Your browser doesn't support javascript.
loading
GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits.
Xu, Hai-Ming; Xu, Li-Feng; Hou, Ting-Ting; Luo, Lin-Feng; Chen, Guo-Bo; Sun, Xi-Wei; Lou, Xiang-Yang.
Afiliación
  • Xu HM; Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, P.R. China.
  • Xu LF; Research Center for Air Pollution and Health, Zhejiang University, Hangzhou, P.R. China.
  • Hou TT; Institute of Computer Application Technology, College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, P.R. China.
  • Luo LF; Institute of Bioinformatics and Institute of Crop Science, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, P.R. China.
  • Chen GB; Institute of Computer Application Technology, College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, P.R. China.
  • Sun XW; Queensland Brain Institute, University of Queensland, St Lucia, Queensland, Australia.
  • Lou XY; Sir Run Run Shaw Hospital and Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, P.R. China.
Curr Genomics ; 17(5): 396-402, 2016 Oct.
Article en En | MEDLINE | ID: mdl-28479868
ABSTRACT
Identification of multifactor gene-gene (G×G) and gene-environment (G×E) interactions underlying complex traits poses one of the great challenges to today's genetic study. Development of the generalized multifactor dimensionality reduction (GMDR) method provides a practicable solution to problems in detection of interactions. To exploit the opportunities brought by the availability of diverse data, it is in high demand to develop the corresponding GMDR software that can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates. We developed a versatile GMDR package to implement this serial of GMDR analyses for various scenarios (e.g., unified analysis of unrelated and family samples) and large-scale (e.g., genome-wide) data. This package includes other desirable features such as data management and preprocessing. Permutation testing strategies are also built in to evaluate the threshold or empirical p values. In addition, its performance is scalable to the computational resources. The software is available at http//www.soph.uab.edu/ssg/software or http//ibi.zju.edu.cn/software.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Curr Genomics Año: 2016 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Curr Genomics Año: 2016 Tipo del documento: Article