FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / BMC Genomics / Genomic insights into the genetic diversity and genetic basi...
BMC genomics2025; 26(1); 292; doi: 10.1186/s12864-025-11484-2

Genomic insights into the genetic diversity and genetic basis of body height in endangered Chinese Ningqiang ponies.

Abstract: Genetic diversity in livestock and poultry is critical for adapting production systems to future challenges. However, inadequate management practices, particularly in developing countries, have led to the extinction or near extinction of several species. Understanding the genetic composition and historical background of local breeds is essential for their effective conservation and sustainable use. This study compared the genomes of 30 newly sequenced Ningqiang ponies with those of 56 other ponies and 104 horses to investigate genetic diversity, genetic differentiation, and the genetic basis of body height differences. Results: Population structure and genetic diversity analyses revealed that Ningqiang ponies belong to southwestern Chinese ponies. They exhibit a moderate level of inbreeding compared to other pony and horse breeds. Mitochondrial DNA analysis indicated that Ningqiang and Debao ponies share the dominant haplogroups A and C, suggesting a likely common maternal origin. Our study identified low genetic differentiation and detectable gene flow between Ningqiang ponies and Datong horses. The study also indicated the effective population size of Ningqiang ponies showed a downward trend. These findings potentially reflect the historical formation of Ningqiang ponies and population size changes. A selection signal scan (CLR and θπ) within Ningqiang ponies detected several key genes associated with bone development (ANKRD11, OSGIN2, JUNB, and RPL13) and immune response (RIPK2). The combination of genome-wide association analysis and selective signature analysis (F) revealed significant single nucleotide polymorphisms and selective genes associated with body height, with the most prominent finding being the TBX3 gene on equine chromosome (ECA) 8. Additionally, TBX5, ASAP1, CDK12, CA10, and CSMD1 were identified as important candidate genes for body height differences between ponies and horses. Conclusions: The results of this study elucidate the genetic diversity, genetic differentiation, and effective population size of Ningqiang ponies compared to other ponies and horses, further deepen the understanding of their small stature, and provide valuable insights into the conservation and breeding of local horse breeds in China.
© 2025. The Author(s).
Get Full Text
Publication Date: 2025-03-24 PubMed ID: 40128652PubMed Central: PMC11934595DOI: 10.1186/s12864-025-11484-2Google 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.

This study dives into the deeper understanding of genetic diversity and the genetic basis of body height in endangered Chinese Ningqiang ponies by studying and comparing their genomes to those of other ponies and horses. The results provide insights into their levels of inbreeding, genetic differentiation, effective population size, and genetic contributors to their small stature.

Objective and Methodology

  • The researchers aim to understand the genetic composition and historical background of Ningqiang ponies in order to ensure their effective conservation and sustainable use.
  • Their method involved comparing the genomes of 30 newly sequenced Ningqiang ponies with those of 56 other ponies and 104 horses.

Findings and Significance

  • Population structure and genetic diversity analyses revealed that Ningqiang ponies belong to southwestern Chinese ponies and exhibit a moderate level of inbreeding compared to other pony and horse breeds.
  • Analysis of mitochondrial DNA suggested common maternal origin between Ningqiang and Debao ponies, as they share the dominant haplogroups A and C.
  • The study also revealed low levels of genetic differentiation and detectable gene flow between Ningqiang ponies and Datong horses.
  • The effective population size of Ningqiang ponies is decreasing. These findings potentially reflect the historical formation of Ningqiang ponies and population size changes.
  • Genes associated with bone development (ANKRD11, OSGIN2, JUNB, and RPL13) and immune response (RIPK2) were detected.
  • Genome-wide association analysis and selective signature analysis (F) revealed significant single nucleotide polymorphisms and selective genes associated with body height.
  • The TBX3 gene was highlighted as fundamental in determining the body height in horses and ponies; other relevant genes included TBX5, ASAP1, CDK12, CA10, and CSMD1.

