A new Mendelian randomization method for causal inference using genetic data

What is it about?

Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.

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Photo by Vitalii Pavlyshynets on Unsplash

Photo by Vitalii Pavlyshynets on Unsplash

Why is it important?

We show that, besides pleiotropy, sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap in Mendelian randomization (MR) analysis. Our method, MR-APSS, accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information. Both simulations and real data analysis results suggest that MR-APSS avoids many false-positive findings and improves the statistical power of detecting causal effects.

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