Robust Inference in Mendelian Randomization with Invalid IVs

Music City Center 

Mendelian randomization employs protein quantitative trait loci (pQTLs) as instruments to address unobserved confounding in protein biomarker discovery for complex diseases like Alzheimer's and autism spectrum disorder. However, the presence of invalid pQTL instruments - those violate core instrument assumptions - can threaten inference validity by introducing bias. While existing methods aim to detect invalid instruments, their susceptibility to selection errors risks propagating bias into causal estimates. To address this, we propose a novel resampling-based approach that accounts for the selection uncertainty, ensuring robustness against misclassified instruments. By incorporating a data-driven prior on pQTL validity, our approach enhances efficiency while maintaining robustness. We showed that our method is free of selection errors across diverse pleiotropic scenarios and improves the CI efficiency by approximately 20% compared to the previous robust MR methods. We applied our method to genome-wide proteomics data from 54,306 UK Biobank individuals and a genome-wide association study of Alzheimer's disease with 455,258 subjects, and identified five causal protein biomarkers.

Pose-selection inference

Mendelian Randomization

Instrumental variable

Protein biomarkers 

Section on Statistical Learning and Data Science