PWAS Hub for exploring gene-based associations of common complex diseases.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) are widely used but do not fully capture genetic effects on protein function.
• Proteome-wide association studies (PWAS) offer a complementary approach by integrating genetic variant data with protein-coding gene functions.
• Understanding gene-disease associations is crucial for personalized medicine and disease mechanism elucidation.

Purpose of the Study:

• To introduce the PWAS Hub, a user-friendly platform for exploring proteome-wide association study results.
• To present findings on gene-disease associations across 99 common diseases using UK Biobank data.
• To investigate sex-specific genetic effects and inheritance modes in common diseases.

Main Methods:

• Utilized machine learning and probabilistic models to quantify genetic variant impact on protein-coding genes.
• Calculated gene-damaging scores by aggregating variants per gene for each individual.
• Performed case-control statistical tests to identify significant gene-phenotype associations, accounting for sex, inheritance mode, and pleiotropy.

Main Results:

• The PWAS Hub provides access to gene-disease associations for 99 common diseases from the UK Biobank, with over 10,000 individuals per phenotype.
• Analyses revealed statistically significant gene associations, with separate results for males and females, considering dominant and recessive inheritance.
• Comparison with Open Targets data showed a significant overlap between PWAS-identified and known gene associations for most diseases. Graphical tools allow comparison between PWAS and coding GWAS results.

Conclusions:

• The PWAS Hub is a valuable resource for researchers and clinicians to explore complex gene-disease relationships and sex-specific genetic influences.
• PWAS approach, as implemented in the PWAS Hub, provides novel insights into the genetic architecture of common diseases.
• The platform facilitates a deeper understanding of cellular and molecular mechanisms underlying diseases, exemplified by asthma analysis.