Dissecting Causal Pathways Using Mendelian Randomization with Summarized Genetic Data: Application to Age at Menarche and Risk of Breast Cancer

Author:

Burgess Stephen12,Thompson Deborah J3,Rees Jessica M B2,Day Felix R4,Perry John R4,Ong Ken K4

Affiliation:

1. MRC Biostatistics Unit, University of Cambridge, CB2 0SR Cambridgeshire, United Kingdom

2. Cardiovascular Epidemiology Unit, University of Cambridge, CB1 8RN Cambridgeshire, United Kingdom

3. Cambridge Centre for Genetic Epidemiology, University of Cambridge, CB1 8RN Cambridgeshire, United Kingdom

4. MRC Epidemiology Unit, University of Cambridge, CB2 0QQ Cambridgeshire, United Kingdom

Abstract

Abstract 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.

Publisher

Oxford University Press (OUP)

Subject

Genetics

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