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Identifying disease-associated copy number variations by a doubly penalized regression model.
Cheng, Yichen; Dai, James Y; Wang, Xiaoyu; Kooperberg, Charles.
Afiliación
  • Cheng Y; Institute for Insight, Georgia State University, Atlanta, Georgia, U.S.A.
  • Dai JY; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
  • Wang X; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
  • Kooperberg C; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
Biometrics ; 74(4): 1341-1350, 2018 12.
Article en En | MEDLINE | ID: mdl-29894562
Copy number variation (CNV) of DNA plays an important role in the development of many diseases. However, due to the irregularity and sparsity of the CNVs, studying the association between CNVs and a disease outcome or a trait can be challenging. Up to now, not many methods have been proposed in the literature for this problem. Most of the current researchers reply on an ad hoc two-stage procedure by first identifying CNVs in each individual genome and then performing an association test using these identified CNVs. This potentially leads to information loss and as a result a lower power to identify disease associated CNVs. In this article, we describe a new method that combines the two steps into a single coherent model to identify the common CNV across patients that are associated with certain diseases. We use a double penalty model to capture CNVs' association with both the intensities and the disease trait. We validate its performance in simulated datasets and a data example on platinum resistance and CNV in ovarian cancer genome.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias Ováricas / Simulación por Computador / Análisis de Regresión / Biometría / Variaciones en el Número de Copia de ADN Tipo de estudio: Diagnostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Biometrics Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias Ováricas / Simulación por Computador / Análisis de Regresión / Biometría / Variaciones en el Número de Copia de ADN Tipo de estudio: Diagnostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Biometrics Año: 2018 Tipo del documento: Article País de afiliación: Estados Unidos