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.

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.