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


BACKGROUND
(http://bioinfo.hku.hk/iggweb) (version 3.0).
Li
Miao-Xin
MX
Department of Biochemistry, The University of Hong Kong, Pokfulam, Hong Kong. limx54@yahoo.com
Jiang
Lin
L
Kao
Patrick Yu-Ping
PY
Sham
Pak-C
PC
Song
You-Qiang
YQ
D000465:Algorithms; D030541:Databases, Genetic; D016678:Genome / Q000235:genetics*; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods; D005838:Genotype; D016247:Information Storage and Retrieval / Q000379:methods; D015201:Meta-Analysis as Topic; D012984:Software