FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / PLoS One / Selection in Australian Thoroughbred horses acts on a locus ...
PloS one2020; 15(2); e0227212; doi: 10.1371/journal.pone.0227212

Selection in Australian Thoroughbred horses acts on a locus associated with early two-year old speed.

Abstract: Thoroughbred horse racing is a global sport with major hubs in Europe, North America, Australasia and Japan. Regional preferences for certain traits have resulted in phenotypic variation that may result from adaptation to the local racing ecosystem. Here, we test the hypothesis that genes selected for regional phenotypic variation may be identified by analysis of selection signatures in pan-genomic SNP genotype data. Comparing Australian to non-Australian Thoroughbred horses (n = 99), the most highly differentiated loci in a composite selection signals (CSS) analysis were on ECA6 (34.75-34.85 Mb), ECA14 (33.2-33.52 Mb and 35.52-36.94 Mb) and ECA16 (24.28-26.52 Mb) in regions containing candidate genes for exercise adaptations including cardiac function (ARHGAP26, HBEGF, SRA1), synapse development and locomotion (APBB3, ATXN7, CLSTN3), stress response (NR3C1) and the skeletal muscle response to exercise (ARHGAP26, NDUFA2). In a genome-wide association study for field-measured speed in two-year-olds (n = 179) SNPs contained within the single association peak (33.2-35.6 Mb) overlapped with the ECA14 CSS signals and spanned a protocadherin gene cluster. Association tests using higher density SNP genotypes across the ECA14 locus identified a SNP within the PCDHGC5 gene associated with elite racing performance (n = 922). These results indicate that there may be differential selection for racing performance under racing and management conditions that are specific to certain geographic racing regions. In Australia breeders have principally selected horses for favourable genetic variants at loci containing genes that modulate behaviour, locomotion and skeletal muscle physiology that together appear to be contributing to early two-year-old speed.
Get Full Text
Publication Date: 2020-02-12 PubMed ID: 32049967PubMed Central: PMC7015314DOI: 10.1371/journal.pone.0227212Google 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
  • Comparative Study
  • Journal Article
  • Research Support
  • Non-U.S. Gov't

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 investigates how regional traits in Thoroughbred horse breeds in Australia may be a result of selective breeding based on pan-genomic SNP genotype data. The study found specific genetic variation in these horses that are linked to adaptations in speed, cardiac function, and muscle physiology.

Overview of Research

  • The research aimed to identify the role of selective breeding in creating distinctive traits in Thoroughbred horses in Australia.
  • Through pan-genomic SNP genotype data analysis, the researchers hypothesized that genes selected for specific regional traits can be identified.
  • Specifically, they were interested in the genes that could influence the speed and physical characteristics of these horses.

Findings

  • Comparative analysis between Australian and non-Australian Thoroughbred horses highlighted discrete genetic variations characteristic of the Australian breeds.
  • Differentiated loci identified were mainly found on ECA6, ECA14, and ECA16, pointing to modifications in genes related to exercise adaptations, including cardiac function, synapse development, locomotion, stress response, and muscular response to exercise.
  • This indicates that Australian breeders may have selected for horses with favorable genetic variants in these particular areas, affecting the horses’ overall performance and speed, particularly in two-year-olds.

Implications

  • The research highlights the potential for selective breeding to influence and enhance specific traits and capabilities in domestic animal species, such as race horses.
  • Understanding this can provide insight into better breeding methodologies to optimize certain desirable traits in animals, either for racing performance or for other purposes, such as specific workhorse traits.

Cite This Article

APA
Han H, McGivney BA, Farries G, Katz LM, MacHugh DE, Randhawa IAS, Hill EW. (2020). Selection in Australian Thoroughbred horses acts on a locus associated with early two-year old speed. PLoS One, 15(2), e0227212. https://doi.org/10.1371/journal.pone.0227212

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 15
Issue: 2
Pages: e0227212

Researcher Affiliations

Han, Haige
  • Plusvital Ltd, The Highline, Dun Laoghaire Business Park, Dublin, Ireland.
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
McGivney, Beatrice A
  • Plusvital Ltd, The Highline, Dun Laoghaire Business Park, Dublin, Ireland.
Farries, Gabriella
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Katz, Lisa M
  • UCD School of Veterinary Medicine, University College Dublin, Dublin, Ireland.
MacHugh, David E
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
  • UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.
Randhawa, Imtiaz A S
  • School of Veterinary Science, University of Queensland, Gatton, Australia.
Hill, Emmeline W
  • Plusvital Ltd, The Highline, Dun Laoghaire Business Park, Dublin, Ireland.
  • UCD School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.

