Indirect genetic effects increase the heritable variation available to selection and are largest for behaviours: a meta-analysis

  • Francesca Santostefano
  • , Maria Moiron
  • , Alfredo Sánchez-Tójar
  • , David Fisher

Research output: Working paperPreprint

Abstract

The rate of trait evolution 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 evolutionary change. We identified 180 effect sizes from 47 studies across 21 species and found that, on average, IGEs 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 (rDGE-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 have a great potential to enhance the rate of evolution. Furthermore, IGEs were most prominent for behavioural traits, 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 rate of evolution, especially for traits tightly related to interactions with other individuals such as behaviour and reproduction.
Original languageEnglish
PublisherbioRxiv
Number of pages38
DOIs
Publication statusPublished - 26 Jul 2024

Bibliographical note

All data and code are available at the following GitHub repository (https://github.com/ASanchez-Tojar/meta-analysis_IGEs). Upon acceptance, all data and code will be provided with a doi via Zenodo.

Version History

Version 1 (May 21, 2024 - 05:49).
Version 2 (July 26, 2024)

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.

FundersFunder number
European Commission101023262, 793550

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