Trans-ancestry Mendelian Randomization Discovers Novel Causal Genes for Autoimmune Disease Traits

Mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) has become a powerful tool for dissecting the causal relationships between exposure and outcomes. As GWAS begins to incorporate samples of diverse ancestries, the optimal strategy to perform MR analysis integrating these GWAS results remains to be determined. To fill in this gap, we proposed a robust method that aggregates the genetic variant-risk factor association summary statistics of multiple ancestries by modelling the genetic effects of the exposure as a function of the principal components of genome-wide allele frequency. This allows borrowing information from different ancestry groups and accounting for potential between-ancestry heterogeneities. The method is general and can be used with different MR methods. We demonstrated that our method improves accuracy and power compared to the inverse-variance weighting method based on only ancestry-stratified samples, with additional benefits in correcting winners curse bias. We also illustrated the flexibility and potential of discovering novel causal genes for autoimmune disease traits of our method in practice.