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A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate

Author

Listed:
  • Padayachee Trishanta

    (Hasselt University, I-BioStat, Diepenbeek, Belgium)

  • Khamiakova Tatsiana

    (Hasselt University, I-BioStat, Diepenbeek, Belgium)

  • Shkedy Ziv

    (Hasselt University, I-BioStat, Diepenbeek, Belgium)

  • Salo Perttu

    (Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland)

  • Perola Markus

    (Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland)

  • Burzykowski Tomasz

    (Hasselt University, I-BioStat, Diepenbeek, Belgium)

Abstract

A way to enhance our understanding of the development and progression of complex diseases is to investigate the influence of cellular environments on gene co-expression (i.e. gene-pair correlations). Often, changes in gene co-expression are investigated across two or more biological conditions defined by categorizing a continuous covariate. However, the selection of arbitrary cut-off points may have an influence on the results of an analysis. To address this issue, we use a general linear model (GLM) for correlated data to study the relationship between gene-module co-expression and a covariate like metabolite concentration. The GLM specifies the gene-pair correlations as a function of the continuous covariate. The use of the GLM allows for investigating different (linear and non-linear) patterns of co-expression. Furthermore, the modeling approach offers a formal framework for testing hypotheses about possible patterns of co-expression. In our paper, a simulation study is used to assess the performance of the GLM. The performance is compared with that of a previously proposed GLM that utilizes categorized covariates. The versatility of the model is illustrated by using a real-life example. We discuss the theoretical issues related to the construction of the test statistics and the computational challenges related to fitting of the proposed model.

Suggested Citation

  • Padayachee Trishanta & Khamiakova Tatsiana & Shkedy Ziv & Salo Perttu & Perola Markus & Burzykowski Tomasz, 2019. "A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-13, April.
    • Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:2:p:13:n:3
    DOI: 10.1515/sagmb-2018-0008
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    References listed on IDEAS

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    1. Steffen Fieuws & Geert Verbeke, 2006. "Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 424-431, June.
    2. Trishanta Padayachee & Tatsiana Khamiakova & Ziv Shkedy & Markus Perola & Perttu Salo & Tomasz Burzykowski, 2016. "The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    3. Gregory Wilding & Xueya Cai & Alan Hutson & Zhangsheng Yu, 2011. "A linear model-based test for the heterogeneity of conditional correlations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2355-2366.
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    5. Peter Libby, 2002. "Inflammation in atherosclerosis," Nature, Nature, vol. 420(6917), pages 868-874, December.
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