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The challenge of estimating indirect genetic effects on behavior: a comment on Bailey et al.

The challenge of estimating indirect genetic effects on behavior: a comment on Bailey et al. The notion that behavioral traits “must often take the lead in evolution” because they are “especially plastic” (West-Eberhard 2003) sits somewhat uncomfortably with standard quantitative genetic theory. Under simple models, plasticity actually slows evolution: more plastic traits have greater variance, reduced heritability and so lowered expected selection response. Here, Bailey et al. (2017) highlight the tantalizing possibility that indirect genetic effects (IGEs) could reconcile these contrasting positions. They provide a neat structured list of questions to guide empirical efforts, effectively asking: Is there plasticity in your behavioral trait of interest? Is there plasticity in response to the “social environment”? Is “social-environment plasticity” due to IGEs? Do IGEs explain more phenotypic variance for behavioral traits than for other trait types? We fully agree that assessing the importance, or otherwise, of IGEs for behavioral traits will be valuable and very interesting. However, for anyone contemplating this challenge several points warrant mention. While questions 1 and 2 may be relatively straightforward to address, 3 and 4 require complex analyses, starting with the decision about whether to use trait-based or variance-partitioning approaches (see Boxes 1 and 2, Bailey et al. 2017). This decision must be made partly on pragmatic grounds, and we expect variance partitioning to be more tractable in many cases: most importantly, it avoids the need to have correctly measured all partner traits with causal effects (see Bijma 2014 for comprehensive discussion). It can also be applied to Q2 (as well as Q3), as social plasticity implies “indirect phenotypic effects” that can be estimated prior to the more data-demanding task of partitioning IGEs. Our comments here therefore relate to this variance-partitioning approach. Several key issues need to be borne in mind for IGE variance-partitioning. First, dyadic interactions—such as those between mothers and offspring, mating partners or contest antagonists—are relatively straightforward to model, but other scenarios are not. Interactions may involve multiple partners, groups of differing size (e.g., number of competing nestlings), and ambiguity in defining group membership. The model presented (Equation 2.1) assumes a uniform group size and fixed group membership with no inter-group interactions. This will suit many experimental studies, but some thought is needed for application to less controlled scenarios (Wilson 2014). Second, statistical models will probably need to be more complex than suggested in Box 2. Repeated observations are increasingly common (and welcome) in behavioral studies, but require explicit modeling of nongenetic among-individual differences to avoid overestimation of genetic variance (Bijma et al. 2007; Kruuk and Hadfield 2007). This consideration applies equally to indirect effects. While “group” effects may be sufficient when group membership is fixed (Bijma et al. 2007), in other situations indirect “permanent environment” effects are required to avoid inflation of IGE variance (e.g., Wilson et al. 2011). Third, Q1-4 are not fully sufficient to characterize the impact of IGEs on evolutionary potential. IGEs represent an additional source of genetic variance, but one that can constrain as well as facilitate selection responses. Evolutionary potential is defined by the “total” variance in breeding values, which depends on the variances of direct and indirect genetic effects, but also, critically, on their covariance (see Equation 2.2). The latter can—and sometimes must—be negative, such that IGEs reduce evolvability. They may even reduce the “total heritable variation” to nothing; in fact IGE models can provide useful fodder for efforts to explain phenotypic stasis in the face of directional selection (Wilson 2014). Answering “yes” to all the questions posed above means IGE are important for determining the evolutionary potential of behavior, but not necessarily that they increase it. In conclusion, we welcome the suggestion that behavioral ecology should better incorporate IGEs. Our thinking about social evolution has long been dominated by relatedness, but IGEs represent a second, distinct mechanism by which associations can arise between the genes of an actor and the fitness of a recipient (Bijma and Wade 2008). Empirical dissection of IGEs is therefore exciting new territory, but it is also challenging, and requires both careful construction of appropriate models and extremely powerful data sets. Arguments that behavior is “special” are not entirely convincing to us—though perhaps because we are not true behavioral ecologists—but such studies will certainly be valuable, regardless of whether they support the idea that IGEs increase evolvabilities of behavioral traits or not. We also think there is great potential in studies that: first, explore the consequences of IGEs on multivariate phenotypes (including, for instance, morphology and life history as well as behavior); and, second, recognize that social plasticity may involve both IGEs and GxE simultaneously, and hence the possibility of (direct) G × (indirect) G interactions. Finally, we hope that growing interest in IGEs will help correct the current imbalance in the literature, where theoretical and methodological treatments abound but empirical estimates, especially from natural systems, are still scarce. REFERENCES Bailey NW, Marie-Orleach L, Moore AJ. 2017. Indirect genetic effects in behavioral ecology: does behavior play a special role in evolution? Behav. Ecol. doi:10.1093/beheco/arx127. Bijma P. 2014. The quantitative genetics of indirect genetic effects: a selective review of modelling issues. Heredity (Edinb) . 112: 61– 69. Google Scholar CrossRef Search ADS PubMed  Bijma P, Muir WM, Ellen ED, Wolf JB, Van Arendonk JAM. 2007. Multilevel selection 2: estimating the genetic parameters determining inheritance and response to selection. Genetics . 175: 289– 299. Google Scholar CrossRef Search ADS PubMed  Bijma P, Wade MJ. 2008. The joint effects of kin, multilevel selection and indirect genetic effects on response to genetic selection. J Evol Biol . 21: 1175– 1188. Google Scholar CrossRef Search ADS PubMed  Kruuk LE, Hadfield JD. 2007. How to separate genetic and environmental causes of similarity between relatives. J Evol Biol . 20: 1890– 1903. Google Scholar CrossRef Search ADS PubMed  West-Eberhard MJ. 2003. Developmental plasticity and evolution . Oxford: Oxford University Press. Wilson AJ. 2014. Competition as a source of constraint on life history evolution in natural populations. Heredity (Edinb) . 112: 70– 78. Google Scholar CrossRef Search ADS PubMed  Wilson AJ, Morrissey MB, Adams MJ, Walling CA, Guinness FE, Pemberton JM, Clutton-Brock TH, Kruuk LE. 2011. Indirect genetics effects and evolutionary constraint: an analysis of social dominance in red deer, Cervus elaphus. J Evol Biol . 24: 772– 783. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Behavioral Ecology Oxford University Press

