Commentary: Mendelian randomization study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer

open access - Commentary

Introduction

In this volume of the IJE, Gao and colleagues explore the causal effect of adiposity on several cancers using two-sample Mendelian randomization (MR), and find some evidence that greater adult body mass index (BMI) causally reduces the risk of breast cancer while increasing ovarian, lung and colorectal cancer.¹ The authors conclude that the study provides ‘…additional understanding of the complex relationship between adiposity and cancer risks’....

 

 As this is the denominator of the MR ratio estimate, it means that the estimated effect of WHR adjusted for BMI for female cancers (breast and ovarian) may be exaggerated and those for prostate cancers underestimated.

But this study does illustrate some of the pitfalls of using summary GWAS data (Genome Wide Association Study)  and methods that might be used to limit these.

 Beyond Mendelian randomization—what can we learn from genetic epidemiology?

What strikes me in watching (and participating in) the development of GWAS and MR over the past decade is how slow those of us largely working in epidemiology, including in intervention research, have been to do what we all know is good science. Our genetic colleagues have led the way in ensuring replication in large collaborations where ‘team science’ is appreciated and for the large part appropriately rewarded. Those developing MR as a method have from the start been very open about its limitations and have worked at developing methods to test and limit sources of bias.^2,3,9 It is notable, for example, that Gao et al. comment on the ‘strong’ assumptions of MR, but rarely do we see such statements about the equally strong, and untestable, assumptions of conventional multivariable regression analyses. Now genetic epidemiologists have shown us how to provide complete open-access summary data, and it is likely that over the coming decade important and impactful use will be made of these data.⁴
 

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1. Top
2. Introduction
3. (One-sample) Mendelian randomization
4. Two-sample Mendelian randomization
5. Overlapping samples and the use of summary or individual participant