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Big and Complex Data Analysis pp 347–367Cite as

Identifying Gene–Environment Interactions Associated with Prognosis Using Penalized Quantile Regression

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Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

In the omics era, it has been well recognized that for complex traits and outcomes, the interactions between genetic and environmental factors (i.e., the G×E interactions) have important implications beyond the main effects. Most of the existing interaction analyses have been focused on continuous and categorical traits. Prognosis is of essential importance for complex diseases. However with significantly more complexity, prognosis outcomes have been less studied. In the existing interaction analysis on prognosis outcomes, the most common practice is to fit marginal (semi)parametric models (for example, Cox) using likelihood-based estimation and then identify important interactions based on significance level. Such an approach has limitations. First data contamination is not uncommon. With likelihood-based estimation, even a single contaminated observation can result in severely biased estimation and misleading conclusions. Second, when sample size is not large, the significance-based approach may not be reliable. To overcome these limitations, in this study, we adopt the quantile-based estimation which is robust to data contamination. Two techniques are adopted to accommodate right censoring. For identifying important interactions, we adopt penalization as an alternative to significance level. An efficient computational algorithm is developed. Simulation shows that the proposed method can significantly outperform the alternative. We analyze a lung cancer prognosis study with gene expression measurements.

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Acknowledgements

We thank the organizers and participants of “The Fourth International Workshop on the Perspectives on High-dimensional Data Analysis.” The authors were supported by the China Postdoctoral Science Foundation (2014M550799), National Science Foundation of China (11401561), National Social Science Foundation of China (13CTJ001, 13&ZD148), National Institutes of Health (CA165923, CA191383, CA016359), and U.S. VA Cooperative Studies Program of the Department of Veterans Affairs, Office of Research and Development.

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Authors and Affiliations

  1. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China

    Guohua Wang, Yangguang Zang & Sanguo Zang

  2. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, China

    Guohua Wang, Yangguang Zang & Sanguo Zang

  3. Department of Biostatistics, Yale University, New Haven, CT, USA

    Yinjun Zhao, Qingzhao Zhang & Shuangge Ma

  4. VA Cooperative Studies Program Coordinating Center, West Haven, CT, USA

    Yinjun Zhao, Qingzhao Zhang & Shuangge Ma

Authors
  1. Guohua Wang
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  2. Yinjun Zhao
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  3. Qingzhao Zhang
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  4. Yangguang Zang
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  5. Sanguo Zang
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  6. Shuangge Ma
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Corresponding author

Correspondence to Shuangge Ma .

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Editors and Affiliations

  1. Department of Mathematics & Statistics, Brock University, St. Catherines, Ontario, Canada

    S. Ejaz Ahmed

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Wang, G., Zhao, Y., Zhang, Q., Zang, Y., Zang, S., Ma, S. (2017). Identifying Gene–Environment Interactions Associated with Prognosis Using Penalized Quantile Regression. In: Ahmed, S. (eds) Big and Complex Data Analysis. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-41573-4_17

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