outram A, Orlando L. The origins and spread of domestic horses from the Western Eurasian steppes.. Nature 2021 Oct;598(7882):634-640.
    doi: 10.1038/s41586-021-04018-9pmc: PMC8550961pubmed: 34671162google scholar: lookup
  2. Kelekna P. The Horse in Human History. .
  3. Hendricks B.L. International Encyclopedia of Horse Breeds. .
  4. McShane C, Tarr J.A. The Horse in the City: Living Machines in the Nineteenth Century. .
  5. FAOSTAT Food and Agriculture Organization License: CC BY-NC-SA 3.0 IGO. Extracted from: http://data.un.org/Data.aspx?d=FAO&f=itemCode%3A1096
  6. 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.2019.03.049pmc: PMC6547883pubmed: 31056281google scholar: lookup
  7. Orlando L, Librado P. Origin and Evolution of Deleterious Mutations in Horses.. Genes (Basel) 2019 Aug 28;10(9).
    doi: 10.3390/genes10090649pmc: PMC6769756pubmed: 31466279google scholar: lookup
  8. Bailey E, Petersen JL, Kalbfleisch TS. Genetics of Thoroughbred Racehorse Performance.. Annu Rev Anim Biosci 2022 Feb 15;10:131-150.
  9. 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-xpmc: PMC5906619pubmed: 29670190google scholar: lookup
  10. Hill EW, Stoffel MA, McGivney BA, MacHugh DE, Pemberton JM. Inbreeding depression and the probability of racing in the Thoroughbred horse.. Proc Biol Sci 2022 Jun 29;289(1977):20220487.
    doi: 10.1098/rspb.2022.0487pmc: PMC9240673pubmed: 35765835google scholar: lookup
  11. Der Sarkissian C, Ermini L, Schubert M, Yang MA, Librado P, Fumagalli M, Jónsson H, Bar-Gal GK, Albrechtsen A, Vieira FG, Petersen B, Ginolhac A, Seguin-Orlando A, Magnussen K, Fages A, Gamba C, Lorente-Galdos B, Polani S, Steiner C, Neuditschko M, Jagannathan V, Feh C, Greenblatt CL, Ludwig A, Abramson NI, Zimmermann W, Schafberg R, Tikhonov A, Sicheritz-Ponten T, Willerslev E, Marques-Bonet T, Ryder OA, McCue M, Rieder S, Leeb T, Slatkin M, Orlando L. Evolutionary Genomics and Conservation of the Endangered Przewalski's Horse.. Curr Biol 2015 Oct 5;25(19):2577-83.
    doi: 10.1016/j.cub.2015.08.032pmc: PMC5104162pubmed: 26412128google scholar: lookup
  12. Gaunitz C, Fages A, Hanghøj K, Albrechtsen A, Khan N, Schubert M, Seguin-Orlando A, Owens IJ, Felkel S, Bignon-Lau O, de Barros Damgaard P, Mittnik A, Mohaseb AF, Davoudi H, Alquraishi S, Alfarhan AH, Al-Rasheid KAS, Crubézy E, Benecke N, Olsen S, Brown D, Anthony D, Massy K, Pitulko V, Kasparov A, Brem G, Hofreiter M, Mukhtarova G, Baimukhanov N, Lõugas L, Onar V, Stockhammer PW, Krause J, Boldgiv B, Undrakhbold S, Erdenebaatar D, Lepetz S, Mashkour M, Ludwig A, Wallner B, Merz V, Merz I, Zaibert V, Willerslev E, Librado P, Outram AK, Orlando L. Ancient genomes revisit the ancestry of domestic and Przewalski's horses.. Science 2018 Apr 6;360(6384):111-114.
    doi: 10.1126/science.aao3297pubmed: 29472442google scholar: lookup
  13. Ludwig A, Reissmann M, Benecke N, Bellone R, Sandoval-Castellanos E, Cieslak M, Fortes GG, Morales-Muñiz A, Hofreiter M, Pruvost M. Twenty-five thousand years of fluctuating selection on leopard complex spotting and congenital night blindness in horses.. Philos Trans R Soc Lond B Biol Sci 2015 Jan 19;370(1660):20130386.
