A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis

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 estimates from association analyses of single-nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a 2-stage instrumental or transcriptome-wide association study (TWAS) analysis, respectively. We propose to extend our previous approach for the joint analysis of marginal summary statistics to incorporate prior information via a hierarchical model (hJAM). In this framework, the use of appropriate estimates as prior information yields an analysis similar to Mendelian randomization (MR) and TWAS approaches. hJAM is applicable to multiple correlated SNPs and intermediates to yield conditional estimates for the intermediates on the outcome, thus providing advantages over alternative approaches. We investigated the performance of hJAM in comparison with existing MR and TWAS approaches and demonstrated that hJAM yields an unbiased estimate, maintains correct type-I error, and has increased power across extensive simulations. We applied hJAM to 2 examples: estimating the causal effects of body mass index (GIANT Consortium) and type 2 diabetes (DIAGRAM data set, GERA Cohort, and UK Biobank) on myocardial infarction (UK Biobank) and estimating the causal effects of the expressions of the genes for nuclear casein kinase and cyclin dependent kinase substrate 1 and peptidase M20 domain containing 1 on the risk of prostate cancer (PRACTICAL and GTEx).