FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Sci Rep / Predicted genetic burden and frequency of phenotype-associat...
Scientific reports2024; 14(1); 8396; doi: 10.1038/s41598-024-57872-8

Predicted genetic burden and frequency of phenotype-associated variants in the horse.

Abstract: Disease-causing variants have been identified for less than 20% of suspected equine genetic diseases. Whole genome sequencing (WGS) allows rapid identification of rare disease causal variants. However, interpreting the clinical variant consequence is confounded by the number of predicted deleterious variants that healthy individuals carry (predicted genetic burden). Estimation of the predicted genetic burden and baseline frequencies of known deleterious or phenotype associated variants within and across the major horse breeds have not been performed. We used WGS of 605 horses across 48 breeds to identify 32,818,945 variants, demonstrate a high predicted genetic burden (median 730 variants/horse, interquartile range: 613-829), show breed differences in predicted genetic burden across 12 target breeds, and estimate the high frequencies of some previously reported disease variants. This large-scale variant catalog for a major and highly athletic domestic animal species will enhance its ability to serve as a model for human phenotypes and improves our ability to discover the bases for important equine phenotypes.
© 2024. The Author(s).
Get Full Text
Publication Date: 2024-04-10 PubMed ID: 38600096PubMed Central: PMC11006912DOI: 10.1038/s41598-024-57872-8Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

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

The research article provides insights into the genetic variants in horses, showing that each horse carries a number of predicted deleterious variants, dubbed ‘predicted genetic burden’, and it’s uneven across different breeds. The researchers did this by performing whole genome sequencing on over 600 horses of 48 breeds.

Identification of Horse Genomic Variants

  • Each horse carries a certain number of predicted harmful or disease-causing variants in their genome, which is referred to as the ‘predicted genetic burden’.
  • The researchers performed whole genome sequencing (WGS) on 605 horses from 48 different breeds.
  • This allowed them to identify as many as 32,818,945 variants, which is a significant finding from a genetic research perspective.

Predicted Genetic Burden in Horses

  • Predicted genetic burden refers to the presence of potentially deleterious genetic variants that a healthy individual carries.
  • The researchers found that the median number of such variants per horse is 730, with an interquartile range between 613 and 829 suggesting variability among individuals.
  • This insight will help further understand the genetic predisposition of horses to certain diseases or physical features.

Breed Differences in Genetic Burden

  • Not only did the researchers identify a large number of variants, but they also noticed a difference in the predicted genetic burden across different breeds of horses.
  • The study examined 12 target breeds and found the burden to vary distinctively among them.
  • This suggests that certain breeds might be genetically predisposed to certain diseases or traits than others.

Previously Reported Disease Variants

  • Interestingly, the study also found that some previously reported disease variants were present at high frequencies.
  • This large-scale variant catalog will be helpful in conducting future studies into these diseases and traits and possibly facilitating early detection or more efficient treatment strategies.

Conclusions and Implications

  • This study of genetic variation within and across horse breeds provides a vital resource for future genetic research in horses.
  • The researchers suggest that this large-scale variant catalog will enhance the ability to use horses as a model for human phenotypes, meaning it could provide valuable insights for human genetic disease research as well.
  • The catalog improves the ability to discover the bases for important equine phenotypes, which could ultimately improve breeding and disease prevention strategies.

Cite This Article

APA
Durward-Akhurst SA, Marlowe JL, Schaefer RJ, Springer K, Grantham B, Carey WK, Bellone RR, Mickelson JR, McCue ME. (2024). Predicted genetic burden and frequency of phenotype-associated variants in the horse. Sci Rep, 14(1), 8396. https://doi.org/10.1038/s41598-024-57872-8

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 14
Issue: 1
Pages: 8396
PII: 8396

Researcher Affiliations

Durward-Akhurst, S A
  • Department of Veterinary Clinical Sciences, University of Minnesota, C339 VMC, 1353 Boyd Avenue, St. Paul, MN, 55108, USA. durwa004@umn.edu.
Marlowe, J L
  • Department of Veterinary Clinical Sciences, University of Minnesota, C339 VMC, 1353 Boyd Avenue, St. Paul, MN, 55108, USA.
Schaefer, R J
  • Department of Veterinary Population Medicine, University of Minnesota, 225 VMC, 1365 Gortner Avenue, St. Paul, MN, 55108, USA.
Springer, K
  • Department of Veterinary Population Medicine, University of Minnesota, 225 VMC, 1365 Gortner Avenue, St. Paul, MN, 55108, USA.
Grantham, B
  • Interval Bio LLC, 408 Stierline Road, Mountain View, CA, 94043, USA.
Carey, W K
  • Interval Bio LLC, 408 Stierline Road, Mountain View, CA, 94043, USA.
Bellone, R R
  • Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California-Davis, Davis, CA, USA.
  • Population Health and Reproduction and Veterinary Genetics Laboratory, School of Veterinary Medicine, University of California, Davis, CA, USA.
Mickelson, J R
  • Department of Veterinary and Biomedical Sciences, University of Minnesota, 295F Animal Science Veterinary Medicine Building, 1988 Fitch Avenue, St. Paul, MN, 55108, USA.
McCue, M E
  • Department of Veterinary Population Medicine, University of Minnesota, 225 VMC, 1365 Gortner Avenue, St. Paul, MN, 55108, USA.