    doi: 10.1098/rstb.2013.0386pmc: PMC4275893pubmed: 25487337google scholar: lookup
  14. Frantz LAF, Bradley DG, Larson G, Orlando L. Animal domestication in the era of ancient genomics.. Nat Rev Genet 2020 Aug;21(8):449-460.
    doi: 10.1038/s41576-020-0225-0pubmed: 32265525google scholar: lookup
  15. Bower MA, McGivney BA, Campana MG, Gu J, Andersson LS, Barrett E, Davis CR, Mikko S, Stock F, Voronkova V, Bradley DG, Fahey AG, Lindgren G, MacHugh DE, Sulimova G, Hill EW. The genetic origin and history of speed in the Thoroughbred racehorse.. Nat Commun 2012 Jan 24;3:643.
    doi: 10.1038/ncomms1644pubmed: 22273681google scholar: lookup
  16. Schubert M, Jónsson H, Chang D, Der Sarkissian C, Ermini L, Ginolhac A, Albrechtsen A, Dupanloup I, Foucal A, Petersen B, Fumagalli M, Raghavan M, Seguin-Orlando A, Korneliussen TS, Velazquez AM, Stenderup J, Hoover CA, Rubin CJ, Alfarhan AH, Alquraishi SA, Al-Rasheid KA, MacHugh DE, Kalbfleisch T, MacLeod JN, Rubin EM, Sicheritz-Ponten T, Andersson L, Hofreiter M, Marques-Bonet T, Gilbert MT, Nielsen R, Excoffier L, Willerslev E, Shapiro B, Orlando L. Prehistoric genomes reveal the genetic foundation and cost of horse domestication.. Proc Natl Acad Sci U S A 2014 Dec 30;111(52):E5661-9.
    doi: 10.1073/pnas.1416991111pmc: PMC4284583pubmed: 25512547google scholar: lookup
  17. Librado P, Gamba C, Gaunitz C, Der Sarkissian C, Pruvost M, Albrechtsen A, Fages A, Khan N, Schubert M, Jagannathan V, Serres-Armero A, Kuderna LFK, Povolotskaya IS, Seguin-Orlando A, Lepetz S, Neuditschko M, Thèves C, Alquraishi S, Alfarhan AH, Al-Rasheid K, Rieder S, Samashev Z, Francfort HP, Benecke N, Hofreiter M, Ludwig A, Keyser C, Marques-Bonet T, Ludes B, Crubézy E, Leeb T, Willerslev E, Orlando L. Ancient genomic changes associated with domestication of the horse.. Science 2017 Apr 28;356(6336):442-445.
    doi: 10.1126/science.aam5298pubmed: 28450643google scholar: lookup
  18. Librado P, Der Sarkissian C, Ermini L, Schubert M, Jónsson H, Albrechtsen A, Fumagalli M, Yang MA, Gamba C, Seguin-Orlando A, Mortensen CD, Petersen B, Hoover CA, Lorente-Galdos B, Nedoluzhko A, Boulygina E, Tsygankova S, Neuditschko M, Jagannathan V, Thèves C, Alfarhan AH, Alquraishi SA, Al-Rasheid KA, Sicheritz-Ponten T, Popov R, Grigoriev S, Alekseev AN, Rubin EM, McCue M, Rieder S, Leeb T, Tikhonov A, Crubézy E, Slatkin M, Marques-Bonet T, Nielsen R, Willerslev E, Kantanen J, Prokhortchouk E, Orlando L. Tracking the origins of Yakutian horses and the genetic basis for their fast adaptation to subarctic environments.. Proc Natl Acad Sci U S A 2015 Dec 15;112(50):E6889-97.
    doi: 10.1073/pnas.1513696112pmc: PMC4687531pubmed: 26598656google scholar: lookup
  19. Orlando L. The Evolutionary and Historical Foundation of the Modern Horse: Lessons from Ancient Genomics.. Annu Rev Genet 2020 Nov 23;54:563-581.
  20. 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.
