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Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects

Nature communications, 2019-04, Vol.10 (1), p.1941-1941, Article 1941 [Peer Reviewed Journal]

The Author(s) 2019. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2019 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-019-09432-2 ;PMID: 31028273

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Title:
Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects
Author: Qi, Guanghao ; Chatterjee, Nilanjan
Subjects: Age ; Age Factors ; Algorithms ; Body mass ; Body Mass Index ; Breast cancer ; Breast Neoplasms - blood ; Breast Neoplasms - diagnosis ; Breast Neoplasms - etiology ; Breast Neoplasms - genetics ; Cardiovascular disease ; Cholesterol, HDL - blood ; Computer simulation ; Coronary artery ; Coronary artery disease ; Coronary Artery Disease - blood ; Coronary Artery Disease - diagnosis ; Coronary Artery Disease - etiology ; Coronary Artery Disease - genetics ; Datasets as Topic ; Depressive Disorder, Major - blood ; Depressive Disorder, Major - diagnosis ; Depressive Disorder, Major - etiology ; Depressive Disorder, Major - genetics ; Female ; Genetic analysis ; Genome, Human ; Genome-wide association studies ; Genome-Wide Association Study ; Genomes ; Health risks ; Heart diseases ; High density lipoprotein ; Humans ; Menarche ; Menarche - blood ; Menarche - genetics ; Mendelian Randomization Analysis - statistics & numerical data ; Mental depression ; Pleiotropy ; Quantitative Trait, Heritable ; Randomization ; Risk ; Risk Factors ; Robustness ; Triglycerides ; Triglycerides - blood
Is Part Of: Nature communications, 2019-04, Vol.10 (1), p.1941-1941, Article 1941
Description: 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.
Publisher: England: Nature Publishing Group
Language: English
Identifier: ISSN: 2041-1723
EISSN: 2041-1723
DOI: 10.1038/s41467-019-09432-2
PMID: 31028273
Source: MEDLINE
PubMed Central
ProQuest Central
DOAJ Directory of Open Access Journals

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Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects

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