FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
BMC genomics2017; 18(1); 565; doi: 10.1186/s12864-017-3943-8

Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds.

Abstract: To date, genome-scale analyses in the domestic horse have been limited by suboptimal single nucleotide polymorphism (SNP) density and uneven genomic coverage of the current SNP genotyping arrays. The recent availability of whole genome sequences has created the opportunity to develop a next generation, high-density equine SNP array. Using whole genome sequence from 153 individuals representing 24 distinct breeds collated by the equine genomics community, we cataloged over 23 million de novo discovered genetic variants. Leveraging genotype data from individuals with both whole genome sequence, and genotypes from lower-density, legacy SNP arrays, a subset of ~5 million high-quality, high-density array candidate SNPs were selected based on breed representation and uniform spacing across the genome. Considering probe design recommendations from a commercial vendor (Affymetrix, now Thermo Fisher Scientific) a set of ~2 million SNPs were selected for a next-generation high-density SNP chip (MNEc2M). Genotype data were generated using the MNEc2M array from a cohort of 332 horses from 20 breeds and a lower-density array, consisting of ~670 thousand SNPs (MNEc670k), was designed for genotype imputation. Here, we document the steps taken to design both the MNEc2M and MNEc670k arrays, report genomic and technical properties of these genotyping platforms, and demonstrate the imputation capabilities of these tools for the domestic horse.
Get Full Text
Publication Date: 2017-07-27 PubMed ID: 28750625PubMed Central: PMC5530493DOI: 10.1186/s12864-017-3943-8Google 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
  • Research Support
  • Non-U.S. Gov't

Summary

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

This research focuses on the development of a genotyping array to identify approximately 2 million genetic variants across 24 horse breeds using 670 thousand single nucleotide polymorphisms (SNPs).

Introduction

  • Genome-scale analyses of domestic horses have been previously limited due to inadequacies in single nucleotide polymorphism (SNP) density and inconsistency in genomic coverage of the available SNP genotyping arrays.
  • The recent availability of whole genome sequences paves the way for the development of a next-generation, high-density equine SNP array.

Development of High-Density Equine SNP Array

  • Using the whole genome sequence data of 153 individuals from 24 different horse breeds, researchers identified over 23 million de novo genetic variants.
  • The team then used genotype data from individuals who had both whole genome sequence data and genotypes from lower-density, older SNP arrays to identify a set of approximately 5 million high-quality candidate SNPs. Selection was based on breed representation and uniform spacing across the genome.

Final SNP Selection

  • Guided by probe design recommendations from commercial vendor Affymetrix, now Thermo Fisher Scientific, the researchers narrowed down a set of about 2 million SNPs to generate a next-generation high-density SNP chip, referred to as MNEc2M.
  • They also designed a lower-density array, consisting of approximately 670 thousand SNPs, referred to as MNEc670k, intended for genotype imputation.

Testing and Validation of the MNEc2M and MNEc670k Arrays

  • The researchers then tested these new arrays by generating genotype data from a cohort of 332 horses from 20 breeds.
  • Their research documents the design steps taken for both the MNEc2M and MNEc670k arrays and reports on the genomic and technical properties of these genotyping platforms.
  • They also demonstrate the potential of these new genotyping arrays in improving genotype imputation, a statistical method used to predict unknown genotypes, for domestic horses.

Cite This Article

APA
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. (2017). Developing a 670k genotyping array to tag ~2M SNPs across 24 horse breeds. BMC Genomics, 18(1), 565. https://doi.org/10.1186/s12864-017-3943-8

Publication

BMC genomics
ISSN: 1471-2164
NlmUniqueID: 100965258
Country: England
Language: English
Volume: 18
Issue: 1
Pages: 565
PII: 565

