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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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EWAS Open Platform: integrated data, knowledge and toolkit for epigenome-wide association study.

Zhuang Xiong1,2,3, Fei Yang1,2,3, Mengwei Li1,2

  • 1National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation, Beijing 100101, China.

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|October 31, 2021
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Summary
This summary is machine-generated.

Epigenome-Wide Association Studies (EWAS) identify DNA methylation variations. The EWAS Open Platform integrates vast EWAS knowledge, data, and tools for comprehensive research, offering open access to over 617,000 associations.

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Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Epigenome-Wide Association Studies (EWAS) are crucial for identifying DNA methylation variations linked to phenotypes.
  • Existing resources like EWAS Atlas and EWAS Data Hub have aggregated significant EWAS knowledge and data.
  • The need for a unified platform to access and analyze EWAS information is growing.

Purpose of the Study:

  • To introduce the EWAS Open Platform, a comprehensive resource integrating EWAS knowledge, data, and analytical tools.
  • To provide open access to a large-scale collection of EWAS associations, DNA methylation data, and metadata.
  • To facilitate EWAS enrichment, annotation, and knowledge network construction through the integrated EWAS Toolkit.

Main Methods:

  • Integration of EWAS Atlas and EWAS Data Hub into a unified EWAS Open Platform.
  • Inclusion of a newly developed EWAS Toolkit for data analysis and visualization.
  • Aggregation of data from 910 publications, encompassing over 617,000 EWAS associations, 51 phenotypes, 275 diseases, and 104 environmental factors.
  • Inclusion of well-normalized DNA methylation array data from 115,852 samples across diverse tissues, cell lines, and disease states.

Main Results:

  • The EWAS Open Platform integrates 617,018 high-quality EWAS associations.
  • The platform covers 51 phenotypes, 275 diseases, and 104 environmental factors.
  • It provides access to DNA methylation array data from 115,852 samples, including 707 tissues, 218 cell lines, and 528 diseases.
  • The EWAS Toolkit enables enrichment, annotation, and knowledge network construction.

Conclusions:

  • The EWAS Open Platform offers a centralized, open-access resource for EWAS knowledge, data, and analytical tools.
  • The platform significantly enhances the utility and accessibility of EWAS findings for researchers.
  • It supports advanced analyses like enrichment, annotation, and network visualization, advancing epigenetic research.