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      EWAS: epigenome-wide association study software 2.0

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          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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          Most cited references4

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          Epigenome-wide association studies for common human diseases.

          Vardhman Rakyan, Thomas A. Down, David Balding … (2011)
          Despite the success of genome-wide association studies (GWASs) in identifying loci associated with common diseases, a substantial proportion of the causality remains unexplained. Recent advances in genomic technologies have placed us in a position to initiate large-scale studies of human disease-associated epigenetic variation, specifically variation in DNA methylation. Such epigenome-wide association studies (EWASs) present novel opportunities but also create new challenges that are not encountered in GWASs. We discuss EWAS design, cohort and sample selections, statistical significance and power, confounding factors and follow-up studies. We also discuss how integration of EWASs with GWASs can help to dissect complex GWAS haplotypes for functional analysis.
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            Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population.

            L Excoffier, M Slatkin (1995)
            Molecular techniques allow the survey of a large number of linked polymorphic loci in random samples from diploid populations. However, the gametic phase of haplotypes is usually unknown when diploid individuals are heterozygous at more than one locus. To overcome this difficulty, we implement an expectation-maximization (EM) algorithm leading to maximum-likelihood estimates of molecular haplotype frequencies under the assumption of Hardy-Weinberg proportions. The performance of the algorithm is evaluated for simulated data representing both DNA sequences and highly polymorphic loci with different levels of recombination. As expected, the EM algorithm is found to perform best for large samples, regardless of recombination rates among loci. To ensure finding the global maximum likelihood estimate, the EM algorithm should be started from several initial conditions. The present approach appears to be useful for the analysis of nuclear DNA sequences or highly variable loci. Although the algorithm, in principle, can accommodate an arbitrary number of loci, there are practical limitations because the computing time grows exponentially with the number of polymorphic loci. Although the algorithm, in principle, can accommodate an arbitrary number of loci, there are practical limitations because the computing time grows exponentially with the number of polymorphic loci.
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              EWAS: epigenome-wide association studies software 1.0 – identifying the association between combinations of methylation levels and diseases

              Jing-Jing Xu, Di Liu, Linna Zhao … (2016)
              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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                Author and article information

                Contributors
                Alfonso Valencia: Role: Associate Editor
                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 01 August 2018
                Publication date (Electronic): 16 March 2018
                Publication date PMC-release: 16 March 2018
                Volume: 34
                Issue: 15
                Pages: 2657-2658
                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
                Author notes

                The authors wish it to be known that, in their opinion, Jing Xu, Linna Zhao, Di Liu and Simeng Hu authors should be regarded as Joint First Authors.

                Article
                Publisher ID: bty163
                DOI: 10.1093/bioinformatics/bty163
                PMC ID: 6061808
                PubMed ID: 29566144
                SO-VID: f5fdeeb4-9105-4542-9b68-8033fcda20c2
                Copyright © © The Author(s) 2018. Published by Oxford University Press.
                License:

                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

                History
                Date received : 05 October 2017
                Date revision received : 27 February 2018
                Date accepted : 15 March 2018
                Page count
                Pages: 2
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 91746113
                Award ID: 81601422
                Award ID: 81600403
                Award ID: 81701350
                Award ID: 31501062
                Funded by: Basic Research Program of Shenzhen
                Award ID: JCYJ20160229203627477
                Categories
                Subject: Applications Notes
                Subject: Genome Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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