Indirect genetic effects and kin recognition: estimating IGEs when interactions differ between kin and strangers

Area of Science:

• Evolutionary biology
• Quantitative genetics
• Behavioral ecology

Background:

• Individuals' traits are influenced by genes of social partners (indirect genetic effects, IGEs).
• Traditional IGE models assume uniform interactions, ignoring kin-specific behaviors predicted by kin-selection theory.
• Divergent behaviors towards kin versus strangers can bias traditional IGE estimates.

Purpose of the Study:

• To investigate the identifiability of genetic parameters for IGEs when they differ between kin and strangers in group-structured populations.
• To develop models for estimating these kin-dependent IGEs.
• To assess the consequences of using traditional models in such scenarios.

Main Methods:

• Extended definitions of total breeding value and heritable variance to account for relatedness-dependent IGEs.
• Demonstrated non-identifiability of full genetic parameters when IGEs vary with relatedness.
• Developed a reduced model for estimating kin, non-kin, and overall IGEs.

Main Results:

• The full set of genetic parameters for indirect genetic effects is not statistically identifiable when interactions differ between kin and strangers.
• A reduced model provides estimates of total heritable effects on kin, non-kin, and all social partners.
• The reduced model also estimates total heritable variance relevant for response to selection.

Conclusions:

• Traditional IGE models are inadequate when individuals interact differently with kin and strangers.
• A reduced model offers a practical approach to estimate key genetic parameters in kin-structured populations.
• Understanding group structures is crucial for accurately estimating kin-dependent IGEs and optimizing selection.