Abstract
The evolutionary potential of traits is governed by the amount of heritable variation available to selection. While this is typically quantified based on genetic variation in a focal individual for its own traits (direct genetic effects, DGEs), when social interactions occur, genetic variation in interacting partners can influence a focal individual's traits (indirect genetic effects, IGEs). Theory and studies on domesticated species have suggested IGEs can greatly impact evolutionary trajectories, but whether this is true more broadly remains unclear. Here, we perform a systematic review and meta-analysis to quantify the amount of trait variance explained by IGEs and the contribution of IGEs to predictions of adaptive potential. We identified 180 effect sizes from 47 studies across 21 species and found that, on average, IGEs of a single social partner account for a small but statistically significant amount of phenotypic variation (0.03). As IGEs affect the trait values of each interacting group member and due to a typically positive-although statistically nonsignificant-correlation with DGEs ( r DGE-IGE = 0.26), IGEs ultimately increase trait heritability substantially from 0.27 (narrow-sense heritability) to 0.45 (total heritable variance). This 66% average increase in heritability suggests IGEs can increase the amount of genetic variation available to selection. Furthermore, whilst showing considerable variation across studies, IGEs were most prominent for behaviors and, to a lesser extent, for reproduction and survival, in contrast to morphological, metabolic, physiological, and development traits. Our meta-analysis, therefore, shows that IGEs tend to enhance the evolutionary potential of traits, especially for those tightly related to interactions with other individuals, such as behavior and reproduction.
| Original language | English |
|---|---|
| Article number | qrae051 |
| Pages (from-to) | 89-104 |
| Number of pages | 16 |
| Journal | Evolution Letters |
| Volume | 9 |
| Issue number | 1 |
| Early online date | 10 Oct 2024 |
| DOIs | |
| Publication status | Published - Feb 2025 |
Bibliographical note
I am grateful to Vikki Entwistle, Sone Erikainen, Sandro Gulì, Gerry Hough, Jesper Kallestrup, Paula Sweeney, Stephan Torre, Ulrich Stegmann and to two anonymous reviewers for helpful comments on a prior version of this paper.We would like to thank Denis Reale, Clint Kelly, and Pierre-Olivier Montiglio for their feedback in the earlier stages of the project, and Daniel W.A. Noble for statistical advice. We thank the participants of the 2023 WAMBAM conference and Alastair Wilson for useful discussion. We also thank Allen Moore, Joel Pick, and one anonymous reviewer for their constructive feedback, which improved the manuscript. We are grateful to the following authors who provided additional data for the meta-analysis when we contacted them: Alastair Wilson, Anasuya Chakrabarty, Cristina Sartori, Esther Ellen, Chang Han, Jane Reid, João Costa e Silva, Jennifer Morinay, Mark Adams, Miriam Piles, Moha Ragab, Ingrid David, Paolo Carnier, Ryan Germain, Setegn Alemu, Simon Evans, and Céline Teplitsky.
Data Availability Statement
All data and code are available at the following GitHub repository:https://github.com/ASanchez-Tojar/meta-analysis_IGEs and via
Zenodo at https://doi.org/10.5281/zenodo.13766382.
Funding
FS was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Individual Fellowship (INTERACTIVE, Grant Agreement Number 101023262). MM was funded by a Marie Skłodowska-Curie Individual Fellowship (PLASTIC TERN, Grant Agreement Number 793550) and an Alexander von Humboldt Research Fellowship for Postdoctoral Researchers. AST was partially funded by the German Research Foundation (DFG:Deutsche Forschungsgemeinschaft) as part of the SFB TRR 212 (NC3)—Project no. 316099922 and 396782608.
| Funders | Funder number |
|---|---|
| H2020 European Research Council | 101023262, 793550 |
| Deutsche Forschungsgemeinschaft | 316099922, 396782608 |
Keywords
- animal model
- associative genetic effects
- interacting phenotypes
- quantitative genetics
- social evolution
- social interactions
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