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Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits.

Authors :
Andrew D Bretherick
Oriol Canela-Xandri
Peter K Joshi
David W Clark
Konrad Rawlik
Thibaud S Boutin
Yanni Zeng
Carmen Amador
Pau Navarro
Igor Rudan
Alan F Wright
Harry Campbell
Veronique Vitart
Caroline Hayward
James F Wilson
Albert Tenesa
Chris P Ponting
J Kenneth Baillie
Chris Haley
Source :
PLoS Genetics, Vol 16, Iss 7, p e1008785 (2020)
Publication Year :
2020
Publisher :
Public Library of Science (PLoS), 2020.

Abstract

To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk.

Subjects

Subjects :
Genetics
QH426-470

Details

Language :
English
ISSN :
15537390 and 15537404
Volume :
16
Issue :
7
Database :
Directory of Open Access Journals
Journal :
PLoS Genetics
Publication Type :
Academic Journal
Accession number :
edsdoj.4343fcaa019c4dbfb203fe430fdf7716
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pgen.1008785