Commentary: Two-sample Mendelian randomization: opportunities and challenges
By Deborah Lawlor. International Journal of Epidemiology, 2016
679 citations recorded by the source corpus.
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.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/e6172458a40f35b55ec81ce45e5d8256ad805ea8
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Citations in the corpus
- Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique (2016, background): “association studies (GWAS), or estimated directly from individual-level participant data.(4) Recent examples include”
- Two-Sample Instrumental Variable Analyses Using Heterogeneous Samples (2017, background): “More recently, there has been growing interest in using two-sample Mendelian randomization that take advantage of large existing Genome-Wide Association Studies (GWAS), as it is often easier to find two GWAS in which one measures the genotypes and the exposure and the other one measures the genotype”