MeSH Terms

  • Animals
  • Australia
  • Genome
  • Genome-Wide Association Study / methods
  • Horses / genetics
  • Locomotion / genetics
  • Phenotype
  • Physical Conditioning, Animal

Conflict of Interest Statement

I have read the journal\'s policy and the authors of this manuscript have the following competing interests: EWH and DEM and shareholders, EWH, HH and BAM are paid employees and LMK is a paid consultant of Plusvital Ltd. EWH is a Director of the company. EWH, DEM and LMK are coinventors on multiple patents relating to the MSTN g.66493737 SNP, which is not relevant to the current manuscript. Plusvital Ltd has not applied for protection of IP arising from the results in the current manuscript and at time of manuscript submission has no commercial offering relating to the results. Due to the confidential nature of the privately owned horses, data are available on request from the UCD Technology Transfer Office and Plusvital Ltd. for researchers who meet the criteria for access to confidential data. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

References

This article includes 99 references
  1. Hill EW, McGivney BA, Rooney MF, Katz LM, Parnell A, MacHugh DE. The contribution of myostatin (MSTN) and additional modifying genetic loci to race distance aptitude in Thoroughbred horses racing in different geographic regions.. Equine Vet J 2019. Epub 2019/01/04.
    doi: 10.1111/evj.13058pubmed: 30604488google scholar: lookup
  2. Gu JJ, Orr N, Park SD, Katz LM, Sulimova G, MacHugh DE. A Genome Scan for Positive Selection in Thoroughbred Horses.. Plos One 2009;4(6).
    pmc: PMC2685479pubmed: 19503617doi: 10.1371/journal.pone.0005767google scholar: lookup
  3. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J. Genome-Wide Analysis Reveals Selection for Important Traits in Domestic Horse Breeds.. Plos Genet 2013;9(1).
    pmc: PMC3547851pubmed: 23349635doi: 10.1371/journal.pgen.1003211google scholar: lookup
  4. Utsunomiya YT, O'Brien AMP, Sonstegard TS, Van Tassell CP, do Carmo AS, Meszaros G. Detecting Loci under Recent Positive Selection in Dairy and Beef Cattle by Combining Different Genome-Wide Scan Methods.. Plos One 2013;8(5).
    pmc: PMC3655949pubmed: 23696874doi: 10.1371/journal.pone.0064280google scholar: lookup
  5. Fariello MI, Servin B, Tosser-Klopp G, Rupp R, Moreno C, San Cristobal M. Selection Signatures in Worldwide Sheep Populations.. Plos One 2014;9(8).
    pmc: PMC4134316pubmed: 25126940doi: 10.1371/journal.pone.0103813google scholar: lookup
  6. Randhawa IAS, Khatkar MS, Thomson PC, Raadsma HW. Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep.. Bmc Genet 2014;15.
    pmc: PMC4101850pubmed: 24636660doi: 10.1186/1471-2156-15-34google scholar: lookup
  7. Metzger J, Karwath M, Tonda R, Beltran S, Agueda L, Gut M. Runs of homozygosity reveal signatures of positive selection for reproduction traits in breed and non-breed horses.. Bmc Genomics 2015;16.
    pmc: PMC4600213pubmed: 26452642doi: 10.1186/s12864-015-1977-3google scholar: lookup
  8. Randhawa IAS, Khatkar MS, Thomson PC, Raadsma HW. Composite Selection Signals for Complex Traits Exemplified Through Bovine Stature Using Multibreed Cohorts of European and African Bos taurus.. G3-Genes Genom Genet 2015;5(7):1391–401.
    pmc: PMC4502373pubmed: 25931611doi: 10.1534/g3.115.017772google scholar: lookup
  9. Frischknecht M, Flury C, Leeb T, Rieder S, Neuditschko M. Selection signatures in Shetland ponies.. Anim Genet 2016;47(3):370–2. Epub 2016/02/10.
    doi: 10.1111/age.12416pubmed: 26857482google scholar: lookup
  10. Taye M, Lee W, Jeon S, Yoon J, Dessie T, Hanotte O. Exploring evidence of positive selection signatures in cattle breeds selected for different traits.. Mamm Genome 2017;28(11–12):528–41.
    doi: 10.1007/s00335-017-9715-6pubmed: 28905131google scholar: lookup
  11. Nielsen R. Molecular signatures of natural selection.. Annu Rev Genet 2005;39:197–218. Epub 2005/11/16.
    doi: 10.1146/annurev.genet.39.073003.112420pubmed: 16285858google scholar: lookup
