The quantitative genetics of indirect genetic effects: a selective review of modelling issues

Indirect genetic effects (IGE) occur when the genotype of an individual affects the phenotypic trait value of another conspecific individual. IGEs can have profound effects on both the magnitude and the direction of response to selection. Models of inheritance and response to selection in traits subject to IGEs have been developed within two frameworks; a trait-based framework in which IGEs are specified as a direct consequence of individual trait values, and a variance-component framework in which phenotypic variance is decomposed into a direct and an indirect additive genetic component. This work is a selective review of the quantitative genetics of traits affected by IGEs, with a focus on modelling, estimation and interpretation issues. It includes a discussion on variance-component vs trait-based models of IGEs, a review of issues related to the estimation of IGEs from field data, including the estimation of the interaction coefficient Ψ (psi), and a discussion on the relevance of IGEs for response to selection in cases where the strength of interaction varies among pairs of individuals. An investigation of the trait-based model shows that the interaction coefficient Ψ may deviate considerably from the corresponding regression coefficient when feedback occurs. The increasing research effort devoted to IGEs suggests that they are a widespread phenomenon, probably particularly in natural populations and plants. Further work in this field should considerably broaden our understanding of the quantitative genetics of inheritance and response to selection in relation to the social organisation of populations.     

Multilevel Selection 4: Modeling the Relationship of Indirect Genetic Effects and Group Size

