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Journal article

Rapid genotype imputation from sequence without reference panels

Abstract:
Inexpensive genotyping methods are essential for genetic studies requiring large sample sizes. In human studies, array-based microarrays and high-density haplotype reference panels allow efficient genotype imputation for this purpose. However, these resources are typically unavailable in non-human settings. Here we describe a method (STITCH) for imputation based only on sequencing read data, without requiring additional reference panels or array data. We demonstrate its applicability even in settings of extremely low sequencing coverage, by accurately imputing 5.7 million SNPs at a mean r2 of 0.98 in 2,073 outbred laboratory mice (0.15X sequencing coverage). In a sample of 11,670 Han Chinese (1.7X), we achieve accuracy similar to alternative approaches that require a reference panel, demonstrating that this approach can work for genetically diverse populations. Our method enables straightforward progression from low-coverage sequence to imputed genotypes, overcoming barriers that at present restrict the application of genome-wide association study technology outside humans.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/ng.3594

Authors

More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Author


More from this funder
Funding agency for:
Myers, S
Davies, R
Grant:
098387/Z/12/Z
097308/Z/11/Z
WT090532/Z/09/Z, WT083573/Z/07/Z, WT089269/Z/09/Z, WT098387/Z/12/Z


Publisher:
Nature Publishing Group
Journal:
Nature Genetics More from this journal
Volume:
48
Pages:
965–969
Publication date:
2016-07-04
Acceptance date:
2016-05-15
DOI:
EISSN:
1546-1718
ISSN:
1061-4036


Pubs id:
pubs:628467
UUID:
uuid:b66a0727-fcdb-4dee-bc6a-8e408e43181e
Local pid:
pubs:628467
Source identifiers:
628467
Deposit date:
2016-06-17
ARK identifier:

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