A Comprehensive Evaluation of Methods for Mendelian Randomization Using Realistic Simulations and an Analysis of 38 Biomarkers for Risk of Type-2 Diabetes

• Published in: bioRxiv
• Main Authors: Guanghao Qi, Chatterjee, Nilanjan
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 05.02.2020
• Subjects: Genomes; Heritability; Single-nucleotide polymorphism; Size distribution
• DOI: 10.1101/702787

Background: Mendelian randomization (MR) has provided major opportunities for understanding the causal relationship among complex traits. Previous studies have often evaluated MR methods based on simulations that do not adequately reflect the data-generating mechanism in GWAS and there are often discrepancies in performance of MR methods in simulations and real datasets. Methods: We use a simulation framework that generates data on full GWAS for two traits under realistic model for effect-size distribution coherent with heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank to investigate their causal effects on risk of type-2 diabetes using externally available GWAS summary-statistics. Results: Simulation studies show that weighted mode and MRMix are the only two methods which maintain correct type-I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS while the opposite being true for smaller sample sizes. Among the other methods, random-effect IVW, MR-Robust and MR-RAPS tend to perform best in maintaining low mean squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on risk of type-2 diabetes across the different methods, with patterns similar to those observed in simulation studies. Conclusions: Relative performance of different MR methods depends heavily on sample sizes of underlying GWAS, proportion of valid instruments and validity of the InSIDE assumption.