Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 1717-1735 |
Journal / Publication | American Journal of Human Genetics |
Volume | 111 |
Issue number | 8 |
Online published | 25 Jul 2024 |
Publication status | Published - 8 Aug 2024 |
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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.
In: American Journal of Human Genetics, Vol. 111, No. 8, 08.08.2024, p. 1717-1735.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review