Conclusion and Future Recommendations

  • The study provides substantial insight into the genetic diversity, genetic differentiation, and effective population size of Ningqiang ponies compared to other ponies and horses.
  • The understanding of their smaller stature is deepened by identifying the genetic contributors.
  • The findings contribute to the ongoing efforts to conserve and breed local horse breeds in China.
  • Future research should focus on further exploring the impact of the detected genes on body height variations among horse and pony breeds.

Cite This Article

APA
Han J, Shao H, Sun M, Gao F, Hu Q, Yang G, Jafari H, Li N, Dang R. (2025). Genomic insights into the genetic diversity and genetic basis of body height in endangered Chinese Ningqiang ponies. BMC Genomics, 26(1), 292. https://doi.org/10.1186/s12864-025-11484-2

Publication

BMC genomics
ISSN: 1471-2164
NlmUniqueID: 100965258
Country: England
Language: English
Volume: 26
Issue: 1
Pages: 292
PII: 292

Researcher Affiliations

Han, Jiale
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, 712100, Yangling, China.
Shao, Hanrui
  • College of Information Engineering, Northwest A&F University, Yangling, 712100, China.
Sun, Minhao
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, 712100, Yangling, China.
Gao, Feng
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, 712100, Yangling, China.
Hu, Qiaoyan
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, 712100, Yangling, China.
Yang, Ge
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, 712100, Yangling, China.
Jafari, Halima
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, 712100, Yangling, China.
Li, Na
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, 712100, Yangling, China.
Dang, Ruihua
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, 712100, Yangling, China. dangruihua@nwsuaf.edu.cn.

MeSH Terms

  • Animals
  • Horses / genetics
  • Genetic Variation
  • Endangered Species
  • Body Height / genetics
  • Genomics / methods
  • Polymorphism, Single Nucleotide
  • China
  • Phylogeny
  • Genetics, Population
  • East Asian People

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: This study was approved by the Institutional Animal Care and Use Committee of Northwest A&F University (FAPWCNWAFU, Protocol number, NWAFAC 1008) following the recommendation of the Regulations for the Administration of Affairs Concerning Experimental Animals of China. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