Indirect genetic effects (IGE) occur when individual trait values depend on genes in others. With IGEs, heritable variance and response to selection depend on the relationship of IGEs and group size. Here I propose a model for this relationship, which can be implemented in standard restricted maximum likelihood software.SOCIAL interactions among individuals are abundant in life (Frank 2007). Trait values of individuals may, therefore, depend on genes in other individuals, a phenomenon known as indirect genetic effects (IGE; Wolf et al. 1998) or associative effects (Griffing 1967; Muir 2005). IGEs may have drastic effects on the rate and direction of response to selection. Moreover, with IGEs, heritable variance and response to selection depend on the size of the interaction group, hereafter denoted group size (Griffing 1967; Bijma et al. 2007; McGlothlin et al. 2010). The magnitude of the IGEs themselves, however, may also depend on group size, because interactions between a specific pair of individuals are probably less intense in larger groups (Arango et al. 2005). The relationship between the magnitude of IGEs and group size is relevant because it affects the dynamics of response to selection, heritable variation, and group size, determining, e.g., whether or not selection is more effective with larger groups. Moreover, a model for this relationship is required to estimate IGEs from data containing varying group sizes. Hadfield and Wilson (2007) proposed a model for the relationship between IGEs and group size. Here I present an alternative.With IGEs, the trait value of focal individual i is the sum of a direct effect rooted in the focal individual itself, P_D,i, and the sum of the indirect effects, P_S,j, of each of its n − 1 group mates j,(1)where A and E represent the heritable and nonheritable component of the full direct and indirect effect, respectively, and n denotes group size (Griffing 1967). When IGEs are independent of group size, total heritable variance in the trait equals (Bijma et al. 2007)(2)For a fixed becomes very large with large groups. This is unrealistic because an individual''s IGE on a single recipient probably becomes smaller in larger groups. The decrease of IGEs with group size, referred to as dilution here, will depend on the trait of interest. With competition for a finite amount of feed per group, for example, an individual consuming 1 kg has an average indirect effect of P_S,i = −1/(n − 1) on feed intake of each of its group mates. Hence, the indirect effect is inversely proportional to the number of group mates, indicating full dilution. The other extreme of no dilution may be illustrated by alarm-calling behavior, where an individual may warn all its group mates when a predator appears, irrespective of group size. Here the indirect effect each group mate receives is independent of group size, indicating no dilution. The degree of dilution is an empirical issue, which may be trait and population specific, and needs to be estimated.Here I propose to model dilution of indirect effects as(3)where P_S,i,n is the indirect effect of individual i in a group of n members, P_S,i,2 the indirect effect of i in a group of two members, and d the degree of dilution. With no dilution, d = 0, indirect effects do not depend on group size, P_S,i,n = P_S,i,2, as with alarm-calling behavior. With full dilution, d = 1, indirect effects are inversely proportional to the number of group mates, P_S,i,n = P_S,i,2/(n − 1), as with competition for a finite amount of feed. Equation 3 is an extension of the model of Arango et al. (2005), who used d = 1.Assuming that IGEs are diluted in the same manner as the full indirect effect, the indirect genetic variance for groups of n members equals(4)and total heritable variance equals(5)Hence, for σ_ADS = 0, total heritable variance increases with group size as long as dilution is incomplete (d < 1). Total heritable variance is independent of group size with full dilution (d = 1). Phenotypic variance also depends on group size. With unrelated group members,(6)which increases with group size for d < 0.5, is independent of group size for d = 0.5, and decreases with group size for d > 0.5.The degree of dilution can be estimated from data containing variation in group size, by using a mixed model with restricted maximum likelihood and evaluating the likelihood for different fixed values of d (Arango et al. 2005; Canario et al. 2010). With Equation 3, however, the estimated genetic (co)variances and breeding values for indirect effect refer to a group size of two individuals, which is inconvenient when actual group size differs considerably. Estimates of A_S, , and σ_ADS referring to the average group size may be obtained from the following mixed model,(7)where z is a vector of observations, Xb are the usual fixed effects, Z_Da_D are the direct genetic effects, Z_gg are random group effects, and e is a vector of residuals. The is a vector of IGEs referring to the average group size, and Z_S(d) is the incidence matrix for IGEs, which depends on the degree of dilution; dilution being specified relative to the average group size. Elements of Z_S(d) are(8)where denotes average group size. This model is equivalent to Equation 3, but yields estimates of genetic parameters and breeding values referring to the average group size because for . When the magnitude of IGEs depends on group size, the group and residual variance in Equation 7 will depend on group size:(9a)(9b)Hence, to obtain unbiased estimates of the genetic parameters and d, it may be required to fit a separate group and residual variance for each group size.To account for the relationship between IGEs and group size, Hadfield and Wilson (2007; HW07) proposed including an additional IGE. In their model, an individual''s full IGE is the sum of an effect independent of group size, and an effect regressed by the reciprocal of the number of group mates,(10)There are a number of differences between both models. First, Equation 3 specifies the relationship between the magnitude of IGEs and group size on the population level, which is sufficient to remedy the problem of increasing variance with group size. The HW07 model, in contrast, specifies the relationship between the magnitude of IGEs and group size on the individual level. In the HW07 model, the absolute value of (1/(n − 1))A_SR,_i decreases with group size, while A_S,i is constant. Consequently, the relationship between an individual''s full IGE and group size depends on the relative magnitudes of its A_S,i and A_SR,_i; the IGEs of individuals with greater |A_SR| show greater change when group size varies. This alters the IGE ranking of individuals when group size varies. The HW07 model, therefore, not only scales IGEs with group size, but also allows for IGE-by-group-size interaction, whereas Equation 3 scales IGEs of all individuals in the same way. Second, the interpretation of the genetic parameters differs between both models. In the HW07 model, lim_n→∞ A_S,i,HW07 = A_S,i, meaning that Var(A_S) represents the variance in IGEs when group size is infinite. With Equation 3 or 7, in contrast, refers to groups of two individuals or to the average group size. Third, in the HW07 model, the dilution of IGEs with group size is implicitly incorporated in the magnitudes of Var(A_S) and Var(A_SR), greater Var(A_SR) implying greater dilution. Equation 3, in contrast, has a single parameter for the degree of dilution, expressed on a 0–1 scale. Finally, implementing the HW07 model involves estimating three additional covariance parameters, Var(A_SR), Cov(A_D, A_SR), and Cov(A_S, A_SR), whereas implementing the model proposed here involves estimating a single additional fixed effect, which is simpler. In conclusion, the HW07 model has greater flexibility than the model proposed here, but is also more difficult to implement and interpret.     

Indirect genetic effects on the relationships between production and feeding behaviour traits in growing Duroc pigs

