The genetic architecture of repeated local adaptation to climate in distantly related plants

Closely related species often use the same genes to adapt to similar environments. However, we know little about why such genes possess increased adaptive potential and whether this is conserved across deeper evolutionary lineages. Adaptation to climate presents a natural laboratory to test these ideas, as even distantly related species must contend with similar stresses. Here, we re-analyse genomic data from thousands of individuals from 25 plant species as diverged as lodgepole pine and Arabidopsis (~300 Myr). We test for genetic repeatability based on within-species associations between allele frequencies in genes and variation in 21 climate variables. Our results demonstrate significant statistical evidence for genetic repeatability across deep time that is not expected under randomness, identifying a suite of 108 gene families (orthogroups) and gene functions that repeatedly drive local adaptation to climate. This set includes many orthogroups with well-known functions in abiotic stress response. Using gene co-expression networks to quantify pleiotropy, we find that orthogroups with stronger evidence for repeatability exhibit greater network centrality and broader expression across tissues (higher pleiotropy), contrary to the 'cost of complexity' theory. These gene families may be important in helping wild and crop species cope with future climate change, representing important candidates for future study.

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Publication Details

Journal
Nature Ecology & Evolution
Published
2024-08-26
DOI
https://doi.org/10.1038/s41559-024-02514-5
Citations
37
Primary Topic
Genetic Mapping and Diversity in Plants and Animals
Type
article
Field-Weighted Citation Impact
20.36

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article

The genetic architecture of repeated local adaptation to climate in distantly related plants

Momena Khandaker, Detlef Weigel, Yvonne Willi, Pascal Milesi et al.
37 citations
Nature Ecology & Evolution
Genetic Mapping and Diversity in Plants and Animals
20.36
article

The genetic architecture of repeated local adaptation to climate in distantly related plants

Momena Khandaker, Detlef Weigel, Yvonne Willi, Pascal Milesi, Clément Rougeux, Pär K. Ingvarsson, Jason A. Holliday, Thibault Leroy, Jaakko Tyrmi, James R. Whiting, Mengmeng Lu, Julia M. Kreiner, Kathryn A. Hodgins, John R. Stinchcombe, Martin C. Fischer, Timothy L. Collins, Stephen Wright, Daniel Koenig, Ian Telford, Tanja Pyhäjärvi, Martin Lascoux, Pooja Singh, Jasmine K. Janes, Fabrice Roux, Antoine Kremer, Jeremy J. Bruhl, Tom R. Booker, Rose L. Andrew, Michael C. Whitlock, Sally N. Aitken, Kevin Murray, Christian Rellstab, Justin Borevitz, Brandon M. Lind, Marco Todesco, Loren H. Rieseberg, Kaichi Huang, Sam Yeaman, Baosheng Wang, Lecong Zhou
article en
37 citations

Abstract

Closely related species often use the same genes to adapt to similar environments. However, we know little about why such genes possess increased adaptive potential and whether this is conserved across deeper evolutionary lineages. Adaptation to climate presents a natural laboratory to test these ideas, as even distantly related species must contend with similar stresses. Here, we re-analyse genomic data from thousands of individuals from 25 plant species as diverged as lodgepole pine and Arabidopsis (~300 Myr). We test for genetic repeatability based on within-species associations between allele frequencies in genes and variation in 21 climate variables. Our results demonstrate significant statistical evidence for genetic repeatability across deep time that is not expected under randomness, identifying a suite of 108 gene families (orthogroups) and gene functions that repeatedly drive local adaptation to climate. This set includes many orthogroups with well-known functions in abiotic stress response. Using gene co-expression networks to quantify pleiotropy, we find that orthogroups with stronger evidence for repeatability exhibit greater network centrality and broader expression across tissues (higher pleiotropy), contrary to the 'cost of complexity' theory. These gene families may be important in helping wild and crop species cope with future climate change, representing important candidates for future study.

Nature Ecology & EvolutionVol. 8(10)
University of California, Riverside (US), University of Notre Dame (US), Australian National University (AU), University of Bern (CH), Uppsala University (SE), CEA CESTA (FR), Centre National de la Recherche Scientifique (FR), NSW Department of Planning and Environment (AU), University of Helsinki (FI), University of British Columbia (CA), Université de Bordeaux (FR), University of Northern British Columbia (CA), University of Calgary (CA), École Nationale Vétérinaire de Toulouse (FR), Université Fédérale de Toulouse Midi-Pyrénées (FR), University of Basel (CH), University of Toronto (CA), Science for Life Laboratory (SE), Swiss Federal Institute for Forest, Snow and Landscape Research (CH), Vancouver Island University (CA), Swedish University of Agricultural Sciences (SE), ETH Zurich (CH), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), International Union for Conservation of Nature (United States) (US), Laboratoire des Interactions Plantes Microbes Environnement (FR), South China Botanical Garden (CN), Canada's Michael Smith Genome Sciences Centre (CA), UMR BIOdiversity, GEnes & Communities (FR), Génétique Physiologie et Systèmes d'Elevage (FR), International Union for Conservation of Nature (CH), Max Planck Institute for Biology (DE), Department of Climate Change, Energy, the Environment and Water (AU), China National Botanical Garden, University of British Columbia, Okanagan Campus (CA), Monash University (AU), Swiss Federal Institute of Aquatic Science and Technology (CH), Virginia Tech (US), University of New England (AU), University of Oulu (FI)
Alberta Innovates - Health Solutions, Natural Sciences and Engineering Research Council of Canada
Climate action
Openalex Percentile: Top 0%
Genetic Mapping and Diversity in Plants and Animals
20.36
DOIOpen Access PDFOpenAlex
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