AI GWAS Post GWAS
Curation of Post GWAS within GWAS — listings under the AI tab.
AI for Post-GWAS Interpretation
ML tools that translate GWAS loci into biological mechanism:
- Gene prioritization: Learning trait-relevant gene features from GWAS summary statistics and functional genomics data (PoPS, Weeks et al. PMID 37106029, Nat Genet 2023). Uses polygenic enrichment to rank genes at GWAS loci.
Current frontier: integrating single-cell and spatial data with GWAS signals for cell-type-specific interpretation.
Summary Table
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| NAME | Main citation | YEAR |
|---|---|---|
| PoPS | Weeks EM et al., Nat Genet, 2023 |
2023 |
PoPS
PUBMED_LINK
FULL NAME
PoPS - Polygenic Priority Score for Gene Prioritization
DESCRIPTION
PoPS (Polygenic Priority Score) is a method that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. It leverages polygenic enrichments across multiple gene features to predict causal genes underlying complex traits and diseases. Published in Nature Genetics.
URL
TITLE
Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.
ABSTRACT
Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. PoPS and the closest gene individually outperform other gene prioritization methods.
DOI
10.1038/s41588-023-01443-6