Genetic architecture and evolutionary constraint SEE COMMENTARY when the environment contains

Jason B. Wolf*

Department of Ecology and , University of Tennessee, Knoxville, TN 37996

Edited by Mary Jane West-Eberhard, Smithsonian Tropical Research Institute, Ciudad Universitaria, Costa Rica, and approved January 21, 2003 (received for review September 19, 2002) The environment provided by conspecifics is often the most im- to investigate the genetic and evolutionary consequences of portant component of the environment experienced by individu- social influences on trait expression (8). IGEs are best under- als, frequently having profound effects on fitness and trait expres- stood by contrasting them with direct genetic effects (DGEs). sion. Although these social effects on fitness and trait expression DGEs occur when genes possessed by an individual directly may appear to be purely environmental, they differ from other influence that individual’s . In contrast, IGEs occur sorts of environmental influences, because they can have a genetic when genes expressed in one individual have phenotypic effects basis and thus can contribute to . Theory has shown that in another (6, 8). IGE models solve the duality of these effects, these effects modify the definition of genetic architecture by as both environmental and genetic, by explicitly including the making the phenotype the property of the genotypes of multiple genetic basis of the environment in the definition of the indi- individuals and alter evolutionary dynamics by introducing addi- vidual phenotype (6, 9, 10). By using this approach, these models tional heritable components contributing to trait evolution. These have demonstrated that IGEs can alter our view of genetic effects suggest that genetic and evolutionary analyses of traits architecture, because the phenotype becomes the property of the influenced by social environments must incorporate the genetic genotypes of multiple individuals (6). The mapping of the components of variation contributed by these environments. How- individual phenotype to multiple genotypes can make the ge- ever, empirical studies incorporating these effects are generally netics of these traits particularly complex, because genetic

lacking. In this paper, I quantify the contribution of genetically analysis requires some understanding of interactions between EVOLUTION based environmental effects arising from social interactions during individuals (9). The altered genetic architecture can also lead to group rearing to the of body size in Dro- very different evolutionary dynamics, such as accelerated or sophila melanogaster. The results demonstrate that the genetic retarded responses to selection (6, 9, 10). Although IGEs can architecture of body size contains an important component of result from any sort of social interaction, with few exceptions (6, variation contributed by the social environment, which is hidden to 9), IGE models have focused on the particular influence that ordinary genetic analyses and opposes the direct effects of genes maternal genotypes have on the expression of traits in their on body-size development within a population. Using a model of offspring (so-called maternal genetic effects; see ref. 10). trait evolution, I show that these effects significantly alter evolu- IGE models are analogous to, but more general than, models tionary predictions by providing hidden constraints on phenotypic derived to examine kin selection (11–15). These models focus on evolution. The importance of relatedness of interactants and the the effect that genes in one individual have on the fitness of potential impact of kin selection on the evolution of body size are related individuals, and thus they are implicitly IGE models (e.g., also examined. equation 1 in ref. 11). These models have primarily been developed to gain an understanding of the conditions under ince its origin, one of the major goals of genetics has been to which altruism can evolve. Cheverud (12) made the relationship Sunderstand the relative contribution of heritable and envi- between kin selection and IGE models explicit by using a QG ronmental factors to trait variation. Quantitative genetic (QG) model of maternal effects on a fitness-related trait of progeny. methods have been developed as the primary means to achieve The QG model of Cheverud, and other more general related this goal, generally with the ultimate goal of understanding the models (13, 14, 16), can be used to model evolution by kin evolutionary potential of traits (1). QG analyses use statistical selection when IGEs directly affect either fitness or fitness- approaches that rely on hypothetical constructs, devised to related traits. Like kin-selection models, IGE models predict reflect causative influences producing variation in traits (e.g., that the evolutionary dynamics of traits can be strongly influ- ref. 2), to partition phenotypic variation into heritable compo- enced by the degree of relatedness of interactants. nents that contribute to trait evolution and nonheritable com- Despite the fact that theoretical models have demonstrated ponents that do not. The success of the QG approach for this that evolutionary dynamics can be quite different when IGEs are purpose depends critically on the validity of the underlying present, there is an absence of empirical studies that have model (3). Because of the primary interest in trait evolution, quantified the occurrence of IGEs outside of the parent– most analyses focus on the genetic components, casting envi- offspring interaction. There have been, however, a number of ronmental influences aside as sources of nonheritable random experiments that have implied the presence of IGEs (17–20), variation. However, in the case of the social environment (i.e., although none of these studies explicitly quantified their impor- the environment provided by conspecifics), there can be a tance. To investigate the importance of IGEs arising from other genetic component to the environment, because it is created by types of social interactions, this study focuses on the develop- traits expressed by individuals. This genetic component of the ment of body size in . Many aspects of environment blurs the distinction between genetic and envi- developmental and quantitative genetics are well understood in ronmental effects and thereby complicates the definition of genetic architecture. Because the environment itself can have a genetic basis, it can evolve. As a result, it must be included This paper was submitted directly (Track II) to the PNAS office. in genetic analysis if one wishes to gain a thorough under- Abbreviations: QG, quantitative genetic; IGE, indirect genetic effect; DGE, direct genetic standing of trait evolution (4–8). effect; sib, sibling. A modeling scheme that incorporates indirect genetic effects See commentary on page 4357. (IGEs) [also known as associate effects (9)] has been developed *E-mail: [email protected].

