EWAS Epigenome-Wide Association Studies Introduction

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# EWAS Epigenome-Wide Association Studies Introduction

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  <a href="/training-material/hall-of-fame/kkamieniecka/" class="contributor-badge contributor-kkamieniecka"><img src="/training-material/assets/images/orcid.png" alt="orcid logo" width="36" height="36"/><img src="https://avatars.githubusercontent.com/kkamieniecka?s=36" alt="Katarzyna Kamieniecka avatar" width="36" class="avatar" />
    Katarzyna Kamieniecka</a>
  
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  <a href="/training-material/hall-of-fame/poterlowicz-lab/" class="contributor-badge contributor-poterlowicz-lab"><img src="/training-material/assets/images/orcid.png" alt="orcid logo" width="36" height="36"/><img src="https://avatars.githubusercontent.com/poterlowicz-lab?s=36" alt="Krzysztof Poterlowicz avatar" width="36" class="avatar" />
    Krzysztof Poterlowicz</a>
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## Requirements

Before diving into this slide deck, we recommend you to have a look at:

- [Introduction to Galaxy Analyses](/training-material/topics/introduction)

- [Sequence analysis](/training-material/topics/sequence-analysis)
    - Quality Control: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/sequence-analysis/tutorials/quality-control/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html)
    - Mapping: [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/sequence-analysis/tutorials/mapping/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/sequence-analysis/tutorials/mapping/tutorial.html)

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# What is the topic?
Epigenome-wide association studies (EWAS) analyse genome-wide activity of epigenetic marks in  cohorts of different individuals to find associations between epigenetic variation and phenotype.

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## Overview

- Applications of EWAS
- Infinium Human Methylation BeadChip
- The Analysis
- Integrative Approach
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## Applications
- **Population Studies**
   - EWAS studies are the solution to explore gene-environment interactions on big scale
- **Clinical**
   - Genetic epidemiology is widely used in many progressive and incurable diseases i.e. cancers, autoimmune and neurological disorders

.image-75[ ![Graphic showing chromosome being unwound into chromatin and dna. DNA methylation is highlighted on individual dna molecules. The graphic shows more and less accessible DNA between histones in a 'histone tail'](../../images/Epigenetic_mechanisms.jpg)]

<small>[*National Institutes of Health, 2005*](http://commonfund.nih.gov/epigenomics/figure.aspx)</small>

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### Infinium Human Methylation BeadChip
<small>One of the most common techniques used in EWAS studies is the Infinium Human Methylation BeadChip array quantifying the DNA methylation level, providing high accuracy with low input DNA requirements.</small>

.image-80[![At a methylated DNA locus, a methylated bead allows for transcription elongation while an unmethylated one doesn't. At an unmethylated site, the opposite happens.](../../images/methylationassay.png)]

<small>[*Illumina, 2018*](https://emea.illumina.com/)</small>

<small>The Infinium Methylation Assay uses two different bead types to detect changes in DNA methylation levels. In the figure we can see M - methylated and U - unmethylated bead types. Depending on the probe design, the bead signals are reported in different colors green or red.</small>
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## The Analysis
.image-90[![.IDAT files are eread, preprocessed and normalised, QC happens, then genetic variation annotation, then DMP DMR finding, and finall functional annotation and visualisation](../../images/ewaschart1.png)]

<small>The analysis combines 6 main steps:
 1. Raw intensity data loading (.idat files)
 2. Preprocessing and optional normalization
 3. Quality assessment and control step
 4. Differentially methylated positions (DMP)  and regions (DMR) finding with respect to a phenotype covariate
 5. Functional annotation and graphical representation
</small>
.image-30[![Screenshot of the above workflow but as a Galaxy workflow](../../images/ewaschart2.png)]
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## Integrative Approach
Methylation patterns obtained from Infinium Human Methylation BeadChip data are compatibile with other platforms (gene expression, microRNA profiling) and thus become one of the most comprehensive solution to invastigate epigenome.

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## Summary
Epigenetic aberrations which involve DNA modifications give researchers an interest to identify novel non-genetic factors responsible for complex human phenotypes such as height, weight, and disease. To identify methylation changes researchers need to perform complicated and time consuming computational analysis.

Here, the Infinium Human Methylation BeadChip pipeline becomes a solution  for this inconvenience and provides a simplified downstream analysis  including preprocessing, quality evaluation and differentially methylated regions detection in one complex set of tools developed and published under the Galaxy platform. Ready to be integrated with existing resources and reused in multiple ways.

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## Thank You!

This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!

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		<td><abbr title="These people wrote the bulk of the tutorial, they may have done the analysis, built the workflow, and wrote the text themselves.">Author(s)</abbr></td>
		<td>
			
  <a href="/training-material/hall-of-fame/kkamieniecka/" class="contributor-badge contributor-kkamieniecka"><img src="/training-material/assets/images/orcid.png" alt="orcid logo" width="36" height="36"/><img src="https://avatars.githubusercontent.com/kkamieniecka?s=36" alt="Katarzyna Kamieniecka avatar" width="36" class="avatar" />
    Katarzyna Kamieniecka</a>
  
  <a href="/training-material/hall-of-fame/poterlowicz-lab/" class="contributor-badge contributor-poterlowicz-lab"><img src="/training-material/assets/images/orcid.png" alt="orcid logo" width="36" height="36"/><img src="https://avatars.githubusercontent.com/poterlowicz-lab?s=36" alt="Krzysztof Poterlowicz avatar" width="36" class="avatar" />
    Krzysztof Poterlowicz</a>
  
		</td>
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		<td><abbr title="These people reviewed this material for accuracy and correctness">Reviewers</abbr></td>
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  <a href="/training-material/hall-of-fame/hexylena/" class="contributor-badge contributor-hexylena">
    <img src="https://avatars.githubusercontent.com/hexylena?s=36" alt="Helena Rasche avatar" width="36" class="avatar" />
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  <a href="/training-material/hall-of-fame/kkamieniecka/" class="contributor-badge contributor-kkamieniecka">
    <img src="https://avatars.githubusercontent.com/kkamieniecka?s=36" alt="Katarzyna Kamieniecka avatar" width="36" class="avatar" />
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  <a href="/training-material/hall-of-fame/shiltemann/" class="contributor-badge contributor-shiltemann">
    <img src="https://avatars.githubusercontent.com/shiltemann?s=36" alt="Saskia Hiltemann avatar" width="36" class="avatar" />
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  <a href="/training-material/hall-of-fame/bebatut/" class="contributor-badge contributor-bebatut">
    <img src="https://avatars.githubusercontent.com/bebatut?s=36" alt="Bérénice Batut avatar" width="36" class="avatar" />
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  <a href="/training-material/hall-of-fame/bgruening/" class="contributor-badge contributor-bgruening">
    <img src="https://avatars.githubusercontent.com/bgruening?s=36" alt="Björn Grüning avatar" width="36" class="avatar" />
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  <a href="/training-material/hall-of-fame/gallardoalba/" class="contributor-badge contributor-gallardoalba">
    <img src="https://avatars.githubusercontent.com/gallardoalba?s=36" alt="Cristóbal Gallardo avatar" width="36" class="avatar" />
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Tutorial Content is licensed under <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.<br/>