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Epistasis

Methods and Protocols

  • Book
  • © 2021

Overview

Editors:
  1. Ka-Chun Wong

    1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

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

Part of the book series: Methods in Molecular Biology (MIMB, volume 2212)

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Table of contents (23 protocols)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Mass-Based Protein Phylogenetic Approach to Identify Epistasis

    • Kevin M. Downard
    Pages 1-15

  3. SNPInt-GPU: Tool for Epistasis Testing with Multiple Methods and GPU Acceleration

    • Lars Wienbrandt, Jan Christian Kässens, David Ellinghaus
    Pages 17-35

  4. Epistasis-Based Feature Selection Algorithm

    • Lauro Cássio Martins de Paula
    Pages 37-44

  5. W-Test for Genetic Epistasis Testing

    • Rui Sun, Haoyi Weng, Maggie Haitian Wang
    Pages 45-53

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

    • Anna L. Tyler, Jake Emerson, Baha El Kassaby, Ann E. Wells, Vivek M. Philip, Gregory W. Carter
    Pages 55-67

  7. Two-Stage Testing for Epistasis: Screening and Verification

    • Jakub Pecanka, Marianne A. Jonker
    Pages 69-92

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

    • Xingjie Shi, Can Yang, Jin Liu
    Pages 93-103

  9. Phenotype Prediction Under Epistasis

    • Elaheh Vojgani, Torsten Pook, Henner Simianer
    Pages 105-120

  10. Simulating Evolution in Asexual Populations with Epistasis

    • Ramon Diaz-Uriarte
    Pages 121-154Open Access

  11. Protocol for Construction of Genome-Wide Epistatic SNP Networks Using WISH-R Package

    • Haja N. Kadarmideen, Victor Adriano Okstoft Carmelo
    Pages 155-168

  12. Brief Survey on Machine Learning in Epistasis

    • Davide Chicco, Trent Faultless
    Pages 169-179

  13. First-Order Correction of Statistical Significance for Screening Two-Way Epistatic Interactions

    • Lu Cheng, Mu Zhu
    Pages 181-190

  14. Gene–Environment Interaction: A Variable Selection Perspective

    • Fei Zhou, Jie Ren, Xi Lu, Shuangge Ma, Cen Wu
    Pages 191-223

  15. Using C-JAMP to Investigate Epistasis and Pleiotropy

    • Stefan Konigorski, Benjamin S. Glicksberg
    Pages 225-243

  16. Identifying the Significant Change of Gene Expression in Genomic Series Data for Epistasis Peaks

    • Hiu-Hin Tam
    Pages 245-264

  17. Analyzing High-Order Epistasis from Genotype-Phenotype Maps Using ‘Epistasis’ Package

    • Junyi Chen, Ka-Chun Wong
    Pages 265-275

  18. Deep Neural Networks for Epistatic Sequence Analysis

    • Jiecong Lin
    Pages 277-289

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

    • Olutomilayo Olayemi Petinrin, Ka-Chun Wong
    Pages 291-305

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

    • Saifur Rahaman, Ka-Chun Wong
    Pages 307-323
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Keywords

About this book

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.

 

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

Editors and Affiliations

  • Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Ka-Chun Wong

Bibliographic Information

  • Book Title: Epistasis

  • Book Subtitle: Methods and Protocols

  • Editors: Ka-Chun Wong

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-0947-7

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2021

  • Hardcover ISBN: 978-1-0716-0946-0Published: 18 March 2021

  • Softcover ISBN: 978-1-0716-0949-1Published: 19 March 2022

  • eBook ISBN: 978-1-0716-0947-7Published: 17 March 2021

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: X, 402

  • Number of Illustrations: 82 b/w illustrations, 85 illustrations in colour

  • Topics: Human Genetics

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