FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Mol Biol Evol / EPAS1 Gain-of-Function Mutation Contributes to High-Altitude...
Molecular biology and evolution2019; 36(11); 2591-2603; doi: 10.1093/molbev/msz158

EPAS1 Gain-of-Function Mutation Contributes to High-Altitude Adaptation in Tibetan Horses.

Abstract: High altitude represents some of the most extreme environments worldwide. The genetic changes underlying adaptation to such environments have been recently identified in multiple animals but remain unknown in horses. Here, we sequence the complete genome of 138 domestic horses encompassing a whole altitudinal range across China to uncover the genetic basis for adaptation to high-altitude hypoxia. Our genome data set includes 65 lowland animals across ten Chinese native breeds, 61 horses living at least 3,300 m above sea level across seven locations along Qinghai-Tibetan Plateau, as well as 7 Thoroughbred and 5 Przewalski's horses added for comparison. We find that Tibetan horses do not descend from Przewalski's horses but were most likely introduced from a distinct horse lineage, following the emergence of pastoral nomadism in Northwestern China ∼3,700 years ago. We identify that the endothelial PAS domain protein 1 gene (EPAS1, also HIF2A) shows the strongest signature for positive selection in the Tibetan horse genome. Two missense mutations at this locus appear strongly associated with blood physiological parameters facilitating blood circulation as well as oxygen transportation and consumption in hypoxic conditions. Functional validation through protein mutagenesis shows that these mutations increase EPAS1 stability and its hetero dimerization affinity to ARNT (HIF1B). Our study demonstrates that missense mutations in the EPAS1 gene provided key evolutionary molecular adaptation to Tibetan horses living in high-altitude hypoxic environments. It reveals possible targets for genomic selection programs aimed at increasing hypoxia tolerance in livestock and provides a textbook example of evolutionary convergence across independent mammal lineages.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Get Full Text
Publication Date: 2019-07-06 PubMed ID: 31273382PubMed Central: PMC6805228DOI: 10.1093/molbev/msz158Google 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.

The research article centers on the exploration of the genetic basis behind the high-altitude adaptation of Tibetan horses. This was done by studying the complete genome of 138 horses from various altitudes across China. They discovered that Tibetan horses likely developed from a different horse lineage, and the EPAS1 gene mutation played a key role in their successful adoption to high-altitude hypoxic environments.

Research Method

  • The researchers sequenced the complete genome of 138 domestic horses from different altitudes across China. The sample set includes lowland animals across ten breeds, horses living above 3,300 meters for seven locations along the Qinghai-Tibetan Plateau, and an additional 7 Thoroughbred and 5 Przewalski’s horses were included for comparative purposes.
  • Their objective was to uncover what genetic changes had occurred to allow these animals to adapt successfully to high altitude hypoxia environments, where oxygen levels in the air are extremely low.

Research Findings

  • The findings revealed that Tibetan horses do not descend from Przewalski’s horses, but likely originated from a different horse lineage. This hypothesis is supported by the emergence of pastoral nomadism in Northwestern China around 3,700 years ago.
  • A key discovery was that a gene known as the endothelial PAS domain protein 1 (EPAS1, also HIF2A) shows the strongest signature for positive selection in the Tibetan horse genome. This means that the mutations associated with this gene were beneficial for the horses’ survival in high altitude environments and thus, were passed down through generations.
  • Two missense mutations at the EPAS1 locus were strongly associated with physiological parameters related to blood circulation and oxygen transportation and consumption under hypoxic conditions. They found through protein mutagenesis that these mutations increased the stability of EPAS1 and its affinity to dimerize with ARNT (HIF1B), another gene.

Implications

  • This study sheds light on how missense mutations in the EPAS1 gene provided key evolutionary molecular adaptation in Tibetan horses for living in high-altitude hypoxic environments.
  • The research findings are not only important in understanding the evolution and adaptation of horses, but they also hold potential for genomic selection programs that aim to enhance hypoxia tolerance in livestock.
  • Furthermore, it is an example of evolutionary convergence across independent mammalian lineages, suggesting there may be a commonality in the genetic mechanism of high-altitude adaptation among various species.

Cite This Article

APA
Liu X, Zhang Y, Li Y, Pan J, Wang D, Chen W, Zheng Z, He X, Zhao Q, Pu Y, Guan W, Han J, Orlando L, Ma Y, Jiang L. (2019). EPAS1 Gain-of-Function Mutation Contributes to High-Altitude Adaptation in Tibetan Horses. Mol Biol Evol, 36(11), 2591-2603. https://doi.org/10.1093/molbev/msz158

Publication

Molecular biology and evolution
ISSN: 1537-1719
NlmUniqueID: 8501455
Country: United States
Language: English
Volume: 36
Issue: 11
Pages: 2591-2603

Researcher Affiliations

Liu, Xuexue
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Zhang, Yanli
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Li, Yefang
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Pan, Jianfei
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Wang, Dandan
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Chen, Weihuang
  • College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, PR China.
Zheng, Zhuqing
  • College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, PR China.
He, Xiaohong
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Zhao, Qianjun
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Pu, Yabin
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Guan, Weijun
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Han, Jianlin
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • International Livestock Research Institute (ILRI), Nairobi, Kenya.
Orlando, Ludovic
  • Lundbeck Foundation GeoGenetics Center, University of Copenhagen, Denmark.
  • Laboratoire d'Anthropobiologie Moléculaire et d'Imagerie de Synthèse, CNRS, UMR 5288, Université Paul Sabatier (UPS), Toulouse, France.
Ma, Yuehui
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
Jiang, Lin
  • Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.
  • CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, PR China.

