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Software for tag single nucleotide polymorphism selection

This paper reviews the theoretical basis for single nucleotide polymorphism (SNP) tagging and considers the use of current software made freely available for this task. A distinction between haplotype block-based and non-block-based approaches yields two classes of procedures. Analysis of two differ...

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Detalles Bibliográficos
Autor principal: Stram, Daniel O
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3525260/
https://www.ncbi.nlm.nih.gov/pubmed/16004730
http://dx.doi.org/10.1186/1479-7364-2-2-144
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author Stram, Daniel O
author_facet Stram, Daniel O
author_sort Stram, Daniel O
collection PubMed
description This paper reviews the theoretical basis for single nucleotide polymorphism (SNP) tagging and considers the use of current software made freely available for this task. A distinction between haplotype block-based and non-block-based approaches yields two classes of procedures. Analysis of two different sets of SNP genotype data from the HapMap is used to judge the practical aspects of using each of the programs considered, as well as to make some general observations about the performance of the programs in finding optimal sets of tagging SNPs. Pairwise R(2 )methods, while the simplest of those considered, do tend to pick more tagging SNPs than are strictly needed to predict unmeasured (non-tagging) SNPs, since a combination of two or more tagging SNPs can form a prediction of SNPs that have no direct (pairwise) surrogate. Block-based methods that exploit the linkage disequilibrium structure within haplotype blocks exploit this sort of redundancy, but run a risk of over-fitting if used without some care. A compromise approach which eliminates the need first to analyse block structure, but which still exploits simple relationships between SNPs, appears promising.
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spelling pubmed-35252602013-05-10 Software for tag single nucleotide polymorphism selection Stram, Daniel O Hum Genomics Software Review This paper reviews the theoretical basis for single nucleotide polymorphism (SNP) tagging and considers the use of current software made freely available for this task. A distinction between haplotype block-based and non-block-based approaches yields two classes of procedures. Analysis of two different sets of SNP genotype data from the HapMap is used to judge the practical aspects of using each of the programs considered, as well as to make some general observations about the performance of the programs in finding optimal sets of tagging SNPs. Pairwise R(2 )methods, while the simplest of those considered, do tend to pick more tagging SNPs than are strictly needed to predict unmeasured (non-tagging) SNPs, since a combination of two or more tagging SNPs can form a prediction of SNPs that have no direct (pairwise) surrogate. Block-based methods that exploit the linkage disequilibrium structure within haplotype blocks exploit this sort of redundancy, but run a risk of over-fitting if used without some care. A compromise approach which eliminates the need first to analyse block structure, but which still exploits simple relationships between SNPs, appears promising. BioMed Central 2005-06-01 /pmc/articles/PMC3525260/ /pubmed/16004730 http://dx.doi.org/10.1186/1479-7364-2-2-144 Text en Copyright ©2005 Henry Stewart Publications
spellingShingle Software Review
Stram, Daniel O
Software for tag single nucleotide polymorphism selection
title Software for tag single nucleotide polymorphism selection
title_full Software for tag single nucleotide polymorphism selection
title_fullStr Software for tag single nucleotide polymorphism selection
title_full_unstemmed Software for tag single nucleotide polymorphism selection
title_short Software for tag single nucleotide polymorphism selection
title_sort software for tag single nucleotide polymorphism selection
topic Software Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3525260/
https://www.ncbi.nlm.nih.gov/pubmed/16004730
http://dx.doi.org/10.1186/1479-7364-2-2-144
work_keys_str_mv AT stramdanielo softwarefortagsinglenucleotidepolymorphismselection