A robust method for testing association in genome-wide association studies.
In genetic association studies, due to the varying underlying genetic models, no single statistical test can be the most powerful test under all situations. Current studies show that if the underlying genetic models are known, trend-based tests, which outperform the classical Pearson χ² test, can be constructed. However, when the underlying genetic models are unknown, the χ² test is usually more robust than trend-based tests. In this paper, we propose a new association test based on a generalized genetic model, namely the generalized order-restricted relative risks model. Through a Monte Carlo simulation study, we show that the proposed association test is generally more powerful than the χ² test, and more robust than those trend-based tests. The proposed methodologies are also illustrated by some real SNP datasets.
Biostatistics Epidemiology Research Design Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, Tex., USA.
Zhongxue
Z
Chen
Hon Keung Tony
HK
Ng
D000465:Algorithms; D016022:Case-Control Studies; D003198:Computer Simulation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide