Easy-PSAP: An Integrated Workflow to Prioritize Pathogenic Variants in Sequence Data from a Single Individual.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-Generation Sequencing (NGS) is crucial for clinical genetic diagnosis.
• Variant prioritization remains a challenge in analyzing NGS data.
• The Population Sampling Probability (PSAP) method aids in prioritizing variants.

Purpose of the Study:

• To introduce Easy-PSAP, a user-friendly implementation of the PSAP method.
• To provide adaptable pipelines for variant prioritization in exome and genome data.
• To enable gene-based recalibration of in silico pathogenicity scores.

Main Methods:

• Easy-PSAP utilizes allele frequencies from population databases and pathogenicity scores.
• It recalibrates in silico pathogenicity scores (e.g., CADD, AlphaMissense) against population data.
• The tool analyzes genetic variants across whole exomes or genomes using current databases.

Main Results:

• Simulations show Easy-PSAP ranks over 50% of causal pathogenic variants in the top 10 for dominant models.
• It ranks over 50% of causal pathogenic variants in the top 1 for recessive models.
• The pipeline demonstrates high efficiency in identifying disease-causing variants.

Conclusions:

• Easy-PSAP is a state-of-the-art tool for variant prioritization in NGS data.
• Its accessibility benefits both researchers and clinicians.
• The tool is designed for continuous evolution with new databases and frameworks.