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PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448521/ https://www.ncbi.nlm.nih.gov/pubmed/32847609 http://dx.doi.org/10.1186/s13059-020-02121-0 |
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author | Tang, Zheng-Zheng Sliwoski, Gregory R. Chen, Guanhua Jin, Bowen Bush, William S. Li, Bingshan Capra, John A. |
author_facet | Tang, Zheng-Zheng Sliwoski, Gregory R. Chen, Guanhua Jin, Bowen Bush, William S. Li, Bingshan Capra, John A. |
author_sort | Tang, Zheng-Zheng |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7448521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74485212020-08-27 PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection Tang, Zheng-Zheng Sliwoski, Gregory R. Chen, Guanhua Jin, Bowen Bush, William S. Li, Bingshan Capra, John A. Genome Biol Method 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. BioMed Central 2020-08-26 /pmc/articles/PMC7448521/ /pubmed/32847609 http://dx.doi.org/10.1186/s13059-020-02121-0 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Method Tang, Zheng-Zheng Sliwoski, Gregory R. Chen, Guanhua Jin, Bowen Bush, William S. Li, Bingshan Capra, John A. PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection |
title | PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection |
title_full | PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection |
title_fullStr | PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection |
title_full_unstemmed | PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection |
title_short | PSCAN: Spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection |
title_sort | pscan: spatial scan tests guided by protein structures improve complex disease gene discovery and signal variant detection |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7448521/ https://www.ncbi.nlm.nih.gov/pubmed/32847609 http://dx.doi.org/10.1186/s13059-020-02121-0 |
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