Performance and feeding behaviour traits in growing pigs could be affected by social interaction effects when animals are raised in group. So, properly knowing the genetic correlations between direct and social interaction effects among performance and feeding behaviour traits could improve the accuracy of the genetic evaluations. Our aim was to explore the role of feeding behaviour traits (FBT) and indirect genetic effects (IGEs) in the genetic evaluations of growing pigs. Thus, genetic parameters were estimated for production traits (PT): average daily gain, average daily feed consumption, feed conversion ratio and backfat thickness; as well as for FBT: average daily feeding rate, average daily feeding frequency, average daily occupation time and average daily time between consecutive visits. Traits were recorded in 1144 Duroc pigs during the fattening period. Two bivariate models were fitted: classic animal model and an animal model fitting IGE. Estimations were done following Bayesian procedures. Heritability estimates obtained with classic animal model for all studied traits were medium-high. The additional heritable variation captured by IGE supposed that the ratios of total genetic variance to phenotypic variance (T²) were higher than the heritability estimates obtained with the classic model, except for occupation time trait, when a lower value (0.20 ± 0.19) was estimated. This is due to a high and negative correlation between IGE and direct genetic effects (DGEs) of this particular trait (−0.78 ± 0.27). Results from classic animal model do not evidence a clear role of FBT to improve the accuracy of breeding value predictions for PT; only average daily feeding rate seems to show a positive correlation (around 0.50 to 0.60) with average daily gain, average daily feed consumption and backfat thickness. However, when IGE model was fitted, the number of estimates of genetic correlations between FBT and PT showing a relevant magnitude increased, generally for the correlations between IGE of FBT and DGE of PT; or particularly for the correlations between IGE of average daily feeding frequency, and the IGE of all the PT, except average daily gain. Thus, in evaluations using the animal model with IGE fitted, the inclusion of FBT could aid the improvement of the accuracy of breeding value predictions for PT. This is a consequence of the improved genetic relationships between traits that can be fitted when considering such models.     

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

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.     

Estimation of social genetic effects on feeding behaviour and production traits in pigs

Pigs are housed in groups during the test period. Social effects between pen mates may affect average daily gain (ADG), backfat thickness (BF), feed conversion rate (FCR), and the feeding behaviour traits of pigs sharing the same pen. The aim of our study was to estimate the genetic parameters of feeding behaviour and production traits with statistical models that include social genetic effects (SGEs). The data contained 3075 Finnish Yorkshire, 3351 Finnish Landrace, and 968 F1-crossbred pigs. Feeding behaviour traits were measured as the number of visits per day (NVD), time spent in feeding per day (TPD), daily feed intake (DFI), time spent in feeding per visit (TPV), feed intake per visit (FPV), and feed intake rate (FR). The test period was divided into five periods of 20 days. The number of pigs per pen varied from 8 to 12. Two model approaches were tested, i.e. a fixed group size model and a variable group size model. For the fixed group size model, eight random pigs per pen were included in the analysis, while all pigs in a pen were included for the variable group size model. The linear mixed-effects model included sex, breed, and herd*year*season as fixed effects and group (batch*pen), litter, the animal itself (direct genetic effect (DGE)), and pen mates (SGEs) as random effects. For feeding behaviour traits, estimates of the total heritable variation (T² ± SE) and classical heritability (h² ± SE, values given in brackets) from the variable group size model (e.g. period 1) were 0.34 ± 0.13 (0.30 ± 0.04) for NVD, 0.41 ± 0.10 (0.37 ± 0.04) for TPD, 0.40 ± 0.15 (0.14 ± 0.03) for DFI, 0.53 ± 0.15 (0.28 ± 0.04) for TPV, 0.66 ± 0.17 (0.28 ± 0.04) for FPV, and 0.29 ± 0.13 (0.22 ± 0.03) for FR. The effect of social interaction was minimal for ADG (T² = 0.29 ± 0.11 and h² = 0.29 ± 0.04), BF (T² = 0.48 ± 0.12 and h² = 0.38 ± 0.07), and FCR (T² = 0.37 ± 0.12 and h² = 0.29 ± 0.04) using the variable group size model. In conclusion, the results indicate that social interactions have a considerable indirect genetic effect on the feeding behaviour and FCR of pigs but not on ADG and BF.     

Genetic composition of social groups influences male aggressive behaviour and fitness in natural genotypes of Drosophila melanogaster

Indirect genetic effects (IGEs) describe how an individual''s behaviour—which is influenced by his or her genotype—can affect the behaviours of interacting individuals. IGE research has focused on dyads. However, insights from social networks research, and other studies of group behaviour, suggest that dyadic interactions are affected by the behaviour of other individuals in the group. To extend IGE inferences to groups of three or more, IGEs must be considered from a group perspective. Here, I introduce the ‘focal interaction’ approach to study IGEs in groups. I illustrate the utility of this approach by studying aggression among natural genotypes of Drosophila melanogaster. I chose two natural genotypes as ‘focal interactants’: the behavioural interaction between them was the ‘focal interaction’. One male from each focal interactant genotype was present in every group, and I varied the genotype of the third male—the ‘treatment male’. Genetic variation in the treatment male''s aggressive behaviour influenced the focal interaction, demonstrating that IGEs in groups are not a straightforward extension of IGEs measured in dyads. Further, the focal interaction influenced male mating success, illustrating the role of IGEs in behavioural evolution. These results represent the first manipulative evidence for IGEs at the group level.     

