Skip to main content
SpringerLink
Log in

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

  • Protocol
  • First Online:
Epistasis

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

  • 902 Accesses

Abstract

Epistasis is the phenomenon about the interactions between genes, leading to complex phenotypic effects. The interactions between three or more mutations called “high-order epistasis” aroused significant interests in recent studies. However, there are still debates for analysis of high-order epistasis due to the non-linear model complexity and statistical artifacts. A recent “epistasis” Python package was therefore developed to characterize high-order epistasis by estimating non-linear scaling for mutation effects to extract high-order epistasis using linear models. This method successfully discovered statistically significant high-order epistasis on several real genotype-phenotype maps. We provided a concise and step-by-step guide to apply the “epistasis” by reproducing the high-order epistasis discoveries on real genotype-phenotype data using the latest API of the package.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tamer YT, Gaszek IK, Abdizadeh H, Batur TA, Reynolds KA, Atilgan AR, Atilgan C, Toprak E (2019) High-order epistasis in catalytic power of dihydrofolate reductase gives rise to a rugged fitness landscape in the presence of trimethoprim selection. Mol Biol Evol 36(7):1533–1550

    Article  CAS  Google Scholar 

  2. Yang G, Anderson DW, Baier F, Dohmen E, Hong N, Carr PD, Kamerlin SCL, Jackson CJ, Bornberg-Bauer E, Tokuriki N (2019) Higher-order epistasis shapes the fitness landscape of a xenobiotic-degrading enzyme. Nat Chem Biol 15(11):1120–1128

    Article  CAS  Google Scholar 

  3. Guerrero RF, Scarpino SV, Rodrigues JV, Hartl DL, Ogbunugafor CB (2019) Proteostasis environment shapes higher-order epistasis operating on antibiotic resistance. Genetics 212(2):565–575

    Article  CAS  Google Scholar 

  4. Yokoyama S, Altun A, Jia H, Yang H, Koyama T, Faggionato D, Liu Y, Starmer WT (2015) Adaptive evolutionary paths from UV reception to sensing violet light by epistatic interactions. Sci Adv 1(8). https://doi.org/10.1126/sciadv.1500162

  5. Anderson DW, McKeown AN, Thornton JW (2015) Intermolecular epistasis shaped the function and evolution of an ancient transcription factor and its DNA binding sites. eLife 4:6

    Article  Google Scholar 

  6. Sun J, Song F, Wang J, Han G, Bai Z, Xie B, Feng X, Jia J, Duan Y, Lei H (2014) Hidden risk genes with high-order intragenic epistasis in Alzheimer’s disease. J Alzheimer’s Dis 41(4):1039–1056

    Article  CAS  Google Scholar 

  7. Hu T, Chen Y, Kiralis JW, Collins RL, Wejse C, Sirugo G, Williams SM, Moore JH (2013) An information-gain approach to detecting three-way epistatic interactions in genetic association studies. J Am Med Inf Assoc 20(4):630–636

    Article  Google Scholar 

  8. Sailer ZR, Harms MJ (2017) High-order epistasis shapes evolutionary trajectories. PLOS Comput Biol 13(5):e1005541

    Article  Google Scholar 

  9. Poelwijk FJ, Krishna V, Ranganathan R (2016) The context-dependence of mutations: a linkage of formalisms. PLoS Comput Biol 12(6):6

    Article  Google Scholar 

  10. Weinreich DM, Lan Y, Scott Wylie C, Heckendorn RB (2013) Should evolutionary geneticists worry about higher-order epistasis? Curr Opin Genet Dev 23(6):700–707

    Article  CAS  Google Scholar 

  11. Joiret M, John JMM, Gusareva ES, Van Steen K (2019) Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies. BioData Min 12(1):11

    Article  Google Scholar 

  12. Szendro IG, Schenk MF, Franke J, Krug J, De Visser JAGM (2013) Quantitative analyses of empirical fitness landscapes. J Stat Mech Theory Exp 2013(1):1

    Article  Google Scholar 

  13. Sun Y, Shang J, Liu J-X, Li S, Zheng C-H (2017) epiACO - a method for identifying epistasis based on ant Colony optimization algorithm. BioData Min 10(1):23

    Google Scholar 

  14. Hu T, Andrew AS, Karagas MR, Moore JH (2015) Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks. BioData Min 8(1):12

    Article  Google Scholar 

  15. Moore JH, Mackay TFC, Williams SM (2019) Testing the assumptions of parametric linear models: the need for biological data mining in disciplines such as human genetics 12:6

    Google Scholar 

  16. Sailer ZR, Harms MJ (2017) Detecting high-order epistasis in nonlinear genotype-phenotype maps. Genetics, 205(3):1079–1088

    Article  CAS  Google Scholar 

  17. Khan AI, Dinh DM, Schneider D, Lenski RE, Cooper TF (2011) Negative epistasis between beneficial mutations in an evolving bacterial population. Science 332(6034):1193–1196

    Article  CAS  Google Scholar 

  18. de Visser JAGM, Park S-C, Krug J (2009) Exploring the effect of sex on empirical fitness landscapes. Am Nat 174 Suppl 1:15–30

    Article  Google Scholar 

  19. Weinreich DM, Delaney NF, DePristo MA, Hartl DL (2006) Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312(5770):111–114

    Article  CAS  Google Scholar 

  20. Anaconda (2016). Anaconda Software Distribution

    Google Scholar 

  21. Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  22. Oliphant T, Millma JK (2006). A guide to NumPy

    Google Scholar 

  23. Oliphant TE (2007) Python for scientific computing. In: Computing in science and engineering.

    Google Scholar 

  24. McKinney W (2010) Data structures for statistical computing in python. In: Proceedings of the 9th python in science conference

    Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Junyi Chen & Ka-Chun Wong

Authors
  1. Junyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ka-Chun Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ka-Chun Wong .

Editor information

Editors and Affiliations

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

    Ka-Chun Wong

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Chen, J., Wong, KC. (2021). Analyzing High-Order Epistasis from Genotype-Phenotype Maps Using ‘Epistasis’ Package. In: Wong, KC. (eds) Epistasis. Methods in Molecular Biology, vol 2212. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0947-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-0947-7_16

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0946-0

  • Online ISBN: 978-1-0716-0947-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation