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. 2018 Sep 17;9:3548. doi: 10.1038/s41467-018-06023-5

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — Analysis of mutation-responsive effects across knockout backgrounds. In a, the number of mutation-responsive effects that interacted with only one knockout (pink) or interacted with multiple knockouts (blue) are shown for each knockout. In b, the phenotypic variance explained (PVE) for each mutation-responsive effect is shown in the relevant knockout (KO) segregants, as well as in the wild-type (WT) segregants. The PVE for each mutation-responsive effect was determined using fixed-effects linear models fit within each individual background (Methods). Mutation-responsive effects are color-coded by the knockout population in which they were identified. In c, the percentage of mutation-responsive effects that showed larger phenotypic effects in mutants than in wild-type segregants (y axis, left side) and mutation-responsive effects that showed larger phenotypic effects in wild-type segregants than in mutants (y axis, right side) is depicted. These values are plotted as a function of the number of knockouts that interact with a given mutation-responsive effect. Error bars represent 95% bootstrap confidence intervals (Methods)