Indirect Genetic Effects and the Dynamics of Social Interactions

BackgroundIndirect genetic effects (IGEs) occur when genes expressed in one individual alter the expression of traits in social partners. Previous studies focused on the evolutionary consequences and evolutionary dynamics of IGEs, using equilibrium solutions to predict phenotypes in subsequent generations. However, whether or not such steady states may be reached may depend on the dynamics of interactions themselves.ResultsIn our study, we focus on the dynamics of social interactions and indirect genetic effects and investigate how they modify phenotypes over time. Unlike previous IGE studies, we do not analyse evolutionary dynamics; rather we consider within-individual phenotypic changes, also referred to as phenotypic plasticity. We analyse iterative interactions, when individuals interact in a series of discontinuous events, and investigate the stability of steady state solutions and the dependence on model parameters, such as population size, strength, and the nature of interactions. We show that for interactions where a feedback loop occurs, the possible parameter space of interaction strength is fairly limited, affecting the evolutionary consequences of IGEs. We discuss the implications of our results for current IGE model predictions and their limitations.     

DETECTING CRYPTIC INDIRECT GENETIC EFFECTS

Indirect genetic effects (IGEs) occur when genes expressed in one individual alter the phenotype of an interacting partner. IGEs can dramatically affect the expression and evolution of social traits. However, the interacting phenotype(s) through which they are transmitted are often unknown, or cryptic, and their detection would enhance our ability to accurately predict evolutionary change. To illustrate this challenge and possible solutions to it, we assayed male leg‐tapping behavior using inbred lines of Drosophila melanogaster paired with a common focal male strain. The expression of tapping in focal males was dependent on the genotype of their interacting partner, but this strong IGE was cryptic. Using a multiple‐regression approach, we identified male startle response as a candidate interacting phenotype: the longer it took interacting males to settle after being startled, the less focal males tapped them. A genome‐wide association analysis identified approximately a dozen candidate protein‐coding genes potentially underlying the IGE, of which the most significant was slowpoke. Our methodological framework provides information about candidate phenotypes and candidate single‐nucleotide polymorphisms that underpin a strong yet cryptic IGE. We discuss how this approach can facilitate the detection of cryptic IGEs contributing to unusual evolutionary dynamics in other study systems.     

Socially flexible female choice differs among populations of the Pacific field cricket: geographical variation in the interaction coefficient psi (Ψ)

Indirect genetic effects (IGEs) occur when genes expressed in one individual affect the phenotype of a conspecific. Theoretical models indicate that the evolutionary consequences of IGEs critically depend on the genetic architecture of interacting traits, and on the strength and direction of phenotypic effects arising from social interactions, which can be quantified by the interaction coefficient Ψ. In the context of sexually selected traits, strong positive Ψ tends to exaggerate evolutionary change, whereas negative Ψ impedes sexual trait elaboration. Despite its theoretical importance, whether and how Ψ varies among geographically distinct populations is unknown. Such information is necessary to evaluate the potential for IGEs to contribute to divergence among isolated or semi-isolated populations. Here, we report substantial variation in Ψ for a behavioural trait involved in sexual selection in the field cricket Teleogryllus oceanicus: female choosiness. Both the strength and direction of Ψ varied among geographically isolated populations. Ψ also changed over time. In a contemporary population of crickets from Kauai, experience of male song increased female choosiness. In contrast, experience of male song decreased choosiness in an ancestral population from the same location. This rapid change corroborates studies examining the evolvability of Ψ and demonstrates how interpopulation variation in the interaction coefficient might influence sexual selection and accelerate divergence of traits influenced by IGEs that contribute to reproductive isolation in nascent species or subspecies.     

