FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / PLoS Genet / An endothelial regulatory module links blood pressure regula...
PLoS genetics2024; 20(6); e1011285; doi: 10.1371/journal.pgen.1011285

An endothelial regulatory module links blood pressure regulation with elite athletic performance.

Abstract: The control of transcription is crucial for homeostasis in mammals. A previous selective sweep analysis of horse racing performance revealed a 19.6 kb candidate regulatory region 50 kb downstream of the Endothelin3 (EDN3) gene. Here, the region was narrowed to a 5.5 kb span of 14 SNVs, with elite and sub-elite haplotypes analyzed for association to racing performance, blood pressure and plasma levels of EDN3 in Coldblooded trotters and Standardbreds. Comparative analysis of human HiCap data identified the span as an enhancer cluster active in endothelial cells, interacting with genes relevant to blood pressure regulation. Coldblooded trotters with the sub-elite haplotype had significantly higher blood pressure compared to horses with the elite performing haplotype during exercise. Alleles within the elite haplotype were part of the standing variation in pre-domestication horses, and have risen in frequency during the era of breed development and selection. These results advance our understanding of the molecular genetics of athletic performance and vascular traits in both horses and humans.
Copyright: © 2024 Fegraeus et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Get Full Text
Publication Date: 2024-06-17 PubMed ID: 38885195PubMed Central: PMC11182536DOI: 10.1371/journal.pgen.1011285Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research investigates the genetic links between blood pressure regulation and athletic performance in horses. By studying variations in the Endothelin3 (EDN3) gene, the researchers were able to correlate different gene variations with differences in blood pressure and racing performance.

Research Background and Methodology

  • The researchers built upon existing genetic studies of horse racing performance. In a prior analysis, a 19.6 kilobase (kb) regulatory region, located 50 kb downstream of the EDN3 gene, was highlighted as a factor potentially influencing performance in horses.
  • The current study further narrowed down this region to a 5.5 kb span composed of 14 single nucleotide variants (SNVs). These SNVs consist of elite and sub-elite haplotypes – groups of genes that have evolved together and are inherited as a single unit.
  • The researchers checked these haplotypes for their association to racing performance, blood pressure, and plasma levels of EDN3 in two types of horses: Coldblooded trotters and Standardbreds.

Significant Findings

  • The analysis confirmed that the 5.5 kb span acts as an enhancer cluster in endothelial cells. These are cells that line the interior surface of blood vessels and lymphatic vessels, playing a critical role in vascular biology.
  • The enhancer cluster was found to interact with genes that are important for regulating blood pressure.
  • Horses carrying the sub-elite haplotype had significantly higher blood pressure during exercise compared to those with the elite haplotype, which suggests that this gene variation could impact both blood pressure regulation and athletic performance.
  • The alleles – different forms of a gene – present in the elite haplotype were also observed in pre-domestication horses. The frequency of these alleles increased during the period of breed development and selection, indicating that they may have been favoured in the breeding of racing horses.

Implications of the Study

  • This study enhances our understanding of the genetic factors influencing athletic performance and vascular traits in both horses and, potentially, humans.
  • Deciphering the connections between blood pressure regulation and athletic performance at the genetic level can help breeders select for traits that improve racing performance in horses.
  • The findings may also have implications for human health and performance, as they offer insights into the genetics of blood pressure regulation.

Cite This Article

APA
Fegraeus K, Rosengren MK, Naboulsi R, Orlando L, Åbrink M, Jouni A, Velie BD, Raine A, Egner B, Mattsson CM, Lång K, Zhigulev A, Björck HM, Franco-Cereceda A, Eriksson P, Andersson G, Sahlén P, Meadows JRS, Lindgren G. (2024). An endothelial regulatory module links blood pressure regulation with elite athletic performance. PLoS Genet, 20(6), e1011285. https://doi.org/10.1371/journal.pgen.1011285

Publication

PLoS genetics
ISSN: 1553-7404
NlmUniqueID: 101239074
Country: United States
Language: English
Volume: 20
Issue: 6
Pages: e1011285
PII: e1011285

Researcher Affiliations

Fegraeus, Kim
  • Department of Medical Sciences, Science for life laboratory, Uppsala University, Sweden.
Rosengren, Maria K
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden.
Naboulsi, Rakan
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden.
  • Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institute, Stockholm.
Orlando, Ludovic
  • Centre d'Anthropobiologie et de Génomique de Toulouse (CNRS UMR 5288), Université Paul Sabatier, Toulouse, France.
Åbrink, Magnus
  • Department of Biomedical Sciences and Veterinary Public Health, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Jouni, Ahmad
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden.
Velie, Brandon D
  • School of Life & Environmental Sciences, University of Sydney, Sydney, Australia.
Raine, Amanda
  • Department of Medical Sciences, Science for life laboratory, Uppsala University, Sweden.
Egner, Beate
  • Department of Cardio-Vascular Research, Veterinary Academy of Higher Learning, Babenhausen, Germany.
Mattsson, C Mikael
  • Silicon Valley Exercise Analytics (svexa), MenloPark, CA, United States of America.
Lång, Karin
  • Division of Cardiovascular Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden.
Zhigulev, Artemy
  • KTH Royal Institute of Technology, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden.
Björck, Hanna M
  • Division of Cardiovascular Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden.
Franco-Cereceda, Anders
  • Section of Cardiothoracic Surgery, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
Eriksson, Per
  • Division of Cardiovascular Medicine, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden.
Andersson, Göran
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden.
Sahlén, Pelin
  • KTH Royal Institute of Technology, School of Chemistry, Biotechnology and Health, Science for Life Laboratory, Stockholm, Sweden.
Meadows, Jennifer R S
  • Department of Medical Biochemistry and Microbiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Lindgren, Gabriella
  • Department of Animal Biosciences, Swedish University of Agricultural Sciences Uppsala, Sweden.
  • Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Leuven, Belgium.

