MDR

Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Motivation: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene–gene interaction or epistasis) and environmental factors (gene–environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. Results: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2–15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. Availability: Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. Contact: moore@phg.mc.vanderbilt.edu Supplementary information: All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR.


References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Mathieu Emily, Nicolas Sounac, Florian Kroell, Magalie Houée-Bigot: Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package (2020) not zbMATH
  2. Yang, Cheng-Hong; Yang, Huai-Shuo; Chuang, Li-Yeh: PBMDR: a particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions (2019)
  3. Zhang, Le; Zheng, Chunqiu; Li, Tian; Xing, Lei; Zeng, Han; Li, Tingting; Yang, Huan; Cao, Jia; Chen, Badong; Zhou, Ziyuan: Building up a robust risk mathematical platform to predict colorectal cancer (2017)
  4. Ricceri, F.; Fassino, C.; Matullo, G.; Roggero, M.; Torrente, M.-L.; Vineis, P.; Terracini, L.: Algebraic methods for studying interactions between epidemiological variables (2012)
  5. Mourad, Raphael; Sinoquet, Christine; Leray, Philippe: A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies (2011) ioport
  6. Amato, Roberto; Pinelli, Michele; D’andrea, Daniel; Miele, Gennaro; Nicodemi, Mario; Raiconi, Giancarlo; Cocozza, Sergio: A novel approach to simulate gene-environment interactions in complex diseases (2010) ioport
  7. Han, Bing; Park, Meeyoung; Chen, Xue-Wen: A Markov blanket-based method for detecting causal SNPs in GWAS (2010) ioport
  8. Moore, Jason H.; Gilbert, Joshua C.; Tsai, Chia-Ti; Chiang, Fu-Tien; Holden, Todd; Barney, Nate; White, Bill C.: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility (2006)
  9. Hanlon, Phil; Lorenz, Andy: A computational method to detect epistatic effects contributing to a quantitative trait (2005)