  12. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E. Genetic Diversity in the Modern Horse Illustrated from Genome-Wide SNP Data.. Plos One 2013;8(1).
    doi: 10.1371/journal.pone.0054997pmc: PMC3559798pubmed: 23383025google scholar: lookup
  13. Hill EW, Eivers SS, McGivney BA, Fonseca RG, Gu J, Smith NA. Moderate and high intensity sprint exercise induce differential responses in COX4I2 and PDK4 gene expression in Thoroughbred horse skeletal muscle.. Equine Vet J Suppl 2010;(38):576–81.
    doi: 10.1111/j.2042-3306.2010.00206.xpubmed: 21059063google scholar: lookup
  14. Andersson LS, Larhammar M, Memic F, Wootz H, Schwochow D, Rubin CJ. Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice.. Nature 2012;488(7413):642–6. Epub 2012/08/31.
    doi: 10.1038/nature11399pmc: PMC3523687pubmed: 22932389google scholar: lookup
  15. Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM. Four loci explain 83% of size variation in the horse.. Plos One 2012;7(7):e39929 Epub 2012/07/19.
    doi: 10.1371/journal.pone.0039929pmc: PMC3394777pubmed: 22808074google scholar: lookup
  16. Gurgul A, Jasielczuk I, Semik-Gurgul E, Pawlina-Tyszko K, Stefaniuk-Szmukier M, Szmatola T. A genome-wide scan for diversifying selection signatures in selected horse breeds.. Plos One 2019;14(1):e0210751 Epub 2019/01/31.
    doi: 10.1371/journal.pone.0210751pmc: PMC6353161pubmed: 30699152google scholar: lookup
  17. Oleksyk TK, Zhao K, De La Vega FM, Gilbert DA, O'Brien SJ, Smith MW. Identifying Selected Regions from Heterozygosity and Divergence Using a Light-Coverage Genomic Dataset from Two Human Populations.. Plos One 2008;3(3).
    pmc: PMC2248624pubmed: 18320033doi: 10.1371/journal.pone.0001712google scholar: lookup
  18. Weir BS, Cockerham CC. Estimating F-Statistics for the Analysis of Population Structure.. Evolution 1984;38(6):1358–70. Epub 1984/11/01.
    doi: 10.1111/j.1558-5646.1984.tb05657.xpubmed: 28563791google scholar: lookup
  19. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C. Genome-wide detection and characterization of positive selection in human populations.. Nature 2007;449(7164):913–8. Epub 2007/10/19.
    doi: 10.1038/nature06250pmc: PMC2687721pubmed: 17943131google scholar: lookup
  20. Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive selection in the human genome.. Plos Biol 2006;4(3):e72 Epub 2006/02/24.
    doi: 10.1371/journal.pbio.0040072pmc: PMC1382018pubmed: 16494531google scholar: lookup
  21. Williamson SH, Hubisz MJ, Clark AG, Payseur BA, Bustamante CD, Nielsen R. Localizing recent adaptive evolution in the human genome.. Plos Genet 2007;3(6):901–15. ARTN e90.
    pmc: PMC1885279pubmed: 17542651doi: 10.1371/journal.pgen.0030090google scholar: lookup
  22. Kim ES, Cole JB, Huson H, Wiggans GR, Van Tassell CP, Crooker BA. Effect of Artificial Selection on Runs of Homozygosity in US Holstein Cattle.. Plos One 2013;8(11).
    doi: 10.1371/journal.pone.0080813pmc: PMC3858116pubmed: 24348915google scholar: lookup
  23. Sabeti PC, Reich DE, Higgins JM, Levine HZ, Richter DJ, Schaffner SF. Detecting recent positive selection in the human genome from haplotype structure.. Nature 2002;419(6909):832–7. Epub 2002/10/25.
    doi: 10.1038/nature01140pubmed: 12397357google scholar: lookup
  24. Tang K, Thornton KR, Stoneking M. A new approach for using genome scans to detect recent positive selection in the human genome.. Plos Biol 2007;5(7):1587–602. ARTN e171.
    doi: 10.1371/journal.pbio.0050171pmc: PMC1892573pubmed: 17579516google scholar: lookup
  25. Grossman SR, Shlyakhter I, Karlsson EK, Byrne EH, Morales S, Frieden G. A composite of multiple signals distinguishes causal variants in regions of positive selection.. Science 2010;327(5967):883–6. Epub 2010/01/09.
    doi: 10.1126/science.1183863pubmed: 20056855google scholar: lookup
  26. Ong RTH, Teo YY. varLD: a program for quantifying variation in linkage disequilibrium patterns between populations.. Bioinformatics 2010;26(9):1269–70.