MeSH Terms

  • Horses / genetics
  • Animals
  • Humans
  • Blood Pressure / genetics
  • Athletic Performance / physiology
  • Haplotypes / genetics
  • Endothelin-3 / genetics
  • Polymorphism, Single Nucleotide
  • Alleles
  • Male
  • Endothelial Cells / metabolism

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 117 references
  1. Andersson L. Domestic animals as models for biomedical research. Ups J Med Sci 2016;121(1):1–11.
    doi: 10.3109/03009734.2015.1091522pmc: PMC4812051pubmed: 26479863google scholar: lookup
  2. Christmas MJ, Kaplow IM, Genereux DP, Dong MX, Hughes GM, Li X. Evolutionary constraint and innovation across hundreds of placental mammals. Science 2023 Apr 28;380(6643):eabn3943.
    doi: 10.1126/science.abn3943pmc: PMC10250106pubmed: 37104599google scholar: lookup
  3. Sullivan PF, Meadows JRS, Gazal S, Phan BN, Li X, Genereux DP. Leveraging base-pair mammalian constraint to understand genetic variation and human disease. Science 2023 Apr 28;380(6643):eabn2937.
    doi: 10.1126/science.abn2937pmc: PMC10259825pubmed: 37104612google scholar: lookup
  4. Fages A, Hanghøj K, Khan N, Gaunitz C, Seguin-Orlando A, Leonardi M. Tracking Five Millennia of Horse Management with Extensive Ancient Genome Time Series. Cell 2019 May;177(6):1419–35.
    doi: 10.1016/j.cell.2019.03.049pmc: PMC6547883pubmed: 31056281google scholar: lookup
  5. McGivney BA, Han H, Corduff LR, Katz LM, Tozaki T, MacHugh DE. Genomic inbreeding trends, influential sire lines and selection in the global Thoroughbred horse population. Sci Rep 2020 Jan 16;10(1):466.
    doi: 10.1038/s41598-019-57389-5pmc: PMC6965197pubmed: 31949252google scholar: lookup
  6. 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):1–17.
    doi: 10.1371/journal.pgen.1003211pmc: PMC3547851pubmed: 23349635google scholar: lookup
  7. Meadows JRS, Lindblad-Toh K. Dissecting evolution and disease using comparative vertebrate genomics. Nat Rev Genet 2017 Oct;18(10):624–36.
    doi: 10.1038/nrg.2017.51pubmed: 28736437google scholar: lookup
  8. 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 Jan 20;5(1):1–6.
    doi: 10.1371/journal.pone.0008645pmc: PMC2808334pubmed: 20098749google scholar: lookup
  9. Tozaki T, Sato F, Hill EW, Miyake T, Endo Y, Kakoi H. Sequence variants at the myostatin gene locus influence the body composition of Thoroughbred horses. J Vet Med Sci 2011 Dec;73(12):1617–24.
    doi: 10.1292/jvms.11-0295pubmed: 21836385google scholar: lookup
  10. McGivney BA, Browne JA, Fonseca RG, Katz LM, Machugh DE, Whiston R. MSTN genotypes in Thoroughbred horses influence skeletal muscle gene expression and racetrack performance. Anim Genet 2012 Dec;43(6):810–2.
    doi: 10.1111/j.1365-2052.2012.02329.xpubmed: 22497477google scholar: lookup
  11. François L, Jäderkvist Fegraeus K, Eriksson S, Andersson LS, Tesfayonas YG, Viluma A. Conformation Traits and Gaits in the Icelandic Horse are Associated with Genetic Variants in Myostatin (MSTN). J Hered 2016 Sep;107(5):431–7.
    doi: 10.1093/jhered/esw031pubmed: 27208149google scholar: lookup
  12. 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 Aug;488(7413):642–6.
    doi: 10.1038/nature11399pmc: PMC3523687pubmed: 22932389google scholar: lookup
  13. Jäderkvist Fegraeus K, Johansson L, Mäenpää M, Mykkänen A, Andersson LS, Velie BD. Different DMRT3 Genotypes Are Best Adapted for Harness Racing and Riding in Finnhorses. J Hered 2015 Dec;106(6):734–40.
    doi: 10.1093/jhered/esv062pubmed: 26285915google scholar: lookup
  14. Jäderkvist K, Andersson LS, Johansson AM, Árnason T, Mikko S, Eriksson S. The DMRT3 “Gait keeper” mutation affects performance of Nordic and Standardbred trotters. J Anim Sci 2014 Oct;92(10):4279–86.
    doi: 10.2527/jas.2014-7803pubmed: 25085403google scholar: lookup
  15. Jäderkvist Fegraeus K, Velie BD, Axelsson J, Ang R, Hamilton NA, Andersson L. A potential regulatory region near the EDN3 gene may control both harness racing performance and coat color variation in horses. Physiol Rep 2018 May;6(10):1–12.
    doi: 10.14814/phy2.13700pmc: PMC5974718pubmed: 29845762google scholar: lookup
