期刊
AMERICAN JOURNAL OF CLINICAL NUTRITION
卷 103, 期 4, 页码 965-978出版社
OXFORD UNIV PRESS
DOI: 10.3945/ajcn.115.118216
关键词
Mendelian randomization; causality; reverse causation; confounding; observational epidemiology; evidence synthesis
资金
- Cancer Research UK [C18281/A19169]
- Roy Castle Lung Cancer Foundation [2013/18/Relton]
- Cancer Research UK Population Research Postdoctoral Fellowship [C52724/A20138]
- [MC_UU_12013/1]
- [MC_UU_12013/2]
- MRC [MC_UU_00002/7, G0800270, MC_UU_12013/2, MC_EX_MR/L012286/1, MC_UU_00002/3, MC_UU_12013/1, MC_UU_12013/3, MR/L003120/1, MC_UU_12013/9, MC_UP_1302/2] Funding Source: UKRI
- British Heart Foundation [RG/13/13/30194, RG/08/014/24067] Funding Source: researchfish
- Cancer Research UK [19169, 20138] Funding Source: researchfish
- Medical Research Council [MC_UP_1302/2, MC_UU_12013/2, MR/N501906/1, MR/L003120/1, MC_UU_12013/3, MC_UU_12013/1, MC_UU_00002/3, MC_UU_12013/9, MC_UU_00002/7, G0800270, MC_EX_MR/L012286/1] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0512-10165] Funding Source: researchfish
Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature and Mendel's first and second laws of inheritance. The approach is, however, subject to important limitations and assumptions that, if unaddressed or compounded by poor study design, can lead to erroneous conclusions. Nevertheless, the advent of 2-sample approaches (in which exposure and outcome are measured in separate samples) and the increasing availability of open-access data from large consortia of genome-wide association studies and population biobanks mean that the approach is likely to become routine practice in evidence synthesis and causal inference research. In this article we provide an overview of the design, analysis, and interpretation of MR studies, with a special emphasis on assumptions and limitations. We also consider different analytic strategies for strengthening causal inference. Although impossible to prove causality with any single approach, MR is a highly cost-effective strategy for prioritizing intervention targets for disease prevention and for strengthening the evidence base for public health policy.
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