  21. Suchan T, Chauvey L, Poullet M, Tonasso-Calvière L, Schiavinato S, Clavel P, Clavel B, Lepetz S, Seguin-Orlando A, Orlando L. Assessing the impact of USER-treatment on hyRAD capture applied to ancient DNA.. Mol Ecol Resour 2022 Aug;22(6):2262-2274.
    doi: 10.1111/1755-0998.13619pubmed: 35398984google scholar: lookup
  22. Suchan T, Kusliy MA, Khan N, Chauvey L, Tonasso-Calvière L, Schiavinato S, Southon J, Keller M, Kitagawa K, Krause J, Bessudnov AN, Bessudnov AA, Graphodatsky AS, Valenzuela-Lamas S, Wilczyński J, Pospuła S, Tunia K, Nowak M, Moskal-delHoyo M, Tishkin AA, Pryor AJE, Outram AK, Orlando L. Performance and automation of ancient DNA capture with RNA hyRAD probes.. Mol Ecol Resour 2022 Apr;22(3):891-907.
    doi: 10.1111/1755-0998.13518pmc: PMC9291508pubmed: 34582623google scholar: lookup
  23. Mathieson I, Lazaridis I, Rohland N, Mallick S, Patterson N, Roodenberg SA, Harney E, Stewardson K, Fernandes D, Novak M, Sirak K, Gamba C, Jones ER, Llamas B, Dryomov S, Pickrell J, Arsuaga JL, de Castro JM, Carbonell E, Gerritsen F, Khokhlov A, Kuznetsov P, Lozano M, Meller H, Mochalov O, Moiseyev V, Guerra MA, Roodenberg J, Vergès JM, Krause J, Cooper A, Alt KW, Brown D, Anthony D, Lalueza-Fox C, Haak W, Pinhasi R, Reich D. Genome-wide patterns of selection in 230 ancient Eurasians.. Nature 2015 Dec 24;528(7583):499-503.
    doi: 10.1038/nature16152pmc: PMC4918750pubmed: 26595274google scholar: lookup
  24. Ausmees K, Sanchez-Quinto F, Jakobsson M, Nettelblad C. An empirical evaluation of genotype imputation of ancient DNA.. G3 (Bethesda) 2022 May 30;12(6).
    doi: 10.1093/g3journal/jkac089pmc: PMC9157144pubmed: 35482488google scholar: lookup
  25. Todd ET, Tonasso-Calvière L, Chauvey L, Schiavinato S, Fages A, Seguin-Orlando A, Clavel P, Khan N, Pérez Pardal L, Patterson Rosa L, Librado P, Ringbauer H, Verdugo M, Southon J, Aury JM, Perdereau A, Vila E, Marzullo M, Prato O, Tecchiati U, Bagnasco Gianni G, Tagliacozzo A, Tinè V, Alhaique F, Cardoso JL, Valente MJ, Telles Antunes M, Frantz L, Shapiro B, Bradley DG, Boulbes N, Gardeisen A, Horwitz LK, Öztan A, Arbuckle BS, Onar V, Clavel B, Lepetz S, Vahdati AA, Davoudi H, Mohaseb A, Mashkour M, Bouchez O, Donnadieu C, Wincker P, Brooks SA, Beja-Pereira A, Wu DD, Orlando L. The genomic history and global expansion of domestic donkeys.. Science 2022 Sep 9;377(6611):1172-1180.
    doi: 10.1126/science.abo3503pubmed: 36074859google scholar: lookup
  26. Erven JAM, Çakirlar C, Bradley DG, Raemaekers DCM, Madsen O. Imputation of Ancient Whole Genome Sus scrofa DNA Introduces Biases Toward Main Population Components in the Reference Panel.. Front Genet 2022;13:872486.
    doi: 10.3389/fgene.2022.872486pmc: PMC9315352pubmed: 35903348google scholar: lookup
  27. Eggertsson HP, Jonsson H, Kristmundsdottir S, Hjartarson E, Kehr B, Masson G, Zink F, Hjorleifsson KE, Jonasdottir A, Jonasdottir A, Jonsdottir I, Gudbjartsson DF, Melsted P, Stefansson K, Halldorsson BV. Graphtyper enables population-scale genotyping using pangenome graphs.. Nat Genet 2017 Nov;49(11):1654-1660.
    doi: 10.1038/ng.3964pubmed: 28945251google scholar: lookup
  28. Browning BL, Zhou Y, Browning SR. A One-Penny Imputed Genome from Next-Generation Reference Panels.. Am J Hum Genet 2018 Sep 6;103(3):338-348.
    doi: 10.1016/j.ajhg.2018.07.015pmc: PMC6128308pubmed: 30100085google scholar: lookup
  29. Beeson SK, Mickelson JR, McCue ME. Equine recombination map updated to Eq쪳.0.. Anim Genet 2020 Mar;51(2):341-342.
    doi: 10.1111/age.12898pmc: PMC7054148pubmed: 31887785google scholar: lookup
  30. 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.