  16. Velie BD, Lillie M, Fegraeus KJ, Rosengren MK, Solé M, Wiklund M. Exploring the genetics of trotting racing ability in horses using a unique Nordic horse model. BMC Genomics 2019 Dec;20(1):104.
    doi: 10.1186/s12864-019-5484-9pmc: PMC6360714pubmed: 30717660google scholar: lookup
  17. Panigrahi A, O’Malley BW. Mechanisms of enhancer action: the known and the unknown. Genome Biol 2021 Dec;22(1):108.
    pmc: PMC8051032pubmed: 33858480
  18. The ENCODE Project Consortium, Abascal F, Acosta R, Addleman NJ, Adrian J, Afzal V. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature 2020 Jul 30;583(7818):699–710.
    doi: 10.1038/s41586-020-2493-4pmc: PMC7410828pubmed: 32728249google scholar: lookup
  19. Sahlén P, Abdullayev I, Ramsköld D, Matskova L, Rilakovic N, Lötstedt B. Genome-wide mapping of promoter-anchored interactions with close to single-enhancer resolution. Genome Biol 2015 Dec;16(1):156.
    doi: 10.1186/s13059-015-0727-9pmc: PMC4557751pubmed: 26313521google scholar: lookup
  20. Åkerborg Ö, Spalinskas R, Pradhananga S, Anil A, Höjer P, Poujade FA. High-Resolution Regulatory Maps Connect Vascular Risk Variants to Disease-Related Pathways. Circ Genomic Precis Med 2019 Mar;12(3).
    doi: 10.1161/CIRCGEN.118.002353pmc: PMC8104016pubmed: 30786239google scholar: lookup
  21. The International Consortium for Blood Pressure Genome-Wide Association Studies. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011 Oct;478(7367):103–9.
    doi: 10.1038/nature10405pmc: PMC3340926pubmed: 21909115google scholar: lookup
  22. Turner ST, Boerwinkle E, O’Connell JR, Bailey KR, Gong Y, Chapman AB. Genomic association analysis of common variants influencing antihypertensive response to hydrochlorothiazide. Hypertens Dallas Tex 1979 2013 Aug;62(2):391–7.
    doi: 10.1161/HYPERTENSIONAHA.111.00436pmc: PMC3780966pubmed: 23753411google scholar: lookup
  23. R. Foundation for Statistical Computing, Vienna, Austria. R: The R Project for Statistical Computing [Internet]. [cited 2023 May 15]. Available from: https://www.r-project.org/
  24. Giuffra E, Tuggle CK, FAANG Consortium. Functional Annotation of Animal Genomes (FAANG): Current Achievements and Roadmap. Annu Rev Anim Biosci 2019 Feb 15;7(1):65–88.
    doi: 10.1146/annurev-animal-020518-114913pubmed: 30427726google scholar: lookup
  25. Lesurf R, Cotto KC, Wang G, Griffith M, Kasaian K, Jones SJM. ORegAnno 3.0: a community-driven resource for curated regulatory annotation. Nucleic Acids Res 2016 Jan 4;44(D1):D126–132.
    doi: 10.1093/nar/gkv1203pmc: PMC4702855pubmed: 26578589google scholar: lookup
  26. The GTEx Consortium, Aguet F, Anand S, Ardlie KG, Gabriel S, Getz GA. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 2020 Sep 11;369(6509):1318–30.
    doi: 10.1126/science.aaz1776pmc: PMC7737656pubmed: 32913098google scholar: lookup
  27. Itoh A, Uchiyama A, Taniguchi S, Sagara J. Phactr3/Scapinin, a Member of Protein Phosphatase 1 and Actin Regulator (Phactr) Family, Interacts with the Plasma Membrane via Basic and Hydrophobic Residues in the N-Terminus. PLoS ONE 2014 Nov 18;9(11):e113289.
    doi: 10.1371/journal.pone.0113289pmc: PMC4236165pubmed: 25405772google scholar: lookup
  28. Wang Y, Chen S, Jiang Q, Deng J, Cheng F, Lin Y. TFAP2C facilitates somatic cell reprogramming by inhibiting c-Myc-dependent apoptosis and promoting mesenchymal-to-epithelial transition. Cell Death Dis 2020 Jun 25;11(6):482.
    doi: 10.1038/s41419-020-2684-9pmc: PMC7316975pubmed: 32587258google scholar: lookup
  29. German CA, Sinsheimer JS, Klimentidis YC, Zhou H, Zhou JJ. Ordered multinomial regression for genetic association analysis of ordinal phenotypes at Biobank scale. Genet Epidemiol 2020 Apr;44(3):248–60.
    doi: 10.1002/gepi.22276pmc: PMC8256450pubmed: 31879980google scholar: lookup
  30. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A. Genome-wide association study of blood pressure and hypertension. Nat Genet 2009 Jun;41(6):677–87.
    doi: 10.1038/ng.384pmc: PMC2998712pubmed: 19430479google scholar: lookup
