Standing genetic variation drives polygenic adaptation to different environmental shifts
- 1Institute for Plant Sciences, University of Cologne, Cologne, Germany
Abstract
Most organisms are well adapted to the environment they have been exposed to for many generations. However, changing environments pose existential threats to populations, unless they are able to adapt. Hence, a central challenge in evolutionary genetics is to understand the adaptation to complex environmental changes. As most traits are controlled by a large number of loci with varying effects on a trait, it is challenging to understand the explicit role of genetic changes during such polygenic adaptation. We use forward-in-time simulations to investigate the evolutionary dynamics of phenotypic and genetic adaptation in populations facing different environmental shifts. Specifically, we simulated sudden, gradual, and fluctuating environmental shifts for multiple trait architectures. Our results show distinct evolutionary paths across different types of environmental shifts and a higher extinction risk during sudden and fluctuating environmental shifts than during gradual shifts. Comparing the contribution of mutations from different sources highlights the critical role of standing genetic variation in driving phenotypic adaptation. We summarize allele frequency trajectories by clustering them by their temporal pattern and reveal how large- and small-effect mutations jointly shape the successful adaptation to changing environments. Additionally, we trained a convolutional neural network (CNN) on "genetic architecture matrices" of populations to jointly infer the type and magnitude of environmental changes and the mutational effect size distribution. The CNN was able to predict all three input parameters with very high accuracy, even on unseen parameter combinations. Our results demonstrate the impact of ecological change on the evolutionary outcome and the assorted mutational changes that enable successful adaptation.
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BibTeX
@misc{zhang2026standing,
title = {{Standing genetic variation drives polygenic adaptation to different environmental shifts}},
author = {Y. Zhang and M. G. Stetter},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.14.751342},
url = {https://www.biorxiv.org/content/10.64898/2026.09.14.751342v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Standing genetic variation drives polygenic adaptation to different environmental shifts
AU - Y. Zhang
AU - M. G. Stetter
PY - 2026
DA - 2026/09/14
PB - bioRxiv
DO - 10.64898/2026.09.14.751342
UR - https://www.biorxiv.org/content/10.64898/2026.09.14.751342v1
AB - Most organisms are well adapted to the environment they have been exposed to for many generations. However, changing environments pose existential threats to populations, unless they are able to adapt. Hence, a central challenge in evolutionary genetics is to understand the adaptation to complex environmental changes. As most traits are controlled by a large number of loci with varying effects on a trait, it is challenging to understand the explicit role of genetic changes during such polygenic adaptation. We use forward-in-time simulations to investigate the evolutionary dynamics of phenotypic and genetic adaptation in populations facing different environmental shifts. Specifically, we simulated sudden, gradual, and fluctuating environmental shifts for multiple trait architectures. Our results show distinct evolutionary paths across different types of environmental shifts and a higher extinction risk during sudden and fluctuating environmental shifts than during gradual shifts. Comparing the contribution of mutations from different sources highlights the critical role of standing genetic variation in driving phenotypic adaptation. We summarize allele frequency trajectories by clustering them by their temporal pattern and reveal how large- and small-effect mutations jointly shape the successful adaptation to changing environments. Additionally, we trained a convolutional neural network (CNN) on "genetic architecture matrices" of populations to jointly infer the type and magnitude of environmental changes and the mutational effect size distribution. The CNN was able to predict all three input parameters with very high accuracy, even on unseen parameter combinations. Our results demonstrate the impact of ecological change on the evolutionary outcome and the assorted mutational changes that enable successful adaptation.
ER -