Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

  • Xianghong Hu
  • Jiashun Xiao
  • Xiaomeng Wan
  • Zhiwei Wang
  • Hongyu Zhao
  • Can Yang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1717-1735
Journal / PublicationAmerican Journal of Human Genetics
Volume111
Issue number8
Online published25 Jul 2024
Publication statusPublished - 8 Aug 2024

Abstract

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference. © 2024 American Society of Human Genetics.

Research Area(s)

  • causal inference, confounding factors, GWAS summary statistics, Mendelian randomization, negative control

Citation Format(s)

Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics. / Hu, Xianghong; Cai, Mingxuan; Xiao, Jiashun et al.
In: American Journal of Human Genetics, Vol. 111, No. 8, 08.08.2024, p. 1717-1735.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review