    doi: 10.1093/bioinformatics/btq125pubmed: 20308177google scholar: lookup
  27. Fu YX, Li WH. Statistical Tests of Neutrality of Mutations.. Genetics 1993;133(3):693–709.
    pmc: PMC1205353pubmed: 8454210
  28. Fay JC, Wu CI. Hitchhiking under positive Darwinian selection.. Genetics 2000;155(3):1405–13.
    pmc: PMC1461156pubmed: 10880498
  29. Tajima F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.. Genetics 1989;123(3):585–95. Epub 1989/11/01.
    pmc: PMC1203831pubmed: 2513255
  30. Qanbari S, Simianer H. Mapping signatures of positive selection in the genome of livestock.. Livest Sci 2014;166:133–43.
    doi: 10.1016/j.livsci.2014.05.003google scholar: lookup
  31. Storz JF. Using genome scans of DNA polymorphism to infer adaptive population divergence.. Mol Ecol 2005;14(3):671–88.
    doi: 10.1111/j.1365-294X.2005.02437.xpubmed: 15723660google scholar: lookup
  32. Liu C, Wu HT, Zhu N, Shi YN, Liu Z, Ao BX. Steroid receptor RNA activator: Biologic function and role in disease.. Clin Chim Acta 2016;459:137–46. Epub 2016/06/11.
    doi: 10.1016/j.cca.2016.06.004pubmed: 27282881google scholar: lookup
  33. Chen WV, Alvarez FJ, Lefebvre JL, Friedman B, Nwakeze C, Geiman E. Functional significance of isoform diversification in the protocadherin gamma gene cluster.. Neuron 2012;75(3):402–9. Epub 2012/08/14.
    doi: 10.1016/j.neuron.2012.06.039pmc: PMC3426296pubmed: 22884324google scholar: lookup
  34. Bryan K, McGivney BA, Farries G, McGettigan PA, McGivney CL, Gough KF. Equine skeletal muscle adaptations to exercise and training: evidence of differential regulation of autophagosomal and mitochondrial components.. BMC Genomics 2017;18(1):595 Epub 2017/08/11.
    doi: 10.1186/s12864-017-4007-9pmc: PMC5551008pubmed: 28793853google scholar: lookup
  35. Hernandez SS, Sandreschi Pf Fau—da Silva FC, da Silva Fc Fau—Arancibia BAV, Arancibia Ba Fau—da Silva R, da Silva R Fau—Gutierres PJB, Gutierres Pj Fau—Andrade A. What are the Benefits of Exercise for Alzheimer's Disease? A Systematic Review of the Past 10 Years.. 2015;(1543-267X (Electronic)).
    pubmed: 25414947
  36. Abramsson A, Kettunen P, Banote RK, Lott E, Li M, Arner A. The zebrafish amyloid precursor protein-b is required for motor neuron guidance and synapse formation.. Dev Biol 2013;381(2):377–88. Epub 2013/07/16.
    doi: 10.1016/j.ydbio.2013.06.026pubmed: 23850871google scholar: lookup
  37. Lin K, Li H, Schlotterer C, Futschik A. Distinguishing positive selection from neutral evolution: boosting the performance of summary statistics.. Genetics 2011;187(1):229–44.
    doi: 10.1534/genetics.110.122614pmc: PMC3018323pubmed: 21041556google scholar: lookup
  38. Randhawa IA, Khatkar MS, Thomson PC, Raadsma HW. Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep.. Bmc Genet 2014;15:34.
    doi: 10.1186/1471-2156-15-34pmc: PMC4101850pubmed: 24636660google scholar: lookup
  39. Randhawa IA, Khatkar MS, Thomson PC, Raadsma HW. Composite Selection Signals for Complex Traits Exemplified Through Bovine Stature Using Multibreed Cohorts of European and African Bos taurus.. G3 (Bethesda) 2015;5(7):1391–401.
    doi: 10.1534/g3.115.017772pmc: PMC4502373pubmed: 25931611google scholar: lookup
  40. Schwarzenbacher H, Dolezal M, Flisikowski K, Seefried F, Wurmser C, Schlotterer C. Combining evidence of selection with association analysis increases power to detect regions influencing complex traits in dairy cattle.. BMC Genomics 2012;13:48.
    doi: 10.1186/1471-2164-13-48pmc: PMC3305582pubmed: 22289501google scholar: lookup
  41. Utsunomiya YT, Perez O'Brien AM, Sonstegard TS, Van Tassell CP, do Carmo AS, Meszaros G. Detecting loci under recent positive selection in dairy and beef cattle by combining different genome-wide scan methods.. PLoS One 2013;8(5):e64280.
    doi: 10.1371/journal.pone.0064280pmc: PMC3655949pubmed: 23696874google scholar: lookup
  42. Huang TY, Zheng D, Hickner RC, Brault JJ, Cortright RN. Peroxisomal gene and protein expression increase in response to a high-lipid challenge in human skeletal muscle.. Metabolism 2019;98:53–61.