  31. Patterson N, Price AL, Reich D. Population structure and eigenanalysis.. PLoS Genet 2006 Dec;2(12):e190.
  32. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies.. Nat Genet 2006 Aug;38(8):904-9.
    doi: 10.1038/ng1847pubmed: 16862161google scholar: lookup
  33. Lefort V, Desper R, Gascuel O. FastME 2.0: A Comprehensive, Accurate, and Fast Distance-Based Phylogeny Inference Program.. Mol Biol Evol 2015 Oct;32(10):2798-800.
    doi: 10.1093/molbev/msv150pmc: PMC4576710pubmed: 26130081google scholar: lookup
  34. Lawson DJ, Hellenthal G, Myers S, Falush D. Inference of population structure using dense haplotype data.. PLoS Genet 2012 Jan;8(1):e1002453.
  35. Alexander DH, Lange K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation.. BMC Bioinformatics 2011 Jun 18;12:246.
    doi: 10.1186/1471-2105-12-246pmc: PMC3146885pubmed: 21682921google scholar: lookup
  36. Hui R, D'Atanasio E, Cassidy LM, Scheib CL, Kivisild T. Evaluating genotype imputation pipeline for ultra-low coverage ancient genomes.. Sci Rep 2020 Oct 29;10(1):18542.
    doi: 10.1038/s41598-020-75387-wpmc: PMC7596702pubmed: 33122697google scholar: lookup
  37. Metzger J, Kreft O, Sieme H, Martinsson G, Reineking W, Hewicker-Trautwein M, Distl O. Hanoverian F/W-line contributes to segregation of Warmblood fragile foal syndrome type 1 variant PLOD1:c.2032G>A in Warmblood horses.. Equine Vet J 2021 Jan;53(1):51-59.
    doi: 10.1111/evj.13271pubmed: 32323341google scholar: lookup
  38. Grillos AS, Roach JM, de Mestre AM, Foote AK, Kinglsey NB, Mienaltowski MJ, Bellone RR. First reported case of fragile foal syndrome type 1 in the Thoroughbred caused by PLOD1 c.2032G>A.. Equine Vet J 2022 Nov;54(6):1086-1093.
    doi: 10.1111/evj.13547pmc: PMC9213567pubmed: 34939209google scholar: lookup
  39. Zhang X, Hirschfeld M, Schafberg R, Swalve H, Brenig B. Skin exhibits of Dark Ronald XX are homozygous wild type at the Warmblood fragile foal syndrome causative missense variant position in lysyl hydroxylase gene PLOD1.. Anim Genet 2020 Oct;51(5):838-840.
    doi: 10.1111/age.12972pubmed: 32557718google scholar: lookup
  40. Corbin LJ, Pope J, Sanson J, Antczak DF, Miller D, Sadeghi R, Brooks SA. An Independent Locus Upstream of ASIP Controls Variation in the Shade of the Bay Coat Colour in Horses.. Genes (Basel) 2020 May 30;11(6).
    doi: 10.3390/genes11060606pmc: PMC7349280pubmed: 32486210google scholar: lookup
  41. Wagner HJ, Reissmann M. New polymorphism detected in the horse MC1R gene.. Anim Genet 2000 Aug;31(4):289-90.
  42. Bellone RR, Holl H, Setaluri V, Devi S, Maddodi N, Archer S, Sandmeyer L, Ludwig A, Foerster D, Pruvost M, Reissmann M, Bortfeldt R, Adelson DL, Lim SL, Nelson J, Haase B, Engensteiner M, Leeb T, Forsyth G, Mienaltowski MJ, Mahadevan P, Hofreiter M, Paijmans JL, Gonzalez-Fortes G, Grahn B, Brooks SA. Evidence for a retroviral insertion in TRPM1 as the cause of congenital stationary night blindness and leopard complex spotting in the horse.. PLoS One 2013;8(10):e78280.
  43. Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM, Loredo AI, Bellone RR, Mezey JG, Brooks SA, Sutter NB. Four loci explain 83% of size variation in the horse.. PLoS One 2012;7(7):e39929.
