Genetic drug target validation using Mendelian randomisation

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作者
Amand F. Schmidt
Chris Finan
Maria Gordillo-Marañón
Folkert W. Asselbergs
Daniel F. Freitag
Riyaz S. Patel
Benoît Tyl
Sandesh Chopade
Rupert Faraway
Magdalena Zwierzyna
Aroon D. Hingorani
机构
[1] University College London,Institute of Cardiovascular Science, Faculty of Population Health
[2] UCL BHF Research Accelerator Centre,Department of Cardiology, Division Heart and Lungs
[3] University Medical Center Utrecht,Center for Therapeutic Innovation, Cardiovascular and Metabolic Disease
[4] Health Data Research UK,undefined
[5] Bayer AG Pharmaceuticals,undefined
[6] Open Innovation & Digital Technologies,undefined
[7] Institut de Recherches Internationales Servier,undefined
[8] The Francis Crick Institute,undefined
来源
Nature Communications | / 11卷
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摘要
Mendelian randomisation (MR) analysis is an important tool to elucidate the causal relevance of environmental and biological risk factors for disease. However, causal inference is undermined if genetic variants used to instrument a risk factor also influence alternative disease-pathways (horizontal pleiotropy). Here we report how the ‘no horizontal pleiotropy assumption’ is strengthened when proteins are the risk factors of interest. Proteins are typically the proximal effectors of biological processes encoded in the genome. Moreover, proteins are the targets of most medicines, so MR studies of drug targets are becoming a fundamental tool in drug development. To enable such studies, we introduce a mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. We illustrate key model decisions and introduce an analytical framework for maximising power and evaluating the robustness of analyses.
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