FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Front Genet / Genetic Variation and the Distribution of Variant Types in t...
Frontiers in genetics2021; 12; 758366; doi: 10.3389/fgene.2021.758366

Genetic Variation and the Distribution of Variant Types in the Horse.

Abstract: Genetic variation is a key contributor to health and disease. Understanding the link between an individual's genotype and the corresponding phenotype is a major goal of medical genetics. Whole genome sequencing (WGS) within and across populations enables highly efficient variant discovery and elucidation of the molecular nature of virtually all genetic variation. Here, we report the largest catalog of genetic variation for the horse, a species of importance as a model for human athletic and performance related traits, using WGS of 534 horses. We show the extent of agreement between two commonly used variant callers. In data from ten target breeds that represent major breed clusters in the domestic horse, we demonstrate the distribution of variants, their allele frequencies across breeds, and identify variants that are unique to a single breed. We investigate variants with no homozygotes that may be potential embryonic lethal variants, as well as variants present in all individuals that likely represent regions of the genome with errors, poor annotation or where the reference genome carries a variant. Finally, we show regions of the genome that have higher or lower levels of genetic variation compared to the genome average. This catalog can be used for variant prioritization for important equine diseases and traits, and to provide key information about regions of the genome where the assembly and/or annotation need to be improved.
Copyright © 2021 Durward-Akhurst, Schaefer, Grantham, Carey, Mickelson and McCue.
Get Full Text
Publication Date: 2021-12-02 PubMed ID: 34925451PubMed Central: PMC8676274DOI: 10.3389/fgene.2021.758366Google 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 is about an extensive catalog of genetic variation in horses created using whole genome sequencing. This comprehensive resource can help in understanding the connection between genotype and phenotype, prioritizing variations for significant equine diseases, and revealing areas of the genome that need better assembly or annotation.

Objective of the Study

  • The study aimed to create a significant catalog of genetic variation in horses, an important model species for human athletic and performance traits, using whole-genome sequencing (WGS).
  • The research sought to link genetic format (genotype) with physical characteristics (phenotype), a major goal in medical genetics.
  • Part of the research also involved comparing the agreement level between two commonly used variant available software to identify genetic variation.

Methodology

  • The researchers performed WGS on 534 horses. The projected target included ten major breeds that represent the major breed clusters in the domestic horse.
  • The distribution of variants and their allele frequencies across breeds were assessed.
  • The team identified variants that were unique to a single breed. They also explored variants with no homozygotes, which could potentially be embryonic lethal variants.
  • They also investigated variants that were present in all individuals. These variants could symbolize regions of the genome with errors, poor annotation, or where the reference genome carries a variant.

Results and Conclusion

  • The research revealed sections of the genome that have comparatively higher or lower levels of genetic variation than the genome average.
  • The catalog can be employed to prioritize variants for essential equine diseases and traits. It also highlights sections of the genome that need improvements in assembly or annotation.

Cite This Article

APA
Durward-Akhurst SA, Schaefer RJ, Grantham B, Carey WK, Mickelson JR, McCue ME. (2021). Genetic Variation and the Distribution of Variant Types in the Horse. Front Genet, 12, 758366. https://doi.org/10.3389/fgene.2021.758366

Publication

Frontiers in genetics
ISSN: 1664-8021
NlmUniqueID: 101560621
Country: Switzerland
Language: English
Volume: 12
Pages: 758366
PII: 758366

Researcher Affiliations

Durward-Akhurst, S A
  • Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, United States.
Schaefer, R J
  • Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, United States.
Grantham, B
  • Interval Bio LLC, Mountain View, CA, United States.
Carey, W K
  • Interval Bio LLC, Mountain View, CA, United States.
Mickelson, J R
  • Department of Veterinary and Biomedical Sciences, University of Minnesota, Minneapolis, MN, United States.
McCue, M E
  • Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, United States.

