Chen Zhongxue, Lu Yan, Lin Tong, Liu Qingzhong, Wang Kai
Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, United States of America.
Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, United States of America.
Genet Epidemiol. 2018 Feb;42(1):95-103. doi: 10.1002/gepi.22098. Epub 2017 Nov 26.
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.
众所周知，在增强基于基因或通路的关联检验的效能方面，为基因变异使用恰当的权重至关重要。为了提高效能，我们提出一种通用方法，该方法在一类权重族中自适应地选择权重，并将其应用于流行的测序核关联检验。通过全面的模拟研究，我们证明了所提出的方法在某些条件下能够大幅提高效能。还展示了其在实际数据中的应用。这种通用方法可以扩展到所有当前基于集合的罕见变异关联检验，其性能取决于变异的权重分配。