References

This article includes 53 references
  1. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals.. Genome Res 2009 Sep;19(9):1655-64.
    pmc: PMC2752134pubmed: 19648217doi: 10.1101/gr.094052.109google scholar: lookup
  2. Beall CM, Cavalleri GL, Deng L, Elston RC, Gao Y, Knight J, Li C, Li JC, Liang Y, McCormack M, Montgomery HE, Pan H, Robbins PA, Shianna KV, Tam SC, Tsering N, Veeramah KR, Wang W, Wangdui P, Weale ME, Xu Y, Xu Z, Yang L, Zaman MJ, Zeng C, Zhang L, Zhang X, Zhaxi P, Zheng YT. Natural selection on EPAS1 (HIF2alpha) associated with low hemoglobin concentration in Tibetan highlanders.. Proc Natl Acad Sci U S A 2010 Jun 22;107(25):11459-64.
    pmc: PMC2895075pubmed: 20534544doi: 10.1073/pnas.1002443107google scholar: lookup
  3. Beall CM, Decker MJ, Brittenham GM, Kushner I, Gebremedhin A, Strohl KP. An Ethiopian pattern of human adaptation to high-altitude hypoxia.. Proc Natl Acad Sci U S A 2002 Dec 24;99(26):17215-8.
    pmc: PMC139295pubmed: 12471159doi: 10.1073/pnas.252649199google scholar: lookup
  4. Bielejec F, Rambaut A, Suchard MA, Lemey P. SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics.. Bioinformatics 2011 Oct 15;27(20):2910-2.
    pmc: PMC3187652pubmed: 21911333doi: 10.1093/bioinformatics/btr481google scholar: lookup
  5. Bigham AW, Lee FS. Human high-altitude adaptation: forward genetics meets the HIF pathway.. Genes Dev 2014 Oct 15;28(20):2189-204.
    pmc: PMC4201282pubmed: 25319824doi: 10.1101/gad.250167.114google scholar: lookup
  6. Fernandes V, Brucato N, Ferreira JC, Pedro N, Cavadas B, Ricaut FX, Alshamali F, Pereira L. Genome-Wide Characterization of Arabian Peninsula Populations: Shedding Light on the History of a Fundamental Bridge between Continents.. Mol Biol Evol 2019 Mar 1;36(3):575-586.
    pubmed: 30649405doi: 10.1093/molbev/msz005google scholar: lookup
  7. Chen FH, Dong GH, Zhang DJ, Liu XY, Jia X, An CB, Ma MM, Xie YW, Barton L, Ren XY, Zhao ZJ, Wu XH, Jones MK. Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 B.P.. Science 2015 Jan 16;347(6219):248-50.
    pubmed: 25593179doi: 10.1126/science.1259172google scholar: lookup
  8. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R. The variant call format and VCFtools.. Bioinformatics 2011 Aug 1;27(15):2156-8.
    pmc: PMC3137218pubmed: 21653522doi: 10.1093/bioinformatics/btr330google scholar: lookup
  9. Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees.. BMC Evol Biol 2007 Nov 8;7:214.
    pmc: PMC2247476pubmed: 17996036doi: 10.1186/1471-2148-7-214google scholar: lookup
  10. Excoffier L, Foll M. fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios.. Bioinformatics 2011 May 1;27(9):1332-4.
    pubmed: 21398675doi: 10.1093/bioinformatics/btr124google scholar: lookup
  11. 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.
    pubmed: 29472442doi: 10.1126/science.aao3297google scholar: lookup
  12. Ge RL, Cai Q, Shen YY, San A, Ma L, Zhang Y, Yi X, Chen Y, Yang L, Huang Y, He R, Hui Y, Hao M, Li Y, Wang B, Ou X, Xu J, Zhang Y, Wu K, Geng C, Zhou W, Zhou T, Irwin DM, Yang Y, Ying L, Bao H, Kim J, Larkin DM, Ma J, Lewin HA, Xing J, Platt RN 2nd, Ray DA, Auvil L, Capitanu B, Zhang X, Zhang G, Murphy RW, Wang J, Zhang YP, Wang J. Draft genome sequence of the Tibetan antelope.. Nat Commun 2013;4:1858.
    pmc: PMC3674232pubmed: 23673643doi: 10.1038/ncomms2860google scholar: lookup
  13. Gou X, Wang Z, Li N, Qiu F, Xu Z, Yan D, Yang S, Jia J, Kong X, Wei Z, Lu S, Lian L, Wu C, Wang X, Li G, Ma T, Jiang Q, Zhao X, Yang J, Liu B, Wei D, Li H, Yang J, Yan Y, Zhao G, Dong X, Li M, Deng W, Leng J, Wei C, Wang C, Mao H, Zhang H, Ding G, Li Y. Whole-genome sequencing of six dog breeds from continuous altitudes reveals adaptation to high-altitude hypoxia.. Genome Res 2014 Aug;24(8):1308-15.
    pmc: PMC4120084pubmed: 24721644doi: 10.1101/gr.171876.113google scholar: lookup
  14. Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data.. PLoS Genet 2009 Oct;5(10):e1000695.
    pmc: PMC2760211pubmed: 19851460doi: 10.1371/journal.pgen.1000695google scholar: lookup
  15. Hendrickson SL. A genome wide study of genetic adaptation to high altitude in feral Andean Horses of the páramo.. BMC Evol Biol 2013 Dec 17;13:273.
