ArticleGenome biology2020
PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The CaThe Journal of biological chemistry · 2026Article
- A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology.Nature communications · 2025Article
- A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology.bioRxiv : the preprint server for biology · 2024Article
- Statistical methods for assessing the effects of de novo variants on birth defects.Human genomics · 2024Review
- Snai2-mediated upregulation of NADSYN1 promotes bladder cancer progression by interacting with PHB.Clinical and translational medicine · 2024Article
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Delineation of functionally essential protein regions for 242 neurodevelopmental genes.Brain : a journal of neurology · 2023Article
- Missense Variants Reveal Functional Insights Into the Human ARID Family of Gene Regulators.Journal of molecular biology · 2022Article
- An association test of the spatial distribution of rare missense variants within protein structures identifies Alzheimer's disease-related patterns.Genome research · 2022Article
- PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection.Genome biology · 2020Article
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Authors and funding
7 authors.
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Abstract
Germline disease-causing variants are generally more spatially clustered in protein 3-dimensional structures than benign variants. Motivated by this tendency, we develop a fast and powerful protein-structure-based scan (PSCAN) approach for evaluating gene-level associations with complex disease and detecting signal variants. We validate PSCAN's performance on synthetic data and two real data sets for lipid traits and Alzheimer's disease. Our results demonstrate that PSCAN performs competitively with existing gene-level tests while increasing power and identifying more specific signal variant sets. Furthermore, PSCAN enables generation of hypotheses about the molecular basis for the associations in the context of protein structures and functional domains.
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