Journal article
A Penalized Robust Method for Identifying Gene-Environment Interactions
Genetic epidemiology, Vol.38(3), pp.220-230
02/24/2014
DOI: 10.1002/gepi.21795
PMCID: PMC4356211
PMID: 24616063
Abstract
In high-throughput studies, an important objective is to identify gene-environment interactions associated with disease outcomes and phenotypes. Many commonly adopted methods assume specific parametric or semiparametric models, which may be subject to model mis-specification. In addition, they usually use significance level as the criterion for selecting important interactions. In this study, we adopt the rank-based estimation, which is much less sensitive to model specification than some of the existing methods and includes several commonly encountered data and models as special cases. Penalization is adopted for the identification of gene-environment interactions. It achieves simultaneous estimation and identification and does not rely on significance level. For computation feasibility, a smoothed rank estimation is further proposed. Simulation shows that under certain scenarios, for example with contaminated or heavy-tailed data, the proposed method can significantly outperform the existing alternatives with more accurate identification. We analyze a lung cancer prognosis study with gene expression measurements under the AFT (accelerated failure time) model. The proposed method identifies interactions different from those using the alternatives. Some of the identified genes have important implications.
Details
- Title: Subtitle
- A Penalized Robust Method for Identifying Gene-Environment Interactions
- Creators
- Xingjie Shi - Shanghai University of Finance and EconomicsJin Liu - University of Illinois ChicagoJian Huang - University of IowaYong Zhou - Shanghai University of Finance and EconomicsYang Xie - The University of Texas Southwestern Medical CenterShuangge Ma - Yale University
- Resource Type
- Journal article
- Publication Details
- Genetic epidemiology, Vol.38(3), pp.220-230
- DOI
- 10.1002/gepi.21795
- PMID
- 24616063
- PMCID
- PMC4356211
- NLM abbreviation
- Genet Epidemiol
- ISSN
- 0741-0395
- eISSN
- 1098-2272
- Grant note
- name: NIH, award: CA165923, CA152301, CA142774; name: National Social Science Foundation of China, award: 13CTJ001; name: National Bureau of Statistics Funds of China, award: 2012LD001
- Language
- English
- Date published
- 02/24/2014
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257739302771
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