www.pnas.org͞cgi͞doi͞10.1073͞pnas.0635741100 PNAS ͉ April 15, 2003 ͉ vol. 100 ͉ no. 8 ͉ 4655–4660 Downloaded by guest on September 29, 2021 ϩ D. melanogaster. This species has been one of the main model The first composite term on the right side of Eq. 3 [(Gff ␤ organisms for studies of developmental genetics (21) and has GfS) f] gives the response to selection when social partners are been extensively studied from a QG perspective (1). Despite this unrelated. This term illustrates that the evolution of a character enormous body of work, no previous investigation has directly described by Eq. 1 is determined by both changes in average ␤ examined the contribution of IGEs to genetic architecture, contribution of DGEs (Gff f) and correlated changes in the ␤ although previous work suggests that they should play an im- average contribution of IGEs via the social environment (GfS f). portant role in trait expression (22–25). Because flies develop The term also demonstrates that, when interacting individuals under high densities in an environment largely created by are unrelated, selection cannot act directly on the IGE compo- conspecifics [through the excretion of biotic residues (24), nent, because IGEs do not directly map to the individual ⌬៮ egestion of digestive enzymes (26), mechanical processing of phenotype (i.e., GSS does not contribute to zf). However, the medium, and direct competition for nutrients], there is consid- IGE component can evolve as a result of a correlated response erable opportunity for IGEs to affect development. To examine to selection when there is a genetic covariance between DGEs the importance of IGEs in this system, I begin by presenting a and IGEs. The change in the IGE component can be seen as an ៮ simple model of trait development and evolution that incorpo- evolutionary change in the mean social environment (⌬S), which rates IGEs (6). This model also forms the foundation for QG contributes to the cross-generational change in the mean phe- analysis by providing the expected components of variation used notype. Traditional QG models do not include this last term and in partition phenotypic variation into IGEs, DGEs, and their predict response to selection solely on the basis of strength of covariance. selection and direct additive genetic variance. Thus, the response to selection can be greater or less than expected from the A Model of Trait Expression and Evolution traditional QG model. When GfS is greater than Gff, the response Influences on the development of body size can be modeled as to selection can even be in the direction opposite that predicted a linear function of the direct effects of an individual’s genes and by the model that lacks IGEs. the effects of the environment provided by conspecifics. Other ϩ ␤ The second composite term in Eq. 3 [r(GSS GfS) f] points functions are possible, but the simple additive model appears out how relatedness and kin effects modify the evolutionary adequate based on the analysis of Drosophila pupa size (i.e., dynamics of traits affected by of IGEs. Interactions among kin epistatic, , and maternal effects are nonsignificant). modify the response to selection, because they alter the geno- With individuals interacting in pairs (i.e., each individual type-phenotype relationship (10) by introducing a sort of ‘‘gen- provides an environment for its partner), the phenotypic value otype-environment’’ covariance (with ‘‘environment’’ referring of an individual (zf) can be expressed as (after ref. 6): to the social environment). The degree of this covariance is z ϭ a ϩ e ϩ SЈ, [1] determined by the coefficient of relatedness of interactants (14, f f f 30). When interacting with kin, individuals experience predict-