    doi: 10.1016/j.metabol.2019.06.009pmc: PMC7031862pubmed: 31226353google scholar: lookup
  43. Kristjansson RP, Oddsson A, Helgason H, Sveinbjornsson G, Arnadottir GA, Jensson BO. Common and rare variants associating with serum levels of creatine kinase and lactate dehydrogenase.. Nat Commun 2016;7:10572.
    doi: 10.1038/ncomms10572pmc: PMC4742860pubmed: 26838040google scholar: lookup
  44. Mami S, Khaje G, Shahriari A, Gooraninejad S. Evaluation of Biological Indicators of Fatigue and Muscle Damage in Arabian Horses After Race.. J Equine Vet Sci 2019;78:74–8.
    doi: 10.1016/j.jevs.2019.04.007pubmed: 31203988google scholar: lookup
  45. Farries G, McGettigan PA, Gough KF, McGivney BA, MacHugh DE, Katz LM. Genetic contributions to precocity traits in racing Thoroughbreds.. Anim Genet 2018;49(3):193–204. Epub 2017/12/13.
    doi: 10.1111/age.12622pubmed: 29230835google scholar: lookup
  46. Hori Y, Tozaki T, Nambo Y, Sato F, Ishimaru M, Inoue-Murayama M. Evidence for the effect of serotonin receptor 1A gene (HTR1A) polymorphism on tractability in Thoroughbred horses.. Anim Genet 2016;47(1):62–7.
    doi: 10.1111/age.12384pubmed: 26763159google scholar: lookup
  47. Moser SC, Bensaddek D, Ortmann B, Maure JF, Mudie S, Blow JJ. PHD1 links cell-cycle progression to oxygen sensing through hydroxylation of the centrosomal protein Cep192.. Dev Cell 2013;26(4):381–92.
    doi: 10.1016/j.devcel.2013.06.014pmc: PMC3757158pubmed: 23932902google scholar: lookup
  48. Rooney MF, Porter RK, Katz LM, Hill EW. Skeletal muscle mitochondrial bioenergetics and associations with myostatin genotypes in the Thoroughbred horse.. Plos One 2017;12(11).
    doi: 10.1371/journal.pone.0186247pmc: PMC5708611pubmed: 29190290google scholar: lookup
  49. Lenaz G, Fato R, Di Bernardo S, Jarreta D, Costa A, Genova ML. Localization and mobility of coenzyme Q in lipid bilayers and membranes.. Biofactors 1999;9(2–4):87–93.
    doi: 10.1002/biof.5520090202pubmed: 10416019google scholar: lookup
  50. Doherty JT, Lenhart KC, Cameron MV, Mack CP, Conlon FL, Taylor JM. Skeletal muscle differentiation and fusion are regulated by the BAR-containing Rho-GTPase-activating protein (Rho-GAP), GRAF1.. J Biol Chem 2011;286(29):25903–21. Epub 2011/05/31.
    doi: 10.1074/jbc.M111.243030pmc: PMC3138304pubmed: 21622574google scholar: lookup
  51. Lenhart KC, Becherer AL, Li J, Xiao X, McNally EM, Mack CP. GRAF1 promotes ferlin-dependent myoblast fusion.. Dev Biol 2014;393(2):298–311.
    doi: 10.1016/j.ydbio.2014.06.025pmc: PMC4535172pubmed: 25019370google scholar: lookup
  52. Lenhart KC, O'Neill TJt, Cheng Z, Dee R, Demonbreun AR, Li J. GRAF1 deficiency blunts sarcolemmal injury repair and exacerbates cardiac and skeletal muscle pathology in dystrophin-deficient mice.. Skelet Muscle 2015;5:27 Epub 2015/08/25.
    doi: 10.1186/s13395-015-0054-6pmc: PMC4546166pubmed: 26301073google scholar: lookup
  53. Fukatsu Y, Noguchi T, Hosooka T, Ogura T, Kotani K, Abe T. Muscle-Specific Overexpression of Heparin-Binding Epidermal Growth Factor-Like Growth Factor Increases Peripheral Glucose Disposal and Insulin Sensitivity.. Endocrinology 2009;150(6):2683–91.
    doi: 10.1210/en.2008-1647pubmed: 19264873google scholar: lookup
  54. Parvaresh KC, Huber AM, Brochin RL, Bacon PL, McCall GE, Huey KA. Acute vascular endothelial growth factor expression during hypertrophy is muscle phenotype specific and localizes as a striated pattern within fibres.. Exp Physiol 2010;95(11):1098–106.
    doi: 10.1113/expphysiol.2010.053959pubmed: 20696782google scholar: lookup
  55. Thornton KJ, Kamange-Sollo E, White ME, Dayton WR. Role of G protein-coupled receptors (GPCR), matrix metalloproteinases 2 and 9 (MMP2 and MMP9), heparin-binding epidermal growth factor-like growth factor (hbEGF), epidermal growth factor receptor (EGFR), erbB2, and insulin-like growth factor 1 receptor (IGF-1R) in trenbolone acetate-stimulated bovine satellite cell proliferation.. Journal of Animal Science 2015;93(9):4291–301.
