Epigenome-Wide Association Studies: Methods and Protocols (Methods in Molecular Biology, 2432)

✍ Scribed by Weihua Guan (editor)

• Publisher: Humana
• Year: 2022
• Tongue: English
• Leaves: 232
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Preface
Contents
Contributors
Chapter 1: Quantification Methods for Methylation Levels in Illumina Arrays
1 Introduction
2 Materials
2.1 Obesity Data Set
2.2 Monte Carlo Simulation Settings
3 Methods
3.1 β-Value
3.2 M-Value
3.3 N-Value
3.4 Comparison of Three Methylation Quantification Methods
3.4.1 Features of Signal Intensities
3.4.2 Distributions of Measures of Methylation Level
3.4.3 Identification of Differential Methylation by Three Methods
3.4.4 Simulation Study
4 Notes
References
Chapter 2: Evaluating Reliability of DNA Methylation Measurement
1 Introduction
2 ICC Estimation and Modeling
3 Real Data Analysis Example
3.1 Replicates from Methylation Chips of Same Model
3.2 Replicates from Different Methylation Arrays
4 Summary and Other Remarks
References
Chapter 3: Accurate Measurement of DNA Methylation: Challenges and Bias Correction
1 Introduction
1.1 Incomplete Bisulfite Conversion
1.2 PCR Bias
1.3 Region-Specific Bias
1.4 Coverage Bias
2 Materials
3 Methods
3.1 Statistical Methods to Correct Methylation Levels
3.2 Model Outline
3.3 Calibration Experiment with Technical Replicates
3.4 Corrected Methylation Degree
3.5 Real Case Applications
3.5.1 Application in Clinical Diagnostic of Celiac Patients
3.5.2 Application in Clinical Diagnostic: Mosaic Beckwith-Wiedemann Syndrome
4 Notes
5 Conclusions
References
Chapter 4: Using R for Cell-Type Composition Imputation in Epigenome-Wide Association Studies
1 Introduction
2 Reference-Based Methods
3 Reference-Free Methods
3.1 ReFACTor
3.2 SVA
4 Discussion
References
Chapter 5: Cell Type-Specific Signal Analysis in Epigenome-Wide Association Studies
1 Introduction: Functional Overlap Analysis, FORGE and eFORGE
2 Materials
3 Methods
3.1 Functional Overlap Analysis and EWAS: The Design of eFORGE
3.2 Cell Type-Specific and Cell Type-Confounding Effects in eFORGE
4 Specific Notes on eFORGE Analysis
4.1 Number of Probes
4.2 Separating Hypo- and Hypermethylated Sites
4.3 Tissue-Specific, General, and Mixed Signals
4.3.1 Tissue-Specific eFORGE Results
4.3.2 General Enrichment Results
4.3.3 Mixed Enrichment
4.4 Choosing Different Datasets to Analyze in eFORGE
4.5 False-Positive Rate
4.6 Reproducibility of eFORGE Results
4.7 Evaluating Enrichments Across Replicate Samples
4.8 Number of Background Repetitions
4.9 Enrichment or Depletion
4.10 Significance Threshold
4.11 Analysis Label
4.12 Broad View and Future Directions
References
Chapter 6: Controlling Batch Effect in Epigenome-Wide Association Study
1 Introduction
1.1 Adjusting Raw Methylation Data Matrix
1.2 Adjusting Model as Included Covariates
2 Methods
2.1 COMBAT: Empirical Bayes Method
2.2 SVA: Surrogate Variable Analysis
2.3 Control Probes
2.4 LMM: Linear-Mixed Effect Model
2.5 PCA
3 Discussion
4 Notes
References
Chapter 7: DNA Methylation and Atopic Diseases
1 Introduction
2 Cross-Sectional Studies
2.1 EWAS on Total Serum IgE Levels
2.2 EWAS on Atopic Asthma
3 Birth Cohort and Longitudinal Studies
3.1 Birth Cohort Studies
3.2 Longitudinal Prospective Studies
4 Tissue Type and Methylation Profiles
5 Discussion
6 Conclusion
References
Chapter 8: Meta-Analysis for Epigenome-Wide Association Studies
1 Introduction
2 Methods
2.1 Fixed-Effects Model
2.2 Random-Effects Model
3 Application
3.1 Data
3.2 Install metafor Package
3.3 Load metafor Package and Import Datasets into R
3.4 Conduct Meta-Analysis
3.5 Calculate Inflation Factor
3.6 Quantile-Quantile (QQ) Plot
3.7 Heterogeneity Analysis
4 Discussion
References
Chapter 9: Increase the Power of Epigenome-Wide Association Testing Using ICC-Based Hypothesis Weighting
