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

  • Guohua Wang
  • Yinjun Zhao
  • Qingzhao Zhang
  • Yangguang Zang
  • Sanguo Zang
  • Shuangge Ma
Chapter
First Online:
Part of the Contributions to Statistics book series (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.

Keywords

Quantile Regression Inverse Probability Prognosis Outcome Data Contamination Minimax Concave Penalty 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Guohua Wang
    • 1
    • 2
  • Yinjun Zhao
    • 3
    • 4
  • Qingzhao Zhang
    • 3
    • 4
  • Yangguang Zang
    • 1
    • 2
  • Sanguo Zang
    • 1
    • 2
  • Shuangge Ma
    • 3
    • 4
  1. 1.School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Key Laboratory of Big Data Mining and Knowledge ManagementChinese Academy of SciencesBeijingChina
  3. 3.Department of BiostatisticsYale UniversityNew HavenUSA
  4. 4.VA Cooperative Studies Program Coordinating CenterWest HavenUSA

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