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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...

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Autores principales: Tang, Zheng-Zheng, Sliwoski, Gregory R., Chen, Guanhua, Jin, Bowen, Bush, William S., Li, Bingshan, Capra, John A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
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.
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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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