MeSH Terms

  • Horses / genetics
  • Animals
  • Humans
  • Genome
  • Phenotype
  • Breeding
  • Polymorphism, Single Nucleotide

Grant Funding

  • 2017-67015-26296 / USDA NIFA-AFRI
  • 2017-67015-26296 / USDA NIFA-AFRI
  • 2017-67015-26296 / USDA NIFA-AFRI
  • 5T320D010993-12 / Office of Research Infrastructure Programs, National Institutes of Health
  • 5T320D010993-12 / Office of Research Infrastructure Programs, National Institutes of Health
  • D20EQ-403 / Morris Animal Foundation

Conflict of Interest Statement

S.A. Durward-Akhurst, J.L. Marlowe, R.J. Schaefer, K. Springer, M.E. McCue and J.R. Mickelson declare no competing interests. B. Grantham and W.K. Carey own IntervalBio LLC, the computational company that was paid to map and perform the variant calling on the original 534 horses. R.R. Bellone is affiliated with the UC Davis Veterinary Genetics Laboratory, which provides genetic diagnostic tests in horses and other species.

References

This article includes 87 references
  1. Petersen JL, Mickelson JR, Rendahl AK, Valberg SJ, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Camara MA, Capomaccio S, Cappelli K, Cothran EG, Distl O, Fox-Clipsham L, Graves KT, Guerin 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, Roed 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.
    doi: 10.1371/journal.pgen.1003211pmc: PMC3547851pubmed: 23349635google scholar: lookup
  2. Hill EW, McGivney BA, Gu J, Whiston R, Machugh DE. A genome-wide SNP-association study confirms a sequence variant (g.66493737C>T) in the equine myostatin (MSTN) gene as the most powerful predictor of optimum racing distance for Thoroughbred racehorses.. BMC Genom 2010;11:552–552.
    doi: 10.1186/1471-2164-11-552google scholar: lookup
  3. Rooney MF, Hill EW, Kelly VP, Porter RK. The “speed gene” effect of myostatin arises in Thoroughbred horses due to a promoter proximal SINE insertion.. PLoS One 2018;13(10):e0205664.
    doi: 10.1371/journal.pone.0205664pmc: PMC6209199pubmed: 30379863google scholar: lookup
  4. McIlwraith CW, Frisbie DD, Kawcak CE. The horse as a model of naturally occurring osteoarthritis.. Bone Jt. Res. 2012;1(11):297–309.
    doi: 10.1302/2046-3758.111.2000132google scholar: lookup
  5. McCoy AM, Toth F, Dolvik NI, Ekman S, Ellermann J, Olstad K, Ytrehus B, Carlson CS. Articular osteochondrosis: A comparison of naturally-occurring human and animal disease.. Osteoarthr. Cartil. 2013;21(11):1638–1647.
    doi: 10.1016/j.joca.2013.08.011google scholar: lookup
  6. Norton EM, Mickelson JR, Binns MM, Blott SC, Caputo P, Isgren CM, McCoy AM, Moore A, Piercy RJ, Swinburne JE, Vaudin M, McCue ME. Heritability of recurrent exertional rhabdomyolysis in Standardbred and Thoroughbred racehorses derived from SNP genotyping data.. J. Hered. 2016;107(6):537–543.
    doi: 10.1093/jhered/esw042pmc: PMC5006745pubmed: 27489252google scholar: lookup
  7. McCue ME, Valberg SJ, Miller MB, Wade C, DiMauro S, Akman HO, Mickelson JR. Glycogen synthase (GYS1) mutation causes a novel skeletal muscle glycogenosis.. Genomics 2008;91(5):458–466.
    doi: 10.1016/j.ygeno.2008.01.011pmc: PMC2430182pubmed: 18358695google scholar: lookup
  8. Ward TL, Valberg SJ, Adelson DL, Abbey CA, Binns MM, Mickelson JR. Glycogen branching enzyme (GBE1) mutation causing equine glycogen storage disease IV.. Mamm. Genome Off. J. Int. Mamm. Genome Soc. 2004;15(7):570–577.
    doi: 10.1007/s00335-004-2369-1google scholar: lookup
  9. Norton EM, Schultz NE, Rendahl AK, Geor RJ, Mickelson JR, McCue ME. Heritability of metabolic traits associated with equine metabolic syndrome in Welsh ponies and Morgan horses.. Equine Vet. J. 2019;51(4):475–480.
    doi: 10.1111/evj.13053pubmed: 30472742google scholar: lookup
