Detecting epistatic effects in association studies at a genomic level based on an ensemble approach | Zendy

Jing Li | Zendy; Benjamin Philip Horstman | Zendy; Yixuan Chen | Zendy

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Detecting epistatic effects in association studies at a genomic level based on an ensemble approach

Author(s) -

Jing Li,

Benjamin Philip Horstman,

Yixuan Chen

Publication year - 2011

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/btr227

Subject(s) - epistasis , genome wide association study , single nucleotide polymorphism , boosting (machine learning) , genetic association , computer science , computational biology , statistical power , permutation (music) , biology , machine learning , genetics , gene , mathematics , statistics , genotype , physics , acoustics

Most complex diseases involve multiple genes and their interactions. Although genome-wide association studies (GWAS) have shown some success for identifying genetic variants underlying complex diseases, most existing studies are based on limited single-locus approaches, which detect single nucleotide polymorphisms (SNPs) essentially based on their marginal associations with phenotypes.

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