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

Abstract

AbstractBackgroundPrevious 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 single nucleotide polymorphisms (SNP)-intermediate or SNP-gene expression, a hierarchical model is equivalent to a two-stage instrumental or transcriptome-wide association study (TWAS) analysis, respectively.MethodsWe 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 FUSION and S-PrediXcan. However, 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.ResultsWe investigate the performance of hJAM in comparison to existing MR approaches (inverse-variance weighted MR and multivariate MR) and existing TWAS approaches (S-PrediXcan) for effect estimation, type-I error and empirical power. We apply hJAM to two examples: estimating the conditional effects of body mass index and type 2 diabetes on myocardial infarction and estimating the effects of the expressions of gene NUCKS1 and PM20D1 on the risk of prostate cancer.ConclusionsAcross numerous causal simulation scenarios, we demonstrate that hJAM is unbiased, maintains correct type-I error and has increased power.Key MessagesMendelian randomization and transcriptome-wide association studies (TWAS) can be viewed as similar approaches via a hierarchical model.The hierarchal joint analysis of marginal summary statistics (hJAM) is a multivariate Mendelian randomization approach which offers a simple way to address the pleiotropy bias that is introduced by genetic variants associated with multiple risk factors or expressions of genes.hJAM incorporates the linkage disequilibrium structure of the single nucleotide polymorphism (SNPs) in a reference population to account for the correlation between SNPs.In addition to Mendelian randomization and TWAS, hJAM offers flexibility to incorporate functional or genomic annotation or information from metabolomic studies.