The use of genomic data and imputation methods in dairy cattle breeding

Klímová A., Kašná E., Machová K., Brzáková M., Přibyl J., Vostrý L. (2020): The use of genomic data and imputation methods in dairy cattle breeding. Czech J. Anim. Sci., 65: 445–453.

download PDF

The inclusion of animal genotype data has contributed to the development of genomic selection. Animals are selected not only based on pedigree and phenotypic data but also on the basis of information about their genotypes. Genomic information helps to increase the accuracy of selection of young animals and thus enables a reduction of the generation interval. Obtaining information about genotypes in the form of SNPs (single nucleotide polymorphisms) has led to the development of new chips for genotyping. Several methods of genomic comparison have been developed as a result. One of the methods is data imputation, which allows the missing SNPs to be calculated using low-density chips to high-density chips. Through imputations, it is possible to combine information from diverse sets of chips and thus obtain more information about genotypes at a lower cost. Increasing the amount of data helps increase the reliability of predicting genomic breeding values. Imputation methods are increasingly used in genome-wide association studies. When classical genotyping and genome-wide sequencing data are combined, this option helps to increase the chances of identifying loci that are associated with economically significant traits.

1000 Bull Genomes Project [Internet]. [Australia]: DairyBio. 2012 - [cited 2020 Mar 29]. Available from:
Aguilar I, Misztal I, Johnson D, Legarra A, Tsuruta S, Lawlor T. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci. 2010 Feb;93(2):743-52.
Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat Rev Genet. 2016 Jan 5;17:81-92.
Bauer J, Pribyl J, Vostry L. Contribution of domestic and Interbull records to reliabilities of single-step genomic breeding values in dairy cattle. Czech J Anim Sci. 2015 Mar;60(3):263-7.
Berry DP, Kearney JF. Imputation of genotypes from low- to high-density genotyping platforms and implications for genomic selection. Animal. 2011 Jun;5(8):1162-9.
Browning B, Browning S. 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 13;84(2):210-23.
Browning B, Browning S. Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics. 2013 Jun 1;194(2):459-71.
Cai Z, Guldbrandtsen B, Lund MS, Sahana G. Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle. BMC Genomics. 2018 Sep 6;19(1): [11 p.].
Carvalheiro R, Solomon B, Neves HR, Sargolzaei M, Schenkel FS, Utsunomiya YT, O’Brien AM, Solkner J, McEwan JC, Van Tassel CP, Sonstegard TS, Garcia JF. Accuracy of genotype imputation in Nelore cattle. Genet Sel Evol. 2014 Oct 10;46: [11 p.].
Cheung CY, Thompson EA, Wijsman EM. GIGI: An approach to effective imputation of dense genotypes on large pedigrees. Am J Hum Genet. 2013 Apr 4;92(4):504-16.
Christensen O, Lund M. Genomic prediction when some animals are not genotyped. Gen Sel Evol. 2010 Jan 27;42(1): [8 p.].
Cooper TA, Wiggans GR, VanRaden PM. Short communication: Relationship of call rate and accuracy of single nucleotide polymorphism genotypes in dairy cattle. J Dairy Sci. 2013 May;96(5):3336-9.
Daetwyler H, Wiggans G, Hayes B, Woolliams J, Goddard M. Imputation of missing genotypes from sparse to high density using long-range phasing. Genetics. 2011 Sep 1;189(1):317-27.
Daetwyler HD, Capitan A, Pausch H, Stothard P, van Binsbergen R, Brondum RF, Liao X, Djari A, Rodriguez SC, Grohs C, Esquerre 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 Jul 13;46(8):858-65.
Druet T, Georges M. A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics. 2010 Mar 1;184(3):789-98.
Druet T, Macleod IM, Hayes B. Toward genomic prediction from whole-genome sequence data: Impact of sequencing design on genotype imputation and accuracy of prediction. Heredity. 2013 Apr 3;112(1):39-47.
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawaniti K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencinc (GBS) approach for high diversity species. PloS One. 2011 May 4;6(5): [10 p.].
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): [10 p.].
Garcia-Ruiz A, Cole J, VanRaden P, Wiggans G, Ruiz-Lopez F, Van Tassell C. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. PNAS. 2016 Jul 12;113(28):E3995-4004.
Gurgul A, Sienko K, Zukowski K, Pawlina K, Bugno-Poniewierska M. Imputation accuracy of bovine spongiform encephalopathy-associated PRNP indel polymorphisms from middle-density SNPs arrays. Czech J Anim Sci. 2014 May;59(5):224-9.
Hayes B, Chamberlain A, Goddard M. Use of markers in linkage disequilibrium with QTL in breeding programs. In: Proceedings of the 8th World Congress on Genetics Applied to Livestock Production; 2006 Aug 13-18; Belo Horizonte, Minas Gerais, Brazil: Instituto Prociencia; 2006. p. 30-6.
Hayes B, Bowman P, Chamberlain A, Goddard M. Invited review: Genomic selection in dairy cattle. J Dairy Sci. 2009a Feb;92(2):433-43.
Hayes BJ, Visscher PM, Goddard ME. Increase accuracy of artificial selection by using the realized relationship matrix. Genet Res. 2009b Feb 17;91(1):47-60.
Hickey J, Kinghorn BP, Tier B, van der Werf JHJ, Cleveland MA. A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation. Gen Sel Evol. 2012 Jun;44(9):1-11.
Hirschhorn J, Daly M. Genome-wide association studies for common diseases and complex traits. Nat Rev Genet. 2005 Feb 1;6(2):95-108.
Howie BN, Donnelly P, Marchini J, Schork NJ. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009 Jun 19;5(6): [15 p.].
Howie B, Marchini J, Stephens M. Genotype imputation with thousands of genomes. G3. 2011 Nov 1;1(6):457-70.
Huang Y, Hickey JM, Cleveland MA, Maltecca C. Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost. Gen Sel Evol. 2012 July 31;44(1): [8 p.].
Jenko J, Wiggans GR, Cooper TA, Eaglen SAE, Luff WGL, Bichard M, Pong-Wong R, Woolliams JA. Cow genotyping strategies for genomic selection in a small dairy cattle population. J Dairy Sci. 2017 Jan;100(1):439-52.
Khatkar MS, Zenger KR, Hobbs M, Hawken RJ, Cavanagh JAL, Barris W, McClintock AE, McClintock S, Thomson PC, Tier B, Nicholas FW, Raadsma HW. A primary assembly of a bovine haplotype block map based on a 15,036-single-nucleotide polymorphism panel genotyped in Holstein-Friesian cattle. Genetics. 2007 Jun 1;176(2):763-72.
Kong A, Masson G, Frigge ML, Gylfason A, Zusmanovich P, Thorleifsson G, Olason PI, Ingason A, Steinberg S, Rafnar T, Sulem P, Mouy M, Jonsson F, Thorsteinsdottir U, Gudbjartsson DF, Stefansson H, Stefansson K. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat Genet. 2008 Aug 17;40(9):1068-75.
Kranjcevicova A, Kasna E, Brzakova M, Pribyl J, Vostry L. Impact of reference population and marker density on accuracy of population imputation. Czech J Anim Sci. 2019 Oct;64(10):405-10.
Larmer A, Sargolzaei M, Brito LF, Ventura RV, Schenkel FS. Novel methods for genotype imputation to whole-genome sequence and simple linear model to predict imputation accuracy. BMC Genet. 2017 Dec 27;18(1): [12 p.].
Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: Using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010 Dec;34(8):816-34.
Meuwissen T, Hayes BJ, Goddard ME. Genomic selection: A paradigm shift in animal breeding. Anim Front. 2016 Jan 1;6(1):6-14.