Researcher Affiliations

Schaefer, Robert J
  • Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Schubert, Mikkel
  • Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.
Bailey, Ernest
  • Maxwell H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
Bannasch, Danika L
  • School of Veterinary Medicine, University of California-Davis, Davis, CA, 95616, USA.
Barrey, Eric
  • Unité de Génétique Animale et Biologie Intégrative- UMR1313, INRA, Université Paris-Saclay, AgroParisTech, 78350, Jouy-en-Josas, France.
Bar-Gal, Gila Kahila
  • The Robert H. Smith Faculty of Agriculture, Food and Environment, The Koret School of Veterinary Medicine, The Hebrew University, 76100, Rehovot, Israel.
Brem, Gottfried
  • Institute of Animal Breeding and Genetics, Department of Biomedical Sciences, University of Veterinary Medicine Vienna, Vienna, Austria.
Brooks, Samantha A
  • Department of Animal Science, University of Florida, Gainesville, FL, USA.
Distl, Ottmar
  • Institute for Animal Breeding and Genetics, University of Veterinary Medicine, Hannover, Germany.
Fries, Ruedi
  • Lehrstuhl für Tierzucht der Technischen Universität München, Liesel-Beckmann-Strasse 1, 85354, Freising, Germany.
Finno, Carrie J
  • School of Veterinary Medicine, University of California-Davis, Davis, CA, 95616, USA.
Gerber, Vinzenz
  • Swiss Institute of Equine Medicine, Department of Clinical Veterinary Medicine, Vetsuisse Faculty, University of Bern, and Agroscope, Länggassstrasse 124, 3001, Bern, Switzerland.
Haase, Bianca
  • School of Life and Environmental Sciences, Faculty of Veterinary Science, University of Sydney, Regimental Drive, B19-301 RMC Gunn, Sydney, NSW, 2006, Australia.
Jagannathan, Vidhya
  • Institute of Genetics, University of Bern, 3001, Bern, Switzerland.
Kalbfleisch, Ted
  • Department of Biochemistry and Molecular Biology, School of Medicine, University of Louisville, Louisville, KY, 40202, USA.
Leeb, Tosso
  • Institute of Genetics, University of Bern, 3001, Bern, Switzerland.
Lindgren, Gabriella
  • Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Lopes, Maria Susana
  • Biotechnology Centre of Azores, University of Azores, Angra do heroísmo, Portugal.
Mach, Núria
  • Unité de Génétique Animale et Biologie Intégrative- UMR1313, INRA, Université Paris-Saclay, AgroParisTech, 78350, Jouy-en-Josas, France.
da Câmara Machado, Artur
  • Biotechnology Centre of Azores, University of Azores, Angra do heroísmo, Portugal.
MacLeod, James N
  • Maxwell H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY, USA.
McCoy, Annette
  • Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61802, USA.
Metzger, Julia
  • Institute for Animal Breeding and Genetics, University of Veterinary Medicine, Hannover, Germany.
Penedo, Cecilia
  • Veterinary Genetics Laboratory, University of California Davis, Davis, CA, USA.
Polani, Sagi
  • The Robert H. Smith Faculty of Agriculture, Food and Environment, The Koret School of Veterinary Medicine, The Hebrew University, 76100, Rehovot, Israel.
Rieder, Stefan
  • Agroscope, Swiss National Stud Farm, 1580, Avenches, Switzerland.
Tammen, Imke
  • School of Life and Environmental Sciences, Faculty of Veterinary Science, University of Sydney, Regimental Drive, B19-301 RMC Gunn, Sydney, NSW, 2006, Australia.
Tetens, Jens
  • Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, Hermann-Rodewald-Strasse 6, 24098, Kiel, Germany.
  • Department of Animal Sciences, Functional Breeding Group, Georg-August University Göttingen, Burckhardtweg 2, 37077, Göttingen, Germany.
Thaller, Georg
  • Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, Hermann-Rodewald-Strasse 6, 24098, Kiel, Germany.
Verini-Supplizi, Andrea
  • Department of Veterinary Medicine - Sport Horse Research Centre, University of Perugia, Perugia, Italy.
Wade, Claire M
  • School of Life and Environmental Sciences, Faculty of Veterinary Science, University of Sydney, Regimental Drive, B19-301 RMC Gunn, Sydney, NSW, 2006, Australia.
Wallner, Barbara
  • Institute of Animal Breeding and Genetics, Department of Biomedical Sciences, University of Veterinary Medicine Vienna, Vienna, Austria.
Orlando, Ludovic
  • Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.
  • Laboratoire d'Anthropobiologie Moléculaire et d'Imagerie de Synthèse, CNRS UMR 5288, Université de Toulouse, Université Paul Sabatier, 31000, Toulouse, France.
Mickelson, James R
  • Department of Veterinary and Biomedical Sciences, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
McCue, Molly E
  • Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA. mccų@umn.edu.

