Journal article
Identifying gene-gene interactions using penalized tensor regression
Statistics in medicine, Vol.37(4), pp.598-610
02/20/2018
DOI: 10.1002/sim.7523
PMCID: PMC5771864
PMID: 29034516
Abstract
Gene-gene (G×G) interactions have been shown to be critical for the fundamental mechanisms and development of complex diseases beyond main genetic effects. The commonly adopted marginal analysis is limited by considering only a small number of G factors at a time. With the "main effects, interactions" hierarchical constraint, many of the existing joint analysis methods suffer from prohibitively high computational cost. In this study, we propose a new method for identifying important G×G interactions under joint modeling. The proposed method adopts tensor regression to accommodate high data dimensionality and the penalization technique for selection. It naturally accommodates the strong hierarchical structure without imposing additional constraints, making optimization much simpler and faster than in the existing studies. It outperforms multiple alternatives in simulation. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer and melanoma demonstrates that it can identify markers with important implications and better prediction performance.
Details
- Title: Subtitle
- Identifying gene-gene interactions using penalized tensor regression
- Creators
- Mengyun Wu - Yale UniversityJian Huang - Hong Kong Polytechnic UniversityShuangge Ma - Yale University
- Resource Type
- Journal article
- Publication Details
- Statistics in medicine, Vol.37(4), pp.598-610
- DOI
- 10.1002/sim.7523
- PMID
- 29034516
- PMCID
- PMC5771864
- NLM abbreviation
- Stat Med
- ISSN
- 0277-6715
- eISSN
- 1097-0258
- Grant note
- R21 CA191383 / NCI NIH HHS R01 CA204120 / NCI NIH HHS
- Language
- English
- Date published
- 02/20/2018
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257742602771
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