Tag SNP
A tag SNP is a representative single nucleotide polymorphism (SNP) in a region of the genome with high linkage disequilibrium that represents a group of SNPs called a haplotype. It is possible to identify genetic variation and association to phenotypes without genotyping every SNP in a chromosomal region. This reduces the expense and time of mapping genome areas associated with disease, since it eliminates the need to study every individual SNP. Tag SNPs are useful in whole-genome SNP association studies in which hundreds of thousands of SNPs across the entire genome are genotyped.
Selection and evaluation[edit]
Steps for tag SNP selection[edit]
Selection of maximum informative tag SNPs is an NP complete problem. However, algorithms can be devised to provide approximate solution within a margin of error.[12] The criteria that are needed to define each tag SNP selection algorithm is the following:
Tools[edit]
Tagger[edit]
Tagger is a web tool available for evaluating and selecting tag SNPs from genotypic data such as the International HapMap Project. It utilizes pairwise methods and multimarker haplotype approaches. Users can upload HapMap genotype data or pedigree format and the linkage disequilibrium patterns will be calculated. Tagger options allow for the user to specify chromosomal landmarks, which indicate regions of interest in the genome for picking tag SNPs. The program then produces a list of tag SNPs and their statistical test values as well as a coverage report. It is developed by Paul de Bakker in the labs of David Altshuler and Mark Daly at the Center for Human Genetic Research of Massachusetts General Hospital and Harvard Medical School, at the Broad Institute.[15]
CLUSTAG and WCLUSTAG[edit]
In the freeware CLUSTAG and WCLUSTAG, there contain cluster and set-cover algorithms to obtain a set of tag SNPs that can represent all the known SNPs in a chromosomal region. The programs are implemented with Java, and they can run in Windows platform as well as the Unix environment. They are developed by SIO-IONG AO et al. in The University of Hong Kong.[16][17]
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