Hierarchical Modeling Framework for Mendelian Randomization and Transcriptome-wide Association Approaches for Correlated SNPs and Intermediates

摘要

Previous research has demonstrated the usefulness of hierarchical modeling for incorporating a flexible array of prior information in genetic association studies. When this prior information consists of effect estimates from association analyses of SNP-intermediate or SNP-gene expression, the hierarchical model is equivalent to a two-stage instrumental or transcriptome-wide association studies (TWAS) analysis, respectively. Here, we propose to extend our previous approach for the joint analysis of marginal summary statistics (JAM) to incorporate prior information via a hierarchical model (hJAM). In this framework, the use of appropriate effect estimates as prior information yields an analysis similar to Mendelian Randomization (MR) and TWAS approaches such as S-PrediXcan. hJAM is applicable to multiple correlated SNPs and multiple correlated intermediates to yield conditional estimates of effect for the intermediate on the outcome, thus providing advantages over alternative approaches. We investigate the performance of our model in comparison to existing MR approaches (e.g. inverse-variance weighted MR, multivariate MR, and MR with Egger regression) and existing TWAS approaches (e.g. S-PrediXcan) for effect estimation, type I error and empirical power. Across numerous causal simulation scenarios, hJAM is unbiased, maintains correct type-I error and has increased power. We apply hJAM to two applied analyses: 1) estimation of the conditional effects of body mass index (BMI), asthma, smoking, and type 2 diabetes on myocardial infarction; and 2) investigation of the impact of gene expression on prostate cancer.