Accounting for the impact of rare variants on causal inference with RARE: a novel multivariable Mendelian...

Area of Science:

• Genetics
• Epidemiology
• Statistical Genetics

Background:

• Mendelian randomization (MR) uses genetic variants to infer causality.
• Traditional MR struggles with rare variants and horizontal pleiotropy (correlated and uncorrelated).

Purpose of the Study:

• To develop a multivariable MR method (RARE) that addresses limitations of traditional MR.
• To incorporate rare variants and account for shared horizontal pleiotropy in causal inference.

Main Methods:

• Proposed MVMR incorporating Rare variants Accounting for multiple Risk factors and shared horizontal plEiotropy (RARE).
• Utilized simulation studies to assess RARE's performance.
• Applied RARE to real-world data for high-density lipoprotein, low-density lipoprotein, type 2 diabetes, and coronary atherosclerosis.

Main Results:

• RARE effectively detects causal effects even with rare variants.
• The method successfully accounts for the impact of rare variants on causal inference.
• Demonstrated robustness and effectiveness in real data analyses.

Conclusions:

• RARE is a robust extension of MR for improved causal inference.
• The method enhances the ability to study complex traits by accounting for rare variants and pleiotropy.
• RARE offers a valuable tool for genetic and epidemiological research.