Main description:

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.

Contents:

1. Mass-based Protein Phylogenetic Approach to Identify Epistasis

Kevin M. Downard

2. SNPInt-GPU: Tool for epistasis testing with multiple methods and GPU acceleration

Lars Wienbrandt, Jan Christian Kassens, and David Ellinghaus

3. Epistasis-based Feature Selection Algorithm

Lauro Cassio Martins de Paula

4. W-test for Genetic Epistasis Testing

Rui Sun, Haoyi Weng, and Maggie Haitian Wang

5. The Combined Analysis of Pleiotropy and Epistasis (CAPE)

Anna L. Tyler, Jake Emerson, Baha El Kassaby, Ann E. Wells, Vivek M. Philip, and Gregory W. Carter

6. Two-Stage Testing for Epistasis: Screening and Veri_cation

Jakub Pecanka and Marianne A. Jonker

7. Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies

Xingjie Shi, Can Yang, and Jin Liu

8. Phenotype Prediction under Epistasis

Elaheh Vojgani, Torsten Pook, and Henner Simianer

9. Simulating Evolution in Asexual Populations with Epistasis

Ramon Diaz-Uriarte

10. Protocol for Construction of Genome-Wide Epistatic SNP Networks using WISH-R Package

Haja N. Kadarmideen and Victor AO. Carmelo

11. Brief survey on Machine Learning in Epistasis

Davide Chicco and Trent Faultless

12. First-Order Correction of Statistical Significance

for Screening Two-Way Epistatic Interactions

Lu Cheng and Mu Zhu

13. Gene-Environment Interaction: AVariable Selection Perspective

Fei Zhou, Jie Ren, Xi Lu, Shuangge Ma, and Cen Wu

14. Using C-JAMP to Investigate Epistasis and Pleiotropy

Stefan Konigorski and Benjamin S. Glicksberg

15. Identifying the Significant Change of Gene Expression in Genomic Series Data

Hiu-Hin Tam

16. Analyzing High-Order Epistasis from Genotype-phenotype Maps Using 'Epistasis' Package

Junyi Chen and Ka-Chun Wong

17. Deep Neural Networks for Epistatic Sequences Analysis

Jiecong Lin

18. Protocol for Epistasis Detection with Machine Learning Using GenEpi Package

Olutomilayo Olayemi Petinrin,and Ka-Chun Wong

19. A Belief Degree Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection

Saifur Rahaman and Ka-Chun Wong

20. Epistasis Detection Based on Epi-GTBN

Xingjian Chen and Ka-Chun Wong

21. Epistasis Analysis: Classification through Machine Learning Methods

Linjing Liu and Ka-Chun Wong

22. Genetic Interaction Network Interpretation: A Tidy Data Science Perspective

Lulu Jiang and Hai Fang

23. Trigenic Synthetic Genetic Array ( -SGA) Technique for Complex Interaction Analysis

Elena Kuzmin, Brenda J. Andrews, and Charles Boone