Main description:

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.

Contents:

1. Quantification Methods for Methylation Levels in Illumina Arrays

Duchwan Ryu and Hao Shen

2. Evaluating Reliability of DNA Methylation Measurement

Rui Cao and Weihua Guan

3. Accurate measurement of DNA methylation: Challenges and Bias Correction

Eguzkine Ochoa, Verena Zuber, and Leonardo Bottolo

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

Chong Wu

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

Charles E. Breeze

6. Controlling Batch Effect in Epigenome-Wide Association Study

Yale Jiang, Jianjiao Chen, and Wei Chen

7. DNA methylation and Atopic Diseases

Yale Jiang, Erick Forno, and Wei Chen

8. Meta-analysis for Epigenome-Wide Association Studies

Nan Wang and Shuilin Jin

9. Increase the Power of Epigenome-Wide Association Testing Using ICC-Based Hypothesis Weighting Bowen Cuia, Shuya Cuib, Jinyan Huanga, and Jun Chenc

10. A Review of High-dimensional Mediation Analyses in DNA Methylation Studies

Haixiang Zhang, Lifang Hou, and Lei Liu

11. DNA Methylation Imputation across Platforms

Gang Li, Yun Li, and Guosheng Zhang

12. Workflow to mine frequent DNA Co-Methylation Clusters in DNA Methylome Data

Jie Zhang and Kun Huang

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

Chenggong Han and Shili Lin

14. Predicting chronological age from DNA methylation data: A machine learning approach for small datasets and limited predictors

Anastasia Aliferi and David Ballard

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

Lechuan Li, Chonghao Zhang, Hannah Guan, and Yu Zhang

16. Differential Methylation Analysis for Bisulfite Sequencing (BS-seq) Data

Hao Feng, Karen Conneely, and Hao Wu