Phenotype database PAVS shows utility for gene prioritization in rare disease cases

Genotype-phenotype databases are essential for variant interpretation and disease gene discovery. Genetic variation differs among human populations, mainly in allele frequencies and haplotype patterns shaped by ancestry and demographic history. Population-specific genotypes can influence traits and disease risk; this makes population specific characterization important. Most existing resources focus on the characterization of a population's genetic background, but do not represent the resulting phenotypes. We have developed PAVS (Phenotype-Associated Variants in Saudi Arabia), a curated, publicly accessible database that integrates 5,132 Saudi clinical cases from four Saudi cohorts and 522 cases from analysis of a mixed-population cohort, together with 1,856 cases from the Deciphering Developmental Disorders study (DDD) and 9,588 literature phenopackets. Each case record describes patient-level phenotypes, encoded with the Human Phenotype Ontology (HPO), and links them to genomic variants, gene identifiers, zygosity, pathogenicity classifications, and disease diagnoses mapped to standardized disease terminologies. The data is represented in Phenopackets format and as a knowledge graph in RDF. Additionally, a web interface provides phenotype-based similarity search, gene and variant browsers, and an HPO hierarchy explorer. We evaluate the utility of the phenotype annotations for gene prioritization using semantic similarity. While there are clear differences to global literature-curated databases, phenotypes in PAVS can successfully rank the correct gene at high rank (ROCAUC: 0.89). PAVS addresses a gap in population-specific genotype-phenotype resources and provides a benchmark for phenotype-driven variant prioritization in under-represented populations.