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Epigenome-Wide Association Studies

Methods and Protocols

  • Book
  • © 2022

Overview

Editors:
  1. Weihua Guan

    1. University of Minnesota, Minneapolis, USA

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  • Includes cutting-edge methods and protocols
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 2432)

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Table of contents (16 protocols)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Quantification Methods for Methylation Levels in Illumina Arrays

    • Duchwan Ryu, Hao Shen
    Pages 1-14

  3. Evaluating Reliability of DNA Methylation Measurement

    • Rui Cao, Weihua Guan
    Pages 15-24

  4. Accurate Measurement of DNA Methylation: Challenges and Bias Correction

    • Eguzkine Ochoa, Verena Zuber, Leonardo Bottolo
    Pages 25-47

  5. Using R for Cell-Type Composition Imputation in Epigenome-Wide Association Studies

    • Chong Wu
    Pages 49-56

  6. Cell Type-Specific Signal Analysis in Epigenome-Wide Association Studies

    • Charles E. Breeze
    Pages 57-71

  7. Controlling Batch Effect in Epigenome-Wide Association Study

    • Yale Jiang, Jianjiao Chen, Wei Chen
    Pages 73-84

  8. DNA Methylation and Atopic Diseases

    • Yale Jiang, Erick Forno, Wei Chen
    Pages 85-99

  9. Meta-Analysis for Epigenome-Wide Association Studies

    • Nan Wang, Shuilin Jin
    Pages 101-111

  10. Increase the Power of Epigenome-Wide Association Testing Using ICC-Based Hypothesis Weighting

    • Bowen Cui, Shuya Cui, Jinyan Huang, Jun Chen
    Pages 113-122

  11. A Review of High-Dimensional Mediation Analyses in DNA Methylation Studies

    • Haixiang Zhang, Lifang Hou, Lei Liu
    Pages 123-135

  12. DNA Methylation Imputation Across Platforms

    • Gang Li, Guosheng Zhang, Yun Li
    Pages 137-151

  13. Workflow to Mine Frequent DNA Co-methylation Clusters in DNA Methylome Data

    • Jie Zhang, Kun Huang
    Pages 153-165

  14. BCurve: Bayesian Curve Credible Bands Approach for the Detection of Differentially Methylated Regions

    • Chenggong Han, Jincheol Park, Shili Lin
    Pages 167-185

  15. Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited Predictors

    • Anastasia Aliferi, David Ballard
    Pages 187-200

  16. Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction

    • Lechuan Li, Chonghao Zhang, Hannah Guan, Yu Zhang
    Pages 201-210

  17. Differential Methylation Analysis for Bisulfite Sequencing (BS-Seq) Data

    • Hao Feng, Karen Conneely, Hao Wu
    Pages 211-226

  18. Back Matter

    Pages 227-229
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Keywords

About this book

This volume details features of DNA methylation data, data processing pipelines, quality control measures, data normalization, and to discussions of statistical methods for data analysis, control of confounding and batch effects, and identification of differentially methylated regions. Chapters focus on microarray-based methylation measures and sequence-based measures. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary methodologies and software packages, step-by-step, readily reproducible analysis pipelines, and tips on troubleshooting and avoiding known pitfalls.

 

Authoritative and cutting-edge, Epigenome- Wide Association Studies: Methods and Protocols: aims to be a useful practical guide to researches to help further their study in this field. 

Editors and Affiliations

  • University of Minnesota, Minneapolis, USA

    Weihua Guan

Bibliographic Information

  • Book Title: Epigenome-Wide Association Studies

  • Book Subtitle: Methods and Protocols

  • Editors: Weihua Guan

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-1994-0

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2022

  • Hardcover ISBN: 978-1-0716-1993-3Published: 04 May 2022

  • Softcover ISBN: 978-1-0716-1996-4Published: 04 May 2023

  • eBook ISBN: 978-1-0716-1994-0Published: 04 May 2022

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: X, 229

  • Number of Illustrations: 18 b/w illustrations, 45 illustrations in colour

  • Topics: Human Genetics

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