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Comparison of tagging single-nucleotide polymorphism methods in association analyses

Several methods to identify tagging single-nucleotide polymorphisms (SNPs) are in common use for genetic epidemiologic studies; however, there may be loss of information when using only a subset of SNPs. We sought to compare the ability of commonly used pairwise, multimarker, and haplotype-based tag...

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Autores principales: Goode, Ellen L, Fridley, Brooke L, Sun, Zhifu, Atkinson, Elizabeth J, Nord, Alex S, McDonnell, Shannon K, Jarvik, Gail P, de Andrade, Mariza, Slager, Susan L
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367496/
https://www.ncbi.nlm.nih.gov/pubmed/18466560
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author Goode, Ellen L
Fridley, Brooke L
Sun, Zhifu
Atkinson, Elizabeth J
Nord, Alex S
McDonnell, Shannon K
Jarvik, Gail P
de Andrade, Mariza
Slager, Susan L
author_facet Goode, Ellen L
Fridley, Brooke L
Sun, Zhifu
Atkinson, Elizabeth J
Nord, Alex S
McDonnell, Shannon K
Jarvik, Gail P
de Andrade, Mariza
Slager, Susan L
author_sort Goode, Ellen L
collection PubMed
description Several methods to identify tagging single-nucleotide polymorphisms (SNPs) are in common use for genetic epidemiologic studies; however, there may be loss of information when using only a subset of SNPs. We sought to compare the ability of commonly used pairwise, multimarker, and haplotype-based tagging SNP selection methods to detect known associations with quantitative expression phenotypes. Using data from HapMap release 21 on unrelated Utah residents with ancestors from northern and western Europe (CEPH-Utah, CEU), we selected tagging SNPs in five chromosomal regions using ldSelect, Tagger, and TagSNPs. We found that SNP subsets did not substantially overlap, and that the use of trio data did not greatly impact SNP selection. We then tested associations between HapMap genotypes and expression phenotypes on 28 CEU individuals as part of Genetic Analysis Workshop 15. Relative to the use of all SNPs (n = 210 SNPs across all regions), most subset methods were able to detect single-SNP and haplotype associations. Generally, pairwise selection approaches worked extremely well, relative to use of all SNPs, with marked reductions in the number of SNPs required. Haplotype-based approaches, which had identified smaller SNP subsets, missed associations in some regions. We conclude that the optimal tagging SNP method depends on the true model of the genetic association (i.e., whether a SNP or haplotype is responsible); unfortunately, this is often unknown at the time of SNP selection. Additional evaluations using empirical and simulated data are needed.
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spelling pubmed-23674962008-05-06 Comparison of tagging single-nucleotide polymorphism methods in association analyses Goode, Ellen L Fridley, Brooke L Sun, Zhifu Atkinson, Elizabeth J Nord, Alex S McDonnell, Shannon K Jarvik, Gail P de Andrade, Mariza Slager, Susan L BMC Proc Proceedings Several methods to identify tagging single-nucleotide polymorphisms (SNPs) are in common use for genetic epidemiologic studies; however, there may be loss of information when using only a subset of SNPs. We sought to compare the ability of commonly used pairwise, multimarker, and haplotype-based tagging SNP selection methods to detect known associations with quantitative expression phenotypes. Using data from HapMap release 21 on unrelated Utah residents with ancestors from northern and western Europe (CEPH-Utah, CEU), we selected tagging SNPs in five chromosomal regions using ldSelect, Tagger, and TagSNPs. We found that SNP subsets did not substantially overlap, and that the use of trio data did not greatly impact SNP selection. We then tested associations between HapMap genotypes and expression phenotypes on 28 CEU individuals as part of Genetic Analysis Workshop 15. Relative to the use of all SNPs (n = 210 SNPs across all regions), most subset methods were able to detect single-SNP and haplotype associations. Generally, pairwise selection approaches worked extremely well, relative to use of all SNPs, with marked reductions in the number of SNPs required. Haplotype-based approaches, which had identified smaller SNP subsets, missed associations in some regions. We conclude that the optimal tagging SNP method depends on the true model of the genetic association (i.e., whether a SNP or haplotype is responsible); unfortunately, this is often unknown at the time of SNP selection. Additional evaluations using empirical and simulated data are needed. BioMed Central 2007-12-18 /pmc/articles/PMC2367496/ /pubmed/18466560 Text en Copyright © 2007 Goode et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Goode, Ellen L
Fridley, Brooke L
Sun, Zhifu
Atkinson, Elizabeth J
Nord, Alex S
McDonnell, Shannon K
Jarvik, Gail P
de Andrade, Mariza
Slager, Susan L
Comparison of tagging single-nucleotide polymorphism methods in association analyses
title Comparison of tagging single-nucleotide polymorphism methods in association analyses
title_full Comparison of tagging single-nucleotide polymorphism methods in association analyses
title_fullStr Comparison of tagging single-nucleotide polymorphism methods in association analyses
title_full_unstemmed Comparison of tagging single-nucleotide polymorphism methods in association analyses
title_short Comparison of tagging single-nucleotide polymorphism methods in association analyses
title_sort comparison of tagging single-nucleotide polymorphism methods in association analyses
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367496/
https://www.ncbi.nlm.nih.gov/pubmed/18466560
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