Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data

被引:204
作者
Burgess, Stephen [1 ,2 ]
Foley, Christopher N. [1 ]
Zuber, Verena [1 ]
机构
[1] Univ Cambridge, MRC Biostat Unit, Cambridge CB2 0SR, England
[2] Univ Cambridge, Cardiovasc Epidemiol Unit, Cambridge CB1 8RN, England
来源
ANNUAL REVIEW OF GENOMICS AND HUMAN GENETICS, VOL 19 | 2018年 / 19卷
基金
英国医学研究理事会; 英国惠康基金;
关键词
genetic epidemiology; causal inference; instrumental variable; target validation; drug discovery; CORONARY-HEART-DISEASE; MENDELIAN RANDOMIZATION ANALYSES; INSTRUMENTAL VARIABLES; GENETIC-VARIANTS; INTERLEUKIN-6; RECEPTOR; METAANALYSIS; BIAS; IDENTIFICATION; AGE; POTENTIALS;
D O I
10.1146/annurev-genom-083117-021731
中图分类号
Q3 [遗传学];
学科分类号
071007 [遗传学];
摘要
An observational correlation between a suspected risk factor and an outcome does not necessarily imply that interventions on levels of the risk factor will have a causal impact on the outcome (correlation is not causation). If genetic variants associated with the risk factor are also associated with the outcome, then this increases the plausibility that the risk factor is a causal determinant of the outcome. However, if the genetic variants in the analysis do not have a specific biological link to the risk factor, then causal claims can be spurious. We review the Mendelian randomization paradigm for making causal inferences using genetic variants. We consider monogenic analysis, in which genetic variants are taken from a single gene region, and polygenic analysis, which includes variants from multiple regions. We focus on answering two questions: When can Mendelian randomization be used to make reliable causal inferences, and when can it be used to make relevant causal inferences?
引用
收藏
页码:303 / 327
页数:25
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