AstraZeneca PheWAS Portal
AstraZeneca PheWAS Portal provides gene-phenotype association analyses from exome sequencing and UK Biobank phenotypes to identify effects of rare protein-coding variants.
Key Features:
- Cohort and data source: Integrates exome sequencing data from approximately 500,000 UK Biobank participants (accessed under application 26041) with comprehensive phenotype information.
- Gene-Based Collapsing Analysis: Performs gene-based collapsing analysis to aggregate rare protein-coding variants and identify gene-phenotype associations not detectable by single-variant tests.
- Phenotype coverage: Analyzes 17,361 binary traits and 1,419 quantitative phenotypes.
- Significant associations: Identified 1,703 statistically significant gene-phenotype associations for binary traits with a median odds ratio of 12.4.
- Enrichment findings: Reports enrichment of associations for loss-of-function-mediated traits and approved drug targets.
- Ancestry-specific and pan-ancestry analyses: Performs ancestry-specific and pan-ancestry analyses including exome data from 11,933 participants of African, East Asian, or South Asian ancestries alongside predominantly European ancestry participants.
Scientific Applications:
- Disease Mechanism Elucidation: Investigating how rare protein-coding variants contribute to disease phenotypes using gene-based association statistics.
- Drug Target Discovery: Prioritizing genes enriched for loss-of-function effects and approved drug targets for therapeutic target validation.
- Population Genetics Studies: Exploring the genetic architecture and effect size differences of rare variants across European, African, East Asian, and South Asian ancestries.
Methodology:
Integration of UK Biobank exome sequencing with phenotype data followed by gene-based collapsing analysis to aggregate rare variants per gene and mitigate allelic heterogeneity, with analyses aligned to reference genome GRCh38.
Topics
Collections
Details
- Tool Type:
- web application
- Added:
- 1/17/2022
- Last Updated:
- 1/17/2022
Operations
Publications
Wang Q, Dhindsa RS, Carss K, Harper AR, Nag A, Tachmazidou I, Vitsios D, Deevi SVV, Mackay A, Muthas D, et al. (7877):527-532. doi:10.1038/s41586-021-03855-y. PMID:34375979. PMCID:PMC8458098.