Grant Funding

  • T32 OD010993 / NIH HHS

Conflict of Interest Statement

Authors BG and WC own and work for IntervalBio LLC, the computational company that was compensated to develop the mapping and variant calling pipeline. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

This article includes 58 references
  1. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella KV, Altshuler D, Gabriel S, DePristo MA. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.. Curr Protoc Bioinformatics 2013;43(1110):11.10.1-11.10.33.
    doi: 10.1002/0471250953.bi1110s43pmc: PMC4243306pubmed: 25431634google scholar: lookup
  2. 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.
    doi: 10.3389/fgene.2018.00249pmc: PMC6060370pubmed: 30105047google scholar: lookup
  3. Beeson SK, Mickelson JR, McCue ME. Exploration of fine-scale recombination rate variation in the domestic horse.. Genome Res 2019 Oct;29(10):1744-1752.
    doi: 10.1101/gr.243311.118pmc: PMC6771410pubmed: 31434677google scholar: lookup
  4. Bellone RR, Liu J, Petersen JL, Mack M, Singer-Berk M, Drögemüller C, Malvick J, Wallner B, Brem G, Penedo MC, Lassaline M. A missense mutation in damage-specific DNA binding protein 2 is a genetic risk factor for limbal squamous cell carcinoma in horses.. Int J Cancer 2017 Jul 15;141(2):342-353.
    doi: 10.1002/ijc.30744pubmed: 28425625google scholar: lookup
  5. Bomba L, Walter K, Soranzo N. The impact of rare and low-frequency genetic variants in common disease.. Genome Biol 2017 Apr 27;18(1):77.
    doi: 10.1186/s13059-017-1212-4pmc: PMC5408830pubmed: 28449691google scholar: lookup
  6. 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.
    doi: 10.3389/fgene.2012.00035pmc: PMC3304048pubmed: 22435069google scholar: lookup
  7. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.. Fly (Austin) 2012 Apr-Jun;6(2):80-92.
    doi: 10.4161/fly.19695pmc: PMC3679285pubmed: 22728672google scholar: lookup
  8. Daetwyler HD, Capitan A, Pausch H, Stothard P, van Binsbergen R, Brøndum RF, Liao X, Djari A, Rodriguez SC, Grohs C, Esquerré D, Bouchez O, Rossignol MN, Klopp C, Rocha D, Fritz S, Eggen A, Bowman PJ, Coote D, Chamberlain AJ, Anderson C, VanTassell CP, Hulsegge I, Goddard ME, Guldbrandtsen B, Lund MS, Veerkamp RF, Boichard DA, Fries R, Hayes BJ. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle.. Nat Genet 2014 Aug;46(8):858-65.
    doi: 10.1038/ng.3034pubmed: 25017103google scholar: lookup
  9. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ. A framework for variation discovery and genotyping using next-generation DNA sequencing data.. Nat Genet 2011 May;43(5):491-8.
    doi: 10.1038/ng.806pmc: PMC3083463pubmed: 21478889google scholar: lookup
  10. Dolzhenko E, van Vugt JJFA, Shaw RJ, Bekritsky MA, van Blitterswijk M, Narzisi G, Ajay SS, Rajan V, Lajoie BR, Johnson NH, Kingsbury Z, Humphray SJ, Schellevis RD, Brands WJ, Baker M, Rademakers R, Kooyman M, Tazelaar GHP, van Es MA, McLaughlin R, Sproviero W, Shatunov A, Jones A, Al Khleifat A, Pittman A, Morgan S, Hardiman O, Al-Chalabi A, Shaw C, Smith B, Neo EJ, Morrison K, Shaw PJ, Reeves C, Winterkorn L, Wexler NS, Housman DE, Ng CW, Li AL, Taft RJ, van den Berg LH, Bentley DR, Veldink JH, Eberle MA. Detection of long repeat expansions from PCR-free whole-genome sequence data.. Genome Res 2017 Nov;27(11):1895-1903.
    doi: 10.1101/gr.225672.117pmc: PMC5668946pubmed: 28887402google scholar: lookup
