Journal
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
Volume 45, Issue 5, Pages 1600-1616Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ije/dyw088
Keywords
Mendelian randomization; genome-wide association study; biomarkers; causal inference
Categories
Funding
- National Institute of Health Research Academic Clinical Fellowship
- British Heart Foundation (Schillingford) Clinical Training Fellowship [FS/07/011]
- Medical Research Council Population Health Scientist Fellowship [G0802432]
- National Institute on Aging [AG034454]
- Medical Research Council [K013351]
- National Heart, Lung and Blood Institute [HL036310]
- NordForsk
- University College London National Institute for Health Research Biomedical Research Centre
- British Heart Foundation programme grant [RG/13/2/30098]
- MooDFOOD Collaborative Project (FP7) [613598]
- MRC [G0802432, MR/K013351/1] Funding Source: UKRI
- British Heart Foundation [RG/10/12/28456, RG/13/2/30098] Funding Source: researchfish
- Medical Research Council [MR/K006584/1, G0802432, MR/K013351/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0515-10091, ACF-2015-21-003] Funding Source: researchfish
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Mendelian randomization (MR) studies typically assess the pathogenic relevance of environmental exposures or disease biomarkers, using genetic variants that instrument these exposures. The approach is gaining popularity-our systematic review reveals a greater than 10-fold increase in MR studies published between 2004 and 2015. When the MR paradigm was first proposed, few biomarker-or exposure-related genetic variants were known, most having been identified by candidate gene studies. However, genome-wide association studies (GWAS) are now providing a rich source of potential instruments for MR analysis. Many early reviews covering the concept, applications and analytical aspects of the MR technique preceded the surge in GWAS, and thus the question of how best to select instruments for MR studies from the now extensive pool of available variants has received insufficient attention. Here we focus on the most common category of MR studies-those concerning disease biomarkers. We consider how the selection of instruments for MR analysis from GWAS requires consideration of: the assumptions underlying the MR approach; the biology of the biomarker; the genome-wide distribution, frequency and effect size of biomarker-associated variants (the genetic architecture); and the specificity of the genetic associations. Based on this, we develop guidance that may help investigators to plan and readers interpret MR studies.
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