Statistics and Its Interface

Volume 4 (2011)

Number 3

Detecting association with rare variants for common diseases using haplotype-based methods

Pages: 273 – 283

DOI: http://dx.doi.org/10.4310/SII.2011.v4.n3.a2

Authors

Tao Feng (Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, U.S.A.)

Yali Li (Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, U.S.A.)

Xiaofeng Zhu (Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, U.S.A.)

Abstract

Current Genome-Wide Association Studies (GWAS) have successfully detected many genetic variants contributing to common diseases but not rare ones. Here two haplotypebased methods are proposed for detecting rare variants contributing a common disease. One method is a haplotypebased truncated product method (HTPM), for which we borrow a p-value combination method from testing for the multiple hypotheses, but use it for the purpose of clustering the information on rare risk haplotypes. The other method is the combined method, for which a set of risk haplotypes are chosen based on haplotype frequency comparison between cases and controls, and then testing for association using the same sample. Our simulation study demonstrates that both methods have improved power for detecting the association between rare variants and diseases, compared with other available methods. Both methods are applied to the Wellcome Trust Case Control Consortium (WTCCC) coronary artery disease and hypertension data and replicated the previous findings of genes associated with hypertension and coronary artery disease respectively at a genome-wide significance level of 5%. These results suggest that haplotypebased methods are powerful methods in searching for rare genetic variants and can be applicable to the data from current GWAS.

Keywords

genome-wide association studies, rare variants, haplotype-based truncated product method, combined method, risk haplotypes

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