  31. CHARGE-Heart Failure Consortium EchoGen Consortium, Consortium METASTROKE, Consortium GIANT, Consortium EPIC-InterAct, Lifelines Cohort Study. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet 2016 Oct;48(10):1151–61.
    doi: 10.1038/ng.3654pmc: PMC5056636pubmed: 27618447google scholar: lookup
  32. CHD Exome+ Consortium, ExomeBP Consortium, GoT2DGenes Consortium, T2D-GENES Consortium, Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia, CKDGen Consortium. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat Genet 2016 Oct;48(10):1162–70.
    doi: 10.1038/ng.3660pmc: PMC5320952pubmed: 27618448google scholar: lookup
  33. Kulakovskiy IV, Medvedeva YA, Schaefer U, Kasianov AS, Vorontsov IE, Bajic VB. HOCOMOCO: a comprehensive collection of human transcription factor binding sites models. Nucleic Acids Res 2013 Jan 1;41(D1):D195–202.
    doi: 10.1093/nar/gks1089pmc: PMC3531053pubmed: 23175603google scholar: lookup
  34. Coetzee SG, Coetzee GA, Hazelett DJ. motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites. Bioinformatics 2015 Dec 1;31(23):3847–9.
    doi: 10.1093/bioinformatics/btv470pmc: PMC4653394pubmed: 26272984google scholar: lookup
  35. Liu X, Orlando L. mapDATAge: a ShinyR package to chart ancient DNA data through space and time. Bioinformatics 2022 Aug 10;38(16):3992–4.
    pmc: PMC9364369pubmed: 35771611
  36. Schubert M, Jónsson H, Chang D, Der Sarkissian C, Ermini L, Ginolhac A. Prehistoric genomes reveal the genetic foundation and cost of horse domestication. Proc Natl Acad Sci 2014 Dec 30;111(52):5661–9.
    doi: 10.1073/pnas.1416991111pmc: PMC4284583pubmed: 25512547google scholar: lookup
  37. Librado P, Der Sarkissian C, Ermini L, Schubert M, Jónsson H, Albrechtsen A. Tracking the origins of Yakutian horses and the genetic basis for their fast adaptation to subarctic environments. Proc Natl Acad Sci 2015 Dec 15;112(50):6889–97.
    doi: 10.1073/pnas.1513696112pmc: PMC4687531pubmed: 26598656google scholar: lookup
  38. Librado P, Gamba C, Gaunitz C, Der Sarkissian C, Pruvost M, Albrechtsen A. Ancient genomic changes associated with domestication of the horse. Science 2017 Apr 28;356(6336):442–5.
    doi: 10.1126/science.aam5298pubmed: 28450643google scholar: lookup
  39. Gaunitz C, Fages A, Hanghøj K, Albrechtsen A, Khan N, Schubert M. Ancient genomes revisit the ancestry of domestic and Przewalski’s horses. Science 2018 Apr 6;360(6384):111–4.
    doi: 10.1126/science.aao3297pubmed: 29472442google scholar: lookup
  40. Librado P, Khan N, Fages A, Kusliy MA, Suchan T, Tonasso-Calvière L. The origins and spread of domestic horses from the Western Eurasian steppes. Nature 2021 Oct;598(7882):634–40.
    doi: 10.1038/s41586-021-04018-9pmc: PMC8550961pubmed: 34671162google scholar: lookup
  41. Stevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ. Blood pressure variability and cardiovascular disease: systematic review and meta-analysis. BMJ 2016 Aug 9;i4098.
    doi: 10.1136/bmj.i4098pmc: PMC4979357pubmed: 27511067google scholar: lookup
  42. Petrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol 2018 May;34(5):575–84.
    doi: 10.1016/j.cjca.2017.12.005pmc: PMC5953551pubmed: 29459239google scholar: lookup
  43. Rossi GP, Sacchetto A, Cesari M, Pessina AC. Interactions between endothelin-1 and the renin-angiotensin-aldosterone system. Cardiovasc Res 1999 Aug 1;43(2):300–7.
    doi: 10.1016/s0008-6363(99)00110-8pubmed: 10536660google scholar: lookup
  44. Emori T, Hirata Y, Ohta K, Kanno K, Eguchi S, Imai T. Cellular mechanism of endothelin-1 release by angiotensin and vasopressin. Hypertension 1991 Aug;18(2):165–70.
    doi: 10.1161/01.hyp.18.2.165pubmed: 1909304google scholar: lookup
  45. Imai T, Hirata Y, Emori T, Yanagisawa M, Masaki T, Marumo F. Induction of endothelin-1 gene by angiotensin and vasopressin in endothelial cells. Hypertension 1992 Jun;19(6_pt_2):753–7.
    doi: 10.1161/01.hyp.19.6.753pubmed: 1592477google scholar: lookup
  46. Rossi NF. Effect of endothelin-3 on vasopressin release in vitro and water excretion in vivo in Long-Evans rats. J Physiol 1993 Feb;461:501–11.
