Identifying regions of disease-related variants in admixed populations with the summation partition approach

We propose a new method for identifying disease-related regions of single nucleotide variants in recently admixed populations. We use principal component analysis to derive both global and local ancestry information. We then use the summation partition approach to search for disease-related regions...

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Detalles Bibliográficos
Autores principales: Auerbach, Jonathan, Agne, Michael, Fan, Rachel, Lo, Adeline, Lo, Shaw-Hwa, Zheng, Tian, Wang, Pei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Proceedings
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133488/
https://www.ncbi.nlm.nih.gov/pubmed/27980624
http://dx.doi.org/10.1186/s12919-016-0018-9
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author Auerbach, Jonathan
Agne, Michael
Fan, Rachel
Lo, Adeline
Lo, Shaw-Hwa
Zheng, Tian
Wang, Pei
author_facet Auerbach, Jonathan
Agne, Michael
Fan, Rachel
Lo, Adeline
Lo, Shaw-Hwa
Zheng, Tian
Wang, Pei
author_sort Auerbach, Jonathan
collection PubMed
description We propose a new method for identifying disease-related regions of single nucleotide variants in recently admixed populations. We use principal component analysis to derive both global and local ancestry information. We then use the summation partition approach to search for disease-related regions based on both rare variants and the local ancestral information of each region. We demonstrate this method using individuals with high systolic blood pressure from a sample of unrelated Mexican American subjects provided in the 19th Genetic Analysis Workshop.
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spelling pubmed-51334882016-12-15 Identifying regions of disease-related variants in admixed populations with the summation partition approach Auerbach, Jonathan Agne, Michael Fan, Rachel Lo, Adeline Lo, Shaw-Hwa Zheng, Tian Wang, Pei BMC Proc Proceedings We propose a new method for identifying disease-related regions of single nucleotide variants in recently admixed populations. We use principal component analysis to derive both global and local ancestry information. We then use the summation partition approach to search for disease-related regions based on both rare variants and the local ancestral information of each region. We demonstrate this method using individuals with high systolic blood pressure from a sample of unrelated Mexican American subjects provided in the 19th Genetic Analysis Workshop. BioMed Central 2016-10-18 /pmc/articles/PMC5133488/ /pubmed/27980624 http://dx.doi.org/10.1186/s12919-016-0018-9 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Proceedings
Auerbach, Jonathan
Agne, Michael
Fan, Rachel
Lo, Adeline
Lo, Shaw-Hwa
Zheng, Tian
Wang, Pei
Identifying regions of disease-related variants in admixed populations with the summation partition approach
title Identifying regions of disease-related variants in admixed populations with the summation partition approach
title_full Identifying regions of disease-related variants in admixed populations with the summation partition approach
title_fullStr Identifying regions of disease-related variants in admixed populations with the summation partition approach
title_full_unstemmed Identifying regions of disease-related variants in admixed populations with the summation partition approach
title_short Identifying regions of disease-related variants in admixed populations with the summation partition approach
title_sort identifying regions of disease-related variants in admixed populations with the summation partition approach
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133488/
https://www.ncbi.nlm.nih.gov/pubmed/27980624
http://dx.doi.org/10.1186/s12919-016-0018-9
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