Gene‐based genetic association test with adaptive optimal weights |
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| Authors: | Zhongxue Chen Yan Lu Tong Lin Qingzhong Liu Kai Wang |
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| Affiliation: | 1. Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, United States of America;2. Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, United States of America;3. The Key Laboratory of Machine Perception (Ministry of Education), School of EECS, Peking University, Beijing, China;4. Department of Computer Science, Sam Houston State University, Huntsville, Texas, United States of America;5. Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, United States of America |
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| Abstract: | It is well known that using proper weights for genetic variants is crucial in enhancing the power of gene‐ or pathway‐based association tests. To increase the power, we propose a general approach that adaptively selects weights among a class of weight families and apply it to the popular sequencing kernel association test. Through comprehensive simulation studies, we demonstrate that the proposed method can substantially increase power under some conditions. Applications to real data are also presented. This general approach can be extended to all current set‐based rare variant association tests whose performances depend on variant's weight assignment. |
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| Keywords: | burden test gene set genetic association SKAT weighting |
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