Misztal I, Legarra A, Aguilar I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci. 2009 Sep;92(9):4648-55.
Misztal I, Aguilar I, Legarra A, Lawlor TJ. Choice of parameters for single-step genomic evaluation for type. In: ASAS Annual Meeting; 2010 Jul 11-15; Tolouse, France. [Tolouse, France]: ASAS; 2010. p. 533.
Misztal I, Aggrey S, Muir W. Experiences with a single-step genome evaluation. Poult. 2013 Sep 1;92(9):2530-4.
Nicolazzi EL, Biffani S, Jansen G. Short communication: Imputing genotypes using PedImpute fast algorithm combining pedigree and population information. J Dairy Sci. 2013 Apr;96(4):2649-53.
Nicolazzi E, Picciolini M, Strozzi F, Schnabel R, Lawley C, Pirani A, Brew F, Stella A. SNPchiMp: A database to disentangle the SNPchip jungle in bovine livestock. BMC Genomics. 2014 Feb 11;15(1): [6 p.].
Robledo D, Palaiokostas C, Bargelloni L, Martinez P, Houston R. Applications of genotyping by sequencing in aquaculture breedin and genetics. Rev Aquac. 2017 Feb;10:670-82.
Sargolzaei M, Schenkel F, Jansen G, Schaeffer L. Extent of linkage disequilibrium in Holstein cattle in North America. J Dairy Sci. 2008 May;91(5):2106-17.
Sargolzaei M, Chesnais J, Schenkel F. A new approach for efficient genotype imputation using information from relatives. BMC Genomics. 2014 Jun 17;15(1): [12 p.].
Schaeffer L. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet. 2006 Aug;123(4):218-23.
Stachowicz K, Larmer S, Jamrozik J, Moore SS, Miller SP. Sequencing and genotypng for the whole genome selection in Canadian beef populations. Proc Assoc Advmt Anim Breed Genet. 2013 Oct;20:344-7.
Starkey M, Elaswarapu R. Genomics: Essential methods. Hoboken: Wiley; 2010. 360 p.
van Binsbergen R, Bink MCAM, Calus MPL, van Eeuwijk FA, Hayes BJ, Hulsegge I, Veerkamp RF. Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle. Gen Sel Evol. 2014 Jul 15;46(1): [13 p.].
VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008 Nov;91(11):4414-23.
VanRaden P, Van Tassell C, Wiggans G, Sonstegard T, Schnabel R, Taylor J, Schenkel F. Invited review: Reliability of genomic predictions for North American Holstein bulls. J Dairy Sci. 2009 Jan;92(1):16-24.
VanRaden P, O’Connell J, Wiggans G, Weigel K. Genomic evaluations with many more genotypes. Gen Sel Evol. 2011 Mar 2;43(1): [11 p.].
VanRaden PM, Null DJ, Sargolzaei M, Wiggans GR, Tooker ME, Cole JB, Sonstegard TS, Connor EE, Winters M, van Kaam JBCHM, Valentini A, Van Doormaal BJ, Faust MA, Doak GA. Genomic imputation and evaluation using high-density Holstein genotypes. J Dairy Sci. 2013 Jan;96(1):668-78.
Walker EJ, Siminovitch KA. Primer: Genomic and proteomic tools for the molecular dissection of disease. Nat Clin Pract Rheumatol. 2007 Oct;3:580-9.
Wang Y, Lin G, Li C, Stothard P. Genotype imputation methods and their effects on genomic predictions in cattle. Springer Sci Rev. 2016 Feb;4:79-98.
Wang X, Su G, Hao D, Lund MS, Kadarmideen HN. Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations. J Anim Sci Biotechnol. 2020 Jan 7;11(1): [12 p.].
Weigel K. Genomic selection of dairy cattle: A review of methods, strategies, and impact. J Anim Breed Genet. 2017 Dec;1(1):1-15.
Wiggans G, Cooper T, VanRaden P, Van Tassell C, Bickhart D, Sonstegard T. Increasing the number of single nucleotide polymorphisms used in genomic evaluation of dairy cattle. J Dairy Sci. 2016 Jun;99(6):4504-11.
Wiggans G, Cole J, Hubbard S, Sonstegard T. Genomic selection in dairy cattle: The USDA experience. Annu Rev Anim Biosci. 2017 Feb;5(1):309-27.
Zhang Z, Druet T. Marker imputation with low-density marker panels in Dutch Holstein cattle. J Dairy Sci. 2010 Nov;93(11):5487-94.
download PDF

© 2022 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti