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Genetics, selection, evolution : GSE2014; 46(1); 9; doi: 10.1186/1297-9686-46-9

The utility of low-density genotyping for imputation in the Thoroughbred horse.

Abstract: Despite the dramatic reduction in the cost of high-density genotyping that has occurred over the last decade, it remains one of the limiting factors for obtaining the large datasets required for genomic studies of disease in the horse. In this study, we investigated the potential for low-density genotyping and subsequent imputation to address this problem. Results: Using the haplotype phasing and imputation program, BEAGLE, it is possible to impute genotypes from low- to high-density (50K) in the Thoroughbred horse with reasonable to high accuracy. Analysis of the sources of variation in imputation accuracy revealed dependence both on the minor allele frequency of the single nucleotide polymorphisms (SNPs) being imputed and on the underlying linkage disequilibrium structure. Whereas equidistant spacing of the SNPs on the low-density panel worked well, optimising SNP selection to increase their minor allele frequency was advantageous, even when the panel was subsequently used in a population of different geographical origin. Replacing base pair position with linkage disequilibrium map distance reduced the variation in imputation accuracy across SNPs. Whereas a 1K SNP panel was generally sufficient to ensure that more than 80% of genotypes were correctly imputed, other studies suggest that a 2K to 3K panel is more efficient to minimize the subsequent loss of accuracy in genomic prediction analyses. The relationship between accuracy and genotyping costs for the different low-density panels, suggests that a 2K SNP panel would represent good value for money. Conclusions: Low-density genotyping with a 2K SNP panel followed by imputation provides a compromise between cost and accuracy that could promote more widespread genotyping, and hence the use of genomic information in horses. In addition to offering a low cost alternative to high-density genotyping, imputation provides a means to combine datasets from different genotyping platforms, which is becoming necessary since researchers are starting to use the recently developed equine 70K SNP chip. However, more work is needed to evaluate the impact of between-breed differences on imputation accuracy.
Publication Date: 2014-02-04 PubMed ID: 24495673PubMed Central: PMC3930001DOI: 10.1186/1297-9686-46-9Google Scholar: Lookup
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  • 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 focused on how low-density genotyping followed by imputation can provide a cost-effective tool in genomic studies of horses, specifically Thoroughbreds. The study discovers that using a 2K Single Nucleotide Polymorphisms (SNP) panel could offer a good balance between cost and accuracy.

Objective and Methodology of the Study

  • The researchers aimed to explore the prospects of using low-density genotyping and subsequent imputation as a cost-effective approach to amass the large datasets required in the genomic study of diseases in horses.
  • They relied on the haplotype phasing and imputation program, BEAGLE, to examine the feasibility of imputing genotypes from low to high density in the Thoroughbred horse.

Key Findings

  • The study showed that it is possible to impute genotypes from low to high-density with reasonable to high accuracy.
  • The accuracy of the imputation was dependent on both the minor allele frequency of the single nucleotide polymorphisms (SNPs) being imputed and the underlying linkage disequilibrium structure.
  • Equally spacing out SNPs on the low-density panel worked fine, however, ramping up the minor allele frequency of the chosen SNPs provided better results. This advantage remained even when the panel was used in a population from a different geographical origin.
  • Replacing base pair position with linkage disequilibrium map distance reduced the variance in imputation accuracy across SNPs.

Implications and Recommendations

  • The utilization of a 1K SNP panel was usually sufficient to ensure that over 80% of genotypes were accurately imputed. However, other studies suggest that a 2K to 3K panel would minimize subsequent loss of accuracy in genomic prediction analyses.
  • The relationship between accuracy and genotyping costs for different low-density panels indicated that a 2K SNP panel would be a worthwhile investment.
  • Low-density genotyping with a 2K SNP panel followed by imputation could strike a balance between cost and accuracy, promoting widespread genotyping and thus the use of genomic information in horses.
  • This methodology also offers a practical alternate to high-density genotyping while providing a means to combine datasets from different genotyping platforms.
  • The study suggests a need for further research to evaluate the impact of between-breed differences on imputation accuracy.

