Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects.
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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.
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The mode-based method has been shown to have low precision in some simulated and real datasets.
“The mode-based method has been shown to have low precision in some simulated and real datasets 26 .”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [26]
Two recent simulation studies recommended the contamination mixture method and MR-Mix as having the lowest mean squared error across a range of different methods.
“Two recent simulation studies that compared different methods recommended the contamination mixture method 26 and MR-Mix 94 as having the lowest mean squared error across a range of different methods”
From Guidelines for performing Mendelian randomization investigations (2020) · cites this paper as [26]
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- Authors
- Qi, G., & Chatterjee, N.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Chatterjee, Nilanjan
- Venue
- Nature Communications
- Published
- 2019-04-26
- Volume
- 10
- Issue
- 1
- Pages
- 1941-1941
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6486646
- OpenAlex
- W2941271956
- Cited by
- 207
- References
- 60
- Citations by year
- 2026: 22 · 2025: 34 · 2024: 52 · 2023: 28 · 2022: 26 · 2021: 20 · 2020: 19 · 2019: 6
- FWCI
- 10.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Johns Hopkins University
- Genome Canada (C1287/A16563, U19 CA148065)
- Government of Canada (C1287/A10710, U19 CA148065, 634935, PSR-SIIRI-701, C1287/A10118, HEALTH-F2-2009-223175, C1287/A16563, 633784)
- Cancer Research UK (223175, U19 CA148065, C1287/A10118, C1287/A16563, C1287/A10710, C1287‐A10118, HEALTHF2-2009-223175, A10118, C1287, 633784, 634935, A16563, C1287-A10710, PSRSIIRI-701, A10710, C1287‐A16563)
- European Commission (PSRSIIRI-701, U19 CA148065, 633784, C1287/A10118, C1287/A10710, 634935, H2020, C1287/A16563, 223175)
- National Institutes of Health (634935, 223175, 633784, PSR-SIIRI-701, C1287/A16563, C1287/A10118, C1287/A10710, HEALTH-F2-2009-223175, X01HG007492, H2020, CA148065, U19 CA148065, X01HG007492, U24OD023382)
- Horizon 2020 Framework Programme (633784, U19 CA148065, 634935, PSR-SIIRI-701, C1287/A10118, C1287/A10710, C1287/A16563, HEALTH-F2-2009-223175, 223175)
- Canadian Institutes of Health Research (C1287/A10710, X01HG007492, C1287/A10118, C1287/A16563, PSR-SIIRI-701, 634935, HEALTH-F2-2009-223175, 633784, U19 CA148065, 223175)
- National Human Genome Research Institute (X01HG007492)
- National Cancer Institute (U19CA148065)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- Mendelian randomization, horizontal pleiotropy, spike detection, normal mixture models, causal effect estimation, genetic instruments, effect size distribution, body mass index, age at menarche, breast cancer risk, coronary artery disease, HDL cholesterol, triglycerides, major depressive disorder
- MeSH terms
- Age Factors, Algorithms, Breast Neoplasms, Coronary Artery Disease, Major Depressive Disorder, Female, Humans, Cholesterol, HDL, Menarche, Risk Factors, Triglycerides, Genome, Human, Body Mass Index, Quantitative Trait, Heritable, Genome-Wide Association Study, Mendelian Randomization Analysis, Datasets as Topic
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