The challenge of estimating indirect genetic effects on behavior: a comment on Bailey et al.

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Oxford University Press
Copyright
© The Author(s) 2017. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
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1045-2249
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1465-7279
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10.1093/beheco/arx162
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Abstract

The notion that behavioral traits “must often take the lead in evolution” because they are “especially plastic” (West-Eberhard 2003) sits somewhat uncomfortably with standard quantitative genetic theory. Under simple models, plasticity actually slows evolution: more plastic traits have greater variance, reduced heritability and so lowered expected selection response. Here, Bailey et al. (2017) highlight the tantalizing possibility that indirect genetic effects (IGEs) could reconcile these contrasting positions. They provide a neat structured list of questions to guide empirical efforts, effectively asking: Is there plasticity in your behavioral trait of interest? Is there plasticity in response to the “social environment”? Is “social-environment plasticity” due to IGEs? Do IGEs explain more phenotypic variance for behavioral traits than for other trait types? We fully agree that assessing the importance, or otherwise, of IGEs for behavioral traits will be valuable and very interesting. However, for anyone contemplating this challenge several points warrant mention. While questions 1 and 2 may be relatively straightforward to address, 3 and 4 require complex analyses, starting with the decision about whether to use trait-based or variance-partitioning approaches (see Boxes 1 and 2, Bailey et al. 2017). This decision must be made partly on pragmatic grounds, and we expect variance partitioning to be more tractable in many cases: most importantly, it avoids the need to have correctly measured all partner traits with causal effects (see Bijma 2014 for comprehensive discussion). It can also be applied to Q2 (as well as Q3), as social plasticity implies “indirect phenotypic effects” that can be estimated prior to the more data-demanding task of partitioning IGEs. Our comments here therefore relate to this variance-partitioning approach. Several key issues need to be borne in mind for IGE variance-partitioning. First, dyadic interactions—such as those between mothers and offspring, mating partners or contest antagonists—are relatively straightforward to model, but other scenarios are not. Interactions may involve multiple partners, groups of differing size (e.g., number of competing nestlings), and ambiguity in defining group membership. The model presented (Equation 2.1) assumes a uniform group size and fixed group membership with no inter-group interactions. This will suit many experimental studies, but some thought is needed for application to less controlled scenarios (Wilson 2014). Second, statistical models will probably need to be more complex than suggested in Box 2. Repeated observations are increasingly common (and welcome) in behavioral studies, but require explicit modeling of nongenetic among-individual differences to avoid overestimation of genetic variance (Bijma et al. 2007; Kruuk and Hadfield 2007). This consideration applies equally to indirect effects. While “group” effects may be sufficient when group membership is fixed (Bijma et al. 2007), in other situations indirect “permanent environment” effects are required to avoid inflation of IGE variance (e.g., Wilson et al. 2011). Third, Q1-4 are not fully sufficient to characterize the impact of IGEs on evolutionary potential. IGEs represent an additional source of genetic variance, but one that can constrain as well as facilitate