    pmc: PMC3878729pubmed: 24344830doi: 10.1186/1471-2148-13-273google scholar: lookup
  16. Hoit BD, Dalton ND, Erzurum SC, Laskowski D, Strohl KP, Beall CM. Nitric oxide and cardiopulmonary hemodynamics in Tibetan highlanders.. J Appl Physiol (1985) 2005 Nov;99(5):1796-801.
    pubmed: 16024527doi: 10.1152/japplphysiol.00205.2005google scholar: lookup
  17. Horscroft JA, Kotwica AO, Laner V, West JA, Hennis PJ, Levett DZH, Howard DJ, Fernandez BO, Burgess SL, Ament Z, Gilbert-Kawai ET, Vercueil A, Landis BD, Mitchell K, Mythen MG, Branco C, Johnson RS, Feelisch M, Montgomery HE, Griffin JL, Grocott MPW, Gnaiger E, Martin DS, Murray AJ. Metabolic basis to Sherpa altitude adaptation.. Proc Natl Acad Sci U S A 2017 Jun 13;114(24):6382-6387.
    pmc: PMC5474778pubmed: 28533386doi: 10.1073/pnas.1700527114google scholar: lookup
  18. Hu XJ, Yang J, Xie XL, Lv FH, Cao YH, Li WR, Liu MJ, Wang YT, Li JQ, Liu YG, Ren YL, Shen ZQ, Wang F, Hehua E, Han JL, Li MH. The Genome Landscape of Tibetan Sheep Reveals Adaptive Introgression from Argali and the History of Early Human Settlements on the Qinghai-Tibetan Plateau.. Mol Biol Evol 2019 Feb 1;36(2):283-303.
    pmc: PMC6367989pubmed: 30445533doi: 10.1093/molbev/msy208google scholar: lookup
  19. Lee JS, Kim Y, Bhin J, Shin HJ, Nam HJ, Lee SH, Yoon JB, Binda O, Gozani O, Hwang D, Baek SH. Hypoxia-induced methylation of a pontin chromatin remodeling factor.. Proc Natl Acad Sci U S A 2011 Aug 16;108(33):13510-5.
    pmc: PMC3158161pubmed: 21825155doi: 10.1073/pnas.1106106108google scholar: lookup
  20. 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.
    pmc: PMC2723002pubmed: 19505943doi: 10.1093/bioinformatics/btp352google scholar: lookup
  21. Ling YH, Ma YH, Guan WJ, Cheng YJ, Wang YP, Han JL, Mang L, Zhao QJ, He XH, Pu YB, Fu BL. Evaluation of the genetic diversity and population structure of Chinese indigenous horse breeds using 27 microsatellite markers.. Anim Genet 2011 Feb;42(1):56-65.
    pubmed: 20477800doi: 10.1111/j.1365-2052.2010.02067.xgoogle scholar: lookup
  22. Liu X, Huang M, Fan B, Buckler ES, Zhang Z. Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies.. PLoS Genet 2016 Feb;12(2):e1005767.
    pmc: PMC4734661pubmed: 26828793doi: 10.1371/journal.pgen.1005767google scholar: lookup
  23. Lorenzo FR, Huff C, Myllymäki M, Olenchock B, Swierczek S, Tashi T, Gordeuk V, Wuren T, Ri-Li G, McClain DA, Khan TM, Koul PA, Guchhait P, Salama ME, Xing J, Semenza GL, Liberzon E, Wilson A, Simonson TS, Jorde LB, Kaelin WG Jr, Koivunen P, Prchal JT. A genetic mechanism for Tibetan high-altitude adaptation.. Nat Genet 2014 Sep;46(9):951-6.
    pmc: PMC4473257pubmed: 25129147doi: 10.1038/ng.3067google scholar: lookup
  24. Luo JC, Shibuya M. A variant of nuclear localization signal of bipartite-type is required for the nuclear translocation of hypoxia inducible factors (1alpha, 2alpha and 3alpha).. Oncogene 2001 Mar 22;20(12):1435-44.
    pubmed: 11313887doi: 10.1038/sj.onc.1204228google scholar: lookup
  25. McCormick RF, Truong SK, Mullet JE. RIG: Recalibration and interrelation of genomic sequence data with the GATK.. G3 (Bethesda) 2015 Feb 13;5(4):655-65.
    pmc: PMC4390580pubmed: 25681258doi: 10.1534/g3.115.017012google scholar: lookup
  26. Nei M, Li WH. Mathematical model for studying genetic variation in terms of restriction endonucleases.. Proc Natl Acad Sci U S A 1979 Oct;76(10):5269-73.
    pmc: PMC413122pubmed: 291943doi: 10.1073/pnas.76.10.5269google scholar: lookup
  27. Orlando L, Ginolhac A, Zhang G, Froese D, Albrechtsen A, Stiller M, Schubert M, Cappellini E, Petersen B, Moltke I, Johnson PL, Fumagalli M, Vilstrup JT, Raghavan M, Korneliussen T, Malaspinas AS, Vogt J, Szklarczyk D, Kelstrup CD, Vinther J, Dolocan A, Stenderup J, Velazquez AM, Cahill J, Rasmussen M, Wang X, Min J, Zazula GD, Seguin-Orlando A, Mortensen C, Magnussen K, Thompson JF, Weinstock J, Gregersen K, Røed KH, Eisenmann V, Rubin CJ, Miller DC, Antczak DF, Bertelsen MF, Brunak S, Al-Rasheid KA, Ryder O, Andersson L, Mundy J, Krogh A, Gilbert MT, Kjær K, Sicheritz-Ponten T, Jensen LJ, Olsen JV, Hofreiter M, Nielsen R, Shapiro B, Wang J, Willerslev E. Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse.. Nature 2013 Jul 4;499(7456):74-8.