References

This article includes 73 references
  1. Outram AK, Stear NA, Bendrey R, Olsen S, Kasparov A, Zaibert V, Thorpe N, Evershed RP. The earliest horse harnessing and milking.. Science 2009;323(5919):1332–5.
    pubmed: 19265018
  2. Gaunitz C, Fages A, Hanghoj K, Albrechtsen A, Khan N, Schubert M, Seguin-Orlando A, Owens IJ, Felkel S, Bignon-Lau O. Ancient genomes revisit the ancestry of domestic and Przewalski’s horses.. Science 2018;360(6384):111–4.
    pubmed: 29472442
  3. Jansen T, Forster P, Levine MA, Oelke H, Hurles M, Renfrew C, Weber J, Olek K. Mitochondrial DNA and the origins of the domestic horse.. Proc Natl Acad Sci U S A 2002;99(16):10905–10.
    pmc: PMC125071pubmed: 12130666
  4. Asadollahpour Nanaei H, Esmailizadeh A, Ayatollahi Mehrgardi A, Han J, Wu D, Li Y, Zhang Y. Comparative population genomic analysis uncovers novel genomic footprints and genes associated with small body size in Chinese pony.. BMC Genomics 2020;21(1):496.
    pmc: PMC7370493pubmed: 32689947
  5. Salek AS, Aminafshar M, Zandi BMM, Banabazi MH, Sargolzaei M, Miar Y. Signatures of selection analysis using whole-genome sequence data reveals novel candidate genes for pony and light horse types.. Genome 2020;63(8):387–96.
    pubmed: 32407640
  6. Kader A, Li Y, Dong K, Irwin DM, Zhao Q, He X, Liu J, Pu Y, Gorkhali NA, Liu X. Population variation reveals independent selection toward small body size in Chinese Debao pony.. Genome Biol Evol 2016;8(1):42–50.
    pmc: PMC4758242pubmed: 26637467
  7. Ningqiang horse. Encyclopaedia of China third edition online. https://www.zgbk.com/ecph/words?SiteID=1&ID=201680&Type=bkzyb&SubID=71827. Accessed 12 Dec 2024.
  8. Ningqiang horse: Animal and Poultry Genetic Resources of China (Horse, Donkey and Camel). 2nd ed. China Agriculture Press; 2011.
  9. Brooks SA, Makvandi Nejad S, Chu E, Allen JJ, Streeter C, Gu E, McCleery B, Murphy BA, Bellone R, Sutter NB. Morphological variation in the horse: defining complex traits of body size and shape.. Anim Genet 2010;41(s2):159–65.
    pubmed: 21070291
  10. Bai H, Lu H, Wang L, Wang S, Zeng W, Zhang T. SNPs analysis of height traits in Ningqiang pony.. Anim Biotechnol 2021;32(5):566–72.
    pubmed: 32091312
  11. Pu Y, Zhang Y, Zhang T, Han J, Ma Y, Liu X. Identification of novel lncRNAs differentially expressed in placentas of Chinese Ningqiang Pony and Yili horse breeds.. Animals (Basel) 2020;10(1):119.
    pmc: PMC7022612pubmed: 31940795
  12. Lv S, Zhang Y, Zhang Z, Meng S, Pu Y, Liu X, Liu L, Ma Y, Liu W, Jiang L. Diversity of the fecal microbiota in Chinese ponies.. Front Vet Sci 2023;10:1102186.
    pmc: PMC9909481pubmed: 36777669
  13. Liu XX, Pan JF, Zhao QJ, He XH, Pu YB, Han JL, Ma YH, Jiang L. Detecting selection signatures on the X chromosome of the Chinese Debao pony.. J Anim Breed Genet 2018;135(1):84–92.
    pubmed: 29345071
  14. Lin X, Feng M, Li Y, Liu Y, Wang M, Li Y, Yang T, Zhao C. Study on the origin of the Baise horse based on whole-genome resequencing.. Anim Genet 2024;55(3):410–9.
    pubmed: 38584302
  15. Li Y, Liu Y, Wang M, Lin X, Li Y, Yang T, Feng M, Ling Y, Zhao C. Whole-genome sequence analysis reveals the origin of the Chakouyi horse.. Genes (Basel) 2022;13(12):2411.
    pmc: PMC9778315pubmed: 36553682
  16. Han J, Lu M, Li C, Sun M, Hu Q, Li Y, Jafari H, Wang Z, Zhao P, Dang R. Whole-genome sequencing of Ganzi horse reveals the genetic diversity and provides unique insights into its plateau adaptation.. Livest Sci 2024;288:105549.
  17. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.. Nucleic Acids Res 2010;38(16):e164.
    pmc: PMC2938201pubmed: 20601685
  18. Pham A, Ghilardi AF, Sun L. Recent advances in the development of RIPK2 modulators for the treatment of inflammatory diseases.. Front Pharmacol 2023;14:14.
    pmc: PMC10028200pubmed: 36959850
