Identification of gene-environment interactions with marginal penalization.

Clicks: 271
ID: 65826
2019
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Abstract
Gene-environment (G-E) interaction analysis has been extensively conducted for complex diseases. In marginal analysis, the common practice is to conduct likelihood-based (and other "standard") estimation with each marginal model, and then select significant G-E interactions and main effects based on p values and multiple comparisons adjustment. One limitation of this approach is that the identification results often do not respect the "main effects, interactions" hierarchy, which has been stressed in recent G-E interaction analyses. There is some recent effort tackling this problem, however, with very complex formulations. Another limitation of the common practice is that it may not perform well when regularization is needed, for example, because of "non-normal" distributions. In this article, we propose a marginal penalization approach which adopts a novel penalty to directly tackle the aforementioned problems. The proposed approach has a framework more coherent with that of the recently developed joint analysis methods and an intuitive formulation, and can be effectively realized. In simulation, it outperforms the popular significance-based analysis and simple penalization-based alternatives. Promising findings are made in the analysis of a single-nucleotide polymorphism and a gene expression data.
Reference Key
zhang2019identificationgenetic Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhang, Sanguo;Xue, Yuan;Zhang, Qingzhao;Ma, Chenjin;Wu, Mengyun;Ma, Shuangge;
Journal genetic epidemiology
Year 2019
DOI
10.1002/gepi.22270
URL
Keywords
gene-environment interaction marginal analysis penalization

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