    doi: 10.1113/jphysiol.1993.sp019525pmc: PMC1175269pubmed: 8350273google scholar: lookup
  47. Rossi NF. Cation channel mechanisms in ET-3-induced vasopressin secretion by rat hypothalamo-neurohypophysial explants. Am J Physiol-Endocrinol Metab 1995 Mar;268(3):E467–75.
    doi: 10.1152/ajpendo.1995.268.3.E467pubmed: 7534990google scholar: lookup
  48. Baeyens N, Bandyopadhyay C, Coon BG, Yun S, Schwartz MA. Endothelial fluid shear stress sensing in vascular health and disease. J Clin Invest 2016 Mar 1;126(3):821–8.
    doi: 10.1172/JCI83083pmc: PMC4767335pubmed: 26928035google scholar: lookup
  49. Wilson C, Zhang X, Buckley C, Heathcote HR, Lee MD, McCarron JG. Increased Vascular Contractility in Hypertension Results From Impaired Endothelial Calcium Signaling. Hypertension 2019 Nov;74(5):1200–14.
    doi: 10.1161/HYPERTENSIONAHA.119.13791pmc: PMC6791503pubmed: 31542964google scholar: lookup
  50. Plagge A, Kelsey G. Imprinting the Gnas locus. Cytogenet Genome Res 2006;113(1–4):178–87.
    doi: 10.1159/000090830pubmed: 16575178google scholar: lookup
  51. Holmes R, Williamson C, Peters J, Denny P, RIKEN GER Group, GSL Members. A Comprehensive Transcript Map of the Mouse Gnas Imprinted Complex. Genome Res 2003 Jun;13(6b):1410–5.
    doi: 10.1101/gr.955503pmc: PMC403675pubmed: 12819140google scholar: lookup
  52. Turan S, Bastepe M. The GNAS Complex Locus and Human Diseases Associated with Loss-of-Function Mutations or Epimutations within This Imprinted Gene. Horm Res Paediatr 2013;80(4):229–41.
    doi: 10.1159/000355384pmc: PMC3874326pubmed: 24107509google scholar: lookup
  53. Weinstein LS, Yu S, Warner DR, Liu J. Endocrine Manifestations of Stimulatory G Protein α-Subunit Mutations and the Role of Genomic Imprinting. Endocr Rev 2001 Oct 1;22(5):675–705.
    pubmed: 11588148
  54. Lewis DL, Weight FF, Luini A. A guanine nucleotide-binding protein mediates the inhibition of voltage-dependent calcium current by somatostatin in a pituitary cell line. Proc Natl Acad Sci 1986 Dec;83(23):9035–9.
    doi: 10.1073/pnas.83.23.9035pmc: PMC387069pubmed: 2431411google scholar: lookup
  55. Codina J, Yatani A, Grenet D, Brown AM, Birnbaumer L. The α Subunit of the GTP Binding Protein Gk Opens Atrial Potassium Channels. Science 1987 Apr 24;236(4800):442–5.
    pubmed: 2436299
  56. Ehret GB, Caulfield MJ. Genes for blood pressure: an opportunity to understand hypertension. Eur Heart J 2013 Apr 1;34(13):951–61.
    doi: 10.1093/eurheartj/ehs455pmc: PMC3612776pubmed: 23303660google scholar: lookup
  57. Keeney S. Spo11 and the Formation of DNA Double-Strand Breaks in Meiosis. Recombination and Meiosis 2008;81–123.
    doi: 10.1007/7050_2007_026pmc: PMC3172816pubmed: 21927624google scholar: lookup
  58. Pober JS, Sessa WC. Evolving functions of endothelial cells in inflammation. Nat Rev Immunol 2007 Oct;7(10):803–15.
    doi: 10.1038/nri2171pubmed: 17893694google scholar: lookup
  59. Dinh QN, Drummond GR, Sobey CG, Chrissobolis S. Roles of inflammation, oxidative stress, and vascular dysfunction in hypertension. BioMed Res Int 2014;2014:1–11.
    doi: 10.1155/2014/406960pmc: PMC4124649pubmed: 25136585google scholar: lookup
  60. Poole DC, Erickson HH. Exercise Physiology of Terrestrial Animals. Duke´s Physiology of Domestic Animals 2015;443–63.
  61. Moris D, Spartalis M, Spartalis E, Karachaliou GS, Karaolanis GI, Tsourouflis G. The role of reactive oxygen species in the pathophysiology of cardiovascular diseases and the clinical significance of myocardial redox. Ann Transl Med 2017 Aug;5(16):326–326.
    doi: 10.21037/atm.2017.06.27pmc: PMC5566734pubmed: 28861423google scholar: lookup
  62. Dubois-Deruy E, Peugnet V, Turkieh A, Pinet F. Oxidative Stress in Cardiovascular Diseases. Antioxidants 2020 Sep 14;9(9):864.
    doi: 10.3390/antiox9090864pmc: PMC7554855pubmed: 32937950google scholar: lookup
  63. Sharma S, Aldred MA. DNA Damage and Repair in Pulmonary Arterial Hypertension. Genes 2020 Oct 19;11(10):1224.
    doi: 10.3390/genes11101224pmc: PMC7603366pubmed: 33086628google scholar: lookup