  19. Parenti I, Mallozzi MB, Hüning I, Gervasini C, Kuechler A, Agolini E, Albrecht B, Baquero-Montoya C, Bohring A, Bramswig NC. ANKRD11 variants: KBG syndrome and beyond.. Clin Genet 2021;100(2):187–200.
    pubmed: 33955014
  20. Shuai Y, Liu B, Rong L, Shao B, Chen B, Jin L. OSGIN2 regulates osteogenesis of jawbone BMSCs in osteoporotic rats.. BMC Mol Cell Biol 2022;23(1):22.
    pmc: PMC9215015pubmed: 35729522
  21. Kenner L, Hoebertz A, Beil FT, Keon N, Karreth F, Eferl R, Scheuch H, Szremska A, Amling M, Schorpp-Kistner M. Mice lacking JunB are osteopenic due to cell-autonomous osteoblast and osteoclast defects.. J Cell Biol 2004;164(4):613–23.
    pmc: PMC2171977pubmed: 14769860
  22. Le Caignec C, Ory B, Lamoureux F, O’Donohue M, Orgebin E, Lindenbaum P, Téletchéa S, Saby M, Hurst A, Nelson K. RPL13 variants cause spondyloepimetaphyseal dysplasia with severe short stature.. Am J Hum Genet 2019;105(5):1040–7.
    pmc: PMC6849359pubmed: 31630789
  23. Marees AT, de Kluiver H, Stringer S, Vorspan F, Curis E, Marie-Claire C, Derks EM. A tutorial on conducting genome-wide association studies: quality control and statistical analysis.. Int J Methods Psychiatr Res 2018;27(2):e1608.
    pmc: PMC6001694pubmed: 29484742
  24. Dabney A, Storey JD. Package‘qvalue.’. Medicine 2011;344:539–48.
  25. Han J, Orlando L, Ma Y, Jiang L. A single-nucleotide mutation within the TBX3 enhancer increased body size in Chinese horses.. Curr Biol 2022;32(2):480–7.
    pmc: PMC8796118pubmed: 34906355
  26. Lyu Y, Guan X, Xu X, Wang P, Li Q, Panigrahi M, Zhang J, Chen N, Huang B, Lei C. A whole genome scan reveals distinct features of selection in Zhaotong cattle of Yunnan province.. Anim Genet 2023;54(6):731–42.
    pubmed: 37796667
  27. Liu D, Li X, Wang L, Pei Q, Zhao J, Sun D, Ren Q, Tian D, Han B, Jiang H. Genome-wide association studies of body size traits in Tibetan sheep.. BMC Genomics 2024;25(1):739.
    pmc: PMC11290296pubmed: 39080522
  28. Werren EA, Peirent ER, Jantti H, Guxholli A, Srivastava KR, Orenstein N, Narayanan V, Wiszniewski W, Dawidziuk M, Gawlinski P. Biallelic variants in CSMD1 are implicated in a neurodevelopmental disorder with intellectual disability and variable cortical malformations.. Cell Death Dis 2024;15(5):315–79.
    pmc: PMC11140003pubmed: 38816421
  29. Yang Y, Zhu Q, Liu S, Zhao C, Wu C. The origin of Chinese domestic horses revealed with novel mtDNA variants.. Animal Science Journal = Nihon Chikusan Gakkaiho 2017;88(1):19–26.
    pubmed: 27071843
  30. Liu S, Fu C, Yang Y, Zhang Y, Ma H, Xiong Z, Ling Y, Zhao C. Current genetic conservation of Chinese indigenous horses revealed with Y-chromosomal and mitochondrial DNA polymorphisms.. G3 (Bethesda, Md) 2021;11(2):jkab008.
    pmc: PMC8022964pubmed: 33604674
  31. Han H, Bryan K, Shiraigol W, Bai D, Zhao Y, Bao W, Yang S, Zhang W, MacHugh DE, Dugarjaviin M. Refinement of global domestic horse biogeography using historic landrace Chinese Mongolian populations.. J Hered 2019;110(7):769–81.
    pubmed: 31628847
  32. Al Abri MA, Holl HM, Kalla SE, Sutter NB, Brooks SA. Whole genome detection of sequence and structural polymorphism in six diverse horses.. PLoS ONE 2020;15(4):e0230899.
    pmc: PMC7144971pubmed: 32271776
  33. McQuillan R, Leutenegger A, 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(3):359–72.
    pmc: PMC2556426pubmed: 18760389
  34. Xu LX, Yang SL, Lin RY, Yang HB, Li AP, Wan QS. Genetic diversity and population structure of Chinese pony breeds using microsatellite markers.. Genet Mol Res 2012;11(3):2629–40.
    pubmed: 22782636
  35. Sigurðardóttir H, Ablondi M, Kristjansson T, Lindgren G, Eriksson S. Genetic diversity and signatures of selection in Icelandic horses and Exmoor ponies.. BMC Genomics 2024;25(1):772.
    pmc: PMC11308356pubmed: 39118059
