The Indirect Genetic Effect Interaction Coefficient ψ: Theoretically Essential and Empirically Neglected
Nathan W Bailey1, Camille Desjonquères1
1Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, Fife KY16 9TH, UK.
The Journal of Heredity
|November 18, 2021
Summary
The interaction effect coefficient (ψ) is key for understanding indirect genetic effects (IGEs) and evolution. However, empirical studies often misinterpret or mismeasure ψ, potentially distorting evolutionary predictions.
Area of Science:
- Evolutionary biology
- Quantitative genetics
- Behavioral ecology
Background:
- Indirect genetic effects (IGEs) are influenced by genes acting through the social environment.
- The interaction effect coefficient (ψ) quantifies these IGEs and their evolutionary impact.
- Theoretical emphasis on ψ contrasts with its varied empirical application.
Purpose of the Study:
- To highlight the mismatch between theoretical importance and empirical use of ψ.
- To identify conceptual and measurement issues with ψ in IGE research.
- To offer guidance for accurate empirical estimation and interpretation of ψ.
Main Methods:
- Systematic survey of published indirect genetic effect (IGE) research.
- Analysis of the conceptualization and measurement of the interaction effect coefficient (ψ).
- Review of statistical quantitative genetics approaches in IGE studies.
Main Results:
- ψ is conceptualized inconsistently across empirical studies.
- Measurement issues may lead to distorted interpretations of evolutionary consequences.
- A majority of published ψ estimates are positive, especially in cases of feedback.
Conclusions:
- Reframe ψ from a descriptive parameter to a predictive tool for evolutionary change.
- Emphasize using ψ to generate falsifiable predictions about trait evolution.
- Integrate theoretical strengths of ψ into empirical IGE research for robust evolutionary insights.
Keywords:
indirect genetic effectinteracting phenotypeinteraction coefficientquantitative geneticssocial evolutiontrait-based analysisvariance partitioningMore Related Videos
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