  10. McCoy AM, Schaefer R, Petersen JL, Morrell PL, Slamka MA, Mickelson JR, Valberg SJ, McCue ME. Evidence of positive selection for a glycogen synthase (GYS1) mutation in domestic horse populations.. J. Hered. 2013;105(2):163–172.
    doi: 10.1093/jhered/est075pmc: PMC3920812pubmed: 24215078google scholar: lookup
  11. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J. Genetic diversity in the modern horse illustrated from genome-wide SNP data.. PloS One 2013;8(1):e54997.
    doi: 10.1371/journal.pone.0054997pmc: PMC3559798pubmed: 23383025google scholar: lookup
  12. Lynch M, Conery J, Burger R. Mutation accumulation and the extinction of small populations.. Am. Nat. 1995;146(4):489–518.
    doi: 10.1086/285812google scholar: lookup
  13. Keller LF, Waller DM. Inbreeding effects in wild populations.. Trends Ecol. Evol. 2002;17(5):230–241.
    doi: 10.1016/S0169-5347(02)02489-8google scholar: lookup
  14. Orr N, Back W, Gu J, Leegwater P, Govindarajan P, Conroy J, Ducro B, Van Arendonk JA, MacHugh DE, Ennis S, Hill EW, Brama PA. Genome-wide SNP association-based localization of a dwarfism gene in Friesian dwarf horses.. Anim. Genet. 2010;41(Suppl 2):2–7.
    doi: 10.1111/j.1365-2052.2010.02091.xpubmed: 21070269google scholar: lookup
  15. Cook D, Gallagher PC, Bailey E. Genetics of swayback in American Saddlebred horses.. Anim. Genet. 2010;41(Suppl 2):64–71.
    doi: 10.1111/j.1365-2052.2010.02108.xpubmed: 21070278google scholar: lookup
  16. Frischknecht M, Neuditschko M, Jagannathan V, Drogemuller C, Tetens J, Thaller G, Leeb T, Rieder S, Drögemüller C, Tetens J, Thaller G, Leeb T, Rieder S. Imputation of sequence level genotypes in the Franches-Montagnes horse breed.. Genet. Sel. Evol. GSE 2014;46:63–67.
    doi: 10.1186/s12711-014-0063-7pmc: PMC4180851pubmed: 25927638google scholar: lookup
  17. Spirito F, Charlesworth A, Linder K, Ortonne JP, Baird J, Meneguzzi G. Animal models for skin blistering conditions: Absence of laminin 5 causes hereditary junctional mechanobullous disease in the Belgian horse.. J. Invest. Dermatol. 2002;119(3):684–691.
    doi: 10.1046/j.1523-1747.2002.01852.xpubmed: 12230513google scholar: lookup
  18. Rudolph JA, Spier SJ, Byrns G, Rojas CV, Bernoco D, Hoffman EP. Periodic paralysis in quarter horses: A sodium channel mutation disseminated by selective breeding.. Nat. Genet. 1992;2(2):144–147.
    doi: 10.1038/ng1092-144pubmed: 1338908google scholar: lookup
  19. Wagner ML, Valberg SJ, Ames EG, Bauer MM, Wiseman JA, Penedo MC, Kinde H, Abbitt B, Mickelson JR. Allele frequency and likely impact of the glycogen branching enzyme deficiency gene in Quarter Horse and Paint Horse populations.. J. Vet. Intern. Med. Am. Coll. Vet. Intern. Med. 2006;20(5):1207–1211.
    doi: 10.1111/j.1939-1676.2006.tb00724.xgoogle scholar: lookup
  20. Tryon RC, White SD, Bannasch DL. Homozygosity mapping approach identifies a missense mutation in equine cyclophilin B (PPIB) associated with HERDA in the American Quarter Horse.. Genomics 2007;90(1):93–102.
    doi: 10.1016/j.ygeno.2007.03.009pubmed: 17498917google scholar: lookup
  21. Aleman M, Nieto JE, Magdesian KG. Malignant hyperthermia associated with ryanodine receptor 1 (C7360G) mutation in Quarter Horses.. J. Vet. Intern. Med. Am. Coll. Vet. Intern. Med. 2009;23(2):329–334.
    doi: 10.1111/j.1939-1676.2009.0274.xgoogle scholar: lookup
  22. Shin EK, Perryman LE, Meek K. A kinase-negative mutation of DNA-PK(CS) in equine SCID results in defective coding and signal joint formation.. J. Immunol. 1997;158(8):3565–3569.
    doi: 10.4049/jimmunol.158.8.3565pubmed: 9103416google scholar: lookup
