Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 423504
Title Imputation of genotypes with low-density chips and its effect on reliabilty of direct genomic values in Dutch Holstein cattle
Author(s) Mulder, H.A.; Calus, M.P.L.; Druet, T.; Schrooten, C.
Source Journal of Dairy Science 95 (2012)2. - ISSN 0022-0302 - p. 876 - 889.
DOI https://doi.org/10.3168/jds.2011-4490
Department(s) WIAS
LR - Backoffice
LR - Animal Breeding & Genomics
Animal Breeding and Genetics
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) nucleotide polymorphism genotypes - haplotype-phase inference - dairy-cattle - unrelated individuals - wide association - breeding values - jersey cattle - marker panels - gene content - accuracy
Abstract 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.
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