PWAS

PWAS detects proteome-mediated gene–phenotype associations by aggregating and modeling the functional effects of genetic variants on protein-coding genes.

Key Features:

• Protein-Centric Aggregation: Aggregates signals from all genetic variants that jointly affect a specific protein-coding gene to assess collective impact on protein function.
• Machine Learning and Probabilistic Modeling: Employs machine learning techniques and probabilistic models to evaluate and predict functional variability of genes across individuals.
• Functional Variability Testing: Tests correlations between gene functional variability and phenotypes, enabling detection of complex heritability patterns including recessive effects.
• Comparative Performance with GWAS: Demonstrated improved ability versus Genome-Wide Association Studies to identify causal protein-coding genes and uncover novel variant–phenotype associations.

Scientific Applications:

• Disease mechanism discovery: Provides insights into how genetic variation affecting protein function translates into phenotypic diversity and disease mechanisms.
• Therapeutic target identification: Prioritizes protein-coding genes whose functional perturbation links to phenotypes, informing potential therapeutic targets.
• Complex trait heritability analysis: Captures complex heritability patterns, including recessive inheritance modes, that may be missed by traditional association methods.

Methodology:

Aggregates variant-level signals per protein-coding gene, applies machine learning and probabilistic models to predict gene functional variability, and tests correlations between gene functional variability and phenotypes.