FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Evol Bioinform Online / Detecting the Population Structure and Scanning for Signatur...
Evolutionary bioinformatics online2018; 14; 1176934318775106; doi: 10.1177/1176934318775106

Detecting the Population Structure and Scanning for Signatures of Selection in Horses (Equus caballus) From Whole-Genome Sequencing Data.

Abstract: Animal domestication gives rise to gradual changes at the genomic level through selection in populations. Selective sweeps have been traced in the genomes of many animal species, including humans, cattle, and dogs. However, little is known regarding positional candidate genes and genomic regions that exhibit signatures of selection in domestic horses. In addition, an understanding of the genetic processes underlying horse domestication, especially the origin of Chinese native populations, is still lacking. In our study, we generated whole genome sequences from 4 Chinese native horses and combined them with 48 publicly available full genome sequences, from which 15 341 213 high-quality unique single-nucleotide polymorphism variants were identified. Kazakh and Lichuan horses are 2 typical Asian native breeds that were formed in Kazakh or Northwest China and South China, respectively. We detected 1390 loss-of-function (LoF) variants in protein-coding genes, and gene ontology (GO) enrichment analysis revealed that some LoF-affected genes were overrepresented in GO terms related to the immune response. Bayesian clustering, distance analysis, and principal component analysis demonstrated that the population structure of these breeds largely reflected weak geographic patterns. Kazakh and Lichuan horses were assigned to the same lineage with other Asian native breeds, in agreement with previous studies on the genetic origin of Chinese domestic horses. We applied the composite likelihood ratio method to scan for genomic regions showing signals of recent selection in the horse genome. A total of 1052 genomic windows of 10 kB, corresponding to 933 distinct core regions, significantly exceeded neutral simulations. The GO enrichment analysis revealed that the genes under selective sweeps were overrepresented with GO terms, including "negative regulation of canonical Wnt signaling pathway," "muscle contraction," and "axon guidance." Frequent exercise training in domestic horses may have resulted in changes in the expression of genes related to metabolism, muscle structure, and the nervous system.
Get Full Text
Publication Date: 2018-06-04 PubMed ID: 29899660PubMed Central: PMC5990873DOI: 10.1177/1176934318775106Google 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 paper explores the selective breeding of domestic horses and its impact on their genomic level. The focus is on Chinese native horses, their genetic history, potential signatures of selection, and identification of distinct regions and genes that may have been affected by selective breeding.

Study Overview and Methodology

In this study, the researchers collected whole genome sequences from four Chinese native horses and supplemented this data with 48 publicly available full genome sequences. Through this, they identified 15,341,213 high-quality unique single-nucleotide polymorphism variants. Loss-of-function (LoF) variants prevalent in these sequences were also examined, particularly those related to protein-coding genes.

  • Two particular Asian breeds, Kazakh and Lichuan horses, were studied in-depth. These breeds originated from different geographical areas in China (Kazakh or Northwest China and South China, respectively).
  • The team conducted a Gene Ontology (GO) enrichment analysis to identify overrepresented GO terms in LoF-affected genes, which notably pointed towards immune response functions.
  • The researchers applied multiple analysis methods, including Bayesian clustering, distance analysis, and principal component analysis, to comprehend the population structure of these breeds and the geographical correlates.

Findings

The study reveals that the population structure of Kazakh and Lichuan horses reflects weak geographical patterns, and these breeds were found to be part of the same lineage with other Asian native breeds. This aligns with previous studies on the genetic origin of Chinese domestic horses.

  • Moreover, the research employs the composite likelihood ratio method to scan the horse genome for regions showing signals of recent selection. The analysis identifies 1052 genomic windows of 10 kB each, corresponding to 933 distinct core regions, significantly exceeding neutral simulations.
  • Further GO enrichment analysis reveals that the genes under selective sweeps were associated with GO terms such as “negative regulation of canonical Wnt signaling pathway,” “muscle contraction,” and “axon guidance.” These findings suggest that the frequent exercise training of domestic horses might have led to changes in gene expression related to metabolism, muscle structure, and the nervous system.

Implications

This study contributes valuable insights into the impact of domestication and selective breeding on the genomic structure of horses, particularly Chinese native breeds. It identifies potential genomic regions and genes that may have been significantly impacted by the breeding selection. The findings of this research could offer a better understanding of the genetic processes underlying horse domestication, fostering improvements in breeding strategies.

Cite This Article

APA
Zhang C, Ni P, Ahmad HI, Gemingguli M, Baizilaitibei A, Gulibaheti D, Fang Y, Wang H, Asif AR, Xiao C, Chen J, Ma Y, Liu X, Du X, Zhao S. (2018). Detecting the Population Structure and Scanning for Signatures of Selection in Horses (Equus caballus) From Whole-Genome Sequencing Data. Evol Bioinform Online, 14, 1176934318775106. https://doi.org/10.1177/1176934318775106

Publication

Evolutionary bioinformatics online
ISSN: 1176-9343
NlmUniqueID: 101256319
Country: United States
Language: English
Volume: 14
Pages: 1176934318775106
PII: 1176934318775106

Researcher Affiliations

Zhang, Cheng
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Ni, Pan
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Ahmad, Hafiz Ishfaq
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Gemingguli, M
  • College of Animal Science, Tarim University, Alar, China.
Baizilaitibei, A
  • College of Animal Science, Tarim University, Alar, China.
Gulibaheti, D
  • College of Animal Science, Tarim University, Alar, China.
Fang, Yaping
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Wang, Haiyang
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Asif, Akhtar Rasool
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Xiao, Changyi
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Chen, Jianhai
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Ma, Yunlong
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Liu, Xiangdong
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
Du, Xiaoyong
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.
  • Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China.
Zhao, Shuhong
  • Key Laboratory of Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Sciences & Technology, Huazhong Agricultural University, Wuhan, People's Republic of China.

Conflict of Interest Statement

Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

This article includes 41 references
  1. Vilà C, Leonard JA, Gotherstrom A, Marklund S, Sandberg K, Liden K, Wayne RK, Ellegren H. Widespread origins of domestic horse lineages.. Science 2001 Jan 19;291(5503):474-7.
    pubmed: 11161199doi: 10.1126/science.291.5503.474google scholar: lookup
  2. Warmuth V, Eriksson A, Bower MA, Barker G, Barrett E, Hanks BK, Li S, Lomitashvili D, Ochir-Goryaeva M, Sizonov GV, Soyonov V, Manica A. Reconstructing the origin and spread of horse domestication in the Eurasian steppe.. Proc Natl Acad Sci U S A 2012 May 22;109(21):8202-6.
    pmc: PMC3361400pubmed: 22566639doi: 10.1073/pnas.1111122109google scholar: lookup
  3. Kayar SR, Hoppeler H, Lindstedt SL, Claassen H, Jones JH, Essen-Gustavsson B, Taylor CR. Total muscle mitochondrial volume in relation to aerobic capacity of horses and steers.. Pflugers Arch 1989 Feb;413(4):343-7.
    pubmed: 2928085doi: 10.1007/BF00584481google scholar: lookup
  4. Lei CZ, Su R, Bower MA, Edwards CJ, Wang XB, Weining S, Liu L, Xie WM, Li F, Liu RY, Zhang YS, Zhang CM, Chen H. Multiple maternal origins of native modern and ancient horse populations in China.. Anim Genet 2009 Dec;40(6):933-44.
    pubmed: 19744143doi: 10.1111/j.1365-2052.2009.01950.xgoogle scholar: lookup
  5. Yang Y, Zhu Q, Liu S, Zhao C, Wu C. The origin of Chinese domestic horses revealed with novel mtDNA variants.. Anim Sci J 2017 Jan;88(1):19-26.
    pubmed: 27071843doi: 10.1111/asj.12583google scholar: lookup
  6. Gemingguli M, Iskhan KR, Li Y, Qi A, Wunirifu W, Ding LY, Wumaierjiang A. Genetic diversity and population structure of Kazakh horses (Equus caballus) inferred from mtDNA sequences.. Genet Mol Res 2016 Oct 5;15(4).
    pubmed: 27808359doi: 10.4238/gmr.15048618google scholar: lookup
  7. Zhao YB, Zhang Y, Zhang QC, Li HJ, Cui YQ, Xu Z, Jin L, Zhou H, Zhu H. Ancient DNA reveals that the genetic structure of the northern Han Chinese was shaped prior to 3,000 years ago.. PLoS One 2015;10(5):e0125676.
    pmc: PMC4418768pubmed: 25938511doi: 10.1371/journal.pone.0125676google scholar: lookup
  8. Ai H, Fang X, Yang B, Huang Z, Chen H, Mao L, Zhang F, Zhang L, Cui L, He W, Yang J, Yao X, Zhou L, Han L, Li J, Sun S, Xie X, Lai B, Su Y, Lu Y, Yang H, Huang T, Deng W, Nielsen R, Ren J, Huang L. Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing.. Nat Genet 2015 Mar;47(3):217-25.
    pubmed: 25621459doi: 10.1038/ng.3199google scholar: lookup
  9. 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
  10. Andersson L. How selective sweeps in domestic animals provide new insight into biological mechanisms.. J Intern Med 2012 Jan;271(1):1-14.
    pubmed: 21895806doi: 10.1111/j.1365-2796.2011.02450.xgoogle 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.
    pmc: PMC5104162pubmed: 26412128doi: 10.1016/j.cub.2015.08.032google scholar: lookup
  12. 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.
    pmc: PMC4284583pubmed: 25512547doi: 10.1073/pnas.1416991111google scholar: lookup
  13. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Capomaccio S, Cappelli K, Cothran EG, Distl O, 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, Swinburne J, Tozaki T, Vaudin M, Wade CM, McCue ME. Genome-wide analysis reveals selection for important traits in domestic horse breeds.. PLoS Genet 2013;9(1):e1003211.
    pmc: PMC3547851pubmed: 23349635doi: 10.1371/journal.pgen.1003211google scholar: lookup
  14. Pavlidis P, Živkovic D, Stamatakis A, Alachiotis N. SweeD: likelihood-based detection of selective sweeps in thousands of genomes.. Mol Biol Evol 2013 Sep;30(9):2224-34.
    pmc: PMC3748355pubmed: 23777627doi: 10.1093/molbev/mst112google scholar: lookup
  15. Park W, Kim J, Kim HJ, Choi J, Park JW, Cho HW, Kim BW, Park MH, Shin TS, Cho SK, Park JK, Kim H, Hwang JY, Lee CK, Lee HK, Cho S, Cho BW. Investigation of de novo unique differentially expressed genes related to evolution in exercise response during domestication in Thoroughbred race horses.. PLoS One 2014;9(3):e91418.
    pmc: PMC3962374pubmed: 24658125doi: 10.1371/journal.pone.0091418google scholar: lookup
  16. 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
  17. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data.. Bioinformatics 2014 Aug 1;30(15):2114-20.
    pmc: PMC4103590pubmed: 24695404doi: 10.1093/bioinformatics/btu170google scholar: lookup
  18. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.. Bioinformatics 2009 Jul 15;25(14):1754-60.
    pmc: PMC2705234pubmed: 19451168doi: 10.1093/bioinformatics/btp324google scholar: lookup
  19. 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.
    pmc: PMC2928508pubmed: 20644199doi: 10.1101/gr.107524.110google scholar: lookup
  20. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.. Fly (Austin) 2012 Apr-Jun;6(2):80-92.
    pmc: PMC3679285pubmed: 22728672doi: 10.4161/fly.19695google scholar: lookup
  21. Korneliussen TS, Albrechtsen A, Nielsen R. ANGSD: Analysis of Next Generation Sequencing Data.. BMC Bioinformatics 2014 Nov 25;15(1):356.
    pmc: PMC4248462pubmed: 25420514doi: 10.1186/s12859-014-0356-4google scholar: lookup
  22. Fumagalli M, Vieira FG, Linderoth T, Nielsen R. ngsTools: methods for population genetics analyses from next-generation sequencing data.. Bioinformatics 2014 May 15;30(10):1486-7.
    pmc: PMC4016704pubmed: 24458950doi: 10.1093/bioinformatics/btu041google scholar: lookup
  23. 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
  24. Lin R, Du X, Peng S, Yang L, Ma Y, Gong Y, Li S. Discovering All Transcriptome Single-Nucleotide Polymorphisms and Scanning for Selection Signatures in Ducks (Anas platyrhynchos).. Evol Bioinform Online 2015;11(Suppl 1):67-76.
    pmc: PMC4721680pubmed: 26819540doi: 10.4137/EBO.S21545google scholar: lookup
  25. Hudson RR. Generating samples under a Wright-Fisher neutral model of genetic variation.. Bioinformatics 2002 Feb;18(2):337-8.
    pubmed: 11847089doi: 10.1093/bioinformatics/18.2.337google scholar: lookup
  26. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA. DAVID: Database for Annotation, Visualization, and Integrated Discovery.. Genome Biol 2003;4(5):P3.
    pubmed: 12734009
  27. Marth GT, Yu F, Indap AR, Garimella K, Gravel S, Leong WF, Tyler-Smith C, Bainbridge M, Blackwell T, Zheng-Bradley X, Chen Y, Challis D, Clarke L, Ball EV, Cibulskis K, Cooper DN, Fulton B, Hartl C, Koboldt D, Muzny D, Smith R, Sougnez C, Stewart C, Ward A, Yu J, Xue Y, Altshuler D, Bustamante CD, Clark AG, Daly M, DePristo M, Flicek P, Gabriel S, Mardis E, Palotie A, Gibbs R. The functional spectrum of low-frequency coding variation.. Genome Biol 2011 Sep 14;12(9):R84.
    pmc: PMC3308047pubmed: 21917140doi: 10.1186/gb-2011-12-9-r84google scholar: lookup
  28. Rohrer DK, Kobilka BK. G protein-coupled receptors: functional and mechanistic insights through altered gene expression.. Physiol Rev 1998 Jan;78(1):35-52.
    pubmed: 9457168doi: 10.1152/physrev.1998.78.1.35google scholar: lookup
  29. MacArthur DG, Balasubramanian S, Frankish A, Huang N, Morris J, Walter K, Jostins L, Habegger L, Pickrell JK, Montgomery SB, Albers CA, Zhang ZD, Conrad DF, Lunter G, Zheng H, Ayub Q, DePristo MA, Banks E, Hu M, Handsaker RE, Rosenfeld JA, Fromer M, Jin M, Mu XJ, Khurana E, Ye K, Kay M, Saunders GI, Suner MM, Hunt T, Barnes IH, Amid C, Carvalho-Silva DR, Bignell AH, Snow C, Yngvadottir B, Bumpstead S, Cooper DN, Xue Y, Romero IG, Wang J, Li Y, Gibbs RA, McCarroll SA, Dermitzakis ET, Pritchard JK, Barrett JC, Harrow J, Hurles ME, Gerstein MB, Tyler-Smith C. A systematic survey of loss-of-function variants in human protein-coding genes.. Science 2012 Feb 17;335(6070):823-8.
