Li Miao-Xin, Jiang Lin, Kao Patrick Yu-Ping, Sham Pak-C, Song You-Qiang
Department of Biochemistry, The University of Hong Kong, Pokfulam, Hong Kong.
Bioinformatics. 2009 Jun 1;25(11):1449-50. doi: 10.1093/bioinformatics/btp183. Epub 2009 Apr 3.
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.
(http://bioinfo.hku.hk/iggweb) (version 3.0).
在全基因组关联研究和HapMap计划中，为了进行全基因组插补和个体水平的荟萃分析，对整合大型基因型数据集的需求日益迫切。我们开发了一种新算法，用于高效合并大型数据集中的原始基因型，并在IGG的最新版本IGG3中实现。此外，IGG3可以整合最新的分阶段和未分阶段的HapMap基因型，并能灵活地为六种流行的基因型插补工具生成完整的输入文件集。我们通过模拟测试证明了IGG3的效率，它可以在普通台式计算机上快速合并数万个大型基因型芯片（如Affymetrix全基因组人类SNP Array 6.0和Illumina Human1m-duo）以及HapMap III计划中的基因型。