    pubmed: 23803765doi: 10.1038/nature12323google scholar: lookup
  28. Pickrell JK, Pritchard JK. Inference of population splits and mixtures from genome-wide allele frequency data.. PLoS Genet 2012;8(11):e1002967.
    pmc: PMC3499260pubmed: 23166502doi: 10.1371/journal.pgen.1002967google scholar: lookup
  29. Prabhakar NR, Semenza GL. Adaptive and maladaptive cardiorespiratory responses to continuous and intermittent hypoxia mediated by hypoxia-inducible factors 1 and 2.. Physiol Rev 2012 Jul;92(3):967-1003.
    pmc: PMC3893888pubmed: 22811423doi: 10.1152/physrev.00030.2011google scholar: lookup
  30. Qiu Q, Zhang G, Ma T, Qian W, Wang J, Ye Z, Cao C, Hu Q, Kim J, Larkin DM, Auvil L, Capitanu B, Ma J, Lewin HA, Qian X, Lang Y, Zhou R, Wang L, Wang K, Xia J, Liao S, Pan S, Lu X, Hou H, Wang Y, Zang X, Yin Y, Ma H, Zhang J, Wang Z, Zhang Y, Zhang D, Yonezawa T, Hasegawa M, Zhong Y, Liu W, Zhang Y, Huang Z, Zhang S, Long R, Yang H, Wang J, Lenstra JA, Cooper DN, Wu Y, Wang J, Shi P, Wang J, Liu J. The yak genome and adaptation to life at high altitude.. Nat Genet 2012 Jul 1;44(8):946-9.
    pubmed: 22751099doi: 10.1038/ng.2343google scholar: lookup
  31. Rubin CJ, Megens HJ, Martinez Barrio A, Maqbool K, Sayyab S, Schwochow D, Wang C, Carlborg Ö, Jern P, Jørgensen CB, Archibald AL, Fredholm M, Groenen MA, Andersson L. Strong signatures of selection in the domestic pig genome.. Proc Natl Acad Sci U S A 2012 Nov 27;109(48):19529-36.
    pmc: PMC3511700pubmed: 23151514doi: 10.1073/pnas.1217149109google scholar: lookup
  32. Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees.. Mol Biol Evol 1987 Jul;4(4):406-25.
    pubmed: 3447015doi: 10.1093/oxfordjournals.molbev.a040454google scholar: lookup
  33. Sanchez-Mazas A. Past human migrations in East Asia: matching archaeology, linguistics and genetics. .
  34. Sato M, Tanaka T, Maeno T, Sando Y, Suga T, Maeno Y, Sato H, Nagai R, Kurabayashi M. Inducible expression of endothelial PAS domain protein-1 by hypoxia in human lung adenocarcinoma A549 cells. Role of Src family kinases-dependent pathway.. Am J Respir Cell Mol Biol 2002 Jan;26(1):127-34.
    pubmed: 11751212doi: 10.1165/ajrcmb.26.1.4319google scholar: lookup
  35. Semagn K, Babu R, Hearne S, Olsen M. Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Mol Breed 331:1–14.
  36. Simonson TS, Yang Y, Huff CD, Yun H, Qin G, Witherspoon DJ, Bai Z, Lorenzo FR, Xing J, Jorde LB, Prchal JT, Ge R. Genetic evidence for high-altitude adaptation in Tibet.. Science 2010 Jul 2;329(5987):72-5.
    pubmed: 20466884doi: 10.1126/science.1189406google scholar: lookup
  37. Song S, Yao N, Yang M, Liu X, Dong K, Zhao Q, Pu Y, He X, Guan W, Yang N, Ma Y, Jiang L. Exome sequencing reveals genetic differentiation due to high-altitude adaptation in the Tibetan cashmere goat (Capra hircus).. BMC Genomics 2016 Feb 18;17:122.
    pmc: PMC4758086pubmed: 26892324doi: 10.1186/s12864-016-2449-0google scholar: lookup
  38. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.. Mol Biol Evol 2011 Oct;28(10):2731-9.
    pmc: PMC3203626pubmed: 21546353doi: 10.1093/molbev/msr121google scholar: lookup
  39. Velie BD, Fegraeus KJ, Solé M, Rosengren MK, Røed KH, Ihler CF, Strand E, Lindgren G. A genome-wide association study for harness racing success in the Norwegian-Swedish coldblooded trotter reveals genes for learning and energy metabolism.. BMC Genet 2018 Aug 29;19(1):80.
    pmc: PMC6114527pubmed: 30157760doi: 10.1186/s12863-018-0670-3google scholar: lookup
  40. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR, Blöcker H, Distl O, Edgar RC, Garber M, Leeb T, Mauceli E, MacLeod JN, Penedo MC, Raison JM, Sharpe T, Vogel J, Andersson L, Antczak DF, Biagi T, Binns MM, Chowdhary BP, Coleman SJ, Della Valle G, Fryc S, Guérin G, Hasegawa T, Hill EW, Jurka J, Kiialainen A, Lindgren G, Liu J, Magnani E, Mickelson JR, Murray J, Nergadze SG, Onofrio R, Pedroni S, Piras MF, Raudsepp T, Rocchi M, Røed KH, Ryder OA, Searle S, Skow L, Swinburne JE, Syvänen AC, Tozaki T, Valberg SJ, Vaudin M, White JR, Zody MC, Lander ES, Lindblad-Toh K. Genome sequence, comparative analysis, and population genetics of the domestic horse.. Science 2009 Nov 6;326(5954):865-7.