  23. Brooks SA, Gabreski N, Miller D, Brisbin A, Brown HE, Streeter C, Mezey J, Cook D, Antczak DF. Whole-genome SNP association in the horse: Identification of a deletion in myosin Va responsible for Lavender Foal Syndrome.. PLoS Genet. 2010;6(4):e1000909.
    doi: 10.1371/journal.pgen.1000909pmc: PMC2855325pubmed: 20419149google scholar: lookup
  24. Monthoux C, de Brot S, Jackson M, Bleul U, Walter J. Skin malformations in a neonatal foal tested homozygous positive for Warmblood Fragile Foal Syndrome.. BMC Vet. Res. 2015;11:1–8.
    doi: 10.1186/s12917-015-0318-8pmc: PMC4300173pubmed: 25582057google scholar: lookup
  25. Metallinos DL, Bowling AT, Rine J. A missense mutation in the endothelin-B receptor gene is associated with Lethal White Foal Syndrome: An equine version of Hirschsprung Disease.. Mamm. Genome. 1998;9(6):426–431.
    doi: 10.1007/s003359900790pubmed: 9585428google scholar: lookup
  26. Finno CJ, Gianino G, Perumbakkam S, Williams ZJ, Bordbari MH, Gardner KL, Burns E, Peng S, Durward-Akhurst SA, Valberg SJ. A missense mutation in MYH1 is associated with susceptibility to immune-mediated myositis in Quarter Horses.. Skelet. Muscle. 2018;8(1):7.
    doi: 10.1186/s13395-018-0155-0pmc: PMC5838957pubmed: 29510741google scholar: lookup
  27. Gianino GM, Valberg SJ, Perumbakkam S, Henry ML, Gardner K, Penedo C, Finno CJ. Prevalence of the E321G MYH1 variant for immune-mediated myositis and nonexertional rhabdomyolysis in performance subgroups of American Quarter Horses.. J. Vet. Intern. Med. 2019;33(2):897–901.
    doi: 10.1111/jvim.15393pmc: PMC6430863pubmed: 30623495google scholar: lookup
  28. Finno CJ, Stevens C, Young A, Affolter V, Joshi NA, Ramsay S, Bannasch DL. SERPINB11 frameshift variant associated with novel hoof specific phenotype in Connemara ponies.. PLoS Genet. 2015;11(4):e1005122–e1005122.
    doi: 10.1371/journal.pgen.1005122pmc: PMC4395385pubmed: 25875171google scholar: lookup
  29. Boycott KM, Vanstone MR, Bulman DE, MacKenzie AE. Rare-disease genetics in the era of next-generation sequencing: discovery to translation.. Nat. Rev. 2013;14(10):681–691.
    doi: 10.1038/nrg3555google scholar: lookup
  30. Consortium UK, Walter K, Min JL, Huang J, Crooks L, Memari Y, McCarthy S, Perry JR, Xu C, Futema M, Lawson D, Iotchkova V, Schiffels S, Hendricks AE, Danecek P, Li R, Floyd J, Wain LV, Barroso I, Humphries SE, Hurles ME, Zeggini E, Barrett JC, Plagnol V, Richards JB, Greenwood CM, Timpson NJ, Durbin R, Soranzo N. The UK10K project identifies rare variants in health and disease.. Nature 2015;526(7571):82–90.
    doi: 10.1038/nature14962pmc: PMC4773891pubmed: 26367797google scholar: lookup
  31. Marwaha S, Knowles JW, Ashley EA. A guide for the diagnosis of rare and undiagnosed disease: Beyond the exome.. Genome Med. 2022;14(1):23.
    doi: 10.1186/s13073-022-01026-wpmc: PMC8883622pubmed: 35220969google scholar: lookup
  32. Wall JD, Stawiski EW, Ratan A. The GenomeAsia 100K Project enables genetic discoveries across Asia.. Nature 2019;576(7785):106–111.
    doi: 10.1038/s41586-019-1793-zpmc: PMC7054211pubmed: 31802016google scholar: lookup
  33. Tachmazidou I. Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits.. American Journal of Human Genetics .
    doi: 10.1016/j.ajhg.2017.04.014google scholar: lookup
  34. MacArthur DG, Balasubramanian S, Frankish A. A systematic survey of loss-of-function variants in human protein-coding genes.. Science 2012;335(6070):823–828.
    doi: 10.1126/science.1215040pmc: PMC3299548pubmed: 22344438google scholar: lookup
  35. Xue Y, Chen Y, Ayub Q. Deleterious- and disease-allele prevalence in healthy individuals: Insights from current predictions, mutation databases, and population-scale resequencing.. Am. J. Hum. Genet. 2012;91(6):1022–1032.
