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Multivariate Dimensionality Reduction Approaches to Identify Gene-Gene and Gene-Environment Interactions Underlying Multiple Complex Traits

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  • Hai-Ming Xu
  • Xi-Wei Sun
  • Ting Qi
  • Wan-Yu Lin
  • Nianjun Liu
  • Xiang-Yang Lou

Abstract

The elusive but ubiquitous multifactor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points). A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that involves a multivariate analysis for grouped phenotypes followed by univariate analysis for the phenotypes in the significant group(s). In this article, we propose a multivariate extension of generalized multifactor dimensionality reduction (GMDR) based on multivariate generalized linear, multivariate quasi-likelihood and generalized estimating equations models. Simulations and real data analysis for the cohort from the Study of Addiction: Genetics and Environment are performed to investigate the properties and performance of the proposed method, as compared with the univariate method. The results suggest that the proposed multivariate GMDR substantially boosts statistical power.

Suggested Citation

  • Hai-Ming Xu & Xi-Wei Sun & Ting Qi & Wan-Yu Lin & Nianjun Liu & Xiang-Yang Lou, 2014. "Multivariate Dimensionality Reduction Approaches to Identify Gene-Gene and Gene-Environment Interactions Underlying Multiple Complex Traits," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-12, September.
  • Handle: RePEc:plo:pone00:0108103
    DOI: 10.1371/journal.pone.0108103
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    Cited by:

    1. Shengqiao Ni & Jiancheng Lv & Zhehao Cheng & Mao Li, 2015. "Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-26, July.

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