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A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies.
Human Heredity ( IF 1.8 ) Pub Date : 2020-09-23 , DOI: 10.1159/000508664
Lei Zhang 1 , Charalampos Papachristou 2 , Pankaj K Choudhary 1 , Swati Biswas 3
Affiliation  

Background: Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway. Methods: We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. To deal with the high dimensionality, we regularize the regression coefficients through an appropriate choice of priors. The model is fit using a combination of iteratively weighted least squares and expectation-maximization algorithms to estimate the posterior modes and their standard errors. A normal approximation is used for inference. Results: We conduct simulations to study the proposed method and find that our method has higher power than some standard approaches in several settings for identifying pathways with multiple modest-sized variants. We illustrate the method by analyzing data from two genome-wide association studies on breast and renal cancers. Conclusion: Our method can be helpful in detecting pathway association.
Hum Hered


中文翻译:

在全基因组关联研究中进行路径分析的贝叶斯层次框架。

背景:途径分析允许共同考虑属于多个基因的多个SNP,而这些基因又属于生物学定义的途径。这种类型的分析通常比单SNP分析更强大,可以检测路径中变体的联合效应。方法:我们通过对3级层次结构(即SNP基因通路)进行完全建模来开发贝叶斯层次模型,这与当前使用的组合此类信息的临时方式不同,自然是通路结构中固有的。我们在广义线性模型框架内以每个级别的效果为条件,对每个级别的效果进行建模。为了应对高维,我们通过先验适当选择正规化的回归系数。该模型使用迭代加权最小二乘和期望最大化算法的组合进行拟合,以估计后验模式及其标准误差。使用法线近似进行推断。结果:我们进行了仿真研究,研究了所提出的方法,发现该方法在几种设置中具有比标准方法更高的能力,可用于识别具有多种中等大小变异的途径。我们通过分析两个关于乳腺癌和肾癌的全基因组关联研究的数据来说明该方法。结论:我们的方法可用于检测通路关联。
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更新日期:2020-09-23
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