A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs | Zendy

Sun Ah Kim | Zendy; Chang-Sung Cho | Zendy; Suh-Ryung Kim | Zendy; Shelley B. Bull | Zendy; Yun Joo Yoo | Zendy

Open Access

A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs

Author(s) -

Sun Ah Kim,

Chang-Sung Cho,

Suh-Ryung Kim,

Shelley B. Bull,

Yun Joo Yoo

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/btx609

Subject(s) - haplotype , single nucleotide polymorphism , computational biology , genetics , graph , interval (graph theory) , genome , biology , block (permutation group theory) , computer science , gene , combinatorics , mathematics , theoretical computer science , genotype

Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation.

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