  44. Gu J, MacHugh DE, McGivney BA, Park SD, Katz LM, Hill EW. Association of sequence variants in CKM (creatine kinase, muscle) and COX4I2 (cytochrome c oxidase, subunit 4, isoform 2) genes with racing performance in Thoroughbred horses.. Equine Vet J Suppl 2010 Nov;(38):569-75.
  45. Hill EW, Gu J, McGivney BA, MacHugh DE. Targets of selection in the Thoroughbred genome contain exercise-relevant gene SNPs associated with elite racecourse performance.. Anim Genet 2010 Dec;41 Suppl 2:56-63.
  46. Tozaki T, Miyake T, Kakoi H, Gawahara H, Sugita S, Hasegawa T, Ishida N, Hirota K, Nakano Y. A genome-wide association study for racing performances in Thoroughbreds clarifies a candidate region near the MSTN gene.. Anim Genet 2010 Dec;41 Suppl 2:28-35.
  47. Blott S, Cunningham H, Malkowski L, Brown A, Rauch C. A Mechanogenetic Model of Exercise-Induced Pulmonary Haemorrhage in the Thoroughbred Horse.. Genes (Basel) 2019 Nov 1;10(11).
    doi: 10.3390/genes10110880pmc: PMC6895809pubmed: 31683933google scholar: lookup
  48. Staiger EA, Albright JD, Brooks SA. Genome-wide association mapping of heritable temperament variation in the Tennessee Walking Horse.. Genes Brain Behav 2016 Jun;15(5):514-26.
    doi: 10.1111/gbb.12290pubmed: 26991152google scholar: lookup
  49. Hori Y, Tozaki T, Nambo Y, Sato F, Ishimaru M, Inoue-Murayama M, Fujita K. Evidence for the effect of serotonin receptor 1A gene (HTR1A) polymorphism on tractability in Thoroughbred horses.. Anim Genet 2016 Feb;47(1):62-7.
    doi: 10.1111/age.12384pubmed: 26763159google scholar: lookup
  50. Boyd L, Houpt K.A. Przewalski's Horse: The History and Biology of an Endangered Species. .
  51. Chacón-Duque JC, Adhikari K, Fuentes-Guajardo M, Mendoza-Revilla J, Acuña-Alonzo V, Barquera R, Quinto-Sánchez M, Gómez-Valdés J, Everardo Martínez P, Villamil-Ramírez H, Hünemeier T, Ramallo V, Silva de Cerqueira CC, Hurtado M, Villegas V, Granja V, Villena M, Vásquez R, Llop E, Sandoval JR, Salazar-Granara AA, Parolin ML, Sandoval K, Peñaloza-Espinosa RI, Rangel-Villalobos H, Winkler CA, Klitz W, Bravi C, Molina J, Corach D, Barrantes R, Gomes V, Resende C, Gusmão L, Amorim A, Xue Y, Dugoujon JM, Moral P, González-José R, Schuler-Faccini L, Salzano FM, Bortolini MC, Canizales-Quinteros S, Poletti G, Gallo C, Bedoya G, Rothhammer F, Balding D, Hellenthal G, Ruiz-Linares A. Latin Americans show wide-spread Converso ancestry and imprint of local Native ancestry on physical appearance.. Nat Commun 2018 Dec 19;9(1):5388.
    doi: 10.1038/s41467-018-07748-zpmc: PMC6300600pubmed: 30568240google scholar: lookup
  52. 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 Nov;192(3):1065-93.
    doi: 10.1534/genetics.112.145037pmc: PMC3522152pubmed: 22960212google scholar: lookup
  53. Peter BM. Admixture, Population Structure, and F-Statistics.. Genetics 2016 Apr;202(4):1485-501.
    doi: 10.1534/genetics.115.183913pmc: PMC4905545pubmed: 26857625google scholar: lookup
  54. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses.. Am J Hum Genet 2007 Sep;81(3):559-75.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  55. Schubert M, Lindgreen S, Orlando L. AdapterRemoval v2: rapid adapter trimming, identification, and read merging.. BMC Res Notes 2016 Feb 12;9:88.
    doi: 10.1186/s13104-016-1900-2pmc: PMC4751634pubmed: 26868221google scholar: lookup
  56. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2.. Nat Methods 2012 Mar 4;9(4):357-9.
    doi: 10.1038/nmeth.1923pmc: PMC3322381pubmed: 22388286google scholar: lookup
  57. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.. Genome Res 2010 Sep;20(9):1297-303.
    doi: 10.1101/gr.107524.110pmc: PMC2928508pubmed: 20644199google scholar: lookup
  58. Jónsson H, Ginolhac A, Schubert M, Johnson PL, Orlando L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters.. Bioinformatics 2013 Jul 1;29(13):1682-4.
