To elucidate the molecular mechanisms underlying genetic variants identified from genome-wide association studies (GWAS) for a variety of phenotypic traits encompassing binary, continuous, count, and survival outcomes, we propose a novel and flexible method to test for mediation that can simultaneously accommodate multiple genetic variants and different types of outcome variables. Specifically, we employ the intersection–union test approach combined with the likelihood ratio test to detect mediation effect of multiple genetic variants via some mediator (e.g., the expression of a neighboring gene) on outcome. We fit high-dimensional generalized linear mixed models under the mediation framework, separately under the null and alternative hypothesis. We leverage Laplace approximation to compute the marginal likelihood of outcome and use coordinate descent algorithm to estimate corresponding parameters. Our extensive simulations demonstrate the validity of our proposed methods and substantial, up to 97%, power gains over alternative methods. Applications to real data for the study of Chlamydia trachomatis infection further showcase advantages of our methods. We believe our proposed methods will be of value and general interest in this post-GWAS era to disentangle the potential causal mechanism from DNA to phenotype for new drug discovery and personalized medicine.

中文翻译：

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广义多 SNP 中介交叉联合检验

为了阐明从全基因组关联研究 (GWAS) 中识别出的遗传变异的分子机制，这些变异针对包括二元、连续、计数和生存结果在内的各种表型性状，我们提出了一种新颖而灵活的方法来测试可以同时适应的中介多种遗传变异和不同类型的结果变量。具体来说，我们采用交叉联合检验方法结合似然比检验来检测多个遗传变异通过某些中介（例如，相邻基因的表达）对结果的中介效应。我们在中介框架下拟合高维广义线性混合模型，分别在零假设和备择假设下。我们利用拉普拉斯近似来计算结果的边际似然，并使用坐标下降算法来估计相应的参数。我们广泛的模拟证明了我们提出的方法的有效性，并且与替代方法相比，功率增益高达 97%。应用真实数据进行研究沙眼衣原体感染进一步展示了我们方法的优势。我们相信，我们提出的方法将在这个后 GWAS 时代具有价值和普遍意义，以解开从 DNA 到表型的潜在因果机制，用于新药发现和个性化医疗。