Evidence map›Paper›PMID 27892496›Full record

ArticleScientific reports2016

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

Jing Xu, Di Liu, Linna Zhao, Ying Li, Zhaoyang Wang, Yang Chen, Changgui Lei, Lin Gao, Fanwu Kong, Lijun Yuan and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Ten Years of EWAS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021
    Review
  2. Article
  3. EWAS: epigenome-wide association study software 2.0.Bioinformatics (Oxford, England) · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Jing XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Di LiuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Linna ZhaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Ying LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Zhaoyang WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Yang ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Changgui LeiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.
Lin GaoDepartment of Ophthalmology, the Second Affiliated Hospital, Harbin Medical University, Harbin, China.
Fanwu KongDepartment of Nephrology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.
Lijun YuanTraining Center for Students Innovation and Entrepreneurship Education of Harbin Medical University, Harbin, China.
Yongshuai JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

DNA MethylationEpigenesis, GeneticSoftwareCase-Control StudiesCpG IslandsGenome-Wide Association StudyHumans

Identifiers

PMID27892496
PMCPMC5125000

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.