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.