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Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.
Won, Sungho; Kim, Wonji; Lee, Sungyoung; Lee, Young; Sung, Joohon; Park, Taesung.
Afiliação
  • Won S; Department of Public Health Science, Seoul National University, Seoul, Korea. won1@snu.ac.kr.
  • Kim W; Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea. won1@snu.ac.kr.
  • Lee S; Institute of Health and Environment, Seoul National University, Seoul, Korea. won1@snu.ac.kr.
  • Lee Y; Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea. dnjswlzz11@gmail.com.
  • Sung J; Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea. biznok@gmail.com.
  • Park T; The Center for Genome Science, Korea National Institute of Health, KCDC, Osong, Korea. lyou7688@gmail.com.
BMC Bioinformatics ; 16: 46, 2015 Feb 15.
Article em En | MEDLINE | ID: mdl-25887481
ABSTRACT

BACKGROUND:

Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.

RESULTS:

The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.

CONCLUSIONS:

The proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL http//healthstat.snu.ac.kr/software/mfqls/ .
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Variação Genética / Simulação por Computador / Análise Multivariada / Modelos Estatísticos / Biologia Computacional / Estudo de Associação Genômica Ampla Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2015 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Variação Genética / Simulação por Computador / Análise Multivariada / Modelos Estatísticos / Biologia Computacional / Estudo de Associação Genômica Ampla Tipo de estudo: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Male Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2015 Tipo de documento: Article