[en] Genomic selection using 50,000 single nucleotide polymorphism (50k SNP) chips has been implemented in many dairy cattle breeding programs. Cheap, low-density chips make genotyping of a larger number of animals cost effective. A commonly proposed strategy is to impute low-density genotypes up to 50,000 genotypes before predicting direct genomic values (DGV). The objectives of this study were to investigate the accuracy of imputation for animals genotyped with a low-density chip and to investigate the effect of imputation on reliability of DGV. Low-density chips contained 384, 3,000, or 6,000 SNP. The SNP were selected based either on the highest minor allele frequency in a bin or the middle SNP in a bin, and DAGPHASE, CHROMIBD, and multivariate BLUP were used for imputation. Genotypes of 9,378 animals were used, from which approximately 2,350 animals had deregressed proofs. Bayesian stochastic search variable selection was used for estimating SNP effects of the 50k chip. Imputation accuracies and imputation error rates were poor for low-density chips with 384 SNP. Imputation accuracies were higher with 3,000 and 6,000 SNP. Performance of DAGPHASE and CHROMIBD was very similar and much better than that of multivariate BLUP for both imputation accuracy and reliability of DGV. With 3,000 SNP and using CHROMIBD or DAGPHASE for imputation, 84 to 90% of the increase in DGV reliability using the 50k chip, compared with a pedigree index, was obtained. With multivariate BLUP, the increase in reliability was only 40%. With 384 SNP, the reliability of DGV was lower than for a pedigree index, whereas with 6,000 SNP, about 93% of the increase in reliability of DGV based on the 50k chip was obtained when using DAGPHASE for imputation. Using genotype probabilities to predict gene content increased imputation accuracy and the reliability of DGV and is therefore recommended for applications of imputation for genomic prediction. A deterministic equation was derived to predict accuracy of DGV based on imputation accuracy, which fitted closely with the observed relationship. The deterministic equation can be used to evaluate the effect of differences in imputation accuracy on accuracy and reliability of DGV.
Berry D.P., Kearney J.F. Imputation of genotypes from low- to high-density genotyping platforms and implications for genomic selection. Animal 2011, 5:1162-1169.
Browning B.L., Browning S.R. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated animals. Am. J. Hum. Genet. 2009, 84:210-223.
Browning S.R. Missing data imputation and haplotype phase inference for genome-wide association studies. Hum. Genet. 2008, 124:439-450.
Browning S.R., Browning B.L. 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, 81:1084-1097.
Calus M.P.L. Genomic breeding value prediction: Methods and procedures. Animal 2010, 4:157-164.
Calus M.P.L., Meuwissen T.H.E., De Roos A.P.W., Veerkamp R.F. Accuracy of genomic selection using different methods to define haplotypes. Genetics 2008, 178:553-561.
Calus M.P.L., Veerkamp R.F., Mulder H.A. Imputation of missing SNP genotypes using a multivariate mixed model framework. J. Anim. Sci. 2011, 89:2042-2049.
Charlier C., Coppieters W., Rollin F., Desmecht D., Agerholm J.S., Cambisano N., Carta E., Dardano S., Dive M., Fasquelle C., Frennet J.C., Hanset R., Hubin X., Jorgensen C., Karim L., Kent M., Harvey K., Pearce B.R., Simon P., Tama N., Nie H., Vandeputte S., Lien S., Longeri M., Fredholm M., Harvey R.J., Georges M. Highly effective SNP-based association mapping and management of recessive defects in livestock. Nat. Genet. 2008, 40:449-454.
Druet T., Farnir F.P. Modeling of identity-by-descent processes along a chromosome between haplotypes and their genotyped ancestors. Genetics 2011, 188:409-419.
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, 184:789-798.
Druet T., Schrooten C., De Roos A.P.W. Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle. J. Dairy Sci. 2010, 93:5443-5454.
Gengler N., Abras S., Verkenne C., Vanderick S., Szydlowski M., Renaville R. Accuracy of prediction of gene content in large animal populations and its use for candidate gene detection and genetic evaluation. J. Dairy Sci. 2008, 91:1652-1659.
Gengler N., Mayeres P., Szydlowski M. A simple method to approximate gene content in large pedigree populations: Application to the myostatin gene in dual-purpose Belgian Blue cattle. Animal 2007, 1:21-27.