Cite This Article

APA
Corbin LJ, Kranis A, Blott SC, Swinburne JE, Vaudin M, Bishop SC, Woolliams JA. (2014). The utility of low-density genotyping for imputation in the Thoroughbred horse. Genet Sel Evol, 46(1), 9. https://doi.org/10.1186/1297-9686-46-9

Publication

ISSN: 1297-9686
NlmUniqueID: 9114088
Country: France
Language: English
Volume: 46
Issue: 1
Pages: 9

Researcher Affiliations

Corbin, Laura J
    Kranis, Andreas
      Blott, Sarah C
        Swinburne, June E
          Vaudin, Mark
            Bishop, Stephen C
              Woolliams, John A
              • Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK. john.woolliams@roslin.ed.ac.uk.

              MeSH Terms

              • Animals
              • Female
              • Gene Frequency
              • Genome
              • Genotype
              • Genotyping Techniques / economics
              • Genotyping Techniques / methods
              • Horses / genetics
              • Linkage Disequilibrium
              • Male
              • Polymorphism, Single Nucleotide
              • Quantitative Trait, Heritable

              Grant Funding

              • BBS/E/D/20211550 / Biotechnology and Biological Sciences Research Council
              • BBS/E/D/20211554 / Biotechnology and Biological Sciences Research Council

