EWAS: epigenome-wide association study software 2.0

Jing Xu^{1

2}, Linna Zhao^{1

2}, Di Liu^{1

2}, Simeng Hu^{1

2}, Xiuling Song^{1

2}, Jin Li¹, Hongchao Lv¹, Lian Duan¹, Mingming Zhang¹, Qinghua Jiang³, Guiyou Liu³, Shuilin Jin⁴, Mingzhi Liao⁵, Meng Zhang^{2

6}, Rennan Feng^{2

6}, Fanwu Kong⁷, Liangde Xu^{1

2}, Yongshuai Jiang^{1

2}

Affiliations

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.

PMID: 29566144
PMCID: PMC6061808
DOI: 10.1093/bioinformatics/bty163

EWAS: epigenome-wide association study software 2.0

Jing Xu et al. Bioinformatics. 2018.

. 2018 Aug 1;34(15):2657-2658.

doi: 10.1093/bioinformatics/bty163.

Authors

Affiliations

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.

PMID: 29566144
PMCID: PMC6061808
DOI: 10.1093/bioinformatics/bty163

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.

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EWAS: epigenome-wide association study software 2.0

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EWAS: epigenome-wide association study software 2.0

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