Journal Article

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

Miao-Xin Li, Lin Jiang, Patrick Yu-Ping Kao, Pak-C. Sham and You-Qiang Song

in Bioinformatics

Volume 25, issue 11, pages 1449-1450
Published in print June 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btp183
IGG3: a tool to rapidly integrate large genotype datasets for whole-genome imputation and individual-level meta-analysis

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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@hkucc.hku.hk; limx54@yahoo.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1530 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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