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Bioinformatics Advance Access originally published online on April 3, 2009
Bioinformatics 2009 25(11):1449-1450; doi:10.1093/bioinformatics/btp183
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

IGG3: a tool to rapidly integrate large genotype datasets for whole-genome imputation and individual-level meta-analysis

Miao-Xin Li 1,*, Lin Jiang 2, Patrick Yu-Ping Kao 1, Pak-C. Sham 3,4 and You-Qiang Song 1,4,*

1Department of Biochemistry, The University of Hong Kong, Pokfulam, Hong Kong, 2The Center for Experiment, Hunan University of Commerce, Changsha, Hunan 410205, China, 3Department of Psychiatry and 4The Centre for Reproduction, Development and Growth, The University of Hong Kong, Pokfulam, Hong Kong

*To whom correspondence should be addressed.


  Abstract

Summary: There is an urgent and increasing demand for integrating large genotype datasets across genome-wide association studies and HapMap project for whole-genome imputation and individual-level meta-analysis. A new algorithm was developed to efficiently merge raw genotypes across large datasets and implemented in the latest version of IGG, IGG3. In addition, IGG3 can integrate the latest phased and unphased HapMap genotypes and can flexibly generate complete sets of input files for six popular genotype imputation tools. We demonstrated the efficiency of IGG3 by simulation tests, which could rapidly merge genotypes in tens of thousands of large genotype chips (e.g. Affymetrix Genome-Wide Human SNP Array 6.0 and Illumina Human1m-duo) and in HapMap III project on an ordinary desktop computer.

Availability: http://bioinfo.hku.hk/iggweb (version 3.0).

Contacts: songy{at}hkucc.hku.hk; limx54{at}yahoo.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Alex Bateman

Received on February 9, 2009; revised on March 25, 2009; accepted on March 30, 2009

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