Functionally informed cis and trans proteome-wide association studies prioritize disease-critical genes

Area of Science:

• Genetics
• Systems Biology
• Computational Biology

Background:

• Proteome-wide association studies (PWAS) traditionally use cis-pQTLs, limited by low heritability and gene tagging.
• Trans-pQTLs offer complementary insights into polygenic effects but require large sample sizes.
• Integrating cis- and trans-regulatory effects is crucial for a comprehensive understanding of protein-mediated disease biology.

Purpose of the Study:

• To develop a novel framework, PolyPWAS, for associating both cis- and trans-predicted protein levels with disease using summary statistics.
• To enhance protein prediction accuracy by integrating functional annotations and proteome-wide pleiotropy.
• To assess the contribution of trans-predicted protein levels to disease heritability and identify novel protein-disease associations.

Main Methods:

• Developed PolyPWAS, a summary statistics-based framework integrating 96 functional annotations and proteome-wide pleiotropy.
• Applied PolyPWAS to UK Biobank-நிறைவுற்ற புரதவியல் திட்டம் (UKB-PPP) plasma proteomic data (2.8K proteins) and GWAS summary statistics for 88 diseases/traits (N=336K).
• Corrected for principal components of predicted protein levels to mitigate tagging effects and improve prediction accuracy.

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 and conditionally significant trans-PWAS associations.
• Combining cis and trans associations improved disease gene prioritization by 11% and 7.0% over cis-only methods.

Conclusions:

• PolyPWAS effectively integrates cis- and trans-predicted protein levels for robust disease association analysis.
• Trans-regulatory effects play a significant role in disease heritability and offer a complementary view to cis-regulatory effects.
• Integrating both cis- and trans-regulatory effects is essential for a complete mapping of protein-mediated disease biology and improved gene prioritization.