    doi: 10.2527/jas.2015-9191pubmed: 26440329google scholar: lookup
  56. Uetani T, Nakayama H, Okayama H, Okura T, Higaki J, Inoue H. Insufficiency of Pro-heparin-binding Epidermal Growth Factor-like Growth Factor Shedding Enhances Hypoxic Cell Death in H9c2 Cardiomyoblasts via the Activation of Caspase-3 and c-Jun N-terminal Kinase.. J Biol Chem 2009;284(18):12399–409.
    doi: 10.1074/jbc.M900463200pmc: PMC2673307pubmed: 19193634google scholar: lookup
  57. Lee KS, Park JH, Lim HJ, Park HY. HB-EGF induces cardiomyocyte hypertrophy via an ERK5-MEF2A-COX2 signaling pathway.. Cell Signal 2011;23(7):1100–9.
    doi: 10.1016/j.cellsig.2011.01.006pubmed: 21244855google scholar: lookup
  58. Friedrichs F, Zugck C, Rauch GJ, Ivandic B, Weichenhan D, Muller-Bardorff M. HBEGF, SRA1, and IK: Three cosegregating genes as determinants of cardiomyopathy.. Genome Research 2009;19(3):395–403.
    doi: 10.1101/gr.076653.108pmc: PMC2661798pubmed: 19064678google scholar: lookup
  59. McGivney BA, Hernandez B, Katz LM, MacHugh DE, McGovern SP, Parnell AC. A genomic prediction model for racecourse starts in the Thoroughbred horse.. Anim Genet 2019;50(4):347–57. Epub 2019/07/02.
    doi: 10.1111/age.12798pubmed: 31257665google scholar: lookup
  60. Cabib S, Oliverio A, Ventura R, Lucchese F, Puglisi-Allegra S. Brain dopamine receptor plasticity: testing a diathesis-stress hypothesis in an animal model.. Psychopharmacology 1997;132(2):153–60.
    doi: 10.1007/s002130050331pubmed: 9266612google scholar: lookup
  61. Reul JM, Collins A, Saliba RS, Mifsud KR, Carter SD, Gutierrez-Mecinas M. Glucocorticoids, epigenetic control and stress resilience.. Neurobiol Stress 2015;1:44–59.
    doi: 10.1016/j.ynstr.2014.10.001pmc: PMC4721318pubmed: 27589660google scholar: lookup
  62. Weaver IC, Cervoni N, Champagne FA, D'Alessio AC, Sharma S, Seckl JR. Epigenetic programming by maternal behavior.. Nature neuroscience 2004;7(8):847–54.
    doi: 10.1038/nn1276pubmed: 15220929google scholar: lookup
  63. Bockmuhl Y, Patchev AV, Madejska A, Hoffmann A, Sousa JC, Sousa N. Methylation at the CpG island shore region upregulates Nr3c1 promoter activity after early-life stress.. Epigenetics 2015;10(3):247–57.
    doi: 10.1080/15592294.2015.1017199pmc: PMC4622987pubmed: 25793778google scholar: lookup
  64. de Ramon Francas G, Alther T, Stoeckli ET. Calsyntenins Are Expressed in a Dynamic and Partially Overlapping Manner during Neural Development.. Front Neuroanat 2017;11:76 Epub 2017/09/16.
    doi: 10.3389/fnana.2017.00076pmc: PMC5582071pubmed: 28912692google scholar: lookup
  65. Chen SQ, Niu Q, Ju LP, Alimujiang M, Yan H, Bai NN. Predicted secreted protein analysis reveals synaptogenic function of Clstn3 during WAT browning and BAT activation in mice.. Acta Pharmacol Sin 2019.
    doi: 10.1038/s41401-019-0211-2pmc: PMC6786409pubmed: 30796355google scholar: lookup
  66. Pettem KL, Yokomaku D, Luo L, Linhoff MW, Prasad T, Connor SA. The specific alpha-neurexin interactor calsyntenin-3 promotes excitatory and inhibitory synapse development.. Neuron 2013;80(1):113–28. Epub 2013/10/08.
    doi: 10.1016/j.neuron.2013.07.016pmc: PMC3821696pubmed: 24094106google scholar: lookup
  67. Avila F, Mickelson JR, Schaefer RJ, McCue ME. Genome-Wide Signatures of Selection Reveal Genes Associated With Performance in American Quarter Horse Subpopulations.. Front Genet 2018;9:249 Epub 2018/08/15.
    doi: 10.3389/fgene.2018.00249pmc: PMC6060370pubmed: 30105047google scholar: lookup
  68. David G, Abbas N, Stevanin G, Durr A, Yvert G, Cancel G. Cloning of the SCA7 gene reveals a highly unstable CAG repeat expansion.. Nat Genet 1997;17(1):65–70.
    doi: 10.1038/ng0997-65pubmed: 9288099google scholar: lookup
  69. dela Pena IJI, dela Pena I, de la Pena JB, Kim HJ, Sohn A, Shin CY. Transcriptional profiling of SHR/NCrl prefrontal cortex shows hyperactivity-associated genes responsive to amphetamine challenge.. Genes Brain Behav 2017;16(7):664–74.
