Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data

Research output: Chapter in Book/Report/Conference proceedingConference paper

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.
Original languageEnglish
Title of host publicationBook of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013
PublisherWageningen Academic Publishers
Pages225-225
Volume19
ISBN (Print)9789086862283
Publication statusPublished - 2013
Event64th Annual Meeting of the European Federation of Animal Science, Nantes, France -
Duration: 26 Aug 201330 Aug 2013

Conference

Conference64th Annual Meeting of the European Federation of Animal Science, Nantes, France
Period26/08/1330/08/13

Fingerprint

Holstein
bulls
genome
genotype
cattle
Beagle
animals
chromosomes
genomics
loci
prediction

Cite this

van Binsbergen, R., Bink, M. C. A. M., Calus, M. P. L., van Eeuwijk, F. A., Hayes, B. J., Hulsegge, B., & Veerkamp, R. F. (2013). Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data. In Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013 (Vol. 19, pp. 225-225). Wageningen Academic Publishers.
van Binsbergen, R. ; Bink, M.C.A.M. ; Calus, M.P.L. ; van Eeuwijk, F.A. ; Hayes, B.J. ; Hulsegge, B. ; Veerkamp, R.F. / Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data. Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013. Vol. 19 Wageningen Academic Publishers, 2013. pp. 225-225
@inproceedings{528e68c713134938aee310b8561171f1,
title = "Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data",
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.",
author = "{van Binsbergen}, R. and M.C.A.M. Bink and M.P.L. Calus and {van Eeuwijk}, F.A. and B.J. Hayes and B. Hulsegge and R.F. Veerkamp",
year = "2013",
language = "English",
isbn = "9789086862283",
volume = "19",
pages = "225--225",
booktitle = "Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013",
publisher = "Wageningen Academic Publishers",

}

van Binsbergen, R, Bink, MCAM, Calus, MPL, van Eeuwijk, FA, Hayes, BJ, Hulsegge, B & Veerkamp, RF 2013, Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data. in Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013. vol. 19, Wageningen Academic Publishers, pp. 225-225, 64th Annual Meeting of the European Federation of Animal Science, Nantes, France, 26/08/13.

Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data. / van Binsbergen, R.; Bink, M.C.A.M.; Calus, M.P.L.; van Eeuwijk, F.A.; Hayes, B.J.; Hulsegge, B.; Veerkamp, R.F.

Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013. Vol. 19 Wageningen Academic Publishers, 2013. p. 225-225.

Research output: Chapter in Book/Report/Conference proceedingConference paper

TY - GEN

T1 - Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data

AU - van Binsbergen, R.

AU - Bink, M.C.A.M.

AU - Calus, M.P.L.

AU - van Eeuwijk, F.A.

AU - Hayes, B.J.

AU - Hulsegge, B.

AU - Veerkamp, R.F.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

M3 - Conference paper

SN - 9789086862283

VL - 19

SP - 225

EP - 225

BT - Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013

PB - Wageningen Academic Publishers

ER -

van Binsbergen R, Bink MCAM, Calus MPL, van Eeuwijk FA, Hayes BJ, Hulsegge B et al. Genotype imputation accuracy in Holstein Friesian cattle in case of whole-genome sequence data. In Book of Abstracts of the 64th Annual Meeting of the European Federation of Animal Science, Nantes, France 26-30 August 2013. Vol. 19. Wageningen Academic Publishers. 2013. p. 225-225