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Commentary: Two-sample Mendelian randomization: opportunities and challenges

International Journal of Epidemiology · 2016 · Vol. 45(3) · pp. 908–915
Debbie A. Lawlor

Abstract

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’. Beyond the study findings themselves, this paper is interesting in its use of publicly available genome-wide association study (GWAS) summary data in a two-sample MR approach. Whereas MR has been increasingly used over the past decade since it was first proposed in the IJE , 2 two-sample MR is a relatively recent extension. 3 With the increasing availability of complete summary results from GWAS consortia that are easily accessible on the internet, the use of two-sample MR is likely to increase considerably over the next decade. 4 By complete summary data I mean results for all genetic loci with a trait or disease outcome, and not just those reaching a pre-specified P -value threshold as shown in journal publications. It is this extensive availability of results that allows Gao et al. to relate genetic variants used as instrumental variables for adiposity traits to the cancer outcomes that they are interested in; the published GWAS for the cancer outcomes would not have reported on the adiposity variants unless they reached a pre-specified GWAS significance value.

Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsMendelian randomizationGenome-wide association studyMedicineBreast cancerTraitCancerDemographySingle-nucleotide polymorphismGeneticsBiology

MeSH terms

GenotypeHumansRandom AllocationMendelian Randomization Analysis
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Triangulation in aetiological epidemiology
International Journal of Epidemiology · 2016 · 1,173 citations
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