Journal
NATURE COMMUNICATIONS
Volume 10, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-019-09432-2
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Funding
- NIH [U24OD023382]
- Bloomberg Distinguished Professorship Endowment at the Johns Hopkins University
- Government of Canada through Genome Canada
- Government of Canada through Canadian Institutes of Health Research
- 'Ministere de l'Economie, de la Science et de l'Innovation du Quebec' through Genome Quebec
- Ministere de l'Economie, de la Science et de l'Innovation du Quebec [PSR-SIIRI-701]
- National Institutes of Health [U19 CA148065, X01HG007492]
- Cancer Research UK [C1287/A10118, C1287/A16563, C1287/A10710]
- European Union [HEALTH-F2-2009-223175, H2020 633784, 634935]
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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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