where zf is the phenotypic value of trait f in the focal individual, able social environments, because they are related to the indi- af is the additive DGE, ef is a random environmental effect (the viduals providing those environments. This means that there is subscript f indicates that this is the focal trait), and SЈ is the effect a correlation between individual DGE values (af) and the IGE Ј of the environment provided by an individual’s social partner, values that they experience (aS), leading to a covariance between which can be negative (the prime indicates that this component individual phenotypic (zf) and IGE values (aS). As a result, is a characteristic of a different individual). The social effect of selection on a trait influenced by IGEs can act on both DGEs and ϭ ϩ the individual’s social partner (SЈ) is, likewise, modeled as a IGEs simultaneously [because cov(zf, aS) GfS rGSS]. De- linear equation: pending on the relative magnitude of the IGE variance (GSS) and the sign and magnitude of the IGE–DGE covariance (G ), the Ј ϭ Ј ϩ Ј fS S a s e s . [2] net response to selection can be increased or decreased when Ј interactants are related (27). as is the additive genetic effect on the quality of the social Ј The evolutionary dynamics of social performance (S) can be environment provided by an interacting partner, and es is the analyzed by using a social selection (16, 31–33) or kin-selection random environmental effect on the social environment pro- framework (12, 13, 15). Although most kin selection models vided. The trait S is analogous to ‘‘maternal performance’’ (the focus on the evolution of altruism, where a trait decreases the effect of the mother on the phenotype of her progeny) consid- individual’s fitness but increases the fitness of the individual’s ered in classic maternal-effect models (see ref. 27) and will be relatives, they can be applied to any type of trait (e.g., compe- referred to as ‘‘social performance.’’ Under this model of tition) (11, 12, 27, 30, 34–36). Because fitness was not directly inheritance, the response to selection (cross-generation change measured in this experiment, to model kin selection, it is in the mean phenotype) (⌬z៮ ) is predicted by the equation (see f necessary to assume that body size is a fitness correlate (i.e., ␤  ref. 6 for details on the derivation): f 0) (12). Because the individual phenotype explains all variance ⌬z៮ ϭ ͑G ϩ G ͒␤ ϩ r͑G ϩ G ͒␤ , [3] in fitness, this is not true social or kin selection (16). However, f ff fS f SS fS f because the characteristics of one individual influence the fitness

where Gff is the direct additive genetic variance of the trait, GSS is of another (with the individual phenotype mediating the map- ␤ the additive indirect genetic variance, f is the selection gradient ping from the traits of one individual to the fitness of another), acting on the phenotype (28), GfS is the genetic covariance between the evolutionary consequences of these social effects can still be direct and indirect genetic effects, and r is the coefficient of interpreted in the framework of social or kin selection (12, 16). relatedness of individuals. The genetic covariance, GfS, differs from To model the evolution of IGEs by kin selection, Cheverud the usually considered genetic covariance, because it measures the (12) considered a case where mothers affect the of degree to which genes simultaneously affect the phenotypes of their offspring. He focused on conditions under which maternal individuals and the phenotypes of their social partners. A positive performance is expected to evolve in an altruistic direction. value of GfS indicates that alleles that make an individual larger also Using that same model, Cheverud also presented a more general make their partner larger. A negative value indicates that alleles equation that can be applied to other types of kin effects (see that make individuals larger make their partners smaller. In the equation 16 in ref. 12). To relate these models, it is necessary to absence of IGEs, Eq. 3 simplifies to a version of the classic first introduce an equation predicting response of the social- ⌬៮ ϭ ␤ ‘‘breeder’s equation,’’ zf Gff f (29). performance trait to selection on body size:

4656 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0635741100 Wolf Downloaded by guest on September 29, 2021 ៮ ki ⌬S ϭ ͑G ϩ rG ͒␤ . [4] random from the LHM population and were placed in a vial with fS SS f one w͞w and one ki͞ki female (dams) and were left to mate for SEE COMMENTARY Note that the terms in Eq. 4 are all present in Eq. 3, because the 3 days. evolution of mean social performance directly contributes to the evolution of mean body size. As pointed out above, when r ϭ 0, Experimental Rearing Scheme. Eggs were collected from individual selection cannot act directly on IGEs, and social performance females every 2 h for 48 h. Eggs from the first 2-h interval were evolves only as a correlated response to selection on body size. discarded to avoid eggs that may have been held by females. Five However, when individuals are related, selection can act directly pairs of dams mated to five sires were randomly assigned to an on genetic variation affecting social performance. experimental block. Each replicate of the experiment contained Eq. 4 can be related to the kin selection models by rearranging five blocks, and the entire experiment was replicated three times it to examine the conditions under which social performance over a 4-mo period. All replicates together included a total of 150 ៮ shows an evolutionary increase (i.e., ⌬S Ͼ 0) due to selection on dams and 75 sires. A total of 2,930 progeny from these families body size (i.e., evolves in an altruistic direction): were used in the analysis. To partition IGEs from DGEs, this study uses a simple social

