Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping | Zendy

Hongjie Zhu | Zendy; Lexin Li | Zendy; Hua Zhou | Zendy

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Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping

Author(s) -

Hongjie Zhu,

Lexin Li,

Hua Zhou

Publication year - 2012

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/bts406

Subject(s) - kernel (algebra) , computer science , snp , dimensionality reduction , single nucleotide polymorphism , genetic association , machine learning , data mining , computational biology , genotype , biology , genetics , mathematics , gene , combinatorics

Association tests based on next-generation sequencing data are often under-powered due to the presence of rare variants and large amount of neutral or protective variants. A successful strategy is to aggregate genetic information within meaningful single-nucleotide polymorphism (SNP) sets, e.g. genes or pathways, and test association on SNP sets. Many existing methods for group-wise tests require specific assumptions about the direction of individual SNP effects and/or perform poorly in the presence of interactions.

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