Epigenome-Wide Association Studies

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.

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

Contenu
Quantification Methods for Methylation Levels in Illumina Arrays.- Evaluating Reliability of DNA Methylation Measurement.- Accurate measurement of DNA methylation: Challenges and Bias Correction. Using R for Cell-Type Composition Imputation in Epigenome-Wide Association Studies.- Cell Type-Specific Signal Analysis in Epigenome-Wide Association Studies.- Controlling Batch Effect in Epigenome-Wide Association Study.- DNA methylation and Atopic Diseases.- Meta-analysis for Epigenome-Wide Association Studies.- Increase the Power of Epigenome-Wide Association Testing Using ICC-Based Hypothesis Weighting.- A Review of High-dimensional Mediation Analyses in DNA Methylation Studies.- DNA Methylation Imputation across Platforms.- Workflow to mine frequent DNA Co-Methylation Clusters in DNA Methylome Data.- BCurve: Bayesian Curve Credible Bands Approach for Detection of Differentially Methylated Regions.- Predicting chronological age from DNA methylation data: A machine learning approach for small datasets and limited predictors.- Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction.- Differential Methylation Analysis for Bisulfite Sequencing (BS-seq) Data.