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Analysis of exome sequences with and without incorporating prior biological knowledge.
Namkung, Junghyun; Raska, Paola; Kang, Jia; Liu, Yunlong; Lu, Qing; Zhu, Xiaofeng.
Afiliación
  • Namkung J; Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA.
Genet Epidemiol ; 35 Suppl 1: S48-55, 2011.
Article en En | MEDLINE | ID: mdl-22128058
Next-generation sequencing technology provides new opportunities and challenges in the search for genetic variants that underlie complex traits. It will also presumably uncover many new rare variants, but exactly how these variants should be incorporated into the data analysis remains a question. Several papers in our group from Genetic Analysis Workshop 17 evaluated different methods of rare variant analysis, including single-variant, gene-based, and pathway-based analyses and analyses that incorporated biological information. Although the performance of some of these methods strongly depends on the underlying disease model, integration of known biological information is helpful in detecting causal genes. Two work groups demonstrated that use of a Bayesian network and a collapsing receiver operating characteristic curve approach improves risk prediction when a disease is caused by many rare variants. Another work group suggested that modeling local rather than global ancestry may be beneficial when controlling the effect of population structure in rare variant association analysis.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Variación Genética / Epidemiología Molecular / Modelos Genéticos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2011 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Variación Genética / Epidemiología Molecular / Modelos Genéticos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2011 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos