Genotype imputation strategies for Portuguese Holstein cattle using different SNP panels A., Silva F., Silva D., Silva H., Costa C., Lopes P., Veroneze R., Thompson G., Carvalheira J. (2019): Genotype imputation strategies for Portuguese Holstein cattle using different SNP panels. Czech J. Anim. Sci., 64: 377-386.
supplementary materialdownload PDF

Although several studies have investigated the factors affecting imputation accuracy, most of these studies involved a large number of genotyped animals. Thus, results from these studies cannot be directly applied to small populations, since the population structure affects imputation accuracy. In addition, factors affecting imputation accuracy may also be intensified in small populations. Therefore, we aimed to compare different imputation strategies for the Portuguese Holstein cattle population considering several commercially available single nucleotide polymorphism (SNP) panels in a relatively small number of genotyped animals. Data from 1359 genotyped animals were used to evaluate imputation in 7 different scenarios. In the S1 to S6 scenarios, imputations were performed from LDv1, 50Kv1, 57K, 77K, HDv3 and Ax58K panels to 50Kv2 panel. In these scenarios, the bulls in 50Kv2 were divided into reference (352) and validation (101) populations based on the year of birth. In the S7 scenario, the validation population consisted of 566 cows genotyped with the Ax58K panel with their genotypes masked to LDv1. In general, all sample imputation accuracies were high with correlations ranging from 0.94 to 0.99 and concordance rate ranging from 92.59 to 98.18%. SNP-specific accuracy was consistent with that of sample imputation. S4 (40.32% of SNPs imputed) had higher accuracy than S2 and S3, both with less than 7.59% of SNPs imputed. Most probably, this was due to the high number of imputed SNPs with minor allele frequency (MAF) < 0.05 in S2 and S3 (by 18.43% and 16.06% higher than in S4, respectively). Therefore, for these two scenarios, MAF was more relevant than the panel density. These results suggest that genotype imputation using several commercially available SNP panels is feasible for the Portuguese national genomic evaluation.

