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.
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Acknowledgments
The work described in this paper was substantially supported by two grants from the Research Grants Council of the Hong Kong Special Administrative Region ([CityU 11203217] and [CityU 11200218]) and the funding from the Hong Kong Institute for Data Science (HKIDS) at the City University of Hong Kong. The work described in this paper was partially supported by a grant from the City University of Hong Kong (CityU 11202219).
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Petinrin, O.O., Wong, KC. (2021). Protocol for Epistasis Detection with Machine Learning Using GenEpi 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_18
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DOI: https://doi.org/10.1007/978-1-0716-0947-7_18
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