    pmc: PMC3299548pubmed: 22344438doi: 10.1126/science.1215040google scholar: lookup
  30. Das A, Panitz F, Gregersen VR, Bendixen C, Holm LE. Deep sequencing of Danish Holstein dairy cattle for variant detection and insight into potential loss-of-function variants in protein coding genes.. BMC Genomics 2015 Dec 9;16:1043.
    pmc: PMC4673847pubmed: 26645365doi: 10.1186/s12864-015-2249-ygoogle scholar: lookup
  31. 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 Aug 6;99(16):10905-10.
    pmc: PMC125071pubmed: 12130666doi: 10.1073/pnas.152330099google scholar: lookup
  32. Luís C, Cothran EG, Oom Mdo M. Inbreeding and genetic structure in the endangered Sorraia horse breed: implications for its conservation and management.. J Hered 2007 May-Jun;98(3):232-7.
    pubmed: 17404326doi: 10.1093/jhered/esm009google scholar: lookup
  33. 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.
    pmc: PMC3559798pubmed: 23383025doi: 10.1371/journal.pone.0054997google scholar: lookup
  34. McCue ME, Bannasch DL, Petersen JL, Gurr J, Bailey E, Binns MM, Distl O, Guérin G, Hasegawa T, Hill EW, Leeb T, Lindgren G, Penedo MC, Røed KH, Ryder OA, Swinburne JE, Tozaki T, Valberg SJ, Vaudin M, Lindblad-Toh K, Wade CM, Mickelson JR. A high density SNP array for the domestic horse and extant Perissodactyla: utility for association mapping, genetic diversity, and phylogeny studies.. PLoS Genet 2012 Jan;8(1):e1002451.
    pmc: PMC3257288pubmed: 22253606doi: 10.1371/journal.pgen.1002451google scholar: lookup
  35. Huang J, Zhao Y, Shiraigol W, Li B, Bai D, Ye W, Daidiikhuu D, Yang L, Jin B, Zhao Q, Gao Y, Wu J, Bao W, Li A, Zhang Y, Han H, Bai H, Bao Y, Zhao L, Zhai Z, Zhao W, Sun Z, Zhang Y, Meng H, Dugarjaviin M. Analysis of horse genomes provides insight into the diversification and adaptive evolution of karyotype.. Sci Rep 2014 May 14;4:4958.
    pmc: PMC4021364pubmed: 24828444doi: 10.1038/srep04958google scholar: lookup
  36. Ahmad HI, Ahmad MJ, Adeel MM, Asif AR, Du X. Positive selection drives the evolution of endocrine regulatory bone morphogenetic protein system in mammals.. Oncotarget 2018 Apr 6;9(26):18435-18445.
    pmc: PMC5915083pubmed: 29719616doi: 10.18632/oncotarget.24240google scholar: lookup
  37. Rao JN, Madasu Y, Dominguez R. Mechanism of actin filament pointed-end capping by tropomodulin.. Science 2014 Jul 25;345(6195):463-7.
    pmc: PMC4367809pubmed: 25061212doi: 10.1126/science.1256159google scholar: lookup
  38. Bates TC, Luciano M, Medland SE, Montgomery GW, Wright MJ, Martin NG. Genetic variance in a component of the language acquisition device: ROBO1 polymorphisms associated with phonological buffer deficits.. Behav Genet 2011 Jan;41(1):50-7.
    pubmed: 20949370doi: 10.1007/s10519-010-9402-9google scholar: lookup
  39. Hannula-Jouppi K, Kaminen-Ahola N, Taipale M, Eklund R, Nopola-Hemmi J, Kääriäinen H, Kere J. The axon guidance receptor gene ROBO1 is a candidate gene for developmental dyslexia.. PLoS Genet 2005 Oct;1(4):e50.
    pmc: PMC1270007pubmed: 16254601doi: 10.1371/journal.pgen.0010050google scholar: lookup
  40. McGivney BA, McGettigan PA, Browne JA, Evans AC, Fonseca RG, Loftus BJ, Lohan A, MacHugh DE, Murphy BA, Katz LM, Hill EW. Characterization of the equine skeletal muscle transcriptome identifies novel functional responses to exercise training.. BMC Genomics 2010 Jun 23;11:398.
    pmc: PMC2900271pubmed: 20573200doi: 10.1186/1471-2164-11-398google scholar: lookup
  41. McGivney BA, Eivers SS, MacHugh DE, MacLeod JN, O'Gorman GM, Park SD, Katz LM, Hill EW. Transcriptional adaptations following exercise in thoroughbred horse skeletal muscle highlights molecular mechanisms that lead to muscle hypertrophy.. BMC Genomics 2009 Dec 30;10:638.
    pmc: PMC2812474pubmed: 20042072doi: 10.1186/1471-2164-10-638google scholar: lookup

