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Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure
BACKGROUND: Although many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection. METHODS: Applying a newly proposed sparse representation based variable selection (SRVS) method to the Genetic...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133507/ https://www.ncbi.nlm.nih.gov/pubmed/27980650 http://dx.doi.org/10.1186/s12919-016-0044-7 |
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author | Cao, Hongbao Guo, Wei Qin, Haide Xu, Mengyuan Lehrman, Benjamin Tao, Yu Shugart, Yin-Yao |
author_facet | Cao, Hongbao Guo, Wei Qin, Haide Xu, Mengyuan Lehrman, Benjamin Tao, Yu Shugart, Yin-Yao |
author_sort | Cao, Hongbao |
collection | PubMed |
description | BACKGROUND: Although many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection. METHODS: Applying a newly proposed sparse representation based variable selection (SRVS) method to the Genetic Analysis Workshop19 data, we analyzed a combined data set consisting of 11522 gene expressions and 354893 single-nucleotide polymorphisms (SNPs) from 397 subjects (case/control: 151/246), with the aim to identify potential biomarkers for blood pressure using both gene expression measures and SNP data. RESULTS: Among the top 1000 variables (SNPs/gene expressions = 575/425) selected, the bioinformatics analysis showed that 302 were plausibly associated with blood pressure. In addition, we identified 173 variables that were associated with body weight and 84 associated with left ventricular contractility. Together, 55.9 % of the top 1000 variables showed associations with blood pressure related phenotypes(SNP/gene expression =348/211). CONCLUSIONS: Our results support the feasibility of the SRVS algorithm in integrating multiple data sets of different structure for comprehensive analysis. |
format | Online Article Text |
id | pubmed-5133507 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-51335072016-12-15 Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure Cao, Hongbao Guo, Wei Qin, Haide Xu, Mengyuan Lehrman, Benjamin Tao, Yu Shugart, Yin-Yao BMC Proc Proceedings BACKGROUND: Although many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection. METHODS: Applying a newly proposed sparse representation based variable selection (SRVS) method to the Genetic Analysis Workshop19 data, we analyzed a combined data set consisting of 11522 gene expressions and 354893 single-nucleotide polymorphisms (SNPs) from 397 subjects (case/control: 151/246), with the aim to identify potential biomarkers for blood pressure using both gene expression measures and SNP data. RESULTS: Among the top 1000 variables (SNPs/gene expressions = 575/425) selected, the bioinformatics analysis showed that 302 were plausibly associated with blood pressure. In addition, we identified 173 variables that were associated with body weight and 84 associated with left ventricular contractility. Together, 55.9 % of the top 1000 variables showed associations with blood pressure related phenotypes(SNP/gene expression =348/211). CONCLUSIONS: Our results support the feasibility of the SRVS algorithm in integrating multiple data sets of different structure for comprehensive analysis. BioMed Central 2016-10-18 /pmc/articles/PMC5133507/ /pubmed/27980650 http://dx.doi.org/10.1186/s12919-016-0044-7 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 Cao, Hongbao Guo, Wei Qin, Haide Xu, Mengyuan Lehrman, Benjamin Tao, Yu Shugart, Yin-Yao Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure |
title | Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure |
title_full | Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure |
title_fullStr | Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure |
title_full_unstemmed | Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure |
title_short | Integrating multiple genomic data: sparse representation based biomarker selection for blood pressure |
title_sort | integrating multiple genomic data: sparse representation based biomarker selection for blood pressure |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5133507/ https://www.ncbi.nlm.nih.gov/pubmed/27980650 http://dx.doi.org/10.1186/s12919-016-0044-7 |
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