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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.
Afiliación
  • Tang ZZ; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, 53715, WI, USA. tang@biostat.wisc.edu.
  • Sliwoski GR; Wisconsin Institute for Discovery, Madison, 53715, WI, USA. tang@biostat.wisc.edu.
  • Chen G; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, 37232, TN, USA.
  • Jin B; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, 53715, WI, USA.
  • Bush WS; Department for Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
  • Li B; Department for Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
  • Capra JA; Institute for Computational Biology, Case Western Reserve University, Cleveland, 44106, OH, USA.
Genome Biol ; 21(1): 217, 2020 08 26.
Article en En | MEDLINE | ID: mdl-32847609
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proteínas / Predisposición Genética a la Enfermedad / Estudios de Asociación Genética Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Genome Biol Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proteínas / Predisposición Genética a la Enfermedad / Estudios de Asociación Genética Tipo de estudio: Diagnostic_studies Límite: Humans Idioma: En Revista: Genome Biol Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos
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