    doi: 10.1111/gbb.12388pubmed: 28422445google scholar: lookup
  70. dela Pena IJI, Botanas CJ, de la Pena JB, Custodio RJ, dela Pena I, Ryoo ZY. The Atxn7-overexpressing mice showed hyperactivity and impulsivity which were ameliorated by atomoxetine treatment: A possible animal model of the hyperactive-impulsive phenotype of ADHD.. Prog Neuro-Psychoph 2019;88:311–9.
    doi: 10.1016/j.pnpbp.2018.08.012pubmed: 30125623google scholar: lookup
  71. Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG.. Nucleic Acids Res 2019;47(D1):D590–D5. Epub 2018/10/16.
    doi: 10.1093/nar/gky962pmc: PMC6324070pubmed: 30321428google scholar: lookup
  72. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.. Nat Genet 2000;25(1):25–9. Epub 2000/05/10.
    doi: 10.1038/75556pmc: PMC3037419pubmed: 10802651google scholar: lookup
  73. The Gene Ontology C. The Gene Ontology Resource: 20 years and still GOing strong.. Nucleic Acids Res 2019;47(D1):D330–D8. Epub 2018/11/06.
    doi: 10.1093/nar/gky1055pmc: PMC6323945pubmed: 30395331google scholar: lookup
  74. Zhou Z, Yon Toh S, Chen Z, Guo K, Peng Ng C, Ponniah S. Cidea-deficient mice have lean phenotype and are resistant to obesity.. Nat Genet 2003;35(1):49–56.
    doi: 10.1038/ng1225pubmed: 12910269google scholar: lookup
  75. Gummesson A, Jernas M, Svensson PA, Larsson I, Glad CA, Schele E. Relations of adipose tissue CIDEA gene expression to basal metabolic rate, energy restriction, and obesity: population-based and dietary intervention studies.. J Clin Endocrinol Metab 2007;92(12):4759–65. Epub 2007/09/27.
    doi: 10.1210/jc.2007-1136pubmed: 17895319google scholar: lookup
  76. Abreu-Vieira G, Fischer AW, Mattsson C, de Jong JMA, Shabalina IG, Rydén M. Cidea improves the metabolic profile through expansion of adipose tissue.. Nat Commun 2015;6:7433.
    doi: 10.1038/ncomms8433pubmed: 26118629google scholar: lookup
  77. Canessa CM, Schild L, Buell G, Thorens B, Gautschi I, Horisberger J-D. Amiloride-sensitive epithelial Na+ channel is made of three homologous subunits.. Nature 1994;367(6462):463–7.
    doi: 10.1038/367463a0pubmed: 8107805google scholar: lookup
  78. Chang SS, Grunder S, Hanukoglu A, Rösler A, Mathew PM, Hanukoglu I. Mutations in subunits of the epithelial sodium channel cause salt wasting with hyperkalaemic acidosis, pseudohypoaldosteronism type 1.. Nat Genet 1996;12(3):248–53.
    doi: 10.1038/ng0396-248pubmed: 8589714google scholar: lookup
  79. Hobbs CA, Blanchard MG, Alijevic O, Tan CD, Kellenberger S, Bencharit S. Identification of the SPLUNC1 ENaC-inhibitory domain yields novel strategies to treat sodium hyperabsorption in cystic fibrosis airway epithelial cultures.. Am J Physiol Lung Cell Mol Physiol 2013;305(12):L990–L1001. Epub 2013/10/15.
    doi: 10.1152/ajplung.00103.2013pmc: PMC3882538pubmed: 24124190google scholar: lookup
  80. Chen WV, Maniatis T. Clustered protocadherins.. Development 2013;140(16):3297–302. Epub 2013/08/01.
    doi: 10.1242/dev.090621pmc: PMC3737714pubmed: 23900538google scholar: lookup
  81. Lu WC, Zhou YX, Qiao P, Zheng J, Wu Q, Shen Q. The protocadherin alpha cluster is required for axon extension and myelination in the developing central nervous system.. Neural Regen Res 2018;13(3):427–33.
    doi: 10.4103/1673-5374.228724pmc: PMC5900504pubmed: 29623926google scholar: lookup
  82. Hasegawa S, Kumagai M, Hagihara M, Nishimaru H, Hirano K, Kaneko R. Distinct and Cooperative Functions for the Protocadherin-alpha, -beta and -gamma Clusters in Neuronal Survival and Axon Targeting.. Front Mol Neurosci 2016;9:155 Epub 2017/01/10.
    doi: 10.3389/fnmol.2016.00155pmc: PMC5179546pubmed: 28066179google scholar: lookup
  83. Hangelbroek RWJ, Fazelzadeh P, Tieland M, Boekschoten MV, Hooiveld GJEJ, van Duynhoven JPM. Expression of protocadherin gamma in skeletal muscle tissue is associated with age and muscle weakness.. J Cachexia Sarcopeni 2016;7(5):604–14.
    doi: 10.1002/jcsm.12099pmc: PMC4863830pubmed: 27239416google scholar: lookup