Citations

This article has been cited 14 times.
  1. Colpitts J, McLoughlin PD, Poissant J. Runs of homozygosity in Sable Island feral horses reveal the genomic consequences of inbreeding and divergence from domestic breeds. BMC Genomics 2022 Jul 12;23(1):501.
    doi: 10.1186/s12864-022-08729-9pubmed: 35820826google scholar: lookup
  2. Criscione A, Mastrangelo S, D'Alessandro E, Tumino S, Di Gerlando R, Zumbo A, Marletta D, Bordonaro S. Genome-wide survey on three local horse populations with a focus on runs of homozygosity pattern. J Anim Breed Genet 2022 Sep;139(5):540-555.
    doi: 10.1111/jbg.12680pubmed: 35445758google scholar: lookup
  3. Lee W, Mun S, Choi SY, Oh DY, Park YS, Han K. Comparative Analysis for Genetic Characterization in Korean Native Jeju Horse. Animals (Basel) 2021 Jun 28;11(7).
    doi: 10.3390/ani11071924pubmed: 34203473google scholar: lookup
  4. Zhou Z, Fan Y, Wang G, Lai Z, Gao Y, Wu F, Lei C, Dang R. Detection of Selection Signatures Underlying Production and Adaptive Traits Based on Whole-Genome Sequencing of Six Donkey Populations. Animals (Basel) 2020 Oct 7;10(10).
    doi: 10.3390/ani10101823pubmed: 33036357google scholar: lookup
  5. Salek Ardestani S, Aminafshar M, Zandi Baghche Maryam MB, Banabazi MH, Sargolzaei M, Miar Y. Whole-Genome Signatures of Selection in Sport Horses Revealed Selection Footprints Related to Musculoskeletal System Development Processes. Animals (Basel) 2019 Dec 26;10(1).
    doi: 10.3390/ani10010053pubmed: 31888018google scholar: lookup
  6. Srikanth K, Kim NY, Park W, Kim JM, Kim KD, Lee KT, Son JH, Chai HH, Choi JW, Jang GW, Kim H, Ryu YC, Nam JW, Park JE, Kim JM, Lim D. Comprehensive genome and transcriptome analyses reveal genetic relationship, selection signature, and transcriptome landscape of small-sized Korean native Jeju horse. Sci Rep 2019 Nov 13;9(1):16672.
    doi: 10.1038/s41598-019-53102-8pubmed: 31723199google scholar: lookup
  7. Raudsepp T, Finno CJ, Bellone RR, Petersen JL. Ten years of the horse reference genome: insights into equine biology, domestication and population dynamics in the post-genome era. Anim Genet 2019 Dec;50(6):569-597.
    doi: 10.1111/age.12857pubmed: 31568563google scholar: lookup
  8. Grilz-Seger G, Neuditschko M, Ricard A, Velie B, Lindgren G, Mesarič M, Cotman M, Horna M, Dobretsberger M, Brem G, Druml T. Genome-Wide Homozygosity Patterns and Evidence for Selection in a Set of European and Near Eastern Horse Breeds. Genes (Basel) 2019 Jun 28;10(7).
    doi: 10.3390/genes10070491pubmed: 31261764google scholar: lookup
  9. Seong HS, Kim NY, Kim DC, Hwang NH, Son DH, Shin JS, Lee JH, Chung WH, Choi JW. Whole genome sequencing analysis of horse populations inhabiting the Korean Peninsula and Przewalski's horse. Genes Genomics 2019 Jun;41(6):621-628.
    doi: 10.1007/s13258-019-00795-wpubmed: 30941726google scholar: lookup
  10. Grilz-Seger G, Druml T, Neuditschko M, Dobretsberger M, Horna M, Brem G. High-resolution population structure and runs of homozygosity reveal the genetic architecture of complex traits in the Lipizzan horse. BMC Genomics 2019 Mar 5;20(1):174.
    doi: 10.1186/s12864-019-5564-xpubmed: 30836959google scholar: lookup
  11. Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Szmatoła T, Polak G, Tomczyk-Wrona I, Bugno-Poniewierska M. A genome-wide scan for diversifying selection signatures in selected horse breeds. PLoS One 2019;14(1):e0210751.
    doi: 10.1371/journal.pone.0210751pubmed: 30699152google scholar: lookup
  12. Chessari G, Reich P, Criscione A, Falker-Gieske C, Mastrangelo S, Tumino S, Bordonaro S, Marletta D, Tetens J. Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds. BMC Genomics 2025 Nov 29;26(1):1086.
    doi: 10.1186/s12864-025-12256-8pubmed: 41318401google scholar: lookup
  13. Fallahi M, Masoudi AA, Vaez Torshizi R, Maghsoudi A. Socio-economic evaluation of human-dog coexistence: A 40,000 years history. Vet Med Sci 2024 Nov;10(6):e70012.
    doi: 10.1002/vms3.70012pubmed: 39385665google scholar: lookup
  14. Orazymbetova Z, Ualiyeva D, Dossybayev K, Torekhanov A, Sydykov D, Mussayeva A, Baktybayev G. Genetic Diversity of Kazakhstani Equus caballus (Linnaeus, 1758) Horse Breeds Inferred from Microsatellite Markers. Vet Sci 2023 Sep 30;10(10).
    doi: 10.3390/vetsci10100598pubmed: 37888550google scholar: lookup
Subscribe to our newsletter
Join 99,308 horse owners like you who receive our email updates on horse health and nutrition.