1 Introduction
2 Materials
3 Methods
3.1 ICC Calculation
3.2 Surrogate Variable Analysis
3.3 Association Testing
3.4 p-Value Adjusting by IHW
3.5 Diagnostic Plots for ICC-Based p-Value Adjusting
4 Notes
References
Chapter 10: A Review of High-Dimensional Mediation Analyses in DNA Methylation Studies
1 Introduction
2 Single Mediator Model
3 Multiple Mediators Model
4 High-Dimensional Mediators Model
4.1 Continuous Outcome
4.2 Binary Outcome
5 Applications
5.1 Normative Aging Study
5.2 Epithelial Ovarian Cancer
6 Concluding Remarks
References
Chapter 11: DNA Methylation Imputation Across Platforms
1 Introduction
2 Materials
3 Methods
3.1 Data Harmonization Via Local Smoothing Among Training Samples
3.2 Penalized Functional Regression (PFR) Model
3.2.1 Estimation of Xi(t)
3.2.2 Estimation of β(t)
3.2.3 Selection of Tuning Parameters
3.2.4 Selection of Local Covariates Z
3.3 Post-imputation Quality Filter
3.4 Imputation Quality Assessment
3.4.1 Cross Validation
3.4.2 Quality Measures
4 Method Performance
5 Future Directions
References
Chapter 12: Workflow to Mine Frequent DNA Co-methylation Clusters in DNA Methylome Data
1 Introduction
2 Materials
3 Methods
3.1 Data Acquisition
3.2 Data Pre-processing
3.3 Individual DNA Co-methylation Cluster (DCMC) Mining
3.4 Cluster Comparison and Visualization (Optional)
3.5 Calculate ``Eigengene´´ Using Singular Value Decomposition for Every Cluster Mined from Individual Dataset
4 Example Study with This Workflow
5 Discussion
References
Chapter 13: BCurve: Bayesian Curve Credible Bands Approach for the Detection of Differentially Methylated Regions
1 Introduction
2 The BCurve Methodology
3 The BCurve Package Implementation and Sample Analysis
3.1 Implementation
3.2 Simulation and Analysis of BS-Seq Data
3.2.1 Simulating BS-Seq Dataset
3.2.2 Input Data for bcurve
3.2.3 Numerical and Graphical Summaries of DMR/DMC Results
3.3 Simulation and Analysis of Microarray Data
3.3.1 Simulating Microarray Dataset
3.3.2 Analysis and Inference of Microarray Data
4 Two Real Data Analysis Examples
4.1 Analysis of a Mouse Brain BS-seq Dataset
4.2 Analysis of the TCGA LUAD Microarray Data
5 Summary and Other Remarks
5.1 Software Availability
References
Chapter 14: Predicting Chronological Age from DNA Methylation Data: A Machine Learning Approach for Small Datasets and Limited...
1 Introduction
2 Materials
2.1 Dataset
2.2 Software
3 Data Preprocessing
3.1 Calculating the β-Values
3.2 Transformation to M-Values
3.3 Centering the Dataset
3.4 Importing the Dataset in R
4 Marker Selection
4.1 Forward Selection and Backward Elimination
4.1.1 Forward Selection
4.1.2 Backward Elimination
4.2 Boruta
4.3 Genetic Algorithm
4.4 Regression
5 Prediction Modeling
5.1 Splitting the Data
5.2 Multiple Linear Regression
5.3 Support Vector Machine with Polynomial Function
6 Notes
References
Chapter 15: Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction
1 Introduction
1.1 Source Code Repository
1.2 Getting Started
2 Materials
3 Method
3.1 Build CPFNN
3.1.1 Training Data Preparation
3.1.2 Model Building
3.1.3 Grid Search for Optimal Parameters
3.2 CPFNN User Instruction
3.2.1 Data Parsing
3.2.2 Age Prediction
3.3 Potential Use of CPFNN
3.3.1 Age Acceleration Detection
3.3.2 Unknown Age Prediction
4 Discussion
References
Chapter 16: Differential Methylation Analysis for Bisulfite Sequencing (BS-Seq) Data
1 Introduction
2 Materials
2.1 Data
2.2 Software
3 Methods
3.1 Download Data
3.2 Install R Language
3.3 Install DSS and Dependency Packages
3.4 Set Working Directory and Load Software
3.5 Read in the Data
3.6 Conduct DM Analysis
3.6.1 Regular Two-Group DM Analysis
3.6.2 Two-Group DM Analysis Without Replicates
3.6.3 Multifactor Design Comparisons
4 Notes
References
Index