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 442952
Title Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data
Author(s) Binsbergen, R. van; Bink, M.C.A.M.; Calus, M.P.L.; Eeuwijk, F.A. van; Hayes, B.J.; Hulsegge, B.; Veerkamp, R.F.
Source In: Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013. - Wageningen Academic Publishers - ISBN 9789086862283 - p. 225 - 225.
Event 64th Annual Meeting of the European Federation of Animal Science, Nantes, France, 2013-08-26/2013-08-30
Department(s) Animal Breeding and Genetics
Biometris (PPO/PRI)
LR - Animal Breeding & Genomics
Biometris (WU MAT)
WIAS
Publication type Contribution in proceedings
Publication year 2013
Abstract The use of whole-genome sequence data can lead to more accurate genomic predictions in animal and plants. Despite the fact that costs of sequencing are falling, sequencing a high number of individuals is still far too expensive. A promising approach is to sequence the genomes of a core set of individuals and impute the missing genotypes for the remaining individuals that are genotyped with currently available marker arrays. Relevant questions are how many animals do we need to sequence and what SNP arrays can we impute from for accurate imputation? Sequence data of 124 Holstein Friesian bulls from different countries were provided by the 1000 bull genomes project consortium (www.1000bullgenomes.com). Two chromosomes with distinct sizes (1 and 29) were selected for this study. The Beagle software was used for imputation and accuracy was assessed via cross validation. The 124 bulls were randomly divided into five sets: four sets were merged into a reference set (n_ref=100), and the remaining set in turn as the validation set. For the validation individuals all markers were set to missing, except for markers that occur on two commonly used arrays that include 777k and 54k SNP across the genome. In a second scenario the same was done, except half of the reference individuals were randomly removed (n_ref=50). Accuracy of imputation was calculated by the correlation between true and imputed genotypes per locus. The results will be presented and the impact of the size of the reference set and the marker density will be discussed.
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