PWAS: Proteome-wide association study

Nadav Brandes*, Nathan Linial, Michal Linial

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Over the last two decades, Genome-Wide Association Study (GWAS) has become a canonical tool for exploratory genetic research, generating countless gene-phenotype associations. Despite its accomplishments, several limitations and drawbacks still hinder its success, including low statistical power and obscurity about the causality of implicated variants. We introduce PWAS (Proteome-Wide Association Study), a new method for detecting protein-coding genes associated with phenotypes through protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the protein’s function using machine-learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. By collecting the genetic signal across many variants in light of their rich proteomic context, PWAS can detect subtle patterns that standard GWAS and other methods overlook. It can also capture more complex modes of heritability, including recessive inheritance. Furthermore, the discovered associations are supported by a concrete molecular model, thus reducing the gap to inferring causality. To demonstrate its applicability for a wide range of human traits, we applied PWAS on a cohort derived from the UK Biobank (~330K individuals) and evaluated it on 49 prominent phenotypes. 23% of the significant PWAS associations on that cohort (2,998 of 12,896) were missed by standard GWAS. A comparison between PWAS to existing methods proves its capacity to recover causal protein-coding genes and highlighting new associations with plausible biological mechanism.

Original languageAmerican English
Title of host publicationResearch in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
EditorsRussell Schwartz
Number of pages3
ISBN (Print)9783030452568
StatePublished - 2020
Event24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 - Padua, Italy
Duration: 10 May 202013 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12074 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.


  • GWAS
  • Machine learning
  • Protein function
  • Recessive heritability
  • UK Biobank


Dive into the research topics of 'PWAS: Proteome-wide association study'. Together they form a unique fingerprint.

Cite this