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Graphical Modeling of Multiple Biological Pathways in Genomic Studies

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Modern Statistical Methods for Health Research

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

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Abstract

Complex diseases are associated with a variety of genomic factors. Identifying such risk factors can help us to better understand the pathogenesis of these diseases, which in turn may help the development of prevention and intervention strategies in terms of personalized treatments. Disease-associated risk factors can be identified by genome-wide association studies (GWAS) or the detection of differentially expressed (DE) genes. Traditional single-marker- or single-gene-based approaches lack adequate statistical powers due to the stringent threshold of multiple testing corrections. To address this problem, researchers have proposed pathway-based approaches, leveraging our knowledge of the gene–gene interactions to increase the power of conducting a large number of statistical tests. Many pathway-based approaches treat pathways as lists of genes that ignore the topology structures of genes. Including the topology structure into analysis allows modeling of specific gene–gene interactions. Previously, such approaches only consider two-way interactions, and they only incorporate a single pathway in GWAS or DE analysis. However, genes participate in different biological processes and thus interact with each other simultaneously in different pathways. We hereby extend the approach by combining multiple biological pathways into genomic studies. In the proposed approaches, the topology structures of biological pathways are modeled by a Markov random field, which is a graphical model to present the dependence structure in the dataset. Finally, a Bayesian framework is constructed to incorporate the knowledge from topological structures of biological pathways with the evidence from biological experiments. The inference of gene status, like disease association status, can be made based on the marginal posterior probability obtained from Bayesian analysis. The proposed approaches are evaluated with simulations studies and a lung cancer dataset. The results show that combining multiple biological pathways can enhance the power of detection and control the false positive rate.

Yujing Cao and Yu Zhang authors contributed equally.

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Acknowledgements

This work was partially supported by the National Institutes of Health [grant R15GM131390 to X.W.].

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Authors and Affiliations

  1. Department of Mathematical Sciences, University of Texas at Dallas, Dallas, TX, USA

    Yujing Cao, Yu Zhang & Min Chen

  2. Department of Statistical Science, Southern Methodist University, Richardson, TX, USA

    Xinlei Wang

  3. Department of Population and Data Sciences, UT Southwestern Medical Center, Richardson, TX, USA

    Min Chen

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  1. Yujing Cao
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  2. Yu Zhang
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  3. Xinlei Wang
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  4. Min Chen
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Correspondence to Min Chen .

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Editors and Affiliations

  1. Department of Mathematics & Statistics, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  2. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, KwaZulu-Natal, South Africa

    (Din) Ding-Geng Chen

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Cao, Y., Zhang, Y., Wang, X., Chen, M. (2021). Graphical Modeling of Multiple Biological Pathways in Genomic Studies. In: Zhao, Y., Chen, (.DG. (eds) Modern Statistical Methods for Health Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-72437-5_19

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