Bayesian analysis of rare variants in genetic association studies.
Bayesian analysis of rare variants in genetic association studies.
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DOI：
10.1002/gepi.20554
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发表时间：
2011-01
影响因子：
2.1
通讯作者：
Zhi, Degui
中科院分区：
文献类型：
--
作者：
Yi, Nengjun;Zhi, Degui
关键词：
新一代测序技术的最新进展促进了罕见变异的检测，使人们有可能发现罕见变异在复杂疾病中的作用。由于任何单一的罕见变异几乎不包含变异，因此罕见变异的关联分析需要能够有效地将变异之间的信息联合收割机组合并估计其总体效应的统计方法。本文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罕见变异。我们的模型可以处理现有方法尚未完全解决的复杂情况，包括不同的影响和非功能性变体的问题。我们的方法联合建模了多个罕见变体的整体效应和权重，并从数据中估计它们。这种方法根据不同变体对表型的贡献为不同变体产生不同的权重，从而产生跨变体的信息的有效汇总。我们评估所提出的方法，并比较其性能与现有的方法在广泛的模拟数据。实验结果表明，该方法在各种情况下都能取得良好的效果，并且比现有的方法更有效。
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. We here 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 non-functional 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.
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