  64. Yengo L, Vedantam S, Marouli E, Sidorenko J, Bartell E, Sakaue S. A saturated map of common genetic variants associated with human height. Nature 2022 Oct 27;610(7933):704–12.
    doi: 10.1038/s41586-022-05275-ypmc: PMC9605867pubmed: 36224396google scholar: lookup
  65. Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D, Mann AL. The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell 2016 Nov;167(5):1415–1429.e19.
    doi: 10.1016/j.cell.2016.10.042pmc: PMC5300907pubmed: 27863252google scholar: lookup
  66. Kichaev G, Bhatia G, Loh PR, Gazal S, Burch K, Freund MK. Leveraging Polygenic Functional Enrichment to Improve GWAS Power. Am J Hum Genet 2019 Jan;104(1):65–75.
    doi: 10.1016/j.ajhg.2018.11.008pmc: PMC6323418pubmed: 30595370google scholar: lookup
  67. Wain LV, Vaez A, Jansen R, Joehanes R, Van Der Most PJ, Erzurumluoglu AM. Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney. Hypertension 2017 Sep;70(3).
    doi: 10.1161/HYPERTENSIONAHA.117.09438pmc: PMC5783787pubmed: 28739976google scholar: lookup
  68. Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinforma 2016 Jun;54(1).
    doi: 10.1002/cpbi.5pubmed: 27322403google scholar: lookup
  69. Müller R, Weirick T, John D, Militello G, Chen W, Dimmeler S. ANGIOGENES: knowledge database for protein-coding and noncoding RNA genes in endothelial cells. Sci Rep 2016 Sep 1;6(1):32475.
    doi: 10.1038/srep32475pmc: PMC5007478pubmed: 27582018google scholar: lookup
  70. Han Y, Ali MK, Dua K, Spiekerkoetter E, Mao Y. Role of Long Non-Coding RNAs in Pulmonary Arterial Hypertension. Cells 2021 Jul 26;10(8):1892.
    doi: 10.3390/cells10081892pmc: PMC8394897pubmed: 34440661google scholar: lookup
  71. Wołowiec Ł, Mędlewska M, Osiak J, Wołowiec A, Grześk E, Jaśniak A. MicroRNA and lncRNA as the Future of Pulmonary Arterial Hypertension Treatment. Int J Mol Sci 2023 Jun 4;24(11):9735.
    doi: 10.3390/ijms24119735pmc: PMC10253568pubmed: 37298685google scholar: lookup
  72. Kingsley NB, Hamilton NA, Lindgren G, Orlando L, Bailey E, Brooks S. “Adopt-a-Tissue” Initiative Advances Efforts to Identify Tissue-Specific Histone Marks in the Mare. Front Genet 2021 Mar 26;12:1–9.
    doi: 10.3389/fgene.2021.649959pmc: PMC8033197pubmed: 33841506google scholar: lookup
  73. Kingsley NB, Kern C, Creppe C, Hales EN, Zhou H, Kalbfleisch TS. Functionally Annotating Regulatory Elements in the Equine Genome Using Histone Mark ChIP-Seq. Genes 2019 Dec 18;11(1):3.
    doi: 10.3390/genes11010003pmc: PMC7017286pubmed: 31861495google scholar: lookup
  74. Hinckley KA, Fearn S, Howard BR, Henderson IW. Nitric oxide donors as treatment for grass induced acute laminitis in ponies. Equine Vet J 1996 Jan;28(1):17–28.
    doi: 10.1111/j.2042-3306.1996.tb01586.xpubmed: 8565949google scholar: lookup
  75. Gauff F, Patan-Zugaj B, Licka TF. Hyperinsulinaemia increases vascular resistance and endothelin-1 expression in the equine digit: Endothelin-1 and vasoconstriction after hyperinsulinaemia. Equine Vet J 2013 Sep;45(5):613–8.
    pmc: PMC4018505pubmed: 23489109
  76. Katwa LC, Johnson PJ, Ganjam VK, Kreeger JM, Messer NT. Expression of endothelin in equine laminitis. Equine Vet J 1999 May;31(3):243–7.
    doi: 10.1111/j.2042-3306.1999.tb03180.xpubmed: 10402139google scholar: lookup
  77. Backus M. Lameness in the horse with special reference to acute laminitis. J Am Vet Med Assoc 1937;(91):64.
  78. Outram AK, Stear NA, Bendrey R, Olsen S, Kasparov A, Zaibert V. The Earliest Horse Harnessing and Milking. Science 2009 Mar 6;323(5919):1332–5.
    doi: 10.1126/science.1168594pubmed: 19265018google scholar: lookup
  79. Heesch CM, Kline DD, Hasser EM. Control Mechanisms of the Circulatory System. Duke´s Physiology of Domestic Animals 2015;353.
  80. Mazic S, Suzic Lazic J, Dekleva M, Antic M, Soldatovic I, Djelic M. The impact of elevated blood pressure on exercise capacity in elite athletes. Int J Cardiol 2015 Feb;180:171–7.
    doi: 10.1016/j.ijcard.2014.11.125pubmed: 25460373google scholar: lookup
  81. Navas de Solis C, Slack J, Boston RC, Reef VB. Hypertensive cardiomyopathy in horses: 5 cases (1995–2011). J Am Vet Med Assoc 2013 Jul 1;243(1):126–30.
    doi: 10.2460/javma.243.1.126pubmed: 23786201google scholar: lookup
  82. Moore JN, Garner HE, Coffman JR. Haematological changes during development of acute laminitis hypertension. Equine Vet J 1981 Oct;13(4):240–2.
    doi: 10.1111/j.2042-3306.1981.tb03506.xpubmed: 7318801google scholar: lookup
  83. Bailey SR, Habershon-Butcher JL, Ransom KJ, Elliott J, Menzies-Gow NJ. Hypertension and insulin resistance in a mixed-breed population of ponies predisposed to laminitis. Am J Vet Res 2008 Jan;69(1):122–9.
    doi: 10.2460/ajvr.69.1.122pubmed: 18167097google scholar: lookup
  84. Lorello O, Heliczer N, Casoni D, Schüpbach G, Navas de Solis C. Correlation of Blood Pressure With Splenic Volume in Horses, Daily Variation in Blood Pressure, and “White Coat Hypertension.”. J Equine Vet Sci 2018 Apr 1;63:41–5.
  85. Söder J, Bröjer JT, Nostell KE. Interday variation and effect of transportation on indirect blood pressure measurements, plasma endothelin-1 and serum cortisol in Standardbred and Icelandic horses. Acta Vet Scand 2012 Jun 10;54:37.
    doi: 10.1186/1751-0147-54-37pmc: PMC3503793pubmed: 22682151google scholar: lookup
  86. Baynash AG, Hosoda K, Giaid A, Richardson JA, Emoto N, Hammer RE. Interaction of endothelin-3 with endothelin-B receptor is essential for development of epidermal melanocytes and enteric neurons. Cell 1994 Dec;79(7):1277–85.
    doi: 10.1016/0092-8674(94)90018-3pubmed: 8001160google scholar: lookup
  87. Kusafuka T, Wang Y, Puri P. Mutation analysis of the RET, the endothelin-B receptor, and the endothelin-3 genes in sporadic cases of Hirschsprung’s disease. J Pediatr Surg 1997 Mar;32(3):501–4.
    doi: 10.1016/s0022-3468(97)90616-3pubmed: 9094028google scholar: lookup
  88. Kusafuka T, Puri P. Mutations of the endothelin-B receptor and endothelin-3 genes in Hirschsprung’s disease. Pediatr Surg Int 1997 Jan;12(1):19–23.
    doi: 10.1007/BF01194795pubmed: 9035203google scholar: lookup
  89. Monti L, Arrigucci U, Rossi A. Insights into Endothelin-3 and Multiple Sclerosis. Biomol Concepts 2020 Jun 25;11(1):137–41.
    doi: 10.1515/bmc-2020-0012pubmed: 32589590google scholar: lookup
  90. Read AP, Newton VE. Waardenburg syndrome. J Med Genet 1997 Aug 1;34(8):656–65.
    doi: 10.1136/jmg.34.8.656pmc: PMC1051028pubmed: 9279758google scholar: lookup
  91. Jastrzebska B. GPCR: G protein complexes—the fundamental signaling assembly. Amino Acids 2013 Dec;45(6):1303–14.
    doi: 10.1007/s00726-013-1593-ypmc: PMC3845202pubmed: 24052187google scholar: lookup
  92. Hepler JR, Gilman AG. G proteins. Trends Biochem Sci 1992 Oct;17(10):383–7.
    doi: 10.1016/0968-0004(92)90005-tpubmed: 1455506google scholar: lookup
  93. Weis WI, Kobilka BK. The Molecular Basis of G Protein–Coupled Receptor Activation. Annu Rev Biochem 2018 Jun 20;87(1):897–919.
    doi: 10.1146/annurev-biochem-060614-033910pmc: PMC6535337pubmed: 29925258google scholar: lookup
  94. Gilman AG. G PROTEINS: TRANSDUCERS OF RECEPTOR-GENERATED SIGNALS. Annu Rev Biochem 1987 Jun;56(1):615–49.
    doi: 10.1146/annurev.bi.56.070187.003151pubmed: 3113327google scholar: lookup
  95. MedlinePlus [Internet]. Bethesda (MD): National Library of Medicine (US).nGNAS gene [Internet]. 2015. [cited 2023 Jun 20]. Available from: https://medlineplus.gov/genetics/gene/gnas/
  96. Danzi S, Klein I. Thyroid hormone and blood pressure regulation. Curr Hypertens Rep 2003 Dec;5(6):513–20.
    doi: 10.1007/s11906-003-0060-7pubmed: 14594573google scholar: lookup
  97. Gu Y, Zheng L, Zhang Q, Liu L, Meng G, Yao Z. Relationship between thyroid function and elevated blood pressure in euthyroid adults. J Clin Hypertens 2018 Oct;20(10):1541–9.
    doi: 10.1111/jch.13369pmc: PMC8031048pubmed: 30260550google scholar: lookup
  98. Kalbfleisch TS, Rice ES, DePriest MS, Walenz BP, Hestand MS, Vermeesch JR. Eq쪳, an Updated Reference Genome for the Domestic Horse. 2018 Apr.
    doi: 10.1101/306928google scholar: lookup
  99. Velie BD, Fegraeus KJ, Solé M, Rosengren MK, Røed KH, Ihler CF. 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 Dec;19(1):80.
    doi: 10.1186/s12863-018-0670-3pmc: PMC6114527pubmed: 30157760google scholar: lookup
  100. Hui L, DelMonte T, Ranade K. Genotyping Using the TaqMan Assay. Curr Protoc Hum Genet 2008 Jan;56(1).
    doi: 10.1002/0471142905.hg0210s56pubmed: 18428424google scholar: lookup
  101. Árnason T. The Importance of Different Traits in Genetic Improvement of Trotters. 1994.
  102. Jurinke C, van den Boom D, Cantor CR, Köster H. Automated genotyping using the DNA MassArray technology. Methods Mol Biol Clifton NJ 2001;170:103–16.
    doi: 10.1385/1-59259-234-1:103pubmed: 11357675google scholar: lookup
  103. Lake SL, Lyon H, Tantisira K, Silverman EK, Weiss ST, Laird NM. Estimation and Tests of Haplotype-Environment Interaction when Linkage Phase Is Ambiguous. Hum Hered 2003;55(1):56–65.
    doi: 10.1159/000071811pubmed: 12890927google scholar: lookup
  104. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinforma Oxf Engl 2010 Mar 15;26(6):841–2.
    doi: 10.1093/bioinformatics/btq033pmc: PMC2832824pubmed: 20110278google scholar: lookup
  105. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009 Aug 15;25(16):2078–9.
    doi: 10.1093/bioinformatics/btp352pmc: PMC2723002pubmed: 19505943google scholar: lookup
  106. Zhigulev A, Sahlén P. Targeted Chromosome Conformation Capture (HiCap). Spatial Genome Organization 2022.
    doi: 10.1007/978-1-0716-2497-5_5pubmed: 35867246google scholar: lookup
  107. Lieberman-Aiden E, Van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A. Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome. Science 2009 Oct 9;326(5950):289–93.
    doi: 10.1126/science.1181369pmc: PMC2858594pubmed: 19815776google scholar: lookup
  108. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012 Apr;9(4):357–9.
    doi: 10.1038/nmeth.1923pmc: PMC3322381pubmed: 22388286google scholar: lookup
  109. Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM. GENCODE 2021. Nucleic Acids Res 2021 Jan 8;49(D1):D916–23.
    doi: 10.1093/nar/gkaa1087pmc: PMC7778937pubmed: 33270111google scholar: lookup
  110. Haider S, Waggott D, Lalonde E, Fung C, Liu FF, Boutros PC. A bedr way of genomic interval processing. Source Code Biol Med 2016 Dec;11(1):14.
    doi: 10.1186/s13029-016-0059-5pmc: PMC5157088pubmed: 27999613google scholar: lookup
  111. Anil A, Spalinskas R, Åkerborg Ö, Sahlén P. HiCapTools: a software suite for probe design and proximity detection for targeted chromosome conformation capture applications. Bioinformatics 2018 Feb 15;34(4):675–7.
    doi: 10.1093/bioinformatics/btx625pmc: PMC6368139pubmed: 29444232google scholar: lookup
  112. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 2009 Jul 1;37(Web Server):W305–11.
    doi: 10.1093/nar/gkp427pmc: PMC2703978pubmed: 19465376google scholar: lookup
  113. Felkel S, Vogl C, Rigler D, Dobretsberger V, Chowdhary BP, Distl O. The horse Y chromosome as an informative marker for tracing sire lines. Sci Rep 2019 Dec;9(1):6095.
    doi: 10.1038/s41598-019-42640-wpmc: PMC6465346pubmed: 30988347google scholar: lookup
  114. Der Sarkissian C, Ermini L, Schubert M, Yang MA, Librado P, Fumagalli M. Evolutionary Genomics and Conservation of the Endangered Przewalski’s Horse. Curr Biol 2015 Oct;25(19):2577–83.
    doi: 10.1016/j.cub.2015.08.032pmc: PMC5104162pubmed: 26412128google scholar: lookup
  115. Orlando L, Librado P. Origin and Evolution of Deleterious Mutations in Horses. Genes 2019 Aug 28;10(9):649.
    doi: 10.3390/genes10090649pmc: PMC6769756pubmed: 31466279google scholar: lookup
  116. Schubert M, Ermini L, Sarkissian CD, Jónsson H, Ginolhac A, Schaefer R. Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX. Nat Protoc 2014 May;9(5):1056–82.
    doi: 10.1038/nprot.2014.063pubmed: 24722405google scholar: lookup
  117. Korneliussen TS, Albrechtsen A, Nielsen R. ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinformatics 2014 Dec;15(1):356.
    doi: 10.1186/s12859-014-0356-4pmc: PMC4248462pubmed: 25420514google scholar: lookup

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.