Robust gene-environment interaction analysis using penalized trimmed regression

doi:10.1080/00949655.2018.1523411

CORC > 上海财经大学 > 上海财经大学

	Robust gene-environment interaction analysis using penalized trimmed regression
	Xu, Yaqing 1; Wu, Mengyun 1,2; Ma, Shuangge 1; Ahmed, Syed Ejaz 3
刊名	JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
	2018-12-12
卷号	88 期号:18 页码:3502-3528
关键词	G-E interaction robustness trimmed regression penalized selection
ISSN号	0094-9655
DOI	10.1080/00949655.2018.1523411
英文摘要	In biomedical and epidemiological studies, gene-environment (G-E) interactions have been shown to importantly contribute to the etiology and progression of many complex diseases. Most existing approaches for identifying G-E interactions are limited by the lack of robustness against outliers/contaminations in response and predictor spaces. In this study, we develop a novel robust G-E identification approach using the trimmed regression technique under joint modelling. A robust data-driven criterion and stability selection are adopted to determine the trimmed subset which is free from both vertical outliers and leverage points. An effective penalization approach is developed to identify important G-E interactions, respecting the 'main effects, interactions' hierarchical structure. Extensive simulations demonstrate the better performance of the proposed approach compared to multiple alternatives. Interesting findings with superior prediction accuracy and stability are observed in the analysis of The Cancer Genome Atlas data on cutaneous melanoma and breast invasive carcinoma.
WOS研究方向	Computer Science ; Mathematics
语种	英语
出版者	TAYLOR & FRANCIS LTD
WOS记录号	WOS:000447378800002
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/429]
专题	上海财经大学
通讯作者	Wu, Mengyun
作者单位	1.Yale Univ, Dept Biostat, New Haven, CT 06520 USA; 2.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China; 3.Brock Univ, Dept Math & Stat, St Catharines, ON, Canada
推荐引用方式 GB/T 7714	Xu, Yaqing,Wu, Mengyun,Ma, Shuangge,et al. Robust gene-environment interaction analysis using penalized trimmed regression[J]. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION,2018,88(18):3502-3528.
APA	Xu, Yaqing,Wu, Mengyun,Ma, Shuangge,&Ahmed, Syed Ejaz.(2018).Robust gene-environment interaction analysis using penalized trimmed regression.JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION,88(18),3502-3528.
MLA	Xu, Yaqing,et al."Robust gene-environment interaction analysis using penalized trimmed regression".JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 88.18(2018):3502-3528.