Indirect Genetic Effects for Survival in Domestic Chickens (Gallus gallus) Are Magnified in Crossbred Genotypes and Show a Parent-of-Origin Effect

Through social interactions, individuals can affect one another’s phenotype. The heritable effect of an individual on the phenotype of a conspecific is known as an indirect genetic effect (IGE). Although IGEs can have a substantial impact on heritable variation and response to selection, little is known about the genetic architecture of traits affected by IGEs. We studied IGEs for survival in domestic chickens (Gallus gallus), using data on two purebred lines and their reciprocal cross. Birds were kept in groups of four. Feather pecking and cannibalism caused mortality, as beaks were kept intact. Survival time was shorter in crossbreds than in purebreds, indicating outbreeding depression and the presence of nonadditive genetic effects. IGEs contributed the majority of heritable variation in crossbreds (87 and 72%) and around half of heritable variation in purebreds (65 and 44%). There was no evidence of dominance variance, neither direct nor indirect. Absence of dominance variance in combination with considerable outbreeding depression suggests that survival is affected by many loci. Direct–indirect genetic correlations were moderately to highly negative in crossbreds (−0.37 ± 0.17 and −0.83 ± 0.10), but low and not significantly different from zero in purebreds (0.20 ± 0.21 and −0.28 ± 0.18). Consequently, unlike purebreds, crossbreds would fail to respond positively to mass selection. The direct genetic correlation between both crosses was high (0.95 ± 0.23), whereas the indirect genetic correlation was moderate (0.41 ± 0.26). Thus, for IGEs, it mattered which parental line provided the sire and which provided the dam. This indirect parent-of-origin effect appeared to be paternally transmitted and is probably Z chromosome linked.     

Genetic Color Morphs in the Eastern Mosquitofish Experience Different Social Environments in the Wild and Laboratory

The social environment of an animal is an especially interesting component of its environment because it can be shaped by both genetic and non‐genetic variation among social partners. Indirect genetic effects (IGEs) are those created when genetic variation in social partners contributes to variation in an individual's phenotype; a potentially common form of IGE occurs when the expression of a behavioral phenotype depends on the particular genotypic combination of interacting individuals. Although IGEs can profoundly affect individual‐ and group‐level fitness, population dynamics, and even community structure, understanding their importance is complicated by two inherent challenges: (1) identifying individuals with genetic differences in social interactions that can contribute to IGEs and (2) characterizing natural social interactions that potentially involve IGEs. As a first step toward addressing both these challenges in the same system, we investigated social interactions involving genetically distinct male color morphs in the poeciliid fish Gambusia holbrooki under natural and laboratory conditions. Previous work indicates that melanic (M) and silver (S) males differ in social behavior and in how conspecifics respond to them, suggesting the potential for IGEs. We used a combination of live and video recording of social groups in two natural populations and in the laboratory to determine the potential for IGEs to contribute to behavioral variation in this species. We found that M males had more social partners, and especially more female social partners than did S males, in nature and in the laboratory. These results suggest that both direct and indirect genetic effects have the potential to play a role in the expression and evolution of social behavior in G. holbrooki.     

The joint effects of kin, multilevel selection and indirect genetic effects on response to genetic selection

Kin and levels-of-selection models are common approaches for modelling social evolution. Indirect genetic effect (IGE) models represent a different approach, specifying social effects on trait values rather than fitness. We investigate the joint effect of relatedness, multilevel selection and IGEs on response to selection. We present a measure for the degree of multilevel selection, which is the natural partner of relatedness in expressions for response. Response depends on both relatedness and the degree of multilevel selection, rather than only one or the other factor. Moreover, response is symmetric in relatedness and the degree of multilevel selection, indicating that both factors have exactly the same effect. Without IGEs, the key parameter is the product of relatedness and the degree of multilevel selection. With IGEs, however, multilevel selection without relatedness can explain evolution of social traits. Thus, next to relatedness and multilevel selection, IGEs are a key element in the genetical theory of social evolution.     

Phenotypic and genetic integration of personality and growth under competition in the sheepshead swordtail,Xiphophorus birchmanni

Competition for resources including food, physical space, and potential mates is a fundamental ecological process shaping variation in individual phenotype and fitness. The evolution of competitive ability, in particular social dominance, depends on genetic (co)variation among traits causal (e.g., behavior) or consequent (e.g., growth) to competitive outcomes. If dominance is heritable, it will generate both direct and indirect genetic effects (IGE) on resource‐dependent traits. The latter are expected to impose evolutionary constraint because winners necessarily gain resources at the expense of losers. We varied competition in a population of sheepshead swordtails, Xiphophorus birchmanni, to investigate effects on behavior, size, growth, and survival. We then applied quantitative genetic analyses to determine (i) whether competition leads to phenotypic and/or genetic integration of behavior with life history and (ii) the potential for IGE to constrain life history evolution. Size, growth, and survival were reduced at high competition. Male dominance was repeatable and dominant individuals show higher growth and survival. Additive genetic contributions to phenotypic covariance were significant, with the G matrix largely recapitulating phenotypic relationships. Social dominance has a low but significant heritability and is strongly genetically correlated with size and growth. Assuming causal dependence of growth on dominance, hidden IGE will therefore reduce evolutionary potential.     