  11. Eldomery MK, Coban-Akdemir Z, Harel T, Rosenfeld JA, Gambin T, Stray-Pedersen A, Küry S, Mercier S, Lessel D, Denecke J, Wiszniewski W, Penney S, Liu P, Bi W, Lalani SR, Schaaf CP, Wangler MF, Bacino CA, Lewis RA, Potocki L, Graham BH, Belmont JW, Scaglia F, Orange JS, Jhangiani SN, Chiang T, Doddapaneni H, Hu J, Muzny DM, Xia F, Beaudet AL, Boerwinkle E, Eng CM, Plon SE, Sutton VR, Gibbs RA, Posey JE, Yang Y, Lupski JR. Lessons learned from additional research analyses of unsolved clinical exome cases.. Genome Med 2017 Mar 21;9(1):26.
    doi: 10.1186/s13073-017-0412-6pmc: PMC5361813pubmed: 28327206google scholar: lookup
  12. Ellingford JM, Barton S, Bhaskar S, Williams SG, Sergouniotis PI, O'Sullivan J, Lamb JA, Perveen R, Hall G, Newman WG, Bishop PN, Roberts SA, Leach R, Tearle R, Bayliss S, Ramsden SC, Nemeth AH, Black GC. Whole Genome Sequencing Increases Molecular Diagnostic Yield Compared with Current Diagnostic Testing for Inherited Retinal Disease.. Ophthalmology 2016 May;123(5):1143-50.
    doi: 10.1016/j.ophtha.2016.01.009pmc: PMC4845717pubmed: 26872967google scholar: lookup
  13. Farwell KD, Shahmirzadi L, El-Khechen D, Powis Z, Chao EC, Tippin Davis B, Baxter RM, Zeng W, Mroske C, Parra MC, Gandomi SK, Lu I, Li X, Lu H, Lu HM, Salvador D, Ruble D, Lao M, Fischbach S, Wen J, Lee S, Elliott A, Dunlop CL, Tang S. Enhanced utility of family-centered diagnostic exome sequencing with inheritance model-based analysis: results from 500 unselected families with undiagnosed genetic conditions.. Genet Med 2015 Jul;17(7):578-86.
    doi: 10.1038/gim.2014.154pubmed: 25356970google scholar: lookup
  14. Field MA, Cho V, Andrews TD, Goodnow CC. Reliably Detecting Clinically Important Variants Requires Both Combined Variant Calls and Optimized Filtering Strategies.. PLoS One 2015;10(11):e0143199.
    doi: 10.1371/journal.pone.0143199pmc: PMC4658170pubmed: 26600436google scholar: lookup
  15. 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 Apr;11(4):e1005122.
    doi: 10.1371/journal.pgen.1005122pmc: PMC4395385pubmed: 25875171google scholar: lookup
  16. 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 Oct 28;467(7319):1061-73.
    doi: 10.1038/nature09534pmc: PMC3042601pubmed: 20981092google scholar: lookup
  17. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA. An integrated map of genetic variation from 1,092 human genomes.. Nature 2012 Nov 1;491(7422):56-65.
    doi: 10.1038/nature11632pmc: PMC3498066pubmed: 23128226google scholar: lookup
  18. Guo Y, Ye F, Sheng Q, Clark T, Samuels DC. Three-stage quality control strategies for DNA re-sequencing data.. Brief Bioinform 2014 Nov;15(6):879-89.
    doi: 10.1093/bib/bbt069pmc: PMC4492405pubmed: 24067931google scholar: lookup
  19. Hartmannová H, Piherová L, Tauchmannová K, Kidd K, Acott PD, Crocker JF, Oussedik Y, Mallet M, Hodaňová K, Stránecký V, Přistoupilová A, Barešová V, Jedličková I, Živná M, Sovová J, Hůlková H, Robins V, Vrbacký M, Pecina P, Kaplanová V, Houštěk J, Mráček T, Thibeault Y, Bleyer AJ, Kmoch S. Acadian variant of Fanconi syndrome is caused by mitochondrial respiratory chain complex I deficiency due to a non-coding mutation in complex I assembly factor NDUFAF6.. Hum Mol Genet 2016 Sep 15;25(18):4062-4079.
    doi: 10.1093/hmg/ddw245pubmed: 27466185google scholar: lookup
  20. 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 Genomics 2010 Oct 11;11:552.
    doi: 10.1186/1471-2164-11-552pmc: PMC3091701pubmed: 20932346google scholar: lookup
  21. Hofer T, Ray N, Wegmann D, Excoffier L. Large allele frequency differences between human continental groups are more likely to have occurred by drift during range expansions than by selection.. Ann Hum Genet 2009 Jan;73(1):95-108.
    doi: 10.1111/j.1469-1809.2008.00489.xpubmed: 19040659google scholar: lookup
  22. Huang N, Lee I, Marcotte EM, Hurles ME. Characterising and predicting haploinsufficiency in the human genome.. PLoS Genet 2010 Oct 14;6(10):e1001154.
