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EWAS: epigenome-wide association study software 2.0

MOTIVATION: With the development of biotechnology, DNA methylation data showed exponential growth. Epigenome-wide association study (EWAS) provide a systematic approach to uncovering epigenetic variants underlying common diseases/phenotypes. But the EWAS software has lagged behind compared with geno...

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Detalles Bibliográficos
Autores principales: Xu, Jing, Zhao, Linna, Liu, Di, Hu, Simeng, Song, Xiuling, Li, Jin, Lv, Hongchao, Duan, Lian, Zhang, Mingming, Jiang, Qinghua, Liu, Guiyou, Jin, Shuilin, Liao, Mingzhi, Zhang, Meng, Feng, Rennan, Kong, Fanwu, Xu, Liangde, Jiang, Yongshuai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061808/
https://www.ncbi.nlm.nih.gov/pubmed/29566144
http://dx.doi.org/10.1093/bioinformatics/bty163
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author Xu, Jing
Zhao, Linna
Liu, Di
Hu, Simeng
Song, Xiuling
Li, Jin
Lv, Hongchao
Duan, Lian
Zhang, Mingming
Jiang, Qinghua
Liu, Guiyou
Jin, Shuilin
Liao, Mingzhi
Zhang, Meng
Feng, Rennan
Kong, Fanwu
Xu, Liangde
Jiang, Yongshuai
author_facet Xu, Jing
Zhao, Linna
Liu, Di
Hu, Simeng
Song, Xiuling
Li, Jin
Lv, Hongchao
Duan, Lian
Zhang, Mingming
Jiang, Qinghua
Liu, Guiyou
Jin, Shuilin
Liao, Mingzhi
Zhang, Meng
Feng, Rennan
Kong, Fanwu
Xu, Liangde
Jiang, Yongshuai
author_sort Xu, Jing
collection PubMed
description MOTIVATION: With the development of biotechnology, DNA methylation data showed exponential growth. Epigenome-wide association study (EWAS) provide a systematic approach to uncovering epigenetic variants underlying common diseases/phenotypes. But the EWAS software has lagged behind compared with genome-wide association study (GWAS). To meet the requirements of users, we developed a convenient and useful software, EWAS2.0. RESULTS: EWAS2.0 can analyze EWAS data and identify the association between epigenetic variations and disease/phenotype. On the basis of EWAS1.0, we have added more distinctive features. EWAS2.0 software was developed based on our ‘population epigenetic framework’ and can perform: (i) epigenome-wide single marker association study; (ii) epigenome-wide methylation haplotype (meplotype) association study and (iii) epigenome-wide association meta-analysis. Users can use EWAS2.0 to execute chi-square test, t-test, linear regression analysis, logistic regression analysis, identify the association between epi-alleles, identify the methylation disequilibrium (MD) blocks, calculate the MD coefficient, the frequency of meplotype and Pearson's correlation coefficients and carry out meta-analysis and so on. Finally, we expect EWAS2.0 to become a popular software and be widely used in epigenome-wide associated studies in the future. AVAILABILITY AND IMPLEMENTATION: The EWAS software is freely available at http://www.ewas.org.cn or http://www.bioapp.org/ewas.
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spelling pubmed-60618082018-08-07 EWAS: epigenome-wide association study software 2.0 Xu, Jing Zhao, Linna Liu, Di Hu, Simeng Song, Xiuling Li, Jin Lv, Hongchao Duan, Lian Zhang, Mingming Jiang, Qinghua Liu, Guiyou Jin, Shuilin Liao, Mingzhi Zhang, Meng Feng, Rennan Kong, Fanwu Xu, Liangde Jiang, Yongshuai Bioinformatics Applications Notes MOTIVATION: With the development of biotechnology, DNA methylation data showed exponential growth. Epigenome-wide association study (EWAS) provide a systematic approach to uncovering epigenetic variants underlying common diseases/phenotypes. But the EWAS software has lagged behind compared with genome-wide association study (GWAS). To meet the requirements of users, we developed a convenient and useful software, EWAS2.0. RESULTS: EWAS2.0 can analyze EWAS data and identify the association between epigenetic variations and disease/phenotype. On the basis of EWAS1.0, we have added more distinctive features. EWAS2.0 software was developed based on our ‘population epigenetic framework’ and can perform: (i) epigenome-wide single marker association study; (ii) epigenome-wide methylation haplotype (meplotype) association study and (iii) epigenome-wide association meta-analysis. Users can use EWAS2.0 to execute chi-square test, t-test, linear regression analysis, logistic regression analysis, identify the association between epi-alleles, identify the methylation disequilibrium (MD) blocks, calculate the MD coefficient, the frequency of meplotype and Pearson's correlation coefficients and carry out meta-analysis and so on. Finally, we expect EWAS2.0 to become a popular software and be widely used in epigenome-wide associated studies in the future. AVAILABILITY AND IMPLEMENTATION: The EWAS software is freely available at http://www.ewas.org.cn or http://www.bioapp.org/ewas. Oxford University Press 2018-08-01 2018-03-16 /pmc/articles/PMC6061808/ /pubmed/29566144 http://dx.doi.org/10.1093/bioinformatics/bty163 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Xu, Jing
Zhao, Linna
Liu, Di
Hu, Simeng
Song, Xiuling
Li, Jin
Lv, Hongchao
Duan, Lian
Zhang, Mingming
Jiang, Qinghua
Liu, Guiyou
Jin, Shuilin
Liao, Mingzhi
Zhang, Meng
Feng, Rennan
Kong, Fanwu
Xu, Liangde
Jiang, Yongshuai
EWAS: epigenome-wide association study software 2.0
title EWAS: epigenome-wide association study software 2.0
title_full EWAS: epigenome-wide association study software 2.0
title_fullStr EWAS: epigenome-wide association study software 2.0
title_full_unstemmed EWAS: epigenome-wide association study software 2.0
title_short EWAS: epigenome-wide association study software 2.0
title_sort ewas: epigenome-wide association study software 2.0
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6061808/
https://www.ncbi.nlm.nih.gov/pubmed/29566144
http://dx.doi.org/10.1093/bioinformatics/bty163
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