| Title: | PWAS Hub for exploring gene-based associations of common complex diseases |
| Journal: | Genome Research |
| Published: | 15 Oct 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39406500/ |
| DOI: | https://doi.org/10.1101/gr.278916.123 |
| URL: | https://genome.cshlp.org/content/early/2024/10/10/gr.278916.123.full.pdf |
Publication 13222
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Abstract
PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects.</p>
4 Authors
- Guy Kelman
- Roei Zucker
- Nadav Brandes
- Michal Linial
1 Application
| Application ID | Title |
|---|---|
| 26664 | Novel machine-learning framework for improved inference in GWAS |
Enabling scientific discoveries that improve human health