    doi: 10.1371/journal.pgen.1001154pmc: PMC2954820pubmed: 20976243google scholar: lookup
  23. Jagannathan V, Drögemüller C, Leeb T. A comprehensive biomedical variant catalogue based on whole genome sequences of 582 dogs and eight wolves.. Anim Genet 2019 Dec;50(6):695-704.
    doi: 10.1111/age.12834pmc: PMC6842318pubmed: 31486122google scholar: lookup
  24. 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 Feb;50(1):74-77.
    doi: 10.1111/age.12753pubmed: 30525216google scholar: lookup
  25. Kalbfleisch TS, Rice ES, DePriest MS, Walenz BP, Hestand MS, Vermeesch JR. Eq쪳, an Updated Reference Genome for the Domestic Horse. bioRxiv .
    doi: 10.1101/306928google scholar: lookup
  26. Känsäkoski J, Jääskeläinen J, Jääskeläinen T, Tommiska J, Saarinen L, Lehtonen R, Hautaniemi S, Frilander MJ, Palvimo JJ, Toppari J, Raivio T. Complete androgen insensitivity syndrome caused by a deep intronic pseudoexon-activating mutation in the androgen receptor gene.. Sci Rep 2016 Sep 9;6:32819.
    doi: 10.1038/srep32819pmc: PMC5016895pubmed: 27609317google scholar: lookup
  27. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q. Variation across 141,456 Human Exomes and Genomes Reveals the Spectrum of Loss-Of-Function Intolerance across Human Protein-Coding Genes. bioRxiv .
    doi: 10.1101/531210google scholar: lookup
  28. Kojima K, Kawai Y, Misawa K, Mimori T, Nagasaki M. STR-realigner: a realignment method for short tandem repeat regions.. BMC Genomics 2016 Dec 3;17(1):991.
    doi: 10.1186/s12864-016-3294-xpmc: PMC5135796pubmed: 27912743google scholar: lookup
  29. Leinonen R, Sugawara H, Shumway M. The sequence read archive.. Nucleic Acids Res 2011 Jan;39(Database issue):D19-21.
    doi: 10.1093/nar/gkq1019pmc: PMC3013647pubmed: 21062823google scholar: lookup
  30. Lenth RV. Emmeans: Estimated Marginal Means, Aka Least-Squares Means. .
  31. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.. Bioinformatics 2011 Nov 1;27(21):2987-93.
    doi: 10.1093/bioinformatics/btr509pmc: PMC3198575pubmed: 21903627google scholar: lookup
  32. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.. Bioinformatics 2009 Jul 15;25(14):1754-60.
    doi: 10.1093/bioinformatics/btp324pmc: PMC2705234pubmed: 19451168google scholar: lookup
  33. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The Sequence Alignment/Map format and SAMtools.. Bioinformatics 2009 Aug 15;25(16):2078-9.
    doi: 10.1093/bioinformatics/btp352pmc: PMC2723002pubmed: 19505943google scholar: lookup
  34. Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K, Wang J. SNP detection for massively parallel whole-genome resequencing.. Genome Res 2009 Jun;19(6):1124-32.
    doi: 10.1101/gr.088013.108pmc: PMC2694485pubmed: 19420381google scholar: lookup
  35. McCoy AM, Beeson SK, Splan RK, Lykkjen S, Ralston SL, Mickelson JR, McCue ME. Identification and validation of risk loci for osteochondrosis in standardbreds.. BMC Genomics 2016 Jan 12;17:41.
    doi: 10.1186/s12864-016-2385-zpmc: PMC4709891pubmed: 26753841google scholar: lookup
  36. 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.. Osteoarthritis Cartilage 2013 Nov;21(11):1638-47.
    doi: 10.1016/j.joca.2013.08.011pmc: PMC3815567pubmed: 23954774google scholar: lookup
  37. 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 May;91(5):458-66.
    doi: 10.1016/j.ygeno.2008.01.011pmc: PMC2430182pubmed: 18358695google scholar: lookup
  38. McIlwraith CW, Frisbie DD, Kawcak CE. The horse as a model of naturally occurring osteoarthritis.. Bone Joint Res 2012 Nov;1(11):297-309.
    doi: 10.1302/2046-3758.111.2000132pmc: PMC3626203pubmed: 23610661google scholar: lookup
  39. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.. Genome Res 2010 Sep;20(9):1297-303.
