ArticleNucleic acids research2022
Organization of gene programs revealed by unsupervised analysis of diverse gene-trait associations.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Methodological quality assessment of genetic studies on brain arteriovenous malformation related hemorrhage: A cross-sectional study.Frontiers in genetics · 2023Pooled it
- Integrated skin-lesion transcriptomics prioritises biomarker-candidate host-response genes with pharmacological annotation in leprosy type 1 reaction.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- A scalable, multi-resolution consensus clustering approach for prioritizing robust signals from high-throughput screens.Briefings in bioinformatics · 2026Article
- Combining Spatial, Genetic, and Environmental Risk Data to Define and Prioritize In Situ Conservation Units.Ecology and evolution · 2025Article
- Atlas of multilineage stem cell differentiation reveals TMEM88 as a developmental regulator of blood pressure.Nature communications · 2025Article
- Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity.Nucleic acids research · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome wide association studies provide statistical measures of gene-trait associations that reveal how genetic variation influences phenotypes. This study develops an unsupervised dimensionality reduction method called UnTANGLeD (Unsupervised Trait Analysis of Networks from Gene Level Data) which organizes 16,849 genes into discrete gene programs by measuring the statistical association between genetic variants and 1,393 diverse complex traits. UnTANGLeD reveals 173 gene clusters enriched for protein-protein interactions and highly distinct biological processes governing development, signalling, disease, and homeostasis. We identify diverse gene networks with robust interactions but not associated with known biological processes. Analysis of independent disease traits shows that UnTANGLeD gene clusters are conserved across all complex traits, providing a simple and powerful framework to predict novel gene candidates and programs influencing orthogonal disease phenotypes. Collectively, this study demonstrates that gene programs co-ordinately orchestrating cell functions can be identified without reliance on prior knowledge, providing a method for use in functional annotation, hypothesis generation, machine learning and prediction algorithms, and the interpretation of diverse genomic data.
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Registered trials
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