EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases

Similar to the SNP (single nucleotide polymorphism) data, there is non-random association of the DNA methylation level (we call it methylation disequilibrium, MD) between neighboring methylation loci. For the case-control study of complex diseases, it is important to identify the association between...

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Autores principales: Xu, Jing, Liu, Di, Zhao, Linna, Li, Ying, Wang, Zhaoyang, Chen, Yang, Lei, Changgui, Gao, Lin, Kong, Fanwu, Yuan, Lijun, Jiang, Yongshuai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5125000/
https://www.ncbi.nlm.nih.gov/pubmed/27892496
http://dx.doi.org/10.1038/srep37951
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author Xu, Jing
Liu, Di
Zhao, Linna
Li, Ying
Wang, Zhaoyang
Chen, Yang
Lei, Changgui
Gao, Lin
Kong, Fanwu
Yuan, Lijun
Jiang, Yongshuai
author_facet Xu, Jing
Liu, Di
Zhao, Linna
Li, Ying
Wang, Zhaoyang
Chen, Yang
Lei, Changgui
Gao, Lin
Kong, Fanwu
Yuan, Lijun
Jiang, Yongshuai
author_sort Xu, Jing
collection PubMed
description Similar to the SNP (single nucleotide polymorphism) data, there is non-random association of the DNA methylation level (we call it methylation disequilibrium, MD) between neighboring methylation loci. For the case-control study of complex diseases, it is important to identify the association between methylation levels combination types (we call it methylecomtype) and diseases/phenotypes. We extended the classical framework of SNP haplotype-based association study in population genetics to DNA methylation level data, and developed a software EWAS to identify the disease-related methylecomtypes. EWAS can provide the following basic functions: (1) calculating the DNA methylation disequilibrium coefficient between two CpG loci; (2) identifying the MD blocks across the whole genome; (3) carrying out case-control association study of methylecomtypes and identifying the disease-related methylecomtypes. For a DNA methylation level data set including 689 samples (354 cases and 335 controls) and 473864 CpG loci, it takes only about 25 min to complete the full scan. EWAS v1.0 can rapidly identify the association between combinations of methylation levels (methylecomtypes) and diseases. EWAS v1.0 is freely available at: http://www.ewas.org.cn or http://www.bioapp.org/ewas.
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spelling pubmed-51250002016-12-08 EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases Xu, Jing Liu, Di Zhao, Linna Li, Ying Wang, Zhaoyang Chen, Yang Lei, Changgui Gao, Lin Kong, Fanwu Yuan, Lijun Jiang, Yongshuai Sci Rep Article Similar to the SNP (single nucleotide polymorphism) data, there is non-random association of the DNA methylation level (we call it methylation disequilibrium, MD) between neighboring methylation loci. For the case-control study of complex diseases, it is important to identify the association between methylation levels combination types (we call it methylecomtype) and diseases/phenotypes. We extended the classical framework of SNP haplotype-based association study in population genetics to DNA methylation level data, and developed a software EWAS to identify the disease-related methylecomtypes. EWAS can provide the following basic functions: (1) calculating the DNA methylation disequilibrium coefficient between two CpG loci; (2) identifying the MD blocks across the whole genome; (3) carrying out case-control association study of methylecomtypes and identifying the disease-related methylecomtypes. For a DNA methylation level data set including 689 samples (354 cases and 335 controls) and 473864 CpG loci, it takes only about 25 min to complete the full scan. EWAS v1.0 can rapidly identify the association between combinations of methylation levels (methylecomtypes) and diseases. EWAS v1.0 is freely available at: http://www.ewas.org.cn or http://www.bioapp.org/ewas. Nature Publishing Group 2016-11-28 /pmc/articles/PMC5125000/ /pubmed/27892496 http://dx.doi.org/10.1038/srep37951 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Xu, Jing
Liu, Di
Zhao, Linna
Li, Ying
Wang, Zhaoyang
Chen, Yang
Lei, Changgui
Gao, Lin
Kong, Fanwu
Yuan, Lijun
Jiang, Yongshuai
EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases
title EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases
title_full EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases
title_fullStr EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases
title_full_unstemmed EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases
title_short EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases
title_sort ewas: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5125000/
https://www.ncbi.nlm.nih.gov/pubmed/27892496
http://dx.doi.org/10.1038/srep37951
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