MeSH Terms

  • Animals
  • Gene Frequency
  • Genotyping Techniques / methods
  • Genotyping Techniques / standards
  • Horses / genetics
  • Linkage Disequilibrium
  • Oligonucleotide Array Sequence Analysis / methods
  • Oligonucleotide Array Sequence Analysis / standards
  • Polymorphism, Single Nucleotide
  • Reference Standards
  • Whole Genome Sequencing

Grant Funding

  • K01 OD015134 / NIH HHS
  • L40 TR001136 / NCATS NIH HHS

Conflict of Interest Statement

ETHICS APPROVAL AND CONSENT TO PARTICIPATE: DNA samples were previously collected with approval from the Animal Care and Use Committees at the respective institutions. All animal work was performed in accordance and with approval from international and national governing bodies at the institutions where the samples were collected (University of Minnesota Institutional Animal Care and Use Committee (IACUC); University of California, Davis Institutional Animal Care and Use Committee (protocol #17491); University of Kentucky Institutional Animal Care and Use Committee (IACUC); Ethics Committee for Animal Experiments in Uppsala, Sweden (Number C121/14); Institutional animal care and use committee at Cornell University (protocol 2008-0121); University of California, Davis IACUC 19205; Hebrew University’s approval number AG-23476-07; Institutional Animal Care and Use Committee (IACUC), the Lower Saxony state veterinary office- registration number 11A 160/7221.3-2.1-015/11, 8.84-02.05.20.12.066; University of Sydney Animal Ethics Committee: AEC APPROVAL NUMBER: N00/9-2009/3/5109; permit no. BE75/16, veterinary service of the Canton of Bern; Institutional ethics committee of the University of Veterinary Medicine Vienna Good Scientific Practice guidelines and national legislation; Italian Ministry of Agricultural, Food and Forestry Policies (Mipaaf); Ethical Committee of the Canton of Bern (BE33/07, BE58/10 and BE10/13)) No commercial animals were used in this study. Written informed client consent describing the purpose and duration of the study, procedures, potential risks and benefits and containing study contact information were obtained from private owners. CONSENT FOR PUBLICATION: Not applicable. COMPETING INTERESTS: The authors declare that they have no competing interests. PUBLISHER’S NOTE: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