    pmc: PMC3785132pubmed: 19892987doi: 10.1126/science.1178158google scholar: lookup
  41. Wang GD, Fan RX, Zhai W, Liu F, Wang L, Zhong L, Wu H, Yang HC, Wu SF, Zhu CL, Li Y, Gao Y, Ge RL, Wu CI, Zhang YP. Genetic convergence in the adaptation of dogs and humans to the high-altitude environment of the tibetan plateau.. Genome Biol Evol 2014 Aug;6(8):2122-8.
    pmc: PMC4231634pubmed: 25091388doi: 10.1093/gbe/evu162google scholar: lookup
  42. Wang MS, Li Y, Peng MS, Zhong L, Wang ZJ, Li QY, Tu XL, Dong Y, Zhu CL, Wang L, Yang MM, Wu SF, Miao YW, Liu JP, Irwin DM, Wang W, Wu DD, Zhang YP. Genomic Analyses Reveal Potential Independent Adaptation to High Altitude in Tibetan Chickens.. Mol Biol Evol 2015 Jul;32(7):1880-9.
    pubmed: 25788450doi: 10.1093/molbev/msv071google scholar: lookup
  43. Wang Q, Li Q, Liu R, Zheng M, Wen J, Zhao G. Host cell interactome of PA protein of H5N1 influenza A virus in chicken cells.. J Proteomics 2016 Mar 16;136:48-54.
    pubmed: 26828018doi: 10.1016/j.jprot.2016.01.018google scholar: lookup
  44. Wei C, Wang H, Liu G, Zhao F, Kijas JW, Ma Y, Lu J, Zhang L, Cao J, Wu M, Wang G, Liu R, Liu Z, Zhang S, Liu C, Du L. Genome-wide analysis reveals adaptation to high altitudes in Tibetan sheep.. Sci Rep 2016 May 27;6:26770.
    pmc: PMC4882523pubmed: 27230812doi: 10.1038/srep26770google scholar: lookup
  45. Weir BS, Cockerham CC. ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE.. Evolution 1984 Nov;38(6):1358-1370.
    pubmed: 28563791doi: 10.1111/j.1558-5646.1984.tb05657.xgoogle scholar: lookup
  46. Xu S, Li S, Yang Y, Tan J, Lou H, Jin W, Yang L, Pan X, Wang J, Shen Y, Wu B, Wang H, Jin L. A genome-wide search for signals of high-altitude adaptation in Tibetans.. Mol Biol Evol 2011 Feb;28(2):1003-11.
    pubmed: 20961960doi: 10.1093/molbev/msq277google scholar: lookup
  47. Yang F. The “ancient tea and horse caravan road”, the “silk road” of Southwest China. Silk Road 2:29–33.
  48. Yang J, Jin ZB, Chen J, Huang XF, Li XM, Liang YB, Mao JY, Chen X, Zheng Z, Bakshi A, Zheng DD, Zheng MQ, Wray NR, Visscher PM, Lu F, Qu J. Genetic signatures of high-altitude adaptation in Tibetans.. Proc Natl Acad Sci U S A 2017 Apr 18;114(16):4189-4194.
    pmc: PMC5402460pubmed: 28373541doi: 10.1073/pnas.1617042114google scholar: lookup
  49. Zhang W, Fan Z, Han E, Hou R, Zhang L, Galaverni M, Huang J, Liu H, Silva P, Li P, Pollinger JP, Du L, Zhang X, Yue B, Wayne RK, Zhang Z. Hypoxia adaptations in the grey wolf (Canis lupus chanco) from Qinghai-Tibet Plateau.. PLoS Genet 2014 Jul;10(7):e1004466.
    pmc: PMC4117439pubmed: 25078401doi: 10.1371/journal.pgen.1004466google scholar: lookup
  50. Zhang Z, Jia Y, Almeida P, Mank JE, van Tuinen M, Wang Q, Jiang Z, Chen Y, Zhan K, Hou S, Zhou Z, Li H, Yang F, He Y, Ning Z, Yang N, Qu L. Whole-genome resequencing reveals signatures of selection and timing of duck domestication.. Gigascience 2018 Apr 1;7(4).
    pmc: PMC6007426pubmed: 29635409doi: 10.1093/gigascience/giy027google scholar: lookup
  51. Zhang W, Fan Z, Han E, Hou R, Zhang L, Galaverni M, Huang J, Liu H, Silva P, Li P, Pollinger JP, Du L, Zhang X, Yue B, Wayne RK, Zhang Z. Hypoxia adaptations in the grey wolf (Canis lupus chanco) from Qinghai-Tibet Plateau.. PLoS Genet 2014 Jul;10(7):e1004466.
    pmc: PMC4117439pubmed: 25078401doi: 10.1371/journal.pgen.1004466google scholar: lookup
  52. Zhang Z, Xu D, Wang L, Hao J, Wang J, Zhou X, Wang W, Qiu Q, Huang X, Zhou J, Long R, Zhao F, Shi P. Convergent Evolution of Rumen Microbiomes in High-Altitude Mammals.. Curr Biol 2016 Jul 25;26(14):1873-9.
    pubmed: 27321997doi: 10.1016/j.cub.2016.05.012google scholar: lookup
  53. Zhou Z, Jiang Y, Wang Z, Gou Z, Lyu J, Li W, Yu Y, Shu L, Zhao Y, Ma Y, Fang C, Shen Y, Liu T, Li C, Li Q, Wu M, Wang M, Wu Y, Dong Y, Wan W, Wang X, Ding Z, Gao Y, Xiang H, Zhu B, Lee SH, Wang W, Tian Z. Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean.. Nat Biotechnol 2015 Apr;33(4):408-14.
    pubmed: 25643055doi: 10.1038/nbt.3096google scholar: lookup