    doi: 10.1016/j.ajhg.2012.10.015pmc: PMC3516590pubmed: 23217326google scholar: lookup
  36. Bell CJ, Dinwiddie DL, Miller NA, Hateley SL, Ganusova EE, Mudge J, Langley RJ, Zhang L, Lee CC, Schilkey FD, Sheth V, Woodward JE, Peckham HE, Schroth GP, Kim RW, Kingsmore SF. Carrier testing for severe childhood recessive diseases by next-generation sequencing.. Sci. Transl. Med. 2011;3(65):65ra4.
    doi: 10.1126/scitranslmed.3001756pmc: PMC3740116pubmed: 21228398google scholar: lookup
  37. Wright CF, West B, Tuke M, Jones SE, Patel K, Laver TW, Beaumont RN, Tyrrell J, Wood AR, Frayling TM, Hattersley AT, Weedon MN. Assessing the pathogenicity, penetrance, and expressivity of putative disease-causing variants in a population setting.. Am. J. Hum. Genet. 2019;104(2):275–286.
    doi: 10.1016/j.ajhg.2018.12.015pmc: PMC6369448pubmed: 30665703google scholar: lookup
  38. Durward-Akhurst SA, Schaefer RJ, Grantham B, Carey WK, Mickelson JR, McCue ME. Genetic variation and the distribution of variant types in the horse.. Front. Genet. 2021;12:758366.
    doi: 10.3389/fgene.2021.758366pmc: PMC8676274pubmed: 34925451google scholar: lookup
  39. Jagannathan V, Gerber V, Rieder S, Tetens J, Thaller G, Drögemüller C, Leeb T. Comprehensive characterization of horse genome variation by whole-genome sequencing of 88 horses.. Anim. Genet. 2019;50(1):74–77.
    doi: 10.1111/age.12753pubmed: 30525216google scholar: lookup
  40. Tozaki T, Ohnuma A, Kikuchi M, Ishige T, Kakoi H, Hirota KI, Kusano K, Nagata SI. Rare and common variant discovery by whole-genome sequencing of 101 Thoroughbred racehorses.. Sci. Rep. 2021;11(1):16057.
    doi: 10.1038/s41598-021-95669-1pmc: PMC8346562pubmed: 34362995google scholar: lookup
  41. Kalbfleisch TS, Rice E, DePriest MS, Walenz BP, Hestand MS, Vermeesch JR, O’Connell BL, Fiddes IT, Vershinina AO, Petersen JL, Finno CJ, Bellone RR, McCue ME, Brooks SA, Bailey E, Orlando L, Green RE, Miller DC, Antczak DF, MacLeod JN. Eq쪳, an updated reference genome for the domestic horse.. bioRxiv 2018.
  42. Cullen JN, Friedenberg SG. Whole Animal Genome Sequencing: User-friendly, rapid, containerized pipelines for processing, variant discovery, and annotation of short-read whole genome sequencing data.. G3 Genes Genomes Genet. 2023;13:jkad117.
    doi: 10.1093/g3journal/jkad117google scholar: lookup
  43. Huang DW. The DAVID Gene Functional Classification Tool: A novel biological module-centric algorithm to functionally analyze large gene lists.. Genome Biology .
    doi: 10.1016/10.1186/gb-2007-8-9-r183google scholar: lookup
  44. Beeson SK, Mickelson JR, McCue ME. Exploration of fine-scale recombination rate variation in the domestic horse.. Genome Res. 2019;29(10):1744–1752.
    doi: 10.1101/gr.243311.118pmc: PMC6771410pubmed: 31434677google scholar: lookup
  45. Edwards J, P S, RB S. Method of detecting inherited equine myopathy.. 2017.
  46. Genomes Project C, Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, Hurles ME, McVean GA. A map of human genome variation from population-scale sequencing.. Nature 2010;467(7319):1061–1073.
    doi: 10.1038/nature09534pmc: PMC3042601pubmed: 20981092google scholar: lookup
  47. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S. Genome sequence, comparative analysis, and population genetics of the domestic horse.. Science 2009;326(5954):865–867.
    doi: 10.1126/science.1178158pmc: PMC3785132pubmed: 19892987google scholar: lookup
  48. Chen L, Chamberlain AJ, Reich CM, Daetwyler HD, Hayes BJ. Detection and validation of structural variations in bovine whole-genome sequence data.. Genet. Sel. Evol. 2017;49:1–13.
    pmc: PMC5240470pubmed: 28093066
