Protocol for Epistasis Detection with Machine Learning Using GenEpi Package

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)12_Chapter in an edited book (Author)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationEpistasis
Subtitle of host publicationMethods and Protocols
EditorsKa-Chun Wong
PublisherHumana Press
Pages291-305
ISBN (Electronic)9781071609477
ISBN (Print)9781071609491, 9781071609460
Publication statusPublished - 2021

Publication series

NameMethods in Molecular Biology
Volume2212
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Abstract

To develop medical treatments and prevention, the association between disease and genetic variants needs to be identified. The main goal of genome-wide association study (GWAS) is to discover the underlying reason for vulnerability to disease and utilize this knowledge for the development of prevention and treatment against these diseases. Given the methods available to address the scientific problems involved in the search for epistasis, there is not any standard for detecting epistasis, and this remains a problem due to limited statistical power. The GenEpi package is a Python package that uses a two-level workflow machine learning model to detect within-gene and cross-gene epistasis. This protocol chapter shows the usage of GenEpi with example data. The package uses a three-step procedure to reduce dimensionality, select the within-gene epistasis, and select the cross-gene epistasis. The package also provides a medium to build prediction models with the combination of genetic features and environmental influences.

Research Area(s)

  • Epistasis, GenEpi, Genotype, Phenotype, SNP

Citation Format(s)

Protocol for Epistasis Detection with Machine Learning Using GenEpi Package. / Petinrin, Olutomilayo Olayemi; Wong, Ka-Chun.

Epistasis: Methods and Protocols. ed. / Ka-Chun Wong. Humana Press, 2021. p. 291-305 (Methods in Molecular Biology; Vol. 2212).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)12_Chapter in an edited book (Author)peer-review