              References

              This article includes 48 references
              1. Andersson LS, Juras R, Ramsey DT, Eason-Butler J, Ewart S, Cothran G, Lindgren G. Equine Multiple Congenital Ocular Anomalies maps to a 4.9 megabase interval on horse chromosome 6.. BMC Genet 2008 Dec 19;9:88.
                pmc: PMC2653074pubmed: 19099555doi: 10.1186/1471-2156-9-88google scholar: lookup
              2. 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 Apr 15;6(4):e1000909.
              3. Fox-Clipsham LY, Carter SD, Goodhead I, Hall N, Knottenbelt DC, May PD, Ollier WE, Swinburne JE. Identification of a mutation associated with fatal Foal Immunodeficiency Syndrome in the Fell and Dales pony.. PLoS Genet 2011 Jul;7(7):e1002133.
              4. Teyssèdre S, Dupuis MC, Guérin G, Schibler L, Denoix JM, Elsen JM, Ricard A. Genome-wide association studies for osteochondrosis in French Trotter horses.. J Anim Sci 2012 Jan;90(1):45-53.
                pubmed: 21841084doi: 10.2527/jas.2011-4031google scholar: lookup
              5. 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.
              6. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.. PLoS Genet 2009 Jun;5(6):e1000529.
              7. Hayes BJ, Bowman PJ, Daetwyler HD, Kijas JW, van der Werf JH. Accuracy of genotype imputation in sheep breeds.. Anim Genet 2012 Feb;43(1):72-80.
              8. Hickey JM, Crossa J, Babu R, de los Campos G. Factors affecting the accuracy of genotype imputation in populations from several maize breeding programs. Crop Sci 2012;52:654–663.
              9. Vereijken ALJ, Albers GAA, Visscher J. Imputation of SNP genotypes in chicken using a reference panel with phased haplotypes. Leipzig; 2010. Proceedings of the 9th World Congress on Genetics Applied to Livestock Production: 1–6 August 2010.
              10. Weigel KA, de Los Campos G, Vazquez AI, Rosa GJ, Gianola D, Van Tassell CP. Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle.. J Dairy Sci 2010 Nov;93(11):5423-35.
                pubmed: 20965358doi: 10.3168/jds.2010-3149google scholar: lookup
              11. Scheet P, Stephens M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.. Am J Hum Genet 2006 Apr;78(4):629-44.
                pmc: PMC1424677pubmed: 16532393doi: 10.1086/502802google scholar: lookup
              12. Li Y, Abecasis GR. Mach 1.0: rapid haplotype reconstruction and missing genotype inference. Am J Hum Genet 2006;79:S2290.
              13. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes.. Nat Genet 2007 Jul;39(7):906-13.
                pubmed: 17572673doi: 10.1038/ng2088google scholar: lookup
              14. Hickey JM, Kinghorn BP, Tier B, Wilson JF, Dunstan N, van der Werf JH. A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes.. Genet Sel Evol 2011 Mar 10;43(1):12.
                pmc: PMC3068938pubmed: 21388557doi: 10.1186/1297-9686-43-12google scholar: lookup
              15. 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.
                pmc: PMC2265661pubmed: 17924348doi: 10.1086/521987google scholar: lookup
              16. Pei YF, Li J, Zhang L, Papasian CJ, Deng HW. Analyses and comparison of accuracy of different genotype imputation methods.. PLoS One 2008;3(10):e3551.
              17. Nothnagel M, Ellinghaus D, Schreiber S, Krawczak M, Franke A. A comprehensive evaluation of SNP genotype imputation.. Hum Genet 2009 Mar;125(2):163-71.
                pubmed: 19089453doi: 10.1007/s00439-008-0606-5google scholar: lookup
              18. Weigel KA, Van Tassell CP, O'Connell JR, VanRaden PM, Wiggans GR. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms.. J Dairy Sci 2010 May;93(5):2229-38.
                pubmed: 20412938doi: 10.3168/jds.2009-2849google scholar: lookup
              19. de Bakker PI, Yelensky R, Pe'er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies.. Nat Genet 2005 Nov;37(11):1217-23.
                pubmed: 16244653doi: 10.1038/ng1669google scholar: lookup
              20. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA. Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium.. Am J Hum Genet 2004 Jan;74(1):106-20.
                pmc: PMC1181897pubmed: 14681826doi: 10.1086/381000google scholar: lookup
              21. Zhang K, Qin Z, Chen T, Liu JS, Waterman MS, Sun F. HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms.. Bioinformatics 2005 Jan 1;21(1):131-4.
                pubmed: 15333454doi: 10.1093/bioinformatics/bth482google scholar: lookup
              22. Halldórsson BV, Bafna V, Lippert R, Schwartz R, De La Vega FM, Clark AG, Istrail S. Optimal haplotype block-free selection of tagging SNPs for genome-wide association studies.. Genome Res 2004 Aug;14(8):1633-40.
                pmc: PMC509273pubmed: 15289481doi: 10.1101/gr.2570004google scholar: lookup
              23. He J, Zelikovsky A. MLR-tagging: informative SNP selection for unphased genotypes based on multiple linear regression.. Bioinformatics 2006 Oct 15;22(20):2558-61.
                pubmed: 16895924doi: 10.1093/bioinformatics/btl420google scholar: lookup
              24. Halldórsson BV, Istrail S, De La Vega FM. Optimal selection of SNP markers for disease association studies.. Hum Hered 2004;58(3-4):190-202.