  49. McGivney BA, Han H, Corduff LR, Katz LM, Tozaki T, MacHugh DE, Hill EW. Genomic inbreeding trends, influential sire lines and selection in the global Thoroughbred horse population.. Sci Rep 2020;10(1):466.
    doi: 10.1038/s41598-019-57389-5pmc: PMC6965197pubmed: 31949252google scholar: lookup
  50. Sidore C, Busonero F, Maschio A, Porcu E, Naitza S, Zoledziewska M, Mulas A, Pistis G, Steri M, Danjou F, Kwong A, Ortega Del Vecchyo VD, Chiang CW, Bragg-Gresham J, Pitzalis M, Nagaraja R, Tarrier B, Brennan C, Uzzau S, Fuchsberger C, Atzeni R, Reinier F, Berutti R, Huang J, Timpson NJ, Toniolo D, Gasparini P, Malerba G, Dedoussis G, Zeggini E, Soranzo N, Jones C, Lyons R, Angius A, Kang HM, Novembre J, Sanna S, Schlessinger D, Cucca F, Abecasis GR. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers.. Nat. Genet. 2015;47(11):1272–1281.
    doi: 10.1038/ng.3368pmc: PMC4627508pubmed: 26366554google scholar: lookup
  51. Tryon RC, Penedo MC, McCue ME, Valberg SJ, Mickelson JR, Famula TR, Wagner ML, Jackson M, Hamilton MJ, Nooteboom S, Bannasch DL. Evaluation of allele frequencies of inherited disease genes in subgroups of American Quarter Horses.. J. Am. Vet. Med. Assoc. 2009;234(1):120–125.
    doi: 10.2460/javma.234.1.120pubmed: 19119976google scholar: lookup
  52. Aleman M, Scalco R, Malvick J, Grahn RA, True A, Bellone RR. Prevalence of genetic mutations in horses with muscle disease from a neuromuscular disease laboratory.. J. Equine Vet. Sci. 2022;1(118):104129.
    doi: 10.1016/j.jevs.2022.104129google scholar: lookup
  53. Brault LS, Cooper CA, Famula TR, Murray JD, Penedo MCT. Mapping of equine cerebellar abiotrophy to ECA2 and identification of a potential causative mutation affecting expression of MUTYH.. Genomics 2011;97(2):121–129.
    doi: 10.1016/j.ygeno.2010.11.006pubmed: 21126570google scholar: lookup
  54. Eberth JE, Graves KT, MacLeod JN, Bailey E. Multiple alleles of ACAN associated with chondrodysplastic dwarfism in Miniature horses.. Anim. Genet. 2018;49(5):413–420.
    doi: 10.1111/age.12682pubmed: 30058072google scholar: lookup
  55. McCoy AM, Beeson SK, Rubin CJ, Andersson L, Caputo P, Lykkjen S, Moore A, Piercy RJ, Mickelson JR, McCue ME. Identification and validation of genetic variants predictive of gait in Standardbred horses.. PLoS Genet. 2019;15(5):e1008146.
    doi: 10.1371/journal.pgen.1008146pmc: PMC6555539pubmed: 31136578google scholar: lookup
  56. Li Y, Liu Y, Wang M, Lin X, Li Y, Yang T, Feng M, Ling Y, Zhao C. Whole-genome sequence analysis reveals the origin of the Chakouyi Horse.. Genes 2022;13(12):2411.
    doi: 10.3390/genes13122411pmc: PMC9778315pubmed: 36553682google scholar: lookup
  57. Promerová M, Andersson LS, Juras R, Penedo MCT, Reissmann M. Worldwide frequency distribution of the ‘Gait keeper’ mutation in the DMRT3 gene.. Anim. Genet. 2014;45(2):274–282.
    doi: 10.1111/age.12120pubmed: 24444049google scholar: lookup
  58. Negro S, Imsland F, Valera M, Molina A, Solé M, Andersson L. Association analysis of KIT, MITF, and PAX3 variants with white markings in Spanish horses.. Anim. Genet. 2017;48(3):349–352.
    doi: 10.1111/age.12528pubmed: 28084638google scholar: lookup
  59. Murgiano L, Waluk DP, Towers R, Wiedemar N, Dietrich J, Jagannathan V, Drögemüller M, Balmer P, Druet T, Galichet A, Penedo MC, Müller EJ, Roosje P, Welle MM, Leeb T. An intronic MBTPS2 variant results in a splicing defect in horses with brindle coat texture.. G3 Genes Genomes Genet. 2016;6(9):2963–2970.
    doi: 10.1534/g3.116.032433google scholar: lookup
  60. Holl HM, Pflug KM, Yates KM, Hoefs-Martin K, Shepard C, Cook DG, Lafayette C, Brooks SA. A candidate gene approach identifies variants in SLC45A2 that explain dilute phenotypes, pearl and sunshine, in compound heterozygote horses.. Anim. Genet. 2019;50(3):271–274.