Evidence for linkage of adolescent-onset idiopathic generalized epilepsies to chromosome 8-and genetic heterogeneity

Several loci and candidate genes for epilepsies or epileptic syndromes map or have been suggested to map to chromosome 8. We investigated families with adolescent-onset idiopathic generalized epilepsy (IGE), for linkage to markers spanning chromosome 8. The IGEs that we studied included juvenile myoclonic epilepsy (JME), epilepsy with only generalized tonic-clonic seizures occurring either randomly during the day (random grand mal) or on awakening (awakening grand mal), and juvenile absence epilepsy (JAE). We looked for a gene common to all these IGEs, but we also investigated linkage to specific subforms of IGE. We found evidence for linkage to chromosome 8 in adolescent-onset IGE families in which JME was not present. The maximum multipoint LOD score was 3.24 when family members with IGE or generalized spike-and-waves (SW) were considered affected. The LOD score remained very similar (3.18) when clinically normal family members with SW were not considered to be affected. Families with either pure grand mal epilepsy or absence epilepsy contributed equally to the positive LOD score. The area where the LOD score reaches the maximum encompasses the location of the gene for the beta3-subunit of the nicotinic acetylcholine receptor (CHRNB3), thus making this gene a possible candidate for these specific forms of adolescent-onset IGE. The data excluded linkage of JME to this region. These results indicate genetic heterogeneity within IGE and provide no evidence, on chromosome 8, for a gene common to all IGEs.     

Group‐level competition influences urinary steroid hormones among wild red–tailed monkeys,indicating energetic costs

Various theories emphasize that intergroup competition should affect intragroup cooperation and social relationships, especially if the cost of intergroup competition outweighs that of intragroup competition. This cost of intergroup competition may be quantified by changes in physiological status, such as in the steroid hormones cortisol (C) and testosterone (T), which rise or are depressed during periods of energetic stress, respectively. Here we tested for changes in urinary C and T after intergroup encounters (IGEs) among wild red‐tailed monkeys (Cercopithecus ascanius), a species that experiences frequent intergroup feeding competition, at the Ngogo station in Kibale National Park, Uganda. We assayed 108 urine samples, of which 36 were collected after IGEs, from 23 individuals in four social groups. Bayesian multilevel models controlling for various confounds revealed that IGEs increased C and decreased T relative to baseline, consistent with an energetic cost to IGEs. The C change was more apparent in samples collected early after IGEs, suggesting an anticipatory increase, whereas the T change was stronger in later samples, suggesting sustained energetic trade‐offs. Hormone responses were not affected by the IGE outcome. This cost to intergroup competition, together with little evidence for intragroup competition in redtails and other guenons, establishes an interesting test case for theories emphasizing the effect of intergroup competition on intragroup cooperation.     

EXPERIMENTAL EVIDENCE FOR THE EVOLUTION OF INDIRECT GENETIC EFFECTS: CHANGES IN THE INTERACTION EFFECT COEFFICIENT,PSI (Ψ), DUE TO SEXUAL SELECTION

Indirect genetics effects (IGEs)—when the genotype of one individual affects the phenotypic expression of a trait in another—may alter evolutionary trajectories beyond that predicted by standard quantitative genetic theory as a consequence of genotypic evolution of the social environment. For IGEs to occur, the trait of interest must respond to one or more indicator traits in interacting conspecifics. In quantitative genetic models of IGEs, these responses (reaction norms) are termed interaction effect coefficients and are represented by the parameter psi (Ψ). The extent to which Ψ exhibits genetic variation within a population, and may therefore itself evolve, is unknown. Using an experimental evolution approach, we provide evidence for a genetic basis to the phenotypic response caused by IGEs on sexual display traits in Drosophila serrata. We show that evolution of the response is affected by sexual but not natural selection when flies adapt to a novel environment. Our results indicate a further mechanism by which IGEs can alter evolutionary trajectories—the evolution of interaction effects themselves.     

The role of indirect genetic effects in the evolution of interacting reproductive behaviors in the burying beetle,Nicrophorus vespilloides

Social interactions can give rise to indirect genetic effects (IGEs), which occur when genes expressed in one individual affect the phenotype of another individual. The evolutionary dynamics of traits can be altered when there are IGEs. Sex often involves indirect effects arising from first‐order (current) or second‐order (prior) social interactions, yet IGEs are infrequently quantified for reproductive behaviors. Here, we use experimental populations of burying beetles that have experienced bidirectional selection on mating rate to test for social plasticity and IGEs associated with focal males mating with a female either without (first‐order effect) or with (second‐order effect) prior exposure to a competitor, and resource defense behavior (first‐order effect). Additive IGEs were detected for mating rate arising from (first‐order) interactions with females. For resource defense behavior, a standard variance partitioning analysis provided no evidence of additive genetic variance—either direct or indirect. However, behavior was predicted by focal size relative to that of the competitor, and size is also heritable. Assuming that behavior is causally dependent on relative size, this implies that both DGEs and IGEs do occur (and may potentially interact). The relative contribution of IGEs may differ among social behaviors related to mating which has consequences for the evolutionary trajectories of multivariate traits.     

Social effects for locomotion vary between environments in Drosophila melanogaster females

Despite strong purifying or directional selection, variation is ubiquitous in populations. One mechanism for the maintenance of variation is indirect genetic effects (IGEs), as the fitness of a given genotype will depend somewhat on the genes of its social partners. IGEs describe the effect of genes in social partners on the expression of the phenotype of a focal individual. Here, we ask what effect IGEs, and variation in IGEs between abiotic environments, has on locomotion in Drosophila. This trait is known to be subject to intralocus sexually antagonistic selection. We estimate the coefficient of interaction, Ψ, using six inbred lines of Drosophila. We found that Ψ varied between abiotic environments, and that it may vary across among male genotypes in an abiotic environment specific manner. We also found evidence that social effects of males alter the value of a sexually dimorphic trait in females, highlighting an interesting avenue for future research into sexual antagonism. We conclude that IGEs are an important component of social and sexual interactions and that they vary between individuals and abiotic environments in complex ways, with the potential to promote the maintenance of phenotypic variation.     

Indirect genetic effects and the spread of infectious disease: are we capturing the full heritable variation underlying disease prevalence?

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment. Selection for host resistance has proven difficult, however, due to low heritability estimates. These low estimates may be caused by a failure to capture all the relevant genetic variance in disease resistance, as genetic analysis currently is not taylored to estimate genetic variation in infectivity. Host infectivity is the propensity of transmitting infection upon contact with a susceptible individual, and can be regarded as an indirect effect to disease status. It may be caused by a combination of physiological and behavioural traits. Though genetic variation in infectivity is difficult to measure directly, Indirect Genetic Effect (IGE) models, also referred to as associative effects or social interaction models, allow the estimation of this variance from more readily available binary disease data (infected/non-infected). We therefore generated binary disease data from simulated populations with known amounts of variation in susceptibility and infectivity to test the adequacy of traditional and IGE models. Our results show that a conventional model fails to capture the genetic variation in infectivity inherent in populations with simulated infectivity. An IGE model, on the other hand, does capture some of the variation in infectivity. Comparison with expected genetic variance suggests that there is scope for further methodological improvement, and that potential responses to selection may be greater than values presented here. Nonetheless, selection using an index of estimated direct and indirect breeding values was shown to have a greater genetic selection differential and reduced future disease risk than traditional selection for resistance only. These findings suggest that if genetic variation in infectivity substantially contributes to disease transmission, then breeding designs which explicitly incorporate IGEs might help reduce disease prevalence.     

Green beards in the light of indirect genetic effects

The green‐beard effect is one proposed mechanism predicted to underpin the evolution of altruistic behavior. It relies on the recognition and the selective help of altruists to each other in order to promote and sustain altruistic behavior. However, this mechanism has often been dismissed as unlikely or uncommon, as it is assumed that both the signaling trait and altruistic trait need to be encoded by the same gene or through tightly linked genes. Here, we use models of indirect genetic effects (IGEs) to find the minimum correlation between the signaling and altruistic trait required for the evolution of the latter. We show that this correlation threshold depends on the strength of the interaction (influence of the green beard on the expression of the altruistic trait), as well as the costs and benefits of the altruistic behavior. We further show that this correlation does not necessarily have to be high and support our analytical results by simulations.