Mendelian randomization: new applications in the coming age of hypothesis free causality

Evans, David M. and Smith, George Davey (2015) Mendelian randomization: new applications in the coming age of hypothesis free causality. Annual Review of Genomics and Human Genetics, 16 327-350. doi:10.1146/annurev-genom-090314-050016

Author Evans, David M.
Smith, George Davey
Title Mendelian randomization: new applications in the coming age of hypothesis free causality
Journal name Annual Review of Genomics and Human Genetics   Check publisher's open access policy
ISSN 1527-8204
Publication date 2015-01-01
Year available 2015
Sub-type Article (original research)
DOI 10.1146/annurev-genom-090314-050016
Open Access Status Not yet assessed
Volume 16
Start page 327
End page 350
Total pages 24
Place of publication Palo Alto, CA United States
Publisher Annual Reviews
Language eng
Subject 1312 Molecular Biology
1311 Genetics
2716 Genetics (clinical)
Abstract Mendelian randomization (MR) is an approach that uses genetic variants associated with a modifiable exposure or biological intermediate to estimate the causal relationship between these variables and a medically relevant outcome. Although it was initially developed to examine the relationship between modifiable exposures/biomarkers and disease, its use has expanded to encompass applications in molecular epidemiology, systems biology, pharmacogenomics, and many other areas. The purpose of this review is to introduce MR, the principles behind the approach, and its limitations. We consider some of the new applications of the methodology, including informing drug development, and comment on some promising extensions, including two-step, two-sample, and bidirectional MR. We show how these new methods can be combined to efficiently examine causality in complex biological networks and provide a new framework to data mine high-dimensional studies as we transition into the age of hypothesis-free causality.
Keyword causal analysis
genetic epidemiology
mining the phenome
structural equation modeling
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID MC_UU_12013/1
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
UQ Diamantina Institute Publications
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Citation counts: TR Web of Science Citation Count  Cited 36 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 35 times in Scopus Article | Citations
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Created: Wed, 12 Aug 2015, 01:34:24 EST by Kylie Hengst on behalf of UQ Diamantina Institute