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

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          Abstract

          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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          Author and article information

          Journal
          Nat Genet
          Nature genetics
          Springer Science and Business Media LLC
          1546-1718
          1061-4036
          Jul 22 2012
          : 44
          : 8
          Affiliations
          [1 ] Department of Human Genetics, University of Chicago, Chicago, Illinois, USA.
          Article
          ng.2354 NIHMS475535
          10.1038/ng.2354
          3696580
          22820512
          be778932-4957-4e9e-a2e0-a3daabeeaf5e
          History

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