This article includes 49 references
  1. Wade CM, Giulotto E, Sigurdsson S, Zoli M, Gnerre S, Imsland F, Lear TL, Adelson DL, Bailey E, Bellone RR, Blöcker H, Distl O, Edgar RC, Garber M, Leeb T, Mauceli E, MacLeod JN, Penedo MC, Raison JM, Sharpe T, Vogel J, Andersson L, Antczak DF, Biagi T, Binns MM, Chowdhary BP, Coleman SJ, Della Valle G, Fryc S, Guérin G, Hasegawa T, Hill EW, Jurka J, Kiialainen A, Lindgren G, Liu J, Magnani E, Mickelson JR, Murray J, Nergadze SG, Onofrio R, Pedroni S, Piras MF, Raudsepp T, Rocchi M, Røed KH, Ryder OA, Searle S, Skow L, Swinburne JE, Syvänen AC, Tozaki T, Valberg SJ, Vaudin M, White JR, Zody MC, Lander ES, Lindblad-Toh K. Genome sequence, comparative analysis, and population genetics of the domestic horse.. Science 2009 Nov 6;326(5954):865-7.
    pmc: PMC3785132pubmed: 19892987doi: 10.1126/science.1178158google scholar: lookup
  2. McCue ME, Bannasch DL, Petersen JL, Gurr J, Bailey E, Binns MM, Distl O, Guérin G, Hasegawa T, Hill EW, Leeb T, Lindgren G, Penedo MC, Røed KH, Ryder OA, Swinburne JE, Tozaki T, Valberg SJ, Vaudin M, Lindblad-Toh K, Wade CM, Mickelson JR. A high density SNP array for the domestic horse and extant Perissodactyla: utility for association mapping, genetic diversity, and phylogeny studies.. PLoS Genet 2012 Jan;8(1):e1002451.
    pmc: PMC3257288pubmed: 22253606doi: 10.1371/journal.pgen.1002451google scholar: lookup
  3. McCoy AM, McCue ME. Validation of imputation between equine genotyping arrays.. Anim Genet 2014 Feb;45(1):153.
    doi: 10.1111/age.12093pmc: PMC4000747pubmed: 24164665google scholar: lookup
  4. Schubert M, Jónsson H, Chang D, Der Sarkissian C, Ermini L, Ginolhac A, Albrechtsen A, Dupanloup I, Foucal A, Petersen B, Fumagalli M, Raghavan M, Seguin-Orlando A, Korneliussen TS, Velazquez AM, Stenderup J, Hoover CA, Rubin CJ, Alfarhan AH, Alquraishi SA, Al-Rasheid KA, MacHugh DE, Kalbfleisch T, MacLeod JN, Rubin EM, Sicheritz-Ponten T, Andersson L, Hofreiter M, Marques-Bonet T, Gilbert MT, Nielsen R, Excoffier L, Willerslev E, Shapiro B, Orlando L. Prehistoric genomes reveal the genetic foundation and cost of horse domestication.. Proc Natl Acad Sci U S A 2014 Dec 30;111(52):E5661-9.
    pmc: PMC4284583pubmed: 25512547doi: 10.1073/pnas.1416991111google scholar: lookup
  5. Jónsson H, Schubert M, Seguin-Orlando A, Ginolhac A, Petersen L, Fumagalli M, Albrechtsen A, Petersen B, Korneliussen TS, Vilstrup JT, Lear T, Myka JL, Lundquist J, Miller DC, Alfarhan AH, Alquraishi SA, Al-Rasheid KA, Stagegaard J, Strauss G, Bertelsen MF, Sicheritz-Ponten T, Antczak DF, Bailey E, Nielsen R, Willerslev E, Orlando L. Speciation with gene flow in equids despite extensive chromosomal plasticity.. Proc Natl Acad Sci U S A 2014 Dec 30;111(52):18655-60.
    pmc: PMC4284605pubmed: 25453089doi: 10.1073/pnas.1412627111google scholar: lookup
  6. 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.
    pmc: PMC4709891pubmed: 26753841doi: 10.1186/s12864-016-2385-zgoogle scholar: lookup
  7. McQueen CM, Dindot SV, Foster MJ, Cohen ND. Genetic Susceptibility to Rhodococcus equi.. J Vet Intern Med 2015 Nov-Dec;29(6):1648-59.
    pmc: PMC4895676pubmed: 26340305doi: 10.1111/jvim.13616google scholar: lookup
  8. Hauswirth R, Haase B, Blatter M, Brooks SA, Burger D, Drögemüller C, Gerber V, Henke D, Janda J, Jude R, Magdesian KG, Matthews JM, Poncet PA, Svansson V, Tozaki T, Wilkinson-White L, Penedo MC, Rieder S, Leeb T. Mutations in MITF and PAX3 cause "splashed white" and other white spotting phenotypes in horses.. PLoS Genet 2012;8(4):e1002653.
    pmc: PMC3325211pubmed: 22511888doi: 10.1371/journal.pgen.1002653google scholar: lookup
  9. 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.
    pmc: PMC3091701pubmed: 20932346doi: 10.1186/1471-2164-11-552google scholar: lookup
  10. Lykkjen S, Dolvik NI, McCue ME, Rendahl AK, Mickelson JR, Roed KH. Genome-wide association analysis of osteochondrosis of the tibiotarsal joint in Norwegian Standardbred trotters.. Anim Genet 2010 Dec;41 Suppl 2:111-20.
    pubmed: 21070284doi: 10.1111/j.1365-2052.2010.02117.xgoogle scholar: lookup
  11. Raudsepp T, McCue ME, Das PJ, Dobson L, Vishnoi M, Fritz KL, Schaefer R, Rendahl AK, Derr JN, Love CC, Varner DD, Chowdhary BP. Genome-wide association study implicates testis-sperm specific FKBP6 as a susceptibility locus for impaired acrosome reaction in stallions.. PLoS Genet 2012;8(12):e1003139.
    pmc: PMC3527208pubmed: 23284302doi: 10.1371/journal.pgen.1003139google scholar: lookup
  12. Lykkjen S, Dolvik NI, McCue ME, Rendahl AK, Mickelson JR, Røed KH. Equine developmental orthopaedic diseases--a genome-wide association study of first phalanx plantar osteochondral fragments in Standardbred trotters.. Anim Genet 2013 Dec;44(6):766-9.
    pubmed: 23742657doi: 10.1111/age.12064google scholar: lookup
  13. Signer-Hasler H, Flury C, Haase B, Burger D, Simianer H, Leeb T, Rieder S. A genome-wide association study reveals loci influencing height and other conformation traits in horses.. PLoS One 2012;7(5):e37282.
    pmc: PMC3353922pubmed: 22615965doi: 10.1371/journal.pone.0037282google scholar: lookup
  14. Corbin LJ, Blott SC, Swinburne JE, Sibbons C, Fox-Clipsham LY, Helwegen M, Parkin TD, Newton JR, Bramlage LR, McIlwraith CW, Bishop SC, Woolliams JA, Vaudin M. A genome-wide association study of osteochondritis dissecans in the Thoroughbred.. Mamm Genome 2012 Apr;23(3-4):294-303.
    pubmed: 22052004doi: 10.1007/s00335-011-9363-1google 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.
    pmc: PMC4395385pubmed: 25875171doi: 10.1371/journal.pgen.1005122google scholar: lookup
  16. Kader A, Li Y, Dong K, Irwin DM, Zhao Q, He X, Liu J, Pu Y, Gorkhali NA, Liu X, Jiang L, Li X, Guan W, Zhang Y, Wu DD, Ma Y. Population Variation Reveals Independent Selection toward Small Body Size in Chinese Debao Pony.. Genome Biol Evol 2015 Dec 3;8(1):42-50.
    pmc: PMC4758242pubmed: 26637467doi: 10.1093/gbe/evv245google scholar: lookup
  17. Petersen JL, Mickelson JR, Cothran EG, Andersson LS, Axelsson J, Bailey E, Bannasch D, Binns MM, Borges AS, Brama P, da Câmara Machado A, Distl O, Felicetti M, Fox-Clipsham L, Graves KT, Guérin G, Haase B, Hasegawa T, Hemmann K, Hill EW, Leeb T, Lindgren G, Lohi H, Lopes MS, McGivney BA, Mikko S, Orr N, Penedo MC, Piercy RJ, Raekallio M, Rieder S, Røed KH, Silvestrelli M, Swinburne J, Tozaki T, Vaudin M, M Wade C, McCue ME. Genetic diversity in the modern horse illustrated from genome-wide SNP data.. PLoS One 2013;8(1):e54997.
    pmc: PMC3559798pubmed: 23383025doi: 10.1371/journal.pone.0054997google scholar: lookup
  18. Schubert M, Ermini L, Der Sarkissian C, Jónsson H, Ginolhac A, Schaefer R, Martin MD, Fernández R, Kircher M, McCue M, Willerslev E, Orlando L. Characterization of ancient and modern genomes by SNP detection and phylogenomic and metagenomic analysis using PALEOMIX.. Nat Protoc 2014 May;9(5):1056-82.
    pubmed: 24722405doi: 10.1038/nprot.2014.063google scholar: lookup
  19. Orlando L, Ginolhac A, Zhang G, Froese D, Albrechtsen A, Stiller M, Schubert M, Cappellini E, Petersen B, Moltke I, Johnson PL, Fumagalli M, Vilstrup JT, Raghavan M, Korneliussen T, Malaspinas AS, Vogt J, Szklarczyk D, Kelstrup CD, Vinther J, Dolocan A, Stenderup J, Velazquez AM, Cahill J, Rasmussen M, Wang X, Min J, Zazula GD, Seguin-Orlando A, Mortensen C, Magnussen K, Thompson JF, Weinstock J, Gregersen K, Røed KH, Eisenmann V, Rubin CJ, Miller DC, Antczak DF, Bertelsen MF, Brunak S, Al-Rasheid KA, Ryder O, Andersson L, Mundy J, Krogh A, Gilbert MT, Kjær K, Sicheritz-Ponten T, Jensen LJ, Olsen JV, Hofreiter M, Nielsen R, Shapiro B, Wang J, Willerslev E. Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse.. Nature 2013 Jul 4;499(7456):74-8.
    pubmed: 23803765doi: 10.1038/nature12323google scholar: lookup
  20. Der Sarkissian C, Ermini L, Schubert M, Yang MA, Librado P, Fumagalli M, Jónsson H, Bar-Gal GK, Albrechtsen A, Vieira FG, Petersen B, Ginolhac A, Seguin-Orlando A, Magnussen K, Fages A, Gamba C, Lorente-Galdos B, Polani S, Steiner C, Neuditschko M, Jagannathan V, Feh C, Greenblatt CL, Ludwig A, Abramson NI, Zimmermann W, Schafberg R, Tikhonov A, Sicheritz-Ponten T, Willerslev E, Marques-Bonet T, Ryder OA, McCue M, Rieder S, Leeb T, Slatkin M, Orlando L. Evolutionary Genomics and Conservation of the Endangered Przewalski's Horse.. Curr Biol 2015 Oct 5;25(19):2577-83.
    pmc: PMC5104162pubmed: 26412128doi: 10.1016/j.cub.2015.08.032google scholar: lookup
  21. Librado P, Der Sarkissian C, Ermini L, Schubert M, Jónsson H, Albrechtsen A, Fumagalli M, Yang MA, Gamba C, Seguin-Orlando A, Mortensen CD, Petersen B, Hoover CA, Lorente-Galdos B, Nedoluzhko A, Boulygina E, Tsygankova S, Neuditschko M, Jagannathan V, Thèves C, Alfarhan AH, Alquraishi SA, Al-Rasheid KA, Sicheritz-Ponten T, Popov R, Grigoriev S, Alekseev AN, Rubin EM, McCue M, Rieder S, Leeb T, Tikhonov A, Crubézy E, Slatkin M, Marques-Bonet T, Nielsen R, Willerslev E, Kantanen J, Prokhortchouk E, Orlando L. Tracking the origins of Yakutian horses and the genetic basis for their fast adaptation to subarctic environments.. Proc Natl Acad Sci U S A 2015 Dec 15;112(50):E6889-97.
    pmc: PMC4687531pubmed: 26598656doi: 10.1073/pnas.1513696112google scholar: lookup
  22. Metzger J, Tonda R, Beltran S, Agueda L, Gut M, Distl O. Next generation sequencing gives an insight into the characteristics of highly selected breeds versus non-breed horses in the course of domestication.. BMC Genomics 2014 Jul 4;15(1):562.
    pmc: PMC4097168pubmed: 24996778doi: 10.1186/1471-2164-15-562google scholar: lookup
  23. Doan R, Cohen ND, Sawyer J, Ghaffari N, Johnson CD, Dindot SV. Whole-genome sequencing and genetic variant analysis of a Quarter Horse mare.. BMC Genomics 2012 Feb 17;13:78.
    pmc: PMC3309927pubmed: 22340285doi: 10.1186/1471-2164-13-78google scholar: lookup
  24. Andersson LS, Larhammar M, Memic F, Wootz H, Schwochow D, Rubin CJ, Patra K, Arnason T, Wellbring L, Hjälm G, Imsland F, Petersen JL, McCue ME, Mickelson JR, Cothran G, Ahituv N, Roepstorff L, Mikko S, Vallstedt A, Lindgren G, Andersson L, Kullander K. Mutations in DMRT3 affect locomotion in horses and spinal circuit function in mice.. Nature 2012 Aug 30;488(7413):642-6.
    pmc: PMC3523687pubmed: 22932389doi: 10.1038/nature11399google scholar: lookup
  25. Frischknecht M, Neuditschko M, Jagannathan V, Drögemüller C, Tetens J, Thaller G, Leeb T, Rieder S. Imputation of sequence level genotypes in the Franches-Montagnes horse breed.. Genet Sel Evol 2014 Oct 1;46(1):63.
    pmc: PMC4180851pubmed: 25927638doi: 10.1186/s12711-014-0063-7google scholar: lookup
  26. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.. Genome Res 2010 Sep;20(9):1297-303.
    pmc: PMC2928508pubmed: 20644199doi: 10.1101/gr.107524.110google scholar: lookup
  27. 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.
    pmc: PMC3198575pubmed: 21903627doi: 10.1093/bioinformatics/btr509google scholar: lookup
  28. Raghavan V, Bollmann P, Jung GS. A critical investigation of recall and precision as measures of retrieval system performance.. ACM Trans Inf Syst 1989;7:205–29.
  29. Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.. PLoS One 2015;10(3):e0118432.
    pmc: PMC4349800pubmed: 25738806doi: 10.1371/journal.pone.0118432google scholar: lookup
  30. 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.
    pmc: PMC3083463pubmed: 21478889doi: 10.1038/ng.806google scholar: lookup
  31. Liu G, Wang Y, Wong L. FastTagger: an efficient algorithm for genome-wide tag SNP selection using multi-marker linkage disequilibrium.. BMC Bioinformatics 2010 Jan 29;11:66.
    pmc: PMC3098109pubmed: 20113476doi: 10.1186/1471-2105-11-66google scholar: lookup
  32. Howie BN, Carlson CS, Rieder MJ, Nickerson DA. Efficient selection of tagging single-nucleotide polymorphisms in multiple populations.. Hum Genet 2006 Aug;120(1):58-68.
    doi: 10.1007/s00439-006-0182-5pubmed: 16680432google scholar: lookup
  33. Browning BL, Browning SR. Genotype Imputation with Millions of Reference Samples.. Am J Hum Genet 2016 Jan 7;98(1):116-26.
    pmc: PMC4716681pubmed: 26748515doi: 10.1016/j.ajhg.2015.11.020google scholar: lookup
  34. von Hippel PT. Mean, Median, and Skew: Correcting a Textbook Rule.. J. Stat. Educ. American Statistical Association 2005;13.
  35. Kranis A, Gheyas AA, Boschiero C, Turner F, Yu L, Smith S, Talbot R, Pirani A, Brew F, Kaiser P, Hocking PM, Fife M, Salmon N, Fulton J, Strom TM, Haberer G, Weigend S, Preisinger R, Gholami M, Qanbari S, Simianer H, Watson KA, Woolliams JA, Burt DW. Development of a high density 600K SNP genotyping array for chicken.. BMC Genomics 2013 Jan 28;14:59.
    pmc: PMC3598943pubmed: 23356797doi: 10.1186/1471-2164-14-59google scholar: lookup
  36. Groenen MAM. Development of a high-density Axiom® porcine genotyping array to meet research and commercial needs.. Plant Anim. Genome XXIII Conf. San Diego, CA: Plant & Animal Genome XXIII Conference; 2015.
  37. Rincon G, Weber KL, Eenennaam AL, Golden BL, Medrano JF. Hot topic: performance of bovine high-density genotyping platforms in Holsteins and Jerseys.. J Dairy Sci 2011 Dec;94(12):6116-21.
