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Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites

Recombination during meiosis is one of the most important biological processes, and the level of recombination rates for a given individual is under genetic control. In this study, we conducted genome-wide association studies to identify chromosomal regions associated with recombination rates. We an...

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Detalles Bibliográficos
Autores principales: Huang, Song, Wang, Shuang, Liu, Nianjun, Chen, Liang, Oh, Cheongeun, Zhao, Hongyu
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866732/
https://www.ncbi.nlm.nih.gov/pubmed/16451663
http://dx.doi.org/10.1186/1471-2156-6-S1-S51
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author Huang, Song
Wang, Shuang
Liu, Nianjun
Chen, Liang
Oh, Cheongeun
Zhao, Hongyu
author_facet Huang, Song
Wang, Shuang
Liu, Nianjun
Chen, Liang
Oh, Cheongeun
Zhao, Hongyu
author_sort Huang, Song
collection PubMed
description Recombination during meiosis is one of the most important biological processes, and the level of recombination rates for a given individual is under genetic control. In this study, we conducted genome-wide association studies to identify chromosomal regions associated with recombination rates. We analyzed genotype data collected on the pedigrees in the Collaborative Study on the Genetics on Alcoholism data provided by Genetic Analysis Workshop 14. A total of 315 microsatellites and 10,081 single-nucleotide polymorphisms from Affymetrix on 22 autosomal chromosomes were used in our association analysis. Genome-wide gender-specific recombination counts for family founders were inferred first and association analysis was performed using multiple linear regressions. We used the positive false discovery rate (pFDR) to account for multiple comparisons in the two genome-wide scans. Eight regions showed some evidence of association with recombination counts based on the single-nucleotide polymorphism analysis after adjusting for multiple comparisons. However, no region was found to be significant using microsatellites.
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spelling pubmed-18667322007-05-11 Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites Huang, Song Wang, Shuang Liu, Nianjun Chen, Liang Oh, Cheongeun Zhao, Hongyu BMC Genet Proceedings Recombination during meiosis is one of the most important biological processes, and the level of recombination rates for a given individual is under genetic control. In this study, we conducted genome-wide association studies to identify chromosomal regions associated with recombination rates. We analyzed genotype data collected on the pedigrees in the Collaborative Study on the Genetics on Alcoholism data provided by Genetic Analysis Workshop 14. A total of 315 microsatellites and 10,081 single-nucleotide polymorphisms from Affymetrix on 22 autosomal chromosomes were used in our association analysis. Genome-wide gender-specific recombination counts for family founders were inferred first and association analysis was performed using multiple linear regressions. We used the positive false discovery rate (pFDR) to account for multiple comparisons in the two genome-wide scans. Eight regions showed some evidence of association with recombination counts based on the single-nucleotide polymorphism analysis after adjusting for multiple comparisons. However, no region was found to be significant using microsatellites. BioMed Central 2005-12-30 /pmc/articles/PMC1866732/ /pubmed/16451663 http://dx.doi.org/10.1186/1471-2156-6-S1-S51 Text en Copyright © 2005 Huang 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
Huang, Song
Wang, Shuang
Liu, Nianjun
Chen, Liang
Oh, Cheongeun
Zhao, Hongyu
Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites
title Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites
title_full Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites
title_fullStr Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites
title_full_unstemmed Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites
title_short Whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites
title_sort whole-genome association analysis to identify markers associated with recombination rates using single-nucleotide polymorphisms and microsatellites
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866732/
https://www.ncbi.nlm.nih.gov/pubmed/16451663
http://dx.doi.org/10.1186/1471-2156-6-S1-S51
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