  36. Orlando L. The evolutionary and historical foundation of the modern horse. Lessons from ancient genomics.. Annu Rev Genet 2020;54:563–81.
    pubmed: 32960653
  37. Achilli A, Olivieri A, Soares P, Lancioni H, Hooshiar KB, Perego UA, Nergadze SG, Carossa V, Santagostino M, Capomaccio S. Mitochondrial genomes from modern horses reveal the major haplogroups that underwent domestication.. Proc Natl Acad Sci U S A 2012;109(7):2449–54.
    pmc: PMC3289334pubmed: 22308342
  38. Yang L, Kong X, Yang S, Dong X, Yang J, Gou X, Zhang H. Haplotype diversity in mitochondrial DNA reveals the multiple origins of Tibetan horse.. PLoS ONE 2018;13(7):e0201564.
    pmc: PMC6063445pubmed: 30052677
  39. Lei CZ, Su R, Bower MA, Edwards CJ, Wang XB, Weining S, Liu L, Xie WM, Li F, Liu RY. Multiple maternal origins of native modern and ancient horse populations in China.. Anim Genet 2009;40(6):933–44.
    pubmed: 19744143
  40. Rege JEO, Gibson JP. Animal genetic resources and economic development: issues in relation to economic valuation.. Ecol Econ 2003;45(3):319–30.
  41. Hare MP, Nunney L, Schwartz MK, Ruzzante DE, Burford M, Waples RS, Ruegg K, Palstra F. Understanding and estimating effective population size for practical application in marine species management.. Conservation Biology: The Journal of the Society for Conservation Biology 2011;25(3):438–49.
    pubmed: 21284731
  42. Sutter NB, Bustamante CD, Chase K, Gray MM, Zhao K, Zhu L, Padhukasahasram B, Karlins E, Davis S, Jones PG. A single IGF1 allele is a major determinant of small size in dogs.. Science (New York, N.Y.) 2007;316(5821):112-115.
    pmc: PMC2789551pubmed: 17412960
  43. Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM, Loredo AI, Bellone RR, Mezey JG, Brooks SA. Four loci explain 83% of size variation in the horse.. PLoS ONE 2012;7(7):e39929.
    pmc: PMC3394777pubmed: 22808074
  44. Frischknecht M, Jagannathan V, Plattet P, Neuditschko M, Signer-Hasler H, Bachmann I, Pacholewska A, Drögemüller C, Dietschi E, Flury C. A non-synonymous HMGA2 variant decreases height in shetland ponies and other small horses.. PLoS ONE 2015;10(10):e0140749.
    pmc: PMC4608717pubmed: 26474182
  45. Clark BL, Bamford NJ, Stewart AJ, McCue ME, Rendahl A, Bailey SR, Bertin F, Norton EM. Evaluation of an variant contribution to height and basal insulin concentrations in ponies.. J Vet Intern Med 2023;37(3):1186–92.
    pmc: PMC10229368pubmed: 37148171
  46. Basson CT, Huang T, Lin RC, Bachinsky DR, Weremowicz S, Vaglio A, Bruzzone R, Quadrelli R, Lerone M, Romeo G. Different TBX5 interactions in heart and limb defined by Holt-Oram syndrome mutations.. Proc Natl Acad Sci U S A 1999;96(6):2919–24.
    pmc: PMC15870pubmed: 10077612
  47. Schreiber C, Saraswati S, Harkins S, Gruber A, Cremers N, Thiele W, Rothley M, Plaumann D, Korn C, Armant O. Loss of ASAP1 in mice impairs adipogenic and osteogenic differentiation of mesenchymal progenitor cells through dysregulation of FAK/Src and AKT signaling.. PLoS Genet 2019;15(6):e1008216.
    pmc: PMC6619832pubmed: 31246957
  48. Wu Z, Hu L, Ru K, Zhang W, Xu X, Liu S, Liu H, Jia Y, Liang S, Chen Z. Ellagic acid inhibits CDK12 to increase osteoblast differentiation and alleviate osteoporosis in hindlimb-unloaded and ovariectomized mice.. Phytomedicine 2023;114:154745.
    pubmed: 36931096
  49. Kraus DM, Elliott GS, Chute H, Horan T, Pfenninger KH, Sanford SD, Foster S, Scully S, Welcher AA, Holers VM. CSMD1 is a novel multiple domain complement-regulatory protein highly expressed in the central nervous system and epithelial tissues.. J Immunol 2006;176(7):4419–30.
    pubmed: 16547280
  50. Brotto M, Bonewald L. Bone and muscle: interactions beyond mechanical.. Bone 2015;80:109–14.
    pmc: PMC4600532pubmed: 26453500
