A Generalized Sequential Bonferroni Procedure Using Smoothed Weights for Genome-Wide Association Studies Incorporating Information on Hardy-Weinberg Disequilibrium among CasesGao G.a · Kang G.b · Wang J.a · Chen W.a · Qin H.c · Jiang B.d · Li Q.e · Sun C.a · Liu N.d · Archer K.J.a · Allison D.B.d
aDepartment of Biostatistics, Virginia Commonwealth University, Richmond, Va., bDepartment of Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, Pa., cDepartment of Biostatistics and Epidemiology, Case Western Reserve University, Cleveland, Ohio, and dDepartment of Biostatistics, University of Alabama at Birmingham, Birmingham, Ala., USA; eAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, PR China
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Background/Objectives: For genome-wide association studies (GWAS) with case-control designs, one of the most widely used association tests is the Cochran-Armitage (CA) trend test assuming an additive mode of inheritance. The CA trend test often has higher power than other association tests under additive and multiplicative disease models. However, it can have very low power under a recessive disease model in GWAS. Although tests (such as MAX3) robust to different genetic models have been developed, they often have relatively lower power than the CA trend test under additive and multiplicative models. The goal of this study is to propose an efficient method that not only has higher power than the CA trend test under dominant and recessive models but also maintains the power of the CA trend test under additive and multiplicative models. Methods: We employed the generalized sequential Bonferroni (GSB) procedure of Holm to incorporate information from a Hardy-Weinberg disequilibrium (HWD) test into the CA trend test based on estimating weights from the p values of the HWD test. We proposed to smooth the weights to reduce possible noise. Results and Conclusions: Results from extensive simulation studies showed that the proposed GSB procedure can achieve the goal described above.
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