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A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis

We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by...

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Autores principales: Song, Yeunjoo E, Namkung, Junghyun, Shields, Robert W, Baechle, Daniel J, Song, Sunah, Elston, Robert C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287925/
https://www.ncbi.nlm.nih.gov/pubmed/22373405
http://dx.doi.org/10.1186/1753-6561-5-S9-S84
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author Song, Yeunjoo E
Namkung, Junghyun
Shields, Robert W
Baechle, Daniel J
Song, Sunah
Elston, Robert C
author_facet Song, Yeunjoo E
Namkung, Junghyun
Shields, Robert W
Baechle, Daniel J
Song, Sunah
Elston, Robert C
author_sort Song, Yeunjoo E
collection PubMed
description We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach.
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spelling pubmed-32879252012-02-28 A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis Song, Yeunjoo E Namkung, Junghyun Shields, Robert W Baechle, Daniel J Song, Sunah Elston, Robert C BMC Proc Proceedings We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach. BioMed Central 2011-11-29 /pmc/articles/PMC3287925/ /pubmed/22373405 http://dx.doi.org/10.1186/1753-6561-5-S9-S84 Text en Copyright ©2011 Song 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
Song, Yeunjoo E
Namkung, Junghyun
Shields, Robert W
Baechle, Daniel J
Song, Sunah
Elston, Robert C
A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis
title A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis
title_full A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis
title_fullStr A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis
title_full_unstemmed A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis
title_short A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis
title_sort method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287925/
https://www.ncbi.nlm.nih.gov/pubmed/22373405
http://dx.doi.org/10.1186/1753-6561-5-S9-S84
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