Leveraging family data to design Mendelian randomization that is provably robust to population stratification

Abstract

Mendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We then conducted an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank dataset. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated due to confounding from population stratification.