    doi: 10.1111/age.12790pubmed: 31006892google scholar: lookup
  61. Dürig N, Jude R, Holl H, Brooks SA, Lafayette C, Jagannathan V, Leeb T. Whole genome sequencing reveals a novel deletion variant in the KIT gene in horses with white spotted coat colour phenotypes.. Anim. Genet. 2017;48(4):483–485.
    doi: 10.1111/age.12556pubmed: 28444912google scholar: lookup
  62. Avila F, Hughes SS, Magdesian KG, Penedo MCT, Bellone RR. Breed distribution and allele frequencies of base coat color, dilution, and white patterning variants across 28 horse breeds.. Genes 2022;13(9):1641.
    doi: 10.3390/genes13091641pmc: PMC9498372pubmed: 36140807google scholar: lookup
  63. Patterson Rosa L, Martin K, Vierra M, Foster G, Lundquist E, Brooks SA, Lafayette C. Two variants of KIT causing white patterning in stock-type horses.. J. Hered. 2021;112(5):447–451.
    doi: 10.1093/jhered/esab033pubmed: 34223905google scholar: lookup
  64. Patterson Rosa L, Martin K, Vierra M, Lundquist E, Foster G, Brooks SA, Lafayette C. A KIT variant associated with increased white spotting epistatic to MC1R genotype in horses (Equus caballus). Animals 2022;12(15):1958.
    doi: 10.3390/ani12151958pmc: PMC9367399pubmed: 35953947google scholar: lookup
  65. Reissmann M, Musa L, Zakizadeh S, Ludwig A. Distribution of coat-color-associated alleles in the domestic horse population and Przewalski’s horse.. J. Appl. Genet. 2016;57(4):519–525.
    doi: 10.1007/s13353-016-0352-7pubmed: 27194311google scholar: lookup
  66. Grilz-Seger G, Reiter S, Neuditschko M, Wallner B, Rieder S, Leeb T, Jagannathan V, Mesarič M, Cotman M, Pausch H, Lindgren G, Velie B, Horna M, Brem G, Druml T. A genome-wide association analysis in noriker horses identifies a SNP associated with roan coat color.. J. Equine Vet. Sci. 2020;1(88):102950.
    doi: 10.1016/j.jevs.2020.102950google scholar: lookup
  67. Voß K, Tetens J, Thaller G, Becker D. Coat color roan shows association with KIT variants and no evidence of lethality in Icelandic horses.. Genes 2020;11(6):680.
    doi: 10.3390/genes11060680pmc: PMC7348759pubmed: 32580410google scholar: lookup
  68. Tozaki T, Kusano K, Ishikawa Y, Kushiro A, Nomura M, Kikuchi M, Kakoi H, Hirota K, Miyake T, Hill EW, Nagata S. A candidate-SNP retrospective cohort study for fracture risk in Japanese Thoroughbred racehorses.. Anim. Genet. 2019;51(1):43–50.
    doi: 10.1111/age.12866pubmed: 31612520google scholar: lookup
  69. Drögemüller M, Jagannathan V, Welle MM, Graubner C, Straub R, Gerber V, Burger D, Signer-Hasler H, Poncet PA, Klopfenstein S, Von Niederhäusern RV, Tetens J, Thaller G, Rieder S, Drögemüller C, Leeb T. Congenital hepatic fibrosis in the Franches-Montagnes horse is associated with the polycystic kidney and hepatic disease 1 (PKHD1) gene.. PLoS ONE 2014;9(10):e110125.
    doi: 10.1371/journal.pone.0110125pmc: PMC4190318pubmed: 25295861google scholar: lookup
  70. Molín J, Asín J, Vitoria A, Sanz A, Gimeno M, Romero A, Sánchez J, Pinczowski P, Vázquez FJ, Rodellar C, Luján L. Congenital hepatic fibrosis in a purebred Spanish horse foal: Pathology and genetic studies on PKHD1 gene mutations.. Vet. Pathol. 2018;55(3):457–461.
    doi: 10.1177/0300985817754122pubmed: 29402207google scholar: lookup
  71. Polani S, Dean M, Lichter-Peled A, Hendrickson S, Tsang S, Fang X, Feng Y, Qiao W, Avni G, Kahila B-G. Sequence variant in the TRIM39-RPP21 gene readthrough is shared across a cohort of arabian foals diagnosed with juvenile idiopathic epilepsy.. J. Genet. Mutat. Disord. 2022;1(1):103.
    pmc: PMC9031527pubmed: 35465405
