Ka-Chun Wong (ed.) Epistasis: Methods and Protocols

Humana, 2021. — 398 p.This volume explores methods and protocols for detecting epistasis from genetic data. Chapters provide methods and protocols demonstrating approaches to identify epistasis, genetic epistasis testing, genome-wide epistatic SNP networks, epistasis detection through machine learning, and complex interaction analysis using trigenic synthetic genetic array (τ-SGA). Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls.
Authoritative and cutting-edge, Epistasis: Methods and Protocols aims to ensure successful results in the further study of this vital field.Preface
Contributors
Mass-Based Protein Phylogenetic Approach to Identify Epistasis
SNPInt-GPU: Tool for Epistasis Testing with Multiple Methods and GPU Acceleration
Epistasis-Based Feature Selection Algorithm
W-Test for Genetic Epistasis Testing
The Combined Analysis of Pleiotropy and Epistasis (CAPE)
Two-Stage Testing for Epistasis: Screening and Verification
Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies
Phenotype Prediction Under Epistasis
Simulating Evolution in Asexual Populations with Epistasis
Protocol for Construction of Genome-Wide Epistatic SNP Networks Using WISH-R Package
Brief Survey on Machine Learning in Epistasis
First-Order Correction of Statistical Significance for Screening Two-Way Epistatic Interactions
Gene-Environment Interaction: A Variable Selection Perspective
Using C-JAMP to Investigate Epistasis and Pleiotropy
Identifying the Significant Change of Gene Expression in Genomic Series Data for Epistasis Peaks
Analyzing High-Order Epistasis from Genotype-Phenotype Maps Using `Epistasis´ Package
Deep Neural Networks for Epistatic Sequence Analysis
Protocol for Epistasis Detection with Machine Learning Using GenEpi Package
A Belief Degree-Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection
Epistasis Detection Based on Epi-GTBN
Epistasis Analysis: Classification Through Machine Learning Methods
Genetic Interaction Network Interpretation: A Tidy Data Science Perspective
Trigenic Synthetic Genetic Array (τ-SGA) Technique for Complex Interaction Analysis
Index