Fast and accurate genotype imputation in genome-wide association studies through pre-phasing

Gonçalo Abecasis, Jonathan Marchini and colleagues report a pre-phasing strategy for genotype imputation in GWAS, which they show maintains accuracy while substantially lowering computational costs. Their approach has been implemented in both MACH and IMPUTE 2.0 software. The 1000 Genomes Project an...

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Bibliographic Details
Published in:Nature genetics Vol. 44; no. 8; pp. 955 - 959
Main Authors: Howie, Bryan, Fuchsberger, Christian, Stephens, Matthew, Marchini, Jonathan, Abecasis, Gonçalo R
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.08.2012
Nature Publishing Group
Subjects:
631/114/2415
631/208/205/2138
Accuracy
Agriculture
Algorithms
Animal Genetics and Genomics
Biological and medical sciences
Biomedical and Life Sciences
Biomedicine
Cancer Research
Computational Biology
Databases, Genetic
DNA sequencing
Fundamental and applied biological sciences. Psychology
Gene Function
Genetics of eukaryotes. Biological and molecular evolution
Genome-Wide Association Study - methods
Genome-Wide Association Study - statistics & numerical data
Genomes
Genotype
Genotypes
Haplotypes
Health promotion
Human Genetics
Human Genome Project
Humans
Methods
Nucleotide sequencing
Physiological aspects
Studies
technical-report
United States
Genotype
Association
ISSN:1061-4036, 1546-1718, 1546-1718
Online Access:Get full text
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Summary:Gonçalo Abecasis, Jonathan Marchini and colleagues report a pre-phasing strategy for genotype imputation in GWAS, which they show maintains accuracy while substantially lowering computational costs. Their approach has been implemented in both MACH and IMPUTE 2.0 software. The 1000 Genomes Project and disease-specific sequencing efforts are producing large collections of haplotypes that can be used as reference panels for genotype imputation in genome-wide association studies (GWAS). However, imputing from large reference panels with existing methods imposes a high computational burden. We introduce a strategy called 'pre-phasing' that maintains the accuracy of leading methods while reducing computational costs. We first statistically estimate the haplotypes for each individual within the GWAS sample (pre-phasing) and then impute missing genotypes into these estimated haplotypes. This reduces the computational cost because (i) the GWAS samples must be phased only once, whereas standard methods would implicitly repeat phasing with each reference panel update, and (ii) it is much faster to match a phased GWAS haplotype to one reference haplotype than to match two unphased GWAS genotypes to a pair of reference haplotypes. We implemented our approach in the MaCH and IMPUTE2 frameworks, and we tested it on data sets from the Wellcome Trust Case Control Consortium 2 (WTCCC2), the Genetic Association Information Network (GAIN), the Women's Health Initiative (WHI) and the 1000 Genomes Project. This strategy will be particularly valuable for repeated imputation as reference panels evolve.
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These authors contributed equally to this work
ISSN:1061-4036
1546-1718
1546-1718
DOI:10.1038/ng.2354