Citations

This article has been cited 36 times.
  1. Jorgensen K, Song D, Weinstein J, Garcia OA, Pearson LN, Inclán M, Rivera-Chira M, León-Velarde F, Kiyamu M, Brutsaert TD, Bigham AW, Lee FS. High-Altitude Andean H194R HIF2A Allele Is a Hypomorphic Allele.. Mol Biol Evol 2023 Jul 5;40(7).
    doi: 10.1093/molbev/msad162pubmed: 37463421google scholar: lookup
  2. Li YP, Li MX, Wang C, Li YD, Sa YP, Guo Y. Bloodletting Acupuncture at Jing-Well Points on Hand Induced Autophagy to Alleviate Brain Injury in Acute Altitude Hypoxic Rats by Activating PINK1/Parkin Pathway.. Chin J Integr Med 2023 Jul 12;.
    doi: 10.1007/s11655-023-3597-0pubmed: 37434031google scholar: lookup
  3. Meng S, Zhang Y, Lv S, Zhang Z, Liu X, Jiang L. Comparison of muscle metabolomics between two Chinese horse breeds.. Front Vet Sci 2023;10:1162953.
    doi: 10.3389/fvets.2023.1162953pubmed: 37215482google scholar: lookup
  4. Tian D, Han B, Li X, Liu D, Zhou B, Zhao C, Zhang N, Wang L, Pei Q, Zhao K. Genetic diversity and selection of Tibetan sheep breeds revealed by whole-genome resequencing.. Anim Biosci 2023 Jul;36(7):991-1002.
    doi: 10.5713/ab.22.0432pubmed: 37170524google scholar: lookup
  5. Zhao J, Yao Y, Li D, Zhu W, Xiao H, Xie M, Xiong Y, Wu J, Ni Q, Zhang M, Xu H. Metagenome and metabolome insights into the energy compensation and exogenous toxin degradation of gut microbiota in high-altitude rhesus macaques (Macaca mulatta).. NPJ Biofilms Microbiomes 2023 Apr 20;9(1):20.
    doi: 10.1038/s41522-023-00387-3pubmed: 37081021google scholar: lookup
  6. Li W, Du J, Yang L, Liang Q, Yang M, Zhou X, Du W. Chromosome-level genome assembly and population genomics of Mongolian racerunner (Eremias argus) provide insights into high-altitude adaptation in lizards.. BMC Biol 2023 Feb 20;21(1):40.
    doi: 10.1186/s12915-023-01535-zpubmed: 36803146google scholar: lookup
  7. Liang C, Liu D, Song P, Zhou Y, Yu H, Sun G, Ma X, Yan J. Transcriptomic Analyses Suggest the Adaptation of Bumblebees to High Altitudes.. Insects 2022 Dec 17;13(12).
    doi: 10.3390/insects13121173pubmed: 36555083google scholar: lookup
  8. Zhong HA, Kong XY, Zhang YW, Su YK, Zhang B, Zhu L, Chen H, Gou X, Zhang H. Microevolutionary mechanism of high-altitude adaptation in Tibetan chicken populations from an elevation gradient.. Evol Appl 2022 Dec;15(12):2100-2112.
    doi: 10.1111/eva.13503pubmed: 36540645google scholar: lookup
  9. Li C, Wu Y, Chen B, Cai Y, Guo J, Leonard AS, Kalds P, Zhou S, Zhang J, Zhou P, Gan S, Jia T, Pu T, Suo L, Li Y, Zhang K, Li L, Purevdorj M, Wang X, Li M, Wang Y, Liu Y, Huang S, Sonstegard T, Wang MS, Kemp S, Pausch H, Chen Y, Han JL, Jiang Y, Wang X. Markhor-derived Introgression of a Genomic Region Encompassing PAPSS2 Confers High-altitude Adaptability in Tibetan Goats.. Mol Biol Evol 2022 Dec 5;39(12).
    doi: 10.1093/molbev/msac253pubmed: 36382357google scholar: lookup
  10. Li X, Yuan L, Wang W, Zhang D, Zhao Y, Chen J, Xu D, Zhao L, Li F, Zhang X. Whole genome re-sequencing reveals artificial and natural selection for milk traits in East Friesian sheep.. Front Vet Sci 2022;9:1034211.
    doi: 10.3389/fvets.2022.1034211pubmed: 36330154google scholar: lookup
  11. Xi Q, Zhao F, Hu J, Wang J, Liu X, Dang P, Luo Y, Li S. Expression and Variations in EPAS1 Associated with Oxygen Metabolism in Sheep.. Genes (Basel) 2022 Oct 15;13(10).
    doi: 10.3390/genes13101871pubmed: 36292756google scholar: lookup
  12. Zhao P, Li S, He Z, Zhao F, Wang J, Liu X, Li M, Hu J, Zhao Z, Luo Y. Physiology and Proteomic Basis of Lung Adaptation to High-Altitude Hypoxia in Tibetan Sheep.. Animals (Basel) 2022 Aug 19;12(16).
    doi: 10.3390/ani12162134pubmed: 36009723google scholar: lookup
  13. Gu X. d(N)/d(S)-H, a New Test to Distinguish Different Selection Modes in Protein Evolution and Cancer Evolution.. J Mol Evol 2022 Oct;90(5):342-351.
    doi: 10.1007/s00239-022-10064-2pubmed: 35920867google scholar: lookup
  14. Shi S, Shao D, Yang L, Liang Q, Han W, Xue Q, Qu L, Leng L, Li Y, Zhao X, Dong P, Walugembe M, Kayang BB, Muhairwa AP, Zhou H, Tong H. Whole genome analyses reveal novel genes associated with chicken adaptation to tropical and frigid environments.. J Adv Res 2023 May;47:13-25.
    doi: 10.1016/j.jare.2022.07.005pubmed: 35907630google scholar: lookup
  15. Zhang DY, Zhang XX, Li FD, Yuan LF, Li XL, Zhang YK, Zhao Y, Zhao LM, Wang JH, Xu D, Cheng JB, Yang XB, Li WX, Lin CC, Zhou BB, Wang WM. Whole-genome resequencing reveals molecular imprints of anthropogenic and natural selection in wild and domesticated sheep.. Zool Res 2022 Sep 18;43(5):695-705.
    doi: 10.24272/j.issn.2095-8137.2022.124pubmed: 35843722google scholar: lookup
  16. Yang L, Wei F, Zhan X, Fan H, Zhao P, Huang G, Chang J, Lei Y, Hu Y. Evolutionary Conservation Genomics Reveals Recent Speciation and Local Adaptation in Threatened Takins.. Mol Biol Evol 2022 Jun 2;39(6).
    doi: 10.1093/molbev/msac111pubmed: 35599233google scholar: lookup
  17. Liu Z, Tan X, Wang J, Jin Q, Meng X, Cai Z, Cui X, Wang K. Whole genome sequencing of Luxi Black Head sheep for screening selection signatures associated with important traits.. Anim Biosci 2022 Sep;35(9):1340-1350.
    doi: 10.5713/ab.21.0533pubmed: 35507856google scholar: lookup
  18. Hou H, Wang X, Ding W, Xiao C, Cai X, Lv W, Tu Y, Zhao W, Yao J, Yang C. Whole-genome sequencing reveals the artificial selection and local environmental adaptability of pigeons (Columba livia).. Evol Appl 2022 Apr;15(4):603-617.
    doi: 10.1111/eva.13284pubmed: 35505885google scholar: lookup
  19. Lu Z, Yuan C, Li J, Guo T, Yue Y, Niu C, Liu J, Yang B. Comprehensive Analysis of Long Non-coding RNA and mRNA Transcriptomes Related to Hypoxia Adaptation in Tibetan Sheep.. Front Vet Sci 2021;8:801278.
    doi: 10.3389/fvets.2021.801278pubmed: 35141308google scholar: lookup
  20. Stephan T, Burgess SM, Cheng H, Danko CG, Gill CA, Jarvis ED, Koepfli KP, Koltes JE, Lyons E, Ronald P, Ryder OA, Schriml LM, Soltis P, VandeWoude S, Zhou H, Ostrander EA, Karlsson EK. Darwinian genomics and diversity in the tree of life.. Proc Natl Acad Sci U S A 2022 Jan 25;119(4).
    doi: 10.1073/pnas.2115644119pubmed: 35042807google scholar: lookup
  21. Zhao P, He Z, Xi Q, Sun H, Luo Y, Wang J, Liu X, Zhao Z, Li S. Variations in HIF-1α Contributed to High Altitude Hypoxia Adaptation via Affected Oxygen Metabolism in Tibetan Sheep.. Animals (Basel) 2021 Dec 28;12(1).
    doi: 10.3390/ani12010058pubmed: 35011164google scholar: lookup
  22. Liu X, Zhang Y, Liu W, Li Y, Pan J, Pu Y, 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 Jan 24;32(2):480-487.e6.
    doi: 10.1016/j.cub.2021.11.052pubmed: 34906355google scholar: lookup
  23. Beckman EJ, Martins F, Suzuki TA, Bi K, Keeble S, Good JM, Chavez AS, Ballinger MA, Agwamba K, Nachman MW. The genomic basis of high-elevation adaptation in wild house mice (Mus musculus domesticus) from South America.. Genetics 2022 Feb 4;220(2).
    doi: 10.1093/genetics/iyab226pubmed: 34897431google scholar: lookup
  24. Yang Y, Yuan H, Yang T, Li Y, Gao C, Jiao T, Cai Y, Zhao S. The Expression Regulatory Network in the Lung Tissue of Tibetan Pigs Provides Insight Into Hypoxia-Sensitive Pathways in High-Altitude Hypoxia.. Front Genet 2021;12:691592.
    doi: 10.3389/fgene.2021.691592pubmed: 34691141google scholar: lookup
  25. Chebii VJ, Mpolya EA, Muchadeyi FC, Domelevo Entfellner JB. Genomics of Adaptations in Ungulates.. Animals (Basel) 2021 May 29;11(6).
    doi: 10.3390/ani11061617pubmed: 34072591google scholar: lookup
  26. Schweizer RM, Jones MR, Bradburd GS, Storz JF, Senner NR, Wolf C, Cheviron ZA. Broad Concordance in the Spatial Distribution of Adaptive and Neutral Genetic Variation across an Elevational Gradient in Deer Mice.. Mol Biol Evol 2021 Sep 27;38(10):4286-4300.
    doi: 10.1093/molbev/msab161pubmed: 34037784google scholar: lookup
  27. Wang T, Guo Y, Liu S, Zhang C, Cui T, Ding K, Wang P, Wang X, Wang Z. KLF4, a Key Regulator of a Transitive Triplet, Acts on the TGF-β Signaling Pathway and Contributes to High-Altitude Adaptation of Tibetan Pigs.. Front Genet 2021;12:628192.
    doi: 10.3389/fgene.2021.628192pubmed: 33936161google scholar: lookup
  28. Storz JF. High-Altitude Adaptation: Mechanistic Insights from Integrated Genomics and Physiology.. Mol Biol Evol 2021 Jun 25;38(7):2677-2691.
    doi: 10.1093/molbev/msab064pubmed: 33751123google scholar: lookup
  29. Zhang Y, Xue X, Liu Y, Abied A, Ding Y, Zhao S, Wang W, Ma L, Guo J, Guan W, Pu Y, Mwacharo JM, Han J, Ma Y, Zhao Q. Genome-wide comparative analyses reveal selection signatures underlying adaptation and production in Tibetan and Poll Dorset sheep.. Sci Rep 2021 Jan 28;11(1):2466.
    doi: 10.1038/s41598-021-81932-ypubmed: 33510350google scholar: lookup
  30. Storz JF, Cheviron ZA. Physiological Genomics of Adaptation to High-Altitude Hypoxia.. Annu Rev Anim Biosci 2021 Feb 16;9:149-171.
    doi: 10.1146/annurev-animal-072820-102736pubmed: 33228375google scholar: lookup
  31. Ababaikeri B, Abduriyim S, Tohetahong Y, Mamat T, Ahmat A, Halik M. Whole-genome sequencing of Tarim red deer (Cervus elaphus yarkandensis) reveals demographic history and adaptations to an arid-desert environment.. Front Zool 2020;17:31.
    doi: 10.1186/s12983-020-00379-5pubmed: 33072165google scholar: lookup
  32. Liu Y, Zhao H, Luo Q, Yang Y, Zhang G, Zhou Z, Naeem M, An J. De Novo Transcriptomic and Metabolomic Analyses Reveal the Ecological Adaptation of High-Altitude Bombus pyrosoma.. Insects 2020 Sep 14;11(9).
    doi: 10.3390/insects11090631pubmed: 32937786google scholar: lookup
  33. Pamenter ME, Hall JE, Tanabe Y, Simonson TS. Cross-Species Insights Into Genomic Adaptations to Hypoxia.. Front Genet 2020;11:743.
    doi: 10.3389/fgene.2020.00743pubmed: 32849780google scholar: lookup
  34. Hall JE, Lawrence ES, Simonson TS, Fox K. Seq-ing Higher Ground: Functional Investigation of Adaptive Variation Associated With High-Altitude Adaptation.. Front Genet 2020;11:471.
    doi: 10.3389/fgene.2020.00471pubmed: 32528523google scholar: lookup
  35. Chen J, Shen Y, Wang J, Ouyang G, Kang J, Lv W, Yang L, He S. Analysis of Multiplicity of Hypoxia-Inducible Factors in the Evolution of Triplophysa Fish (Osteichthyes: Nemacheilinae) Reveals Hypoxic Environments Adaptation to Tibetan Plateau.. Front Genet 2020;11:433.
    doi: 10.3389/fgene.2020.00433pubmed: 32477402google scholar: lookup
  36. Schweizer RM, Velotta JP, Ivy CM, Jones MR, Muir SM, Bradburd GS, Storz JF, Scott GR, Cheviron ZA. Physiological and genomic evidence that selection on the transcription factor Epas1 has altered cardiovascular function in high-altitude deer mice.. PLoS Genet 2019 Nov;15(11):e1008420.
    doi: 10.1371/journal.pgen.1008420pubmed: 31697676google scholar: lookup
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.