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Bioinformatics. 2018 Aug 1;34(15):2657-2658. doi: 10.1093/bioinformatics/bty163.

EWAS: epigenome-wide association study software 2.0.

Xu J1,2, Zhao L1,2, Liu D1,2, Hu S1,2, Song X1,2, Li J1, Lv H1, Duan L1, Zhang M1, Jiang Q3, Liu G3, Jin S4, Liao M5, Zhang M2,6, Feng R2,6, Kong F7, Xu L1,2, Jiang Y1,2.

Author information

1
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
2
Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China.
3
Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
4
Department of Mathematics, Harbin Institute of Technology, Harbin, China.
5
College of Life Science, Northwest A&F University, Yangling, Shaanxi, China.
6
Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China.
7
Department of Nephrology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.

Abstract

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.

PMID:
29566144
PMCID:
PMC6061808
DOI:
10.1093/bioinformatics/bty163
[Indexed for MEDLINE]
Free PMC Article

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