Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, Alabama 35294-0022, USA.
Genet Epidemiol. 2011 Jan;35(1):57-69. doi: 10.1002/gepi.20554.
Recent advances in next-generation sequencing technologies facilitate the detection of rare variants, making it possible to uncover the roles of rare variants in complex diseases. As any single rare variants contain little variation, association analysis of rare variants requires statistical methods that can effectively combine the information across variants and estimate their overall effect. In this study, we propose a novel Bayesian generalized linear model for analyzing multiple rare variants within a gene or genomic region in genetic association studies. Our model can deal with complicated situations that have not been fully addressed by existing methods, including issues of disparate effects and nonfunctional variants. Our method jointly models the overall effect and the weights of multiple rare variants and estimates them from the data. This approach produces different weights to different variants based on their contributions to the phenotype, yielding an effective summary of the information across variants. We evaluate the proposed method and compare its performance to existing methods on extensive simulated data. The results show that the proposed method performs well under all situations and is more powerful than existing approaches.
近年来，下一代测序技术的进步促进了稀有变异的检测，使得揭示稀有变异在复杂疾病中的作用成为可能。由于任何单一的稀有变异都包含很少的变异，因此稀有变异的关联分析需要能够有效地整合变异信息并估计其总体效应的统计方法。在这项研究中，我们提出了一种新的贝叶斯广义线性模型，用于分析遗传关联研究中基因或基因组区域内的多个稀有变异。我们的模型可以处理现有方法尚未完全解决的复杂情况，包括效应不一致和非功能变异的问题。我们的方法联合建模了多个稀有变异的总体效应和权重，并从数据中对它们进行估计。这种方法根据不同变异对表型的贡献为不同的变异赋予不同的权重，从而有效地对变异之间的信息进行汇总。我们在广泛的模拟数据上评估了所提出的方法，并将其性能与现有方法进行了比较。结果表明，该方法在所有情况下都表现良好，比现有方法更有效。