GfS structure that corresponds to the structure assumed in the above Ϫͫ ͬ Ͻ r. [5] G model. Same-age progeny from each family were reared from SS egg to pupation with, and thus experience an environment 16 provided by, either their full siblings (sibs), half sibs, or individ- This equation is equivalent to the Cheverud model (equation ͞ of ref. 12) under the assumptions used here. From Eq. 5,itis uals from an unrelated family. Eggs from each wi wi dam (which have the genotype w͞w) were reared with eggs from every ki͞ki clear that, when the covariance is positive, these conditions are ͞ always met. The conditions become more interesting when the dam (which have the genotype w ki) in the same block, and each covariance is negative (i.e., DGEs and IGEs are antagonistic), dam combination was replicated six times. These combinations because the term on the left is positive. The conditions are met allowed parentage to be assigned based on the ki phenotype. when the ratio of genetic covariance to indirect genetic variance Eggs derived from a single female were also reared together. A is less than the coefficient of relatedness. The ratio in Eq. 5 is single egg from a dam was placed in a rearing tube (details analogous to the cost–benefit ratio (k)in‘‘Hamilton’s rule’’ (11), below) along with an egg, collected within the same time period, from one other dam from her block. Pairs were reared under or what he called the ‘‘ratio of diminution’’ when referring to EVOLUTION competition. The negative covariance (G ) can be viewed as the competitive conditions in 0.4-ml polypropylene microcentrifuge fS tubes containing 50 ␮l of cornmeal molasses medium (the same selective component due to negative impacts on fitness of kin recipe as in the population tubes) and 5 ␮l of a yeast solution (1 and genetic variance (G ) as the selective component associated SS g of yeast per 10 ml of distilled water). Tubes were placed in with the positive effects on fitness (12). random locations in humidified boxes inside an environmentally Eq. 3 can also be rearranged to examine conditions for positive controlled chamber at 25°C on a 12:12-h light͞dark cycle and or negative responses to selection under the various combina- were randomly repositioned every day to diminish the influence tions of positive or negative selection and positive or negative of environmental differences among locations. At pupation, genetic covariances (as in section 5 of ref. 11). The evolutionary pupae were placed individually in 0.5-ml microcentrifuge tubes dynamics in the presence of nonkin social selection can be and held in the environmental control chamber. Images of each examined by setting r equal to zero in the above equations (e.g., pupa were taken under a stereomicroscope by using NIH IMAGE equation 14 in ref. 16). software. Length was measured to the nearest 5 ␮m (yielding Methods values with four significant digits). Pupae were held until eclo- ki sion, at which time parentage was determined based on the ki Study Population. The focal population (LHM) is a wild-type genotype. Dry body weights, including the pupal case, were highly variable laboratory adapted population segregating for a measured on a small subsample of 175 male and 175 female flies codominant marker (kinked [ki] on the third chromosome). This to establish the relationship between pupa size and dry weight. population is adapted to a 2-week generation cycle, with larvae These individuals were dried for2hat60°C and then kept in a developing under controlled intermediate densities on standard- dessication chamber until being weighed. Dry weights were ized cornmeal molasses medium seeded with yeast in 10-dram ␮ ki taken to the nearest 1.0 g by using a microbalance (yielding vials. The LHM population was created by introgressing ki from values with three significant digits). a marker stock (Bloomington Stock Center no. 2975, Bloom- ington, IN) into 120 lineages derived from the LHM population Genetic Analysis. QG analysis of pupa length was performed by by 18 generations of selective backcrossing to LHM (see ref. 37 using restricted maximum likelihood to fit a mixed-model for details on the LHM population). The retention of the 2975 ANOVA (38, 39). Separate analyses were done for the three genome in each of these lineages was estimated by testcrosses social structures: (i) pairs of unrelated individuals reared in back to 2975 and scoring of the nine linked recessive third- combination (analysis U; n ϭ 724), where each paternal half-sib chromosome markers that flank the ki in 2975. Lines family is combined with just one other family; (ii) individuals retaining a large portion of the 2975 genome were discarded, reared with half sibs (analysis H; n ϭ 708), and (iii) full sibs ki leaving 101 lines that were combined to found the LHM popu- reared together (analysis F; n ϭ 1498). All three analyses lation. The resulting population contains between 97.5% and included two random effects: sire and dam(sire), where the 99% of its genome from the laboratory population with the parentheses indicate a nested effect. The sire component esti- remaining 1–2.5% coming from the marker stock. mates the covariance of half-sibs (40), whereas dam(sire) esti- mates the covariance of full-sibs minus the covariance of half- Experimental Crosses. Virgin females were collected during the sibs. These two terms correspond to the ordinary sire and first 4 days of adult emergence and were mixed together to create dam(sire) terms from a paternal half-sib breeding design (40). an all-female population with a representation of genotypes All models also included the fixed effect of sex. Other fixed from the entire period of normal adult emergence. Females that effects were tested (e.g., block and effects of the ki) but were not were either homozygous wild-type at the ki locus (w͞w)or significant and were not included in the final models. homozygous kinked (ki͞ki) were chosen at random from this Expected covariances for each of the analyses were derived by mixed population of virgins. w͞w males (sires) were chosen at using the model for trait expression presented above (Eqs. 1 and

