Hai-Ming Xu, Li-Feng Xu, Ting-Ting Hou, Lin-Feng Luo, Guo-Bo Chen, Xi-Wei Sun and Xiang-Yang Lou Pages 396 - 402 ( 7 )
Identification of multifactor gene-gene (GxG) and gene-environment (GxE) 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.
Generalized multifactor dimensionality reduction, Gene-gene interactions, Gene-environment interactions, Complex traits, Unrelated sample, Family sample, Computer software.
Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal St., Suite 2001, New Orleans, LA 70112-2632, USA.