    pubmed: 22118099doi: 10.3168/jds.2011-4764google scholar: lookup
  38. Salomón-Torres R, González-Vizcarra VM, Medina-Basulto GE, Montaño-Gómez MF, Mahadevan P, Yaurima-Basaldúa VH, Villa-Angulo C, Villa-Angulo R. Genome-wide identification of copy number variations in Holstein cattle from Baja California, Mexico, using high-density SNP genotyping arrays.. Genet Mol Res 2015 Oct 2;14(4):11848-59.
    pubmed: 26436509doi: 10.4238/2015.october.2.18google scholar: lookup
  39. Salomon-Torres R, Villa-Angulo R, Villa-Angulo C. Analysis of copy number variations in Mexican Holstein cattle using axiom genome-wide Bos 1 array.. Genom Data 2016 Mar;7:97-100.
    pmc: PMC4778655pubmed: 26981375doi: 10.1016/j.gdata.2015.12.007google scholar: lookup
  40. Romé H, Varenne A, Hérault F, Chapuis H, Alleno C, Dehais P, Vignal A, Burlot T, Le Roy P. GWAS analyses reveal QTL in egg layers that differ in response to diet differences.. Genet Sel Evol 2015 Oct 19;47:83.
    pmc: PMC4617898pubmed: 26482360doi: 10.1186/s12711-015-0160-2google scholar: lookup
  41. Lu D, Akanno EC, Crowley JJ, Schenkel F, Li H, De Pauw M, Moore SS, Wang Z, Li C, Stothard P, Plastow G, Miller SP, Basarab JA. Accuracy of genomic predictions for feed efficiency traits of beef cattle using 50K and imputed HD genotypes.. J Anim Sci 2016 Apr;94(4):1342-53.
    doi: 10.2527/jas.2015-0126pubmed: 27135994google scholar: lookup
  42. Li G, Li D, Yang N, Qu L, Hou Z, Zheng J, Xu G, Chen S. A genome-wide association study identifies novel single nucleotide polymorphisms associated with dermal shank pigmentation in chickens.. Poult Sci 2014 Dec;93(12):2983-7.
    pubmed: 25260525doi: 10.3382/ps.2014-04164google scholar: lookup
  43. Corbin LJ, Kranis A, Blott SC, Swinburne JE, Vaudin M, Bishop SC, Woolliams JA. The utility of low-density genotyping for imputation in the Thoroughbred horse.. Genet Sel Evol 2014 Feb 4;46(1):9.
    pmc: PMC3930001pubmed: 24495673doi: 10.1186/1297-9686-46-9google scholar: lookup
  44. Lindgreen S. AdapterRemoval: easy cleaning of next-generation sequencing reads.. BMC Res Notes 2012 Jul 2;5:337.
    pmc: PMC3532080pubmed: 22748135doi: 10.1186/1756-0500-5-337google scholar: lookup
  45. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.. Bioinformatics 2009 Jul 15;25(14):1754-60.
    pmc: PMC2705234pubmed: 19451168doi: 10.1093/bioinformatics/btp324google scholar: lookup
  46. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs.. Genome Res 2008 May;18(5):821-9.
    pmc: PMC2336801pubmed: 18349386doi: 10.1101/gr.074492.107google scholar: lookup
  47. Smit AFA, Hubley R, Green P. RepeatMasker Open-4.0.2013-2015. .
  48. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering.. Am J Hum Genet 2007 Nov;81(5):1084-97.
    doi: 10.1086/521987pmc: PMC2265661pubmed: 17924348google scholar: lookup
  49. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R. The variant call format and VCFtools.. Bioinformatics 2011 Aug 1;27(15):2156-8.
    doi: 10.1093/bioinformatics/btr330pmc: PMC3137218pubmed: 21653522google scholar: lookup

Citations

This article has been cited 59 times.
Subscribe to our newsletter
Join 99,970 horse owners like you who receive our email updates on horse health and nutrition.