    doi: 10.1101/gr.107524.110pmc: PMC2928508pubmed: 20644199google scholar: lookup
  40. Mu XJ, Lu ZJ, Kong Y, Lam HY, Gerstein MB. Analysis of genomic variation in non-coding elements using population-scale sequencing data from the 1000 Genomes Project.. Nucleic Acids Res 2011 Sep 1;39(16):7058-76.
    doi: 10.1093/nar/gkr342pmc: PMC3167619pubmed: 21596777google scholar: lookup
  41. Noll AC, Miller NA, Smith LD, Yoo B, Fiedler S, Cooley LD, Willig LK, Petrikin JE, Cakici J, Lesko J, Newton A, Detherage K, Thiffault I, Saunders CJ, Farrow EG, Kingsmore SF. Clinical detection of deletion structural variants in whole-genome sequences.. NPJ Genom Med 2016;1:16026.
    doi: 10.1038/npjgenmed.2016.26pmc: PMC5685307pubmed: 29263817google scholar: lookup
  42. Norton EM, Avila F, Schultz NE, Mickelson JR, Geor RJ, McCue ME. Evaluation of an HMGA2 variant for pleiotropic effects on height and metabolic traits in ponies.. J Vet Intern Med 2019 Mar;33(2):942-952.
    doi: 10.1111/jvim.15403pmc: PMC6430908pubmed: 30666754google scholar: lookup
  43. 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 Nov;107(6):537-43.
    doi: 10.1093/jhered/esw042pmc: PMC5006745pubmed: 27489252google scholar: lookup
  44. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Distl O, Felicetti M, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Silvestrelli M, Swinburne J, Tozaki T, Vaudin M, M Wade C, McCue ME. Genetic diversity in the modern horse illustrated from genome-wide SNP data.. PLoS One 2013;8(1):e54997.
    doi: 10.1371/journal.pone.0054997pmc: PMC3559798pubmed: 23383025google scholar: lookup
  45. Raudsepp T, Finno CJ, Bellone RR, Petersen JL. Ten years of the horse reference genome: insights into equine biology, domestication and population dynamics in the post-genome era.. Anim Genet 2019 Dec;50(6):569-597.
    doi: 10.1111/age.12857pmc: PMC6825885pubmed: 31568563google scholar: lookup
  46. Rebolledo-Mendez J, Hestand MS, Coleman SJ, Zeng Z, Orlando L, MacLeod JN, Kalbfleisch T. Comparison of the Equine Reference Sequence with Its Sanger Source Data and New Illumina Reads.. PLoS One 2015;10(6):e0126852.
    doi: 10.1371/journal.pone.0126852pmc: PMC4479572pubmed: 26107638google scholar: lookup
  47. 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
  48. Schaefer RJ, Schubert M, Bailey E, Bannasch DL, Barrey E, Bar-Gal GK, Brem G, Brooks SA, Distl O, Fries R, Finno CJ, Gerber V, Haase B, Jagannathan V, Kalbfleisch T, Leeb T, Lindgren G, Lopes MS, Mach N, da Câmara Machado A, MacLeod JN, McCoy A, Metzger J, Penedo C, Polani S, Rieder S, Tammen I, Tetens J, Thaller G, Verini-Supplizi A, Wade CM, Wallner B, Orlando L, Mickelson JR, McCue ME. Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds.. BMC Genomics 2017 Jul 27;18(1):565.
    doi: 10.1186/s12864-017-3943-8pmc: PMC5530493pubmed: 28750625google scholar: lookup
  49. Schneider VA, Graves-Lindsay T, Howe K, Bouk N, Chen HC, Kitts PA, Murphy TD, Pruitt KD, Thibaud-Nissen F, Albracht D, Fulton RS, Kremitzki M, Magrini V, Markovic C, McGrath S, Steinberg KM, Auger K, Chow W, Collins J, Harden G, Hubbard T, Pelan S, Simpson JT, Threadgold G, Torrance J, Wood JM, Clarke L, Koren S, Boitano M, Peluso P, Li H, Chin CS, Phillippy AM, Durbin R, Wilson RK, Flicek P, Eichler EE, Church DM. Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly.. Genome Res 2017 May;27(5):849-864.
    doi: 10.1101/gr.213611.116pmc: PMC5411779pubmed: 28396521google scholar: lookup
  50. Schultz N. Characterization of Equine Metabolic Syndrome and Mapping of Candidate Genetic Loci. .
