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Identifying disease-associated SNP clusters via contiguous outlier detection
Author(s) -
Can Yang,
Xiaowei Zhou,
Xiang Wan,
Qiang Yang,
Hong Xue,
Weichuan Yu
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/btr424
Subject(s) - linkage disequilibrium , single nucleotide polymorphism , tag snp , snp , genome wide association study , outlier , genetic association , snp genotyping , computer science , biology , computational biology , genetics , data mining , artificial intelligence , genotype , gene
Although genome-wide association studies (GWAS) have identified many disease-susceptibility single-nucleotide polymorphisms (SNPs), these findings can only explain a small portion of genetic contributions to complex diseases, which is known as the missing heritability. A possible explanation is that genetic variants with small effects have not been detected. The chance is < 8 that a causal SNP will be directly genotyped. The effects of its neighboring SNPs may be too weak to be detected due to the effect decay caused by imperfect linkage disequilibrium. Moreover, it is still challenging to detect a causal SNP with a small effect even if it has been directly genotyped.

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