Learning epistatic gene interactions from perturbation screens.

The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fas...

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Autores principales:	Kieran Elmes, Fabian Schmich, Ewa Szczurek, Jeremy Jenkins, Niko Beerenwinkel, Alex Gavryushkin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/902eddb853e84651a7a7ce1e81fa053f
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id	oai:doaj.org-article:902eddb853e84651a7a7ce1e81fa053f
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spelling	oai:doaj.org-article:902eddb853e84651a7a7ce1e81fa053f2021-12-02T20:15:24ZLearning epistatic gene interactions from perturbation screens.1932-620310.1371/journal.pone.0254491https://doaj.org/article/902eddb853e84651a7a7ce1e81fa053f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254491https://doaj.org/toc/1932-6203The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.Kieran ElmesFabian SchmichEwa SzczurekJeremy JenkinsNiko BeerenwinkelAlex GavryushkinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254491 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kieran Elmes Fabian Schmich Ewa Szczurek Jeremy Jenkins Niko Beerenwinkel Alex Gavryushkin Learning epistatic gene interactions from perturbation screens.
description	The treatment of complex diseases often relies on combinatorial therapy, a strategy where drugs are used to target multiple genes simultaneously. Promising candidate genes for combinatorial perturbation often constitute epistatic genes, i.e., genes which contribute to a phenotype in a non-linear fashion. Experimental identification of the full landscape of genetic interactions by perturbing all gene combinations is prohibitive due to the exponential growth of testable hypotheses. Here we present a model for the inference of pairwise epistatic, including synthetic lethal, gene interactions from siRNA-based perturbation screens. The model exploits the combinatorial nature of siRNA-based screens resulting from the high numbers of sequence-dependent off-target effects, where each siRNA apart from its intended target knocks down hundreds of additional genes. We show that conditional and marginal epistasis can be estimated as interaction coefficients of regression models on perturbation data. We compare two methods, namely glinternet and xyz, for selecting non-zero effects in high dimensions as components of the model, and make recommendations for the appropriate use of each. For data simulated from real RNAi screening libraries, we show that glinternet successfully identifies epistatic gene pairs with high accuracy across a wide range of relevant parameters for the signal-to-noise ratio of observed phenotypes, the effect size of epistasis and the number of observations per double knockdown. xyz is also able to identify interactions from lower dimensional data sets (fewer genes), but is less accurate for many dimensions. Higher accuracy of glinternet, however, comes at the cost of longer running time compared to xyz. The general model is widely applicable and allows mining the wealth of publicly available RNAi screening data for the estimation of epistatic interactions between genes. As a proof of concept, we apply the model to search for interactions, and potential targets for treatment, among previously published sets of siRNA perturbation screens on various pathogens. The identified interactions include both known epistatic interactions as well as novel findings.
format	article
author	Kieran Elmes Fabian Schmich Ewa Szczurek Jeremy Jenkins Niko Beerenwinkel Alex Gavryushkin
author_facet	Kieran Elmes Fabian Schmich Ewa Szczurek Jeremy Jenkins Niko Beerenwinkel Alex Gavryushkin
author_sort	Kieran Elmes
title	Learning epistatic gene interactions from perturbation screens.
title_short	Learning epistatic gene interactions from perturbation screens.
title_full	Learning epistatic gene interactions from perturbation screens.
title_fullStr	Learning epistatic gene interactions from perturbation screens.
title_full_unstemmed	Learning epistatic gene interactions from perturbation screens.
title_sort	learning epistatic gene interactions from perturbation screens.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/902eddb853e84651a7a7ce1e81fa053f
work_keys_str_mv	AT kieranelmes learningepistaticgeneinteractionsfromperturbationscreens AT fabianschmich learningepistaticgeneinteractionsfromperturbationscreens AT ewaszczurek learningepistaticgeneinteractionsfromperturbationscreens AT jeremyjenkins learningepistaticgeneinteractionsfromperturbationscreens AT nikobeerenwinkel learningepistaticgeneinteractionsfromperturbationscreens AT alexgavryushkin learningepistaticgeneinteractionsfromperturbationscreens
_version_	1718374594863169536

Learning epistatic gene interactions from perturbation screens.

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