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Updated: May 8, 2026
03:08
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Functionally informed cis and trans proteome-wide association studies prioritize disease-critical genes
Kangcheng Hou1,2,3, Ali Pazokitoroudi1,2, Benjamin Strober4,5
1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Summary
PolyPWAS improves disease association studies by integrating cis- and trans-predicted protein levels. Trans-predicted protein levels explain more disease heritability and identify more associations than cis-only approaches, enhancing gene prioritization.
Area of Science:
- Genetics
- Proteomics
- Systems Biology
Background:
- Proteome-wide association studies (PWAS) typically use cis-pQTLs, limited by low heritability and gene tagging.
- Trans-pQTLs offer complementary insights but require large sample sizes for weak polygenic effects.
Purpose of the Study:
- Develop PolyPWAS, a framework for associating both cis- and trans-predicted protein levels with disease using summary statistics.
- Improve protein prediction accuracy by integrating functional annotations and proteome-wide pleiotropy.
- Correct for principal components to mitigate tagging effects in predicted protein levels.
Main Methods:
- Applied PolyPWAS to 2.8K plasma proteins in 34K UKB-PPP participants, analyzing GWAS summary statistics for 88 diseases/traits.
- Integrated 96 functional annotations and proteome-wide pleiotropy for improved protein prediction.
- Corrected for principal components of predicted protein levels to reduce tagging.
Main Results:
- Trans-predicted protein levels explained 21% of disease heritability, compared to 9.6% for cis-predicted levels.
- Functional priors improved trans-prediction accuracy by 24%, leading to more significant Trans-PWAS associations.
- Combining cis and trans associations improved disease gene prioritization by 11% (rare variants) and 7.0% (PoPS).
Conclusions:
- Trans-regulatory effects are crucial for understanding protein-mediated disease biology, linking multiple disease-critical genes.
- Integrating cis- and trans-PWAS provides a more comprehensive approach to disease gene discovery.
- PolyPWAS framework enhances replication and concordance across cohorts, validating its utility.
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