                pubmed: 15812176doi: 10.1159/000083546google scholar: lookup
              25. 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
              26. 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.
              27. 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
              28. Swinburne JE, Boursnell M, Hill G, Pettitt L, Allen T, Chowdhary B, Hasegawa T, Kurosawa M, Leeb T, Mashima S, Mickelson JR, Raudsepp T, Tozaki T, Binns M. Single linkage group per chromosome genetic linkage map for the horse, based on two three-generation, full-sibling, crossbred horse reference families.. Genomics 2006 Jan;87(1):1-29.
                pubmed: 16314071doi: 10.1016/j.ygeno.2005.09.001google scholar: lookup
              29. Solberg TR, Sonesson AK, Woolliams JA, Meuwissen TH. Genomic selection using different marker types and densities.. J Anim Sci 2008 Oct;86(10):2447-54.
                pubmed: 18407980doi: 10.2527/jas.2007-0010google scholar: lookup
              30. Maniatis N, Collins A, Xu CF, McCarthy LC, Hewett DR, Tapper W, Ennis S, Ke X, Morton NE. The first linkage disequilibrium (LD) maps: delineation of hot and cold blocks by diplotype analysis.. Proc Natl Acad Sci U S A 2002 Feb 19;99(4):2228-33.
                pmc: PMC122347pubmed: 11842208doi: 10.1073/pnas.042680999google scholar: lookup
              31. Malecot G. Les Mathématiques de l’Hérédité. Paris: Maison et Cie; 1948.
              32. Morton NE, Zhang W, Taillon-Miller P, Ennis S, Kwok PY, Collins A. The optimal measure of allelic association.. Proc Natl Acad Sci U S A 2001 Apr 24;98(9):5217-21.
                pmc: PMC33190pubmed: 11309498doi: 10.1073/pnas.091062198google scholar: lookup
              33. Khatkar MS, Collins A, Cavanagh JA, Hawken RJ, Hobbs M, Zenger KR, Barris W, McClintock AE, Thomson PC, Nicholas FW, Raadsma HW. A first-generation metric linkage disequilibrium map of bovine chromosome 6.. Genetics 2006 Sep;174(1):79-85.
                pmc: PMC1569786pubmed: 16816421doi: 10.1534/genetics.106.060418google scholar: lookup
              34. Zhang W, Collins A, Maniatis N, Tapper W, Morton NE. Properties of linkage disequilibrium (LD) maps.. Proc Natl Acad Sci U S A 2002 Dec 24;99(26):17004-7.
                pmc: PMC139259pubmed: 12486239doi: 10.1073/pnas.012672899google scholar: lookup
              35. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals.. Am J Hum Genet 2009 Feb;84(2):210-23.
                pmc: PMC2668004pubmed: 19200528doi: 10.1016/j.ajhg.2009.01.005google scholar: lookup
              36. Purcell S. PLINK. v 1.06. 2009.
              37. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses.. Am J Hum Genet 2007 Sep;81(3):559-75.
                pmc: PMC1950838pubmed: 17701901doi: 10.1086/519795google scholar: lookup
              38. Weir BS. Genetic Data Analysis II: Methods for Discrete Population Genetic Data. Sunderland MA: Sinauer Associates; 1996. p. 126.
              39. Browning SR. Missing data imputation and haplotype phase inference for genome-wide association studies.. Hum Genet 2008 Dec;124(5):439-50.
                pmc: PMC2731769pubmed: 18850115doi: 10.1007/s00439-008-0568-7google scholar: lookup
              40. Kijas JW, Lenstra JA, Hayes B, Boitard S, Porto Neto LR, San Cristobal M, Servin B, McCulloch R, Whan V, Gietzen K, Paiva S, Barendse W, Ciani E, Raadsma H, McEwan J, Dalrymple B. Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong recent selection.. PLoS Biol 2012 Feb;10(2):e1001258.
              41. Dalrymple BP, Kirkness EF, Nefedov M, McWilliam S, Ratnakumar A, Barris W, Zhao S, Shetty J, Maddox JF, O'Grady M, Nicholas F, Crawford AM, Smith T, de Jong PJ, McEwan J, Oddy VH, Cockett NE. Using comparative genomics to reorder the human genome sequence into a virtual sheep genome.. Genome Biol 2007;8(7):R152.
                pmc: PMC2323240pubmed: 17663790doi: 10.1186/gb-2007-8-7-r152google scholar: lookup
              42. Corbin LJ, Blott SC, Swinburne JE, Vaudin M, Bishop SC, Woolliams JA. Linkage disequilibrium and historical effective population size in the Thoroughbred horse.. Anim Genet 2010 Dec;41 Suppl 2:8-15.
              43. Cunningham EP, Dooley JJ, Splan RK, Bradley DG. Microsatellite diversity, pedigree relatedness and the contributions of founder lineages to thoroughbred horses.. Anim Genet 2001 Dec;32(6):360-4.
              44. Daetwyler HD, Wiggans GR, Hayes BJ, Woolliams JA, Goddard ME. Imputation of missing genotypes from sparse to high density using long-range phasing.. Genetics 2011 Sep;189(1):317-27.
                pmc: PMC3176129pubmed: 21705746doi: 10.1534/genetics.111.128082google scholar: lookup
              45. R Development Core Team. R: A Language and Environment for Computing. Vienna, Austria: R Foundation for Statistical Computing; 2009.
              46. Becker RA, Chambers JM, Wilks AR. The New S Language. Wadsworth & Brooks/Cole: Pacific Grove; 1988.
              47. Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Statist Assoc 1979;74:829–836.
              48. Cleveland WS. Lowess - A program for smoothing scatterplots by robust locally weighted regression. Am Stat 1981;35:54.

              Citations

              This article has been cited 14 times.
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                doi: 10.1093/jas/skab118pubmed: 33860324google scholar: lookup
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                doi: 10.2147/IJGM.S297371pubmed: 33833559google scholar: lookup
              5. 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 Jan 16;10(1):466.
                doi: 10.1038/s41598-019-57389-5pubmed: 31949252google scholar: lookup
              6. Farries G, Bryan K, McGivney CL, McGettigan PA, Gough KF, Browne JA, MacHugh DE, Katz LM, Hill EW. Expression Quantitative Trait Loci in Equine Skeletal Muscle Reveals Heritable Variation in Metabolism and the Training Responsive Transcriptome.. Front Genet 2019;10:1215.
                doi: 10.3389/fgene.2019.01215pubmed: 31850069google scholar: lookup
              7. O'Brien AC, Judge MM, Fair S, Berry DP. High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1.. J Anim Sci 2019 Apr 3;97(4):1550-1567.
                doi: 10.1093/jas/skz043pubmed: 30722011google scholar: lookup
              8. Frischknecht M, Pausch H, Bapst B, Signer-Hasler H, Flury C, Garrick D, Stricker C, Fries R, Gredler-Grandl B. Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle.. BMC Genomics 2017 Dec 29;18(1):999.
                doi: 10.1186/s12864-017-4390-2pubmed: 29284405google scholar: lookup
              9. Ponomarenko P, Ryutov A, Maglinte DT, Baranova A, Tatarinova TV, Gai X. Clinical utility of the low-density Infinium QC genotyping Array in a genomics-based diagnostics laboratory.. BMC Med Genomics 2017 Oct 6;10(1):57.
                doi: 10.1186/s12920-017-0297-7pubmed: 28985730google scholar: lookup
              10. 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/s12864-017-3943-8pubmed: 28750625google scholar: lookup
              11. Ventura RV, Miller SP, Dodds KG, Auvray B, Lee M, Bixley M, Clarke SM, McEwan JC. Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population.. Genet Sel Evol 2016 Sep 23;48(1):71.
                doi: 10.1186/s12711-016-0244-7pubmed: 27663120google scholar: lookup
              12. Wu XL, Xu J, Feng G, Wiggans GR, Taylor JF, He J, Qian C, Qiu J, Simpson B, Walker J, Bauck S. Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.. PLoS One 2016;11(9):e0161719.
                doi: 10.1371/journal.pone.0161719pubmed: 27583971google scholar: lookup
              13. Piccoli ML, Braccini J, Cardoso FF, Sargolzaei M, Larmer SG, Schenkel FS. Accuracy of genome-wide imputation in Braford and Hereford beef cattle.. BMC Genet 2014 Dec 29;15:157.
                doi: 10.1186/s12863-014-0157-9pubmed: 25543517google scholar: lookup
              14. Yu X, Woolliams JA, Meuwissen TH. Prioritizing animals for dense genotyping in order to impute missing genotypes of sparsely genotyped animals.. Genet Sel Evol 2014 Aug 26;46(1):46.
                doi: 10.1186/1297-9686-46-46pubmed: 25158690google scholar: lookup