Multiple Subsampling of Dense SNP Data Localizes Disease Genes with Increased Precision | Zendy

William C. Stewart | Zendy; Anna L. Peljto | Zendy; David A. Greenberg | Zendy

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Multiple Subsampling of Dense SNP Data Localizes Disease Genes with Increased Precision

Author(s) -

William C. Stewart,

Anna L. Peljto,

David A. Greenberg

Publication year - 2009

Publication title -

human heredity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.423

H-Index - 62

eISSN - 1423-0062

pISSN - 0001-5652

DOI - 10.1159/000267995

Subject(s) - estimator , single nucleotide polymorphism , statistics , correlation , pairwise comparison , linkage (software) , quantitative trait locus , mathematics , trait , snp , genetics , biology , computer science , genotype , gene , geometry , programming language

Current linkage studies detect and localize trait loci using genotypes sampled at hundreds of thousands of single nucleotide polymorphisms (SNPs). Such data should provide precise estimates of trait location once linkage has been established. However, correlations between nearby SNPs can distort the information about trait location. Traditionally, when faced with this dilemma, three approaches have been used: (1) ignore the correlation; (2) approximate the correlation; or, (3) analyze a single, approximately uncorrelated subset of the original dense data.

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