Fast implementation of a scan statistic for identifying chromosomal patterns of genome wide association studies
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- 作者：Yan V. Sun ; Douglas M. Jacobsen ; Stephen T. Turner ; Eric Boerwinkle ; Sharon L.R. Kardia
- 刊名：Computational Statistics and Data Analysis
- 出版年：2009
- 出版时间：15 March 2009
- 年：2009
- 卷：53
- 期：5
- 页码：1794-1801
- 全文大小：684 K
文摘
In order to take into account the complex genomic distribution of SNP variations when identifying chromosomal regions with significant SNP effects, a single nucleotide polymorphism (SNP) association scan statistic was developed. To address the computational needs of genome wide association (GWA) studies, a fast Java application, which combines single-locus SNP tests and a scan statistic for identifying chromosomal regions with significant clusters of significant SNP effects, was developed and implemented. To illustrate this application, SNP associations were analyzed in a pharmacogenomic study of the blood pressure lowering effect of thiazide-diuretics (N=195) using the Affymetrix Human Mapping 100 K Set. 55,335 tagSNPs (pair-wise linkage disequilibrium R2<0.5) were selected to reduce the frequency correlation between SNPs. A typical workstation can complete the whole genome scan including 10,000 permutation tests within 3 h. The most significant regions locate on chromosome 3, 6, 13 and 16, two of which contain candidate genes that may be involved in the underlying drug response mechanism. The computational performance of ChromoScan-GWA and its scalability were tested with up to 1,000,000 SNPs and up to 4000 subjects. Using 10,000 permutations, the computation time grew linearly in these datasets. This scan statistic application provides a robust statistical and computational foundation for identifying genomic regions associated with disease and provides a method to compare GWA results even across different platforms.