Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects

TLDR

Mendelian randomization uses large-scale GWAS to infer causal relationships among traits, but horizontal pleiotropy remains a major source of bias. The study aims to develop a robust and efficient MR analysis method using many genetic instruments. This method, called MRMix, employs a spike‑detection algorithm under a normal‑mixture model to estimate effect‑size distributions. Simulations demonstrate that MRMix yields nearly unbiased, more efficient causal effect estimates than existing methods, and its application to public datasets identifies BMI and age‑at‑menarche as causal for breast cancer, no causal effect of HDL or triglycerides on coronary artery disease, and a strong detrimental effect of BMI on major depressive disorder.

Abstract

Abstract Mendelian randomization (MR) has emerged as a major tool for the investigation of causal relationship among traits, utilizing results from large-scale genome-wide association studies. Bias due to horizontal pleiotropy, however, remains a major concern. We propose a novel approach for robust and efficient MR analysis using large number of genetic instruments, based on a novel spike-detection algorithm under a normal-mixture model for underlying effect-size distributions. Simulations show that the new method, MRMix, provides nearly unbiased or/and less biased estimates of causal effects compared to alternative methods and can achieve higher efficiency than comparably robust estimators. Application of MRMix to publicly available datasets leads to notable observations, including identification of causal effects of BMI and age-at-menarche on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI on the risk of major depressive disorder.