  51. Smedley D, Schubach M, Jacobsen JOB, Köhler S, Zemojtel T, Spielmann M, Jäger M, Hochheiser H, Washington NL, McMurry JA, Haendel MA, Mungall CJ, Lewis SE, Groza T, Valentini G, Robinson PN. A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease.. Am J Hum Genet 2016 Sep 1;99(3):595-606.
    doi: 10.1016/j.ajhg.2016.07.005pmc: PMC5011059pubmed: 27569544google scholar: lookup
  52. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH, Konkel MK, Malhotra A, Stütz AM, Shi X, Casale FP, Chen J, Hormozdiari F, Dayama G, Chen K, Malig M, Chaisson MJP, Walter K, Meiers S, Kashin S, Garrison E, Auton A, Lam HYK, Mu XJ, Alkan C, Antaki D, Bae T, Cerveira E, Chines P, Chong Z, Clarke L, Dal E, Ding L, Emery S, Fan X, Gujral M, Kahveci F, Kidd JM, Kong Y, Lameijer EW, McCarthy S, Flicek P, Gibbs RA, Marth G, Mason CE, Menelaou A, Muzny DM, Nelson BJ, Noor A, Parrish NF, Pendleton M, Quitadamo A, Raeder B, Schadt EE, Romanovitch M, Schlattl A, Sebra R, Shabalin AA, Untergasser A, Walker JA, Wang M, Yu F, Zhang C, Zhang J, Zheng-Bradley X, Zhou W, Zichner T, Sebat J, Batzer MA, McCarroll SA, Mills RE, Gerstein MB, Bashir A, Stegle O, Devine SE, Lee C, Eichler EE, Korbel JO. An integrated map of structural variation in 2,504 human genomes.. Nature 2015 Oct 1;526(7571):75-81.
    doi: 10.1038/nature15394pmc: PMC4617611pubmed: 26432246google scholar: lookup
  53. Tenesa A, Navarro P, Hayes BJ, Duffy DL, Clarke GM, Goddard ME, Visscher PM. Recent human effective population size estimated from linkage disequilibrium.. Genome Res 2007 Apr;17(4):520-6.
    doi: 10.1101/gr.6023607pmc: PMC1832099pubmed: 17351134google scholar: lookup
  54. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR, Blöcker H, Distl O, Edgar RC, Garber M, Leeb T, Mauceli E, MacLeod JN, Penedo MC, Raison JM, Sharpe T, Vogel J, Andersson L, Antczak DF, Biagi T, Binns MM, Chowdhary BP, Coleman SJ, Della Valle G, Fryc S, Guérin G, Hasegawa T, Hill EW, Jurka J, Kiialainen A, Lindgren G, Liu J, Magnani E, Mickelson JR, Murray J, Nergadze SG, Onofrio R, Pedroni S, Piras MF, Raudsepp T, Rocchi M, Røed KH, Ryder OA, Searle S, Skow L, Swinburne JE, Syvänen AC, Tozaki T, Valberg SJ, Vaudin M, White JR, Zody MC, Lander ES, Lindblad-Toh K. Genome sequence, comparative analysis, and population genetics of the domestic horse.. Science 2009 Nov 6;326(5954):865-7.
    doi: 10.1126/science.1178158pmc: PMC3785132pubmed: 19892987google scholar: lookup
  55. Wang J, Raskin L, Samuels DC, Shyr Y, Guo Y. Genome measures used for quality control are dependent on gene function and ancestry.. Bioinformatics 2015 Feb 1;31(3):318-23.
    doi: 10.1093/bioinformatics/btu668pmc: PMC4308666pubmed: 25297068google scholar: lookup
  56. 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 2004 Jul;15(7):570-7.
    doi: 10.1007/s00335-004-2369-1pubmed: 15366377google scholar: lookup
  57. Yang Y, Muzny DM, Xia F, Niu Z, Person R, Ding Y, Ward P, Braxton A, Wang M, Buhay C, Veeraraghavan N, Hawes A, Chiang T, Leduc M, Beuten J, Zhang J, He W, Scull J, Willis A, Landsverk M, Craigen WJ, Bekheirnia MR, Stray-Pedersen A, Liu P, Wen S, Alcaraz W, Cui H, Walkiewicz M, Reid J, Bainbridge M, Patel A, Boerwinkle E, Beaudet AL, Lupski JR, Plon SE, Gibbs RA, Eng CM. Molecular findings among patients referred for clinical whole-exome sequencing.. JAMA 2014 Nov 12;312(18):1870-9.
    doi: 10.1001/jama.2014.14601pmc: PMC4326249pubmed: 25326635google scholar: lookup
  58. Yngvadottir B, Xue Y, Searle S, Hunt S, Delgado M, Morrison J, Whittaker P, Deloukas P, Tyler-Smith C. A genome-wide survey of the prevalence and evolutionary forces acting on human nonsense SNPs.. Am J Hum Genet 2009 Feb;84(2):224-34.
    doi: 10.1016/j.ajhg.2009.01.008pmc: PMC2668024pubmed: 19200524google scholar: lookup

Citations

This article has been cited 10 times.
  1. 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 (Bethesda) 2023 Aug 9;13(8).
    doi: 10.1093/g3journal/jkad117pubmed: 37243692google scholar: lookup
  2. Tozaki T, Ohnuma A, Kikuchi M, Ishige T, Kakoi H, Hirota KI, Takahashi Y, Nagata SI. Short Insertion and Deletion Discoveries via Whole-Genome Sequencing of 101 Thoroughbred Racehorses. Genes (Basel) 2023 Mar 3;14(3).
    doi: 10.3390/genes14030638pubmed: 36980910google scholar: lookup
  3. van der Graaf L, Leigh W, Szmatoła T, Roberts K, Ryan S, Brown B, Van Buren S, Finno CJ, Petersen JL. A missense mutation in the KCNE4 gene is not predictive of equine anhidrosis. Anim Genet 2025 Feb;56(1):e70004.
    doi: 10.1111/age.70004pubmed: 39953936google scholar: lookup
  4. Bailey E, Finno CJ, Cullen JN, Kalbfleisch T, Petersen JL. Analyses of whole-genome sequences from 185 North American Thoroughbred horses, spanning 5 generations. Sci Rep 2024 Oct 2;14(1):22930.
    doi: 10.1038/s41598-024-73645-9pubmed: 39358442google scholar: lookup
  5. Lovász L, Sommer-Trembo C, Barth JMI, Scasta JD, Grancharova-Hill R, Lemoine RT, Kerekes V, Merckling L, Bouskila A, Svenning JC, Fages A. Rewilded horses in European nature conservation - a genetics, ethics, and welfare perspective. Biol Rev Camb Philos Soc 2025 Feb;100(1):407-427.
    doi: 10.1111/brv.13146pubmed: 39279124google scholar: lookup
  6. Durward-Akhurst SA, Marlowe JL, Schaefer RJ, Springer K, Grantham B, Carey WK, Bellone RR, Mickelson JR, McCue ME. Predicted genetic burden and frequency of phenotype-associated variants in the horse. Sci Rep 2024 Apr 10;14(1):8396.
    doi: 10.1038/s41598-024-57872-8pubmed: 38600096google scholar: lookup
  7. Palomino Lago E, Baird A, Blott SC, McPhail RE, Ross AC, Durward-Akhurst SA, Guest DJ. A Functional Single-Nucleotide Polymorphism Upstream of the Collagen Type III Gene Is Associated with Catastrophic Fracture Risk in Thoroughbred Horses. Animals (Basel) 2023 Dec 28;14(1).
    doi: 10.3390/ani14010116pubmed: 38200847google scholar: lookup
  8. Ma Y, Peng S, Donnelly CG, Ghosh S, Miller AD, Woolard K, Finno CJ. Genetic polymorphisms in vitamin E transport genes as determinants for risk of equine neuroaxonal dystrophy. J Vet Intern Med 2024 Jan-Feb;38(1):417-423.
    doi: 10.1111/jvim.16924pubmed: 37937700google scholar: lookup
  9. Zhang YJ, Luo Z, Sun Y, Liu J, Chen Z. From beasts to bytes: Revolutionizing zoological research with artificial intelligence. Zool Res 2023 Nov 18;44(6):1115-1131.
    doi: 10.24272/j.issn.2095-8137.2023.263pubmed: 37933101google scholar: lookup
  10. Tozaki T, Ohnuma A, Kikuchi M, Ishige T, Kakoi H, Hirota KI, Nagata SI. Construction of an individual identification panel for horses using insertion and deletion markers. J Equine Sci 2023 Sep;34(3):83-92.
    doi: 10.1294/jes.34.83pubmed: 37781568google scholar: lookup
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.