  72. Rivas VN, Aleman M, Peterson JA, Dahlgren AR, Hales EN, Finno CJ. TRIM39-RPP21 variants (∆19InsCCC) are not associated with juvenile idiopathic epilepsy in Egyptian Arabian horses.. Genes 2019;10(10):816.
    doi: 10.3390/genes10100816pmc: PMC6826448pubmed: 31623255google scholar: lookup
  73. Aleman M, Finno CJ, Weich K, Penedo MCT. Investigation of known genetic mutations of Arabian horses in Egyptian Arabian foals with juvenile idiopathic epilepsy.. J. Vet. Intern. Med. 2018;32(1):465–468.
    doi: 10.1111/jvim.14873pmc: PMC5787150pubmed: 29171123google scholar: lookup
  74. Valberg SJ, Henry ML, Herrick KL, Velez-Irizarry D, Finno CJ, Petersen JL. Absence of myofibrillar myopathy in Quarter Horses with a histopathological diagnosis of type 2 polysaccharide storage myopathy and lack of association with commercial genetic tests.. Equine Vet. J. 2023;55(2):230–238.
    doi: 10.1111/evj.13574pmc: PMC10084132pubmed: 35288976google scholar: lookup
  75. Valberg SJ, Finno CJ, Henry ML, Schott M, Velez-Irizarry D, Peng S, McKenzie EC, Petersen JL. Commercial genetic testing for type 2 polysaccharide storage myopathy and myofibrillar myopathy does not correspond to a histopathological diagnosis.. Equine Vet. J. 2021;53(4):690–700.
    doi: 10.1111/evj.13345pmc: PMC7937766pubmed: 32896939google scholar: lookup
  76. Tandy-Connor S, Guiltinan J, Krempely K, LaDuca H, Reineke P, Gutierrez S, Gray P, Tippin DB. False-positive results released by direct-to-consumer genetic tests highlight the importance of clinical confirmation testing for appropriate patient care.. Genet. Med. 2018;20(12):1515–1521.
    doi: 10.1038/gim.2018.38pmc: PMC6301953pubmed: 29565420google scholar: lookup
  77. Goldstein DB, Allen A, Keebler J, Margulies EH, Petrou S, Petrovski S, Sunyaev S. Sequencing studies in human genetics: Design and interpretation.. Nat. Rev. 2013;14(7):460–470.
    doi: 10.1038/nrg3455google scholar: lookup
  78. Mitchell AA, Chakravarti A, Cutler DJ. On the probability that a novel variant is a disease-causing mutation.. Genome Res. 2005;15(7):960–966.
    doi: 10.1101/gr.3761405pmc: PMC1172040pubmed: 15965029google scholar: lookup
  79. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL, Committee ALQA. Standards and guidelines for the interpretation of sequence variants: A joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.. Genet. Med. Off. J. Am. Coll. Med. Genet. 2015;17(5):405–424.
  80. Nykamp K, Anderson M, Powers M, Garcia J, Herrera B. Sherloc: A comprehensive refinement of the ACMG-AMP variant classification criteria.. Genet. Med. 2017;19(10):1105–1117.
    doi: 10.1038/gim.2017.37pmc: PMC5632818pubmed: 28492532google scholar: lookup
  81. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.. Bioinformatics 2011;27:2987–2993.
  82. McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, Flicek P, Cunningham F. The ensembl variant effect predictor.. Genome Biol. 2016;17:1–14.
    doi: 10.1186/s13059-016-0974-4pmc: PMC4707776pubmed: 26753840google scholar: lookup
  83. Cingolani P, Platts A, le Wang 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;6(2):80–92.
    doi: 10.4161/fly.19695pmc: PMC3679285pubmed: 22728672google scholar: lookup
  84. Cingolani P, Patel VM, Coon M, Nguyen T, Land SJ, Ruden DM, Lu X. Using Drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift.. Front. Genet. 2012;3:35–35.
    doi: 10.3389/fgene.2012.00035pmc: PMC3304048pubmed: 22435069google scholar: lookup
  85. Robinson JT. Integrative genomics viewer.. Nature biotechnology 2011;29(1):24–26.
  86. Lenth RV. emmeans: Estimated Marginal Means, aka Least-Squares Means.. 2018.
  87. Team RC. R: A language environment for statistical computing.. 2013.

Citations

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