  84. Li Y, Xiao H, Chiou TT, Jin H, Bonhomme B, Miralles CP. Molecular and Functional Interaction between Protocadherin- C5 and GABAA Receptors.. J Neurosci 2012;32(34):11780–97.
    doi: 10.1523/JNEUROSCI.0969-12.2012pmc: PMC3445339pubmed: 22915120google scholar: lookup
  85. Xie GQ, Myint PK, Voora D, Laskowitz DT, Shi P, Ren FX. Genome-wide association study on progression of carotid artery intima media thickness over 10 years in a Chinese cohort.. Atherosclerosis 2015;243(1):30–7.
    doi: 10.1016/j.atherosclerosis.2015.08.034pubmed: 26343869google scholar: lookup
  86. Hemnes AR, Zhao M, West J, Newman JH, Rich S, Archer SL. Critical Genomic Networks and Vasoreactive Variants in Idiopathic Pulmonary Arterial Hypertension.. Am J Respir Crit Care Med 2016;194(4):464–75. Epub 2016/03/02.
    doi: 10.1164/rccm.201508-1678OCpmc: PMC5003329pubmed: 26926454google scholar: lookup
  87. McGowan PO, Suderman M, Sasaki A, Huang TC, Hallett M, Meaney MJ. Broad epigenetic signature of maternal care in the brain of adult rats.. Plos One 2011;6(2):e14739 Epub 2011/03/10.
    doi: 10.1371/journal.pone.0014739pmc: PMC3046141pubmed: 21386994google scholar: lookup
  88. Browning BL, Browning SR. Improving the Accuracy and Efficiency of Identity-by-Descent Detection in Population Data.. Genetics 2013;194(2):459–+.
    doi: 10.1534/genetics.113.150029pmc: PMC3664855pubmed: 23535385google scholar: lookup
  89. Farries G, McGettigan PA, Gough KF, McGivney BA, MacHugh DE, Katz LM. Genetic contributions to precocity traits in racing Thoroughbreds.. Anim Genet 2018;49(3):193–204.
    doi: 10.1111/age.12622pubmed: 29230835google scholar: lookup
  90. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies.. Nat Genet 2006;38(8):904–9.
    doi: 10.1038/ng1847pubmed: 16862161google scholar: lookup
  91. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets.. Gigascience 2015;4:7 Epub 2015/02/28.
    doi: 10.1186/s13742-015-0047-8pmc: PMC4342193pubmed: 25722852google scholar: lookup
  92. Hill EW, Gu J, Eivers SS, Fonseca RG, McGivney BA, Govindarajan P. A sequence polymorphism in MSTN predicts sprinting ability and racing stamina in thoroughbred horses.. Plos One 2010;5(1):e8645 Epub 2010/01/26.
    doi: 10.1371/journal.pone.0008645pmc: PMC2808334pubmed: 20098749google scholar: lookup
  93. Browett S, McHugo G, Richardson IW, Magee DA, Park SDE, Fahey AG. Genomic Characterisation of the Indigenous Irish Kerry Cattle Breed.. Front Genet 2018;9:51 Epub 2018/03/10.
    doi: 10.3389/fgene.2018.00051pmc: PMC5827531pubmed: 29520297google scholar: lookup
  94. McCue ME, Bannasch DL, Petersen JL, Gurr J, Bailey E, Binns MM. A high density SNP array for the domestic horse and extant Perissodactyla: utility for association mapping, genetic diversity, and phylogeny studies.. Plos Genet 2012;8(1):e1002451 Epub 2012/01/19.
    doi: 10.1371/journal.pgen.1002451pmc: PMC3257288pubmed: 22253606google scholar: lookup
  95. Fonseca RG, Kenny DA, Hill EW, Katz LM. The association of various speed indices to training responses in Thoroughbred flat racehorses measured with a global positioning and heart rate monitoring system.. Equine Vet J 2010;42(s38):51–7.
    doi: 10.1111/j.2042-3306.2010.00272.xpubmed: 21058982google scholar: lookup
  96. R Core Team. R: A language and environment for statistical computing.. Vienna, Austria: R Foundation for Statistical Computing; 2017.
  97. Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots.. bioRxiv 2014.
  98. Chen H, Wang C, Conomos MP, Stilp AM, Li Z, Sofer T. Control for Population Structure and Relatedness for Binary Traits in Genetic Association Studies via Logistic Mixed Models.. Am J Hum Genet 2016;98(4):653–66.
    doi: 10.1016/j.ajhg.2016.02.012pmc: PMC4833218pubmed: 27018471google scholar: lookup
  99. Li MX, Yeung JM, Cherny SS, Sham PC. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets.. Hum Genet 2012;131(5):747–56. Epub 2011/12/07.
    doi: 10.1007/s00439-011-1118-2pmc: PMC3325408pubmed: 22143225google scholar: lookup
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.