selection responses. Evolutionary potential is defined by the “total” variance in breeding values, which depends on the variances of direct and indirect genetic effects, but also, critically, on their covariance (see Equation 2.2). The latter can—and sometimes must—be negative, such that IGEs reduce evolvability. They may even reduce the “total heritable variation” to nothing; in fact IGE models can provide useful fodder for efforts to explain phenotypic stasis in the face of directional selection (Wilson 2014). Answering “yes” to all the questions posed above means IGE are important for determining the evolutionary potential of behavior, but not necessarily that they increase it. In conclusion, we welcome the suggestion that behavioral ecology should better incorporate IGEs. Our thinking about social evolution has long been dominated by relatedness, but IGEs represent a second, distinct mechanism by which associations can arise between the genes of an actor and the fitness of a recipient (Bijma and Wade 2008). Empirical dissection of IGEs is therefore exciting new territory, but it is also challenging, and requires both careful construction of appropriate models and extremely powerful data sets. Arguments that behavior is “special” are not entirely convincing to us—though perhaps because we are not true behavioral ecologists—but such studies will certainly be valuable, regardless of whether they support the idea that IGEs increase evolvabilities of behavioral traits or not. We also think there is great potential in studies that: first, explore the consequences of IGEs on multivariate phenotypes (including, for instance, morphology and life history as well as behavior); and, second, recognize that social plasticity may involve both IGEs and GxE simultaneously, and hence the possibility of (direct) G × (indirect) G interactions. Finally, we hope that growing interest in IGEs will help correct the current imbalance in the literature, where theoretical and methodological treatments abound but empirical estimates, especially from natural systems, are still scarce. REFERENCES Bailey NW, Marie-Orleach L, Moore AJ. 2017. Indirect genetic effects in behavioral ecology: does behavior play a special role in evolution? Behav. Ecol. doi:10.1093/beheco/arx127. Bijma P. 2014. The quantitative genetics of indirect genetic effects: a selective review of modelling issues. Heredity (Edinb) . 112: 61– 69. Google Scholar CrossRef Search ADS PubMed  Bijma P, Muir WM, Ellen ED, Wolf JB, Van Arendonk JAM. 2007. Multilevel selection 2: estimating the genetic parameters determining inheritance and response to selection. Genetics . 175: 289– 299. Google Scholar CrossRef Search ADS PubMed  Bijma P, Wade MJ. 2008. The joint effects of kin, multilevel selection and indirect genetic effects on response to genetic selection. J Evol Biol . 21: 1175– 1188. Google Scholar CrossRef Search ADS PubMed  Kruuk LE, Hadfield JD. 2007. How to separate genetic and environmental causes of similarity between relatives. J Evol Biol . 20: 1890– 1903. Google Scholar CrossRef Search ADS PubMed  West-Eberhard MJ. 2003. Developmental plasticity and evolution . Oxford: Oxford University Press. Wilson AJ. 2014. Competition as a source of constraint on life history evolution in natural populations. Heredity (Edinb) . 112: 70– 78. Google Scholar CrossRef Search ADS PubMed  Wilson AJ, Morrissey MB, Adams MJ, Walling CA, Guinness FE, Pemberton JM, Clutton-Brock TH, Kruuk LE. 2011. Indirect genetics effects and evolutionary constraint: an analysis of social dominance in red deer, Cervus elaphus. J Evol Biol . 24: 772– 783. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

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Behavioral Ecology – Oxford University Press

Published: Jan 1, 2018

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