Epistasis

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 (-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.

"Simulating Evolution in Asexual Populations with Epistasis is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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

Inhalt
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 Veri_cation.- 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: AVariable Selection Perspective.- Using C-JAMP to Investigate Epistasis and Pleiotropy.- Identifying the Significant Change of Gene Expression in Genomic Series Data.- Analyzing High-Order Epistasis from Genotype-phenotype Maps Using 'Epistasis' Package.- Deep Neural Networks for Epistatic Sequences 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 (-SGA) Technique for Complex Interaction Analysis.