  51. Moser SC, van der Eerden BCJ. Osteocalcin-A versatile bone-derived hormone.. Front Endocrinol (Lausanne) 2019;9:794.
    pmc: PMC6335246pubmed: 30687236
  52. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data.. Bioinformatics 2014;30(15):2114–20.
    pmc: PMC4103590pubmed: 24695404
  53. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.. Bioinformatics (Oxford, England) 2009;25(14):1754–60.
    pmc: PMC2705234pubmed: 19451168
  54. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.. Genome Res 2010;20(9):1297–303.
    pmc: PMC2928508pubmed: 20644199
  55. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ. PLINK: a tool set for whole-genome association and population-based linkage analyses.. Am J Hum Genet 2007;81(3):559–75.
    pmc: PMC1950838pubmed: 17701901
  56. Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets.. Mol Biol Evol 2016;33(7):1870–4.
    pmc: PMC8210823pubmed: 27004904
  57. Patterson N, Price AL, Reich D. Population structure and eigenanalysis.. PLoS Genet 2006;2(12):e190.
    pmc: PMC1713260pubmed: 17194218
  58. Alexander DH, Lange K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation.. BMC Bioinformatics 2011;12:246.
    pmc: PMC3146885pubmed: 21682921
  59. Zhang C, Dong SS, Xu JY, He WM, Yang TL. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files.. Bioinformatics 2019;35(10):1786–8.
    pubmed: 30321304
  60. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST. The variant call format and VCFtools.. Bioinformatics 2011;27(15):2156–8.
    pmc: PMC3137218pubmed: 21653522
  61. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering.. Am J Hum Genet 2007;81(5):1084–97.
    pmc: PMC2265661pubmed: 17924348
  62. Pickrell JK, Pritchard JK. Inference of population splits and mixtures from genome-wide allele frequency data.. PLoS Genet 2012;8(11):e1002967.
    pmc: PMC3499260pubmed: 23166502
  63. Fitak RR. OptM: estimating the optimal number of migration edges on population trees using Treemix.. Biol Methods Protocols 2021;6(1):bpab017.
    pmc: PMC8476930pubmed: 34595352
  64. Terhorst J, Kamm JA, Song YS. Robust and scalable inference of population history from hundreds of unphased whole genomes.. Nat Genet 2017;49(2):303–9.
    pmc: PMC5470542pubmed: 28024154
  65. Orlando L, Ginolhac A, Zhang G, Froese D, Albrechtsen A, Stiller M, Schubert M, Cappellini E, Petersen B, Moltke I. Recalibrating Equus evolution using the genome sequence of an early middle Pleistocene horse.. Nature 2013;499(7456):74–8.
    pubmed: 23803765
  66. 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 (Oxford, England) 2009;25(16):2078–9.
    pmc: PMC2723002pubmed: 19505943
  67. Green RE, Malaspinas A, Krause J, Briggs AW, Johnson PLF, Uhler C, Meyer M, Good JM, Maricic T, Stenzel U. A complete Neandertal mitochondrial genome sequence determined by high-throughput sequencing.. Cell 2008;134(3):416–26.
    pmc: PMC2602844pubmed: 18692465
  68. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput.. Nucleic Acids Res 2004;32(5):1792–7.
    pmc: PMC390337pubmed: 15034147
  69. Librado P, Rozas J. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data.. Bioinformatics 2009;25(11):1451–2.
    pubmed: 19346325
  70. Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees.. Nucleic Acids Res 2016;44(W1):W242–5.
    pmc: PMC4987883pubmed: 27095192
  71. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.. Nat Commun 2019;10(1):1523.
    pmc: PMC6447622pubmed: 30944313
  72. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies.. Nat Genet 2012;44(7):821–4.
    pmc: PMC3386377pubmed: 22706312
  73. Liu N, Zhang L, Tian T, Cheng J, Zhang B, Qiu S, Geng Z, Cui G, Zhang Q, Liao W. Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes.. Nat Genet 2023;55(7):1126–37.
    pubmed: 37337106

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.