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A model-free approach for detecting interactions in genetic association studies.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Li, Jiahan Dan, Jun Li, Chunlei Wu, Rongling
Abstract	Over the past few decades, genome-wide association studies analyzed by efficient statistical procedures have successfully identified single-nucleotide polymorphisms (SNPs) that are associated with complex traits or human diseases. However, due to the overwhelming number of SNPs, most approaches have focused on additive genetic model without genome-wide SNP-SNP interactions. In this study, we propose an efficient statistical procedure in a genetic model-free framework for detecting SNPs exhibiting main genetic effects as well as epistatic interactions. Specifically, the association between phenotype and genotype is characterized by an unknown function to be estimated using nonparametric techniques, and a two-stage non-parametric independence screening procedure is proposed to sequentially identify potentially important main genetic effects and interactions. Finally, the subset of genetic predictors implied by two-stage non-parametric independence screening is analyzed by penalized regressions such as LASSO, and a final model is identified. In this framework, specific genetic model is not assumed and interactions are not only among marginally important SNPs. Therefore, SNPs that are involved in genetic regulatory networks but missed by previous studies are expected to be recognized. In simulation studies, we show that the procedure is computationally efficient and has an outstanding finite sample performance in selecting potential SNPs as well as SNP-SNP interactions. A real data analysis further indicates the importance of epistatic interactions in explaining body mass index.
File Format	HTM / HTML
ISSN	14675463
e-ISSN	14774054
DOI	10.1093/bib/bbt082
Journal	Briefings in Bioinformatics
Issue Number	6
Volume Number	15
Language	English
Publisher	Oxford University Press
Publisher Date	2014-11-01
Publisher Place	Great Britain (UK)
Access Restriction	Open
Subject Keyword	Discipline Computational Biology Genetic Association Studies Statistics & Numerical Data Body Mass Index Computational Biology Computer Simulation Epistasis, Genetic Gene Regulatory Networks Genome-wide Association Study Models, Genetic Models, Statistical Polymorphism, Single Nucleotide Statistics, Nonparametric Research Support, N.i.h., Extramural Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Information Systems

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