Genetic drug target validation using Mendelian randomisation

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 and Aroon D. Hingorani
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Amand F. Schmidt: University College London
Chris Finan: University College London
Maria Gordillo-Marañón: University College London
Folkert W. Asselbergs: University College London
Daniel F. Freitag: Bayer AG Pharmaceuticals, Open Innovation & Digital Technologies
Riyaz S. Patel: University College London
Benoît Tyl: Institut de Recherches Internationales Servier
Sandesh Chopade: University College London
Rupert Faraway: University College London
Magdalena Zwierzyna: University College London
Aroon D. Hingorani: University College London

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-16969-0

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