Identifying gene-gene interactions using penalized tensor regression

Mengyun Wu, Jian Huang, Shuangge Ma

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)598-610
Number of pages13
JournalStatistics in Medicine
Volume37
Issue number4
DOIs
Publication statusPublished - 20 Feb 2018

Keywords

  • gene-gene interactions
  • penalized selection
  • tensor regression

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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