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

S.W. Alemu, P. Berg, L.L.G. Janss, P. Bijma

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11 Citations (Scopus)


Social interactions among individuals are widespread, both in natural and domestic populations. As a result, trait values of individuals may be affected by genes in other individuals, a phenomenon known as indirect genetic effects (IGEs). IGEs can be estimated using linear mixed models. The traditional IGE model assumes that an individual interacts equally with all its partners, whether kin or strangers. There is abundant evidence, however, that individuals behave differently towards kin as compared with strangers, which agrees with predictions from kin-selection theory. With a mix of kin and strangers, therefore, IGEs estimated from a traditional model may be incorrect, and selection based on those estimates will be suboptimal. Here we investigate whether genetic parameters for IGEs are statistically identifiable in group-structured populations when IGEs differ between kin and strangers, and develop models to estimate such parameters. First, we extend the definition of total breeding value and total heritable variance to cases where IGEs depend on relatedness. Next, we show that the full set of genetic parameters is not identifiable when IGEs differ between kin and strangers. Subsequently, we present a reduced model that yields estimates of the total heritable effects on kin, on non-kin and on all social partners of an individual, as well as the total heritable variance for response to selection. Finally we discuss the consequences of analysing data in which IGEs depend on relatedness using a traditional IGE model, and investigate group structures that may allow estimation of the full set of genetic parameters when IGEs depend on kin.
Original languageEnglish
Pages (from-to)197-206
Publication statusPublished - 2014


  • multilevel selection
  • sibling recognition
  • biological groups
  • forest tree
  • group-size
  • evolution
  • parameters
  • individuals
  • populations
  • competition

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