@article{3c34af58aedb4815900bb29f85cacf5c,
title = "Fitness variation across subtle environmental perturbations reveals local modularity and global pleiotropy of adaptation",
abstract = "Building a genotype-phenotype-fitness map of adaptation is a central goal in evolutionary biology. It is difficult even when adaptive mutations are known because it is hard to enumerate which phenotypes make these mutations adaptive. We address this problem by first quantifying how the fitness of hundreds of adaptive yeast mutants responds to subtle environmental shifts. We then model the number of phenotypes these mutations collectively influence by phenotypes can predict fitness of the adaptive mutations near their original glucose-limited evolution condition. Importantly, inferred phenotypes that matter little to fitness at or near the decomposing these patterns of fitness variation. We find that a small number of inferred evolution condition can matter strongly in distant environments. This suggests that adaptive mutations are locally modular—affecting a small number of phenotypes that matter to fitness in the environment where they evolved—yet globally pleiotropic—affecting additional phenotypes that may reduce or improve fitness in new environments.",
author = "Grant Kinsler and Kerry Geiler-Samerotte and Dmitri Petrov",
note = "Funding Information: The authors thank Sandeep Venkataram for the BarcodeCounter2 script; Yuping Li, Monica Sanchez, Tuya Yokoyama, Chris McFarland, Grace Lam, Ellie Armstrong, and Dimitra Aggeli for technical sasistance; Atish gAarwala, Marc aSlit, Sasha eLvy, Gavin hSerlock, Ben oGod, IvanavCijovic, DavidoGkhman, EmilybEel, SimoneLvin, MollycShumer, JankSotheim, Moises Exposito-Alonso, Mikhail Tikhonov, Hunter Fraser, Michael Desai and all members of the ePtrov nad eGiler-Samerotte Labs for helpful comments and discussions. We are grateful to the twitter communityatt fohllowed1#BigBatchnadrpvoidedsuiwtverhy hpfulefeedback.l We are grateful to Enrico Coen for very helpful discussions and specifically for the suggestion of the term t“ontyfpie”. Some fo e tocmhputngi rfo is trphjeoct asweprrfeomd noethSeroclk lucster. We would like to thank Stanford University and the Stanford Research Computing Center for providing computational resources and support that contributed to these research results. This work was supported by National Institutes of Health grant R35GM118165 (to DAP) and National Institutes foHalthegrant R35GM133674 (to KGS). Publisher Copyright: {\textcopyright} 2020, eLife Sciences Publications Ltd. All rights reserved.",
year = "2020",
month = dec,
doi = "10.7554/ELIFE.61271",
language = "English (US)",
volume = "9",
pages = "1--52",
journal = "eLife",
issn = "2050-084X",
publisher = "eLife Sciences Publications",
}