Journal
GENETICS
Volume 207, Issue 2, Pages 481-487Publisher
GENETICS SOCIETY AMERICA
DOI: 10.1534/genetics.117.300191
Keywords
Mendelian randomization; instrumental variable; mediation analysis; direct effect; causal inference
Categories
Funding
- European Community's Seventh Framework Programme [223175 (HEALTH-F2-2009-223175)]
- Cancer Research UK [C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692]
- National Institutes of Health [CA128978]
- Post-Cancer GWAS initiative (GAME-ON initiative) [1U19 CA148537, 1U19 CA148065, 1U19 CA148112]
- Department of Defence [W81XWH-10-1-0341]
- Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer
- Komen Foundation for the Cure
- Breast Cancer Research Foundation
- Ovarian Cancer Research Fund
- Wellcome Trust
- Royal Society [204623/Z/16/Z]
- Medical Research Council [MC_UU_12015/2]
- MRC [MR/L003120/1, G0700463, MC_UU_12015/2, MC_UU_00002/7] Funding Source: UKRI
- British Heart Foundation [RG/08/014/24067] Funding Source: researchfish
- Medical Research Council [MC_UU_12015/2, MC_UU_00002/7, G0700463, MR/L003120/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0512-10165] Funding Source: researchfish
- Wellcome Trust [204623/Z/16/Z] Funding Source: researchfish
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Mendelian randomization is the use of genetic variants as instrumental variables to estimate causal effects of risk factors on outcomes. The total causal effect of a risk factor is the change in the outcome resulting from intervening on the risk factor. This total causal effect may potentially encompass multiple mediating mechanisms. For a proposed mediator, the direct effect of the risk factor is the change in the outcome resulting from a change in the risk factor, keeping the mediator constant. A difference between the total effect and the direct effect indicates that the causal pathway from the risk factor to the outcome acts at least in part via the mediator (an indirect effect). Here, we show that Mendelian randomization estimates of total and direct effects can be obtained using summarized data on genetic associations with the risk factor, mediator, and outcome, potentially from different data sources. We perform simulations to test the validity of this approach when there is unmeasured confounding and/or bidirectional effects between the risk factor and mediator. We illustrate this method using the relationship between age at menarche and risk of breast cancer, with body mass index (BMI) as a potential mediator. We show an inverse direct causal effect of age at menarche on risk of breast cancer (independent of BMI), and a positive indirect effect via BMI. In conclusion, multivariable Mendelian randomization using summarized genetic data provides a rapid and accessible analytic strategy that can be undertaken using publicly available data to better understand causal mechanisms.
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