George E.I., McCulloch R.E. Variable selection via Gibbs sampling. J. Am. Stat. Assoc. 1993, 88:881-889.
Habier D., Fernando R.L., Dekkers J.C.M. Genomic selection using low-density marker panels. Genetics 2009, 182:343-353.
Habier D., Tetens J., Seefried F.-R., Lichtner P., Thaller G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet. Sel. Evol. 2010, 42:5.
Hayes B.J., Bowman P.J., Chamberlain A.J., Goddard M.E. Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 2009, 92:433-443.
Hill W.G., Robertson A. Linkage disequilibrium in finite populations. Theor. Appl. Genet. 1968, 38:226-231.
Howie B.N., Donnelly P., Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009, 5:e1000529.
Huang L., Wang C., Rosenberg N.A. The relationship between imputation error and statistical power in genetic association studies in diverse populations. Am. J. Hum. Genet. 2009, 85:692-698.
Interbull. 2010. Interbull validation test for genomic evaluations-GEBV test. Accessed Mar. 29, 2011. http://www.interbull.org/images/stories/GEBV_validationtest_June2010.pdf.
Li Y., Willer C., Sanna S., Abecasis G. Genotype imputation. Annu. Rev. Genomics Hum. Genet. 2009, 10:387-406.
Lin P., Hartz S.M., Zhang Z.H., Saccone S.F., Wang J., Tischfield J.A., Edenberg H.J., Kramer J.R., Goate A.M., Bierut L.J., Rice J.P. A new statistic to evaluate imputation reliability. PLoS ONE 2010, 5:e9697.
Marchini J., Howie B. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 2010, 11:499-511.
Meuwissen T.H.E. Accuracy of breeding values of " unrelated" individuals predicted by dense SNP genotyping. Genet. Sel. Evol. 2009, 41:35.
Meuwissen T.H.E., Goddard M.E. Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multi-trait data. Genet. Sel. Evol. 2004, 2004:261-279.
Meuwissen T.H.E., Hayes B.J., Goddard M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001, 157:1819-1829.
Moser G., Khatkar M.S., Hayes B., Raadsma H. Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP. Genet. Sel. Evol. 2010, 42:37.
Mulder, H. A., M. Lidauer, I. Stranden, E. A. Mantysaari, M. H. Pool, and R. F. Veerkamp. 2010a. MiXBLUP manual. Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Lelystad, the Netherlands.
Mulder H.A., Meuwissen T.H.E., Calus M.P.L., Veerkamp R.F. The effect of missing marker genotypes on the accuracy of gene-assisted breeding value estimation: A comparison of methods. Animal 2010, 4:9-19.
Pszczola M., Mulder H.A., Calus M.P.L. Effect of enlarging the reference population by (un)genotyped animals on the accuracy. J. Dairy Sci. 2011, 94:431-441.
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, 78:629-644.
VanRaden P.M., Van Tassell C.P., Wiggans G.R., Sonstegard T.S., Schnabel R.D., Taylor J.F., Schenkel F.S. Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 2009, 92:16-24.
Weigel K.A., de los Campos G., Gonzalez-Recio O., Naya H., Wu X.L., Long N., Rosa G.J.M., Gianola D. Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. J. Dairy Sci. 2009, 92:5248-5257.
Weigel K.A., De los Campos G., Vazquez A.I., Rosa G.J.M., Gianola D., Van Tassell C.P. Accuracy of direct genomic values derived from imputed single nucleotide polymorphism genotypes in Jersey cattle. J. Dairy Sci. 2010, 93:5423-5435.
Weigel K.A., Van Tassell C.P., O'Connell J.R., VanRaden P.M., Wiggans G.R. Prediction of unobserved single nucleotide polymorphism genotypes of Jersey cattle using reference panels and population-based imputation algorithms. J. Dairy Sci. 2010, 93:2229-2238.
Wiggans G.R., Sonstegard T.S., Vanraden P.M., Matukumalli L.K., Schnabel R.D., Taylor J.F., Schenkel F.S., Van Tassell C.P. Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada. J. Dairy Sci. 2009, 92:3431-3436.
Zhang Z., Druet T. Marker imputation with low-density marker panels in Dutch Holstein cattle. J. Dairy Sci. 2010, 93:5487-5494.