Berry D.P., O’Brien A., Wall E., McDermott K., Randles S., Flynn P., Park S., Grose J., Weld R., McHugh N. (2016): Inter- and intra-reproducibility of genotypes from sheep technical replicates on Illumina and Affymetrix platforms. Genetics Selection Evolution, 48, 1–5.
Boison S.A., Santos D.J.A., Utsunomiya A.H.T., Carvalheiro R., Neves H.H.R., O’Brien A.M.P., Garcia J.F., Solkner J., da Silva M.V.G.B. (2015): Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: Comparison of commercially available SNP chips. Journal of Dairy Science, 98, 4969–4989.
Carvalheiro R., Boison S.A., Neves H.H.R., Sargolzaei M., Schenkel F.S., Utsunomiya Y.T., O’Brien A.M.P., Solkner J., McEwan J.C., Van Tassell C.P., Sonstegard T.S., Garcia J.F. (2014): Accuracy of genotype imputation in Nelore cattle. Genetics Selection Evolution, 46, 1–11.
Chud T.C.S., Ventura R.V., Schenkel F.S., Carvalheiro R., Buzanskas M.E., Rosa J.O., de Alvarenga Mudadu M., da Silva M.V.G.B., Mokry F.B., Marcondes C.R., Regitano L.C.A., Munari D.P. (2015): Strategies for genotype imputation in composite beef cattle. BMC Genetics, 16, 1–10.
Daetwyler H.D., Calus M.P.L., Pong-Wong R., de los Campos G., Hickey J.M. (2013): Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking. Genetics, 193, 347–365.
Garcia-Ruiz A., Ruiz-Lopez F.J., Wiggans G.R., Van Tassell C.P., Montaldo H.H. (2015): Effect of reference population size and available ancestor genotypes on imputation of Mexican Holstein genotypes. Journal of Dairy Science, 98, 3478–3484.
Goktas A., Isci O. (2011): A comparison and normality test of some measures of association via simulation for rectangular doubly ordered cross tables. Metodoloski Zvezki, 8, 17–37.
Gu Z., Gu L., Eils R., Schlesner M., Brors B. (2014): circlize implements and enhances circular visualization in R. Bioinformatics, 30, 2811–2812.
Heidaritabar M., Calus M.P.L., Vereijken A., Groenen M.A.M., Bastiaansen J.W.M. (2015): Accuracy of imputation using the most common sires as reference population in layer chickens. BMC Genetics, 16, 1–14.
Hickey J.M., Crossa J., Babu R., de los Campos G. (2012): Factors affecting the accuracy of genotype imputation in populations from several maize breeding programs. Crop Science, 52, 654–663.
Hill W.G., Robertson A. (1968): Linkage disequilibrium in finite populations. TAG Theoretical and Applied Genetics, 38, 226–231.
Jattawa D., Elzo M.A., Koonawootrittriron S., Suwanasopee T. (2016): Imputation accuracy from low to moderate density single nucleotide polymorphism chips in a Thai multibreed dairy cattle population. Asian-Australasian Journal of Animal Sciences, 29, 464–470.
Khatkar M.S., Moser G., Hayes B.J., Raadsma H.W. (2012): Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle. BMC Genomics, 13, Article No. 538.
Larmer S.G., Sargolzaei M., Schenkel F.S. (2014): Extent of linkage disequilibrium, consistency of gametic phase, and imputation accuracy within and across Canadian dairy breeds. Journal of Dairy Science, 97, 3128–3141.
Misztal I., Tsuruta S., Lourenco D.A.L., Aguilar I., Legarra A., Vitezica Z.G. (2014): Manual for BLUPF90 Family of Programs. University of Georgia, Athens, USA. Available at (accessed Feb 15, 2018).
Nicolazzi E.L., Biffani S., Biscarini F., Orozco P., Wengel T., Caprera A., Nazzicari N., Stella A. (2015): Software solutions for the livestock genomics SNP array revolution. Animal Genetics, 46, 343–353.
Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A.R., Bender D., Maller J., Sklar P., de Bakker P.I.W., Daly M.J., Sham P.C. (2007): PLINK: A tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 81, 559–575.
R Core Team (2018): R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at (accessed May 10, 2018).
Salem M.M.I., Thompson G., Chen S., Beja-Pereira A., Carvalheira J. (2018): Linkage disequilibrium and haplotype block structure in Portuguese Holstein cattle. Czech Journal of Animal Science, 63, 61–69.
Sargolzaei M., Chesnais J.P., Schenkel F.S. (2014): A new approach for efficient genotype imputation using information from relatives. BMC Genomics, 15, Article No. 478.
VanRaden P.M. (2008): Efficient methods to compute genomic predictions. Journal of Dairy Science, 91, 4414–4423.
VanRaden P.M., O’Connell J.R., Wiggans G.R., Weigel K.A. (2011): Genomic evaluations with many more genotypes. Genetics Selection Evolution, 43, 1–11.
VanRaden P.M., Null D.J., Sargolzaei M., Wiggans G.R., Tooker M.E., Cole J.B., Sonstegard T.S., Connor E.E., Winters M., van Kaam J.B.C.H.M., Valentini A., Van Doormaal B.J., Faust M.A., Doak G.A. (2013): Genomic imputation and evaluation using high-density Holstein genotypes. Journal of Dairy Science, 96, 668–678.
Ventura R.V., Lu D., Schenkel F.S., Wang Z., Li C., Miller S.P. (2014): Impact of reference population on accuracy of imputation from 6K to 50K single nucleotide polymorphism chips in purebred and crossbreed beef cattle. Journal of Animal Science, 92, 1433–1444.
Ventura R.V., Miller S.P., Dodds K.G., Auvray B., Lee M., Bixley M., Clarke S.M., McEwan J.C. (2016): Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population. Genetics Selection Evolution, 48, 1–20.
Wang Y., Lin G., Li C., Stothard P. (2016): Genotype imputation methods and their effects on genomic predictions in cattle. Springer Science Reviews, 4, 79–98.
Zhou L., Heringstad B., Su G., Guldbrandtsen B., Meuwissen T.H.E., Svendsen M., Grove H., Nielsen U.S., Lund M.S. (2014): Genomic predictions based on a joint reference population for the Nordic Red cattle breeds. Journal of Dairy Science, 97, 4485–4496.
Zimin A.V., Delcher A.L., Florea L., Kelley D.R., Schatz M.C., Puiu D., Hanrahan F., Pertea G., Van Tassell C.P., Sonstegard T.S., Marcais G., Roberts M., Subramanian P., Yorke J.A., Salzberg S.L. (2009): A whole-genome assembly of the domestic cow, Bos taurus. Genome Biology, 10, Article R42.
supplementary materialdownload PDF

© 2021 Czech Academy of Agricultural Sciences | Prohlášení o přístupnosti