| Title: | PWAS Hub: exploring gene-based associations of complex diseases with sex dependency |
| Journal: | Nucleic Acids Research |
| Published: | 20 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39565197/ |
| DOI: | https://doi.org/10.1093/nar/gkae1125 |
Publication 14612
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Abstract
The Proteome-Wide Association Study (PWAS) is a protein-based genetic association approach designed to complement traditional variant-based methods like GWAS. PWAS operates in two stages: first, machine learning models predict the impact of genetic variants on protein-coding genes, generating effect scores. These scores are then aggregated into a gene-damaging score for each individual. This score is then used in case-control statistical tests to significantly link to specific phenotypes. PWAS Hub (v1.2) is a user-friendly platform that facilitates the exploration of gene-disease associations using clinical and genetic data from the UK Biobank (UKB), encompassing 500k individuals. PWAS Hub reports on 819 diseases and phenotypes determined by PheCode and ICD-10 clinical codes, each with a minimum of 400 affected individuals. PWAS-derived gene associations were reported for 72% of the tested phenotypes. The PWAS Hub also analyzes gene associations separately for males and females, considering sex-specific genetic effects, inheritance patterns (dominant and recessive), and gene pleiotropy. We illustrated the utility of the PWAS Hub for primary (essential) hypertension (I10), type 2 diabetes mellitus (E11), and specified haematuria (R31) that showed sex-dependent genetic signals. The PWAS Hub, available at pwas.huji.ac.il, is a valuable resource for studying genetic contributions to common diseases and sex-specific effects.</p>
11 Keywords
- Diabetes Mellitus, Type 2
- Female
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Hypertension
- Machine Learning
- Male
- Phenotype
- Proteome
- Sex Factors
3 Authors
- Roei Zucker
- Guy Kelman
- Michal Linial
1 Application
| Application ID | Title |
|---|---|
| 26664 | Novel machine-learning framework for improved inference in GWAS |
Enabling scientific discoveries that improve human health