Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

Jie Zheng*, Valeriia Haberland, Denis A Baird, Venexia M Walker, Philip C Haycock, Mark R. Hurle, Alex Gutteridge, Pau Erola, Yi Liu, Shan Luo, Jamie W Robinson, Tom G Richardson, James R Staley, Benjamin L Elsworth, Stephen Burgess, Benjamin B Sun, John Danesh , Heiko Runz, Joseph C. Maranville, Hannah M MartinJames Yarmolinsky, Charles A Laurin, Michael V Holmes, Jimmy Z Liu, Karol Estrada, Rita Santos, Linda McCarthy, Dawn Waterworth, Matthew R. Nelson, George Davey Smith, Adam S Butterworth , Gibran Hemani, Robert A. Scott*, Tom R Gaunt*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

Abstract

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes (https://www.epigraphdb.org/pqtl/). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.
Original languageEnglish
Pages (from-to)1122–1131
Number of pages15
JournalNature Genetics
Volume52
DOIs
Publication statusPublished - 7 Sep 2020

Keywords

  • Drug discovery
  • Genetics
  • High-throughput screening
  • Proteomics

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