MR-EILLS: an invariance-based Mendelian randomization method integrating multiple heterogeneous GWAS summary datasets.

ABSTRACT: Diverse genetic structures can lead to heterogeneity among GWAS summary datasets from distinct populations. This makes it more difficult to infer causal effects of exposures on the outcome when multiple GWAS summary datasets are integrated. Here, we propose a Mendelian randomization method called MR-EILLS, which leverages environment invariant linear least squares to establish whether there is a causal relationship that is invariant in all heterogeneous populations. The MR-EILLS model works in both univariate and multivariate scenarios and allows for invalid instrumental variables that violate the exchangeability and exclusion restriction assumptions. In addition, MR-EILLS shows the unbiased causal effect estimations of one or multiple exposures on the outcome, whether there are valid or i

SUBMITTER: Hou L 

PROVIDER: S-EPMC12361485 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature