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Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past

Cortés, Andrés J. and López-Hernández, Felipe and Osorio-Rodriguez, Daniela (2020) Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past. Frontiers in Genetics, 11 . Art. No. 564515. ISSN 1664-8021.

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Molecular evolution offers an insightful theory to interpret the genomic consequences of thermal adaptation to previous events of climate change beyond range shifts. However, disentangling often mixed footprints of selective and demographic processes from those due to lineage sorting, recombination rate variation, and genomic constrains is not trivial. Therefore, here we condense current and historical population genomic tools to study thermal adaptation and outline key developments (genomic prediction, machine learning) that might assist their utilization for improving forecasts of populations’ responses to thermal variation. We start by summarizing how recent thermal-driven selective and demographic responses can be inferred by coalescent methods and in turn how quantitative genetic theory offers suitable multi-trait predictions over a few generations via the breeder’s equation. We later assume that enough generations have passed as to display genomic signatures of divergent selection to thermal variation and describe how these footprints can be reconstructed using genome-wide association and selection scans or, alternatively, may be used for forward prediction over multiple generations under an infinitesimal genomic prediction model. Finally, we move deeper in time to comprehend the genomic consequences of thermal shifts at an evolutionary time scale by relying on phylogeographic approaches that allow for reticulate evolution and ecological parapatric speciation, and end by envisioning the potential of modern machine learning techniques to better inform long-term predictions. We conclude that foreseeing future thermal adaptive responses requires bridging the multiple spatial scales of historical and predictive environmental change research under modern cohesive approaches such as genomic prediction and machine learning frameworks.

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Osorio-Rodriguez, Daniela0000-0001-6676-4124
Additional Information:© 2020 Cortés, López-Hernández and Osorio-Rodriguez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 21 May 2020; Accepted: 24 August 2020; Published: 25 September 2020. We acknowledge thoughtful discussions with M. W. Blair regarding the genetic basis of thermal adaptation that took place with AC and DO-R during the Erasmus funded workshop “Molecular Breeding for Abiotic Constraints in Plants” held in Montpellier (France) during the summer of 2012. Some of the ideas discussed here were also framed into perspective, thanks to suggestions from A. A. Hoffmann to AC as part of the “Climate Change and Evolution” symposium during the XIV Congress of the European Society for Evolutionary Biology (ESEB) held at Lisbon (Portugal) in August 2013. The Evolutionary Biology Centre (EBC) Graduate School on Genomes and Phenotypes from Uppsala University is recognized for promoting AC participation in this meeting. AGROSAVIA’s Department for Research Capacity Building is credited for granting time to AC to carry out synergistic discussions and progress meetings during 2016 and 2017 in order to pursue this mini-review, as well as for sponsoring FL-H’s internship during 2018. We thank D. Royer for the Cenozoic temperature, CO2, and species richness dataset. Special thanks are given to M. J. Torres-Urrego for support while drafting and revising this mini-review. The topic editor and the two reviewers are recognized for their thoughtful suggestions to improve the scope of the mini-review, as well as for making possible the insightful special issue on “Coping with Climate Change: A Genomic Perspective on Thermal Adaptation.” AC was supported by grants 4.1-2016-00418 and BS2017-0036 from Vetenskapsrådet (VR) and Kungliga Vetenskapsakademien (KVA), respectively. The National Science Foundation (NSF) and the SIMONS Collaboration on the Origins of Life support DO-R. The editorial fund from the Colombian Corporation for Agricultural Research (AGROSAVIA) was thanked for subsidizing the mini-review B-type processing charge. Author Contributions. AC conceived this mini-review. FL-H collected the literature and prepared diagrams. DO-R compiled the historical climate data. AC wrote the first draft of the mini-review with further contributions from FL-H and DO-R. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Edited by: Margarida Matos, University of Lisbon, Portugal. Reviewed by: Anti Vasemägi, Swedish University of Agricultural Sciences, Sweden. Takeshi Kawakami, Independent Researcher, Boston, United States.
Funding AgencyGrant Number
Swedish Research Council4.1-2016-00418
Royal Swedish Academy of SciencesBS2017-0036
Simons FoundationUNSPECIFIED
Colombian Corporation for Agricultural ResearchUNSPECIFIED
Subject Keywords:coalescent theory, genome-wide association studies, genome-wide selection scans, genome–environment associations, phylogeography, breeder’s equation, genomic prediction, machine learning
Record Number:CaltechAUTHORS:20201022-112713886
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Official Citation:Cortés AJ, López-Hernández F and Osorio-Rodriguez D (2020) Predicting Thermal Adaptation by Looking Into Populations’ Genomic Past. Front. Genet. 11:564515. doi: 10.3389/fgene.2020.564515
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106232
Deposited By: George Porter
Deposited On:22 Oct 2020 21:03
Last Modified:22 Oct 2020 21:03

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