TSGSIS: a high-dimensional grouped variable selection approach for detection of whole-genome SNP–SNP interactions | Zendy

YaoHwei Fang | Zendy; Jie-Huei Wang | Zendy; Chao A. Hsiung | Zendy

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TSGSIS: a high-dimensional grouped variable selection approach for detection of whole-genome SNP–SNP interactions

Author(s) -

YaoHwei Fang,

Jie-Huei Wang,

Chao A. Hsiung

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx409

Subject(s) - snp , selection (genetic algorithm) , variable (mathematics) , feature selection , computer science , computational biology , genome , snp array , biology , genetics , single nucleotide polymorphism , artificial intelligence , mathematics , genotype , gene , mathematical analysis

Identification of single nucleotide polymorphism (SNP) interactions is an important and challenging topic in genome-wide association studies (GWAS). Many approaches have been applied to detecting whole-genome interactions. However, these approaches to interaction analysis tend to miss causal interaction effects when the individual marginal effects are uncorrelated to trait, while their interaction effects are highly associated with the trait.

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