  59. Skoglund P, Northoff BH, Shunkov MV, Derevianko AP, Pääbo S, Krause J, Jakobsson M. Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal.. Proc Natl Acad Sci U S A 2014 Feb 11;111(6):2229-34.
    doi: 10.1073/pnas.1318934111pmc: PMC3926038pubmed: 24469802google scholar: lookup
  60. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The Sequence Alignment/Map format and SAMtools.. Bioinformatics 2009 Aug 15;25(16):2078-9.
  61. Korneliussen TS, Albrechtsen A, Nielsen R. ANGSD: Analysis of Next Generation Sequencing Data.. BMC Bioinformatics 2014 Nov 25;15(1):356.
    doi: 10.1186/s12859-014-0356-4pmc: PMC4248462pubmed: 25420514google scholar: lookup
  62. Browning BL, Browning SR. Genotype Imputation with Millions of Reference Samples.. Am J Hum Genet 2016 Jan 7;98(1):116-26.
    doi: 10.1016/j.ajhg.2015.11.020pmc: PMC4716681pubmed: 26748515google scholar: lookup
  63. Sieck RL, Fuller AM, Bedwell PS, Ward JA, Sanders SK, Xiang SH, Peng S, Petersen JL, Steffen DJ. Mandibulofacial Dysostosis Attributed to a Recessive Mutation of CYP26C1 in Hereford Cattle.. Genes (Basel) 2020 Oct 22;11(11).
    doi: 10.3390/genes11111246pmc: PMC7690606pubmed: 33105751google scholar: lookup
  64. Seguin-Orlando A, Donat R, Der Sarkissian C, Southon J, Thèves C, Manen C, Tchérémissinoff Y, Crubézy E, Shapiro B, Deleuze JF, Dalén L, Guilaine J, Orlando L. Heterogeneous Hunter-Gatherer and Steppe-Related Ancestries in Late Neolithic and Bell Beaker Genomes from Present-Day France.. Curr Biol 2021 Mar 8;31(5):1072-1083.e10.
    doi: 10.1016/j.cub.2020.12.015pubmed: 33434506google scholar: lookup
  65. Gamba C, Hanghøj K, Gaunitz C, Alfarhan AH, Alquraishi SA, Al-Rasheid KA, Bradley DG, Orlando L. Comparing the performance of three ancient DNA extraction methods for high-throughput sequencing.. Mol Ecol Resour 2016 Mar;16(2):459-69.
    doi: 10.1111/1755-0998.12470pubmed: 26401836google scholar: lookup
  66. Rohland N, Harney E, Mallick S, Nordenfelt S, Reich D. Partial uracil-DNA-glycosylase treatment for screening of ancient DNA.. Philos Trans R Soc Lond B Biol Sci 2015 Jan 19;370(1660):20130624.
    doi: 10.1098/rstb.2013.0624pmc: PMC4275898pubmed: 25487342google scholar: lookup
  67. Kalbfleisch TS, Rice ES, DePriest MS Jr, Walenz BP, Hestand MS, Vermeesch JR, O Connell BL, Fiddes IT, Vershinina AO, Saremi NF, Petersen JL, Finno CJ, Bellone RR, McCue ME, Brooks SA, Bailey E, Orlando L, Green RE, Miller DC, Antczak DF, MacLeod JN. Improved reference genome for the domestic horse increases assembly contiguity and composition.. Commun Biol 2018;1:197.
    doi: 10.1038/s42003-018-0199-zpmc: PMC6240028pubmed: 30456315google scholar: lookup
  68. Poullet M, Orlando L. Assessing DNA sequence alignment methods for characterizing ancient genomes and methylomes. Front. Ecol. Evol. 2020;8.
    doi: 10.3389/fevo.2020.00105google scholar: lookup
  69. Bates D.W, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. BMJ Qual. Saf. 2015;24:1–3.
    doi: 10.18637/jss.v067.i01google scholar: lookup

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