Wolf PNAS ͉ April 15, 2003 ͉ vol. 100 ͉ no. 8 ͉ 4657 Downloaded by guest on September 29, 2021 Table 1. Expected covariances for individuals reared with Table 2. Estimated quantitative genetic variance components individuals that are their full sibs or half sibs, or are unrelated Parameter Estimate 95% C.I. Relationship of Direct additive genetic variance (G ) 11,290 Ϯ1,497 social partners ANOVA term Expected covariance FF Indirect additive genetic variance (GSS) 5,865 Ϯ1,520 2 1 1 1 Full sibs Sire ␴ ϭ Gff ϩ GfS ϩ GSS Direct–indirect genetic covariance (GFS) Ϫ6,955 Ϯ1,488 SireFull 4 2 4 Ϯ 2 1 1 1 Total phenotypic variance 32,815 1,199 Dam(Sire) ␴ ϭ Gff ϩ GfS ϩ GSS DamFull 4 2 4 Values are the mean of 15 individual estimates (in ␮m2) taken from the 15 2 1 1 1 Half sibs Sire ␴ ϭ Gff ϩ GfS ϩ GSS SireHalf 4 2 2 independent replicates of the experimental design with 95% confidence 2 1 intervals (C.I.). Dam(Sire) ␴ ϭ Gff DamHalf 4 ␴2 ϭ 1 ϩ 1 Unrelated Sire SireUnrel Gff GSS 4 4 accounts for 17.9% of phenotypic variation. Note that DGEs 2 1 1 Dam(Sire) ␴ ϭ Gff ϩ GSS DamUnrel 4 4 account for just less than twice as much phenotypic variation as For the case where individuals are reared with individuals they are not IGEs. The genetic covariance between direct and indirect effects related to, each half-sib family is reared with individuals from one other is large and negative, corresponding to a (RfS) Ϫ Ϯ unrelated half-sib family. Covariances were derived by using Eqs. 1 and 2. Gff of 0.85, with an approximate 95% confidence interval of 0.20 is the additive genetic variance of the trait measured in the focal individual; (see ref. 1). This indicates a large degree of nonindependence of GSS is the additive genetic variance of the indirect genetic effect (i.e., genetic these effects (due to either or linkage disequilibrium). variance of the environment provided by another individual); and GfS is the The magnitude of the narrow-sense is in accord genetic covariance of direct and indirect effects. Dam(Sire) indicates that with the general findings of other studies that have estimated the dams are nested within sires in the half-sib design. heritability of body size in Drosophila. Most heritability estimates for body size (weight or thorax length) in other populations fall Ϸ 2) and are given in Table 1. The expected covariances of full and within the range of 0.2–0.4 (reviewed in ref. 42). The herita- half-sibs (i.e., the sire and dam[sire] terms) under the IGE model bility estimated here is also very close to the mean heritability differ for each of the three social structures. The expected (which equals 0.32) estimated from a large suite of morpholog- covariances were calculated by using a model that matches this ical traits in Drosophila (43). particular social system (pairwise social interactions) and thus Discussion would differ for other possible social structures. This further illustrates the potential problems that arise when considering the The results of this experiment demonstrate that the view of role of IGEs in genetic analyses and trait evolution; for any single genetic architecture, derived from an analysis that includes both experiment, it may be necessary to derive expected covariances DGEs and IGEs, is very different from what we would have by using a hypothetical construct that matches the expected found had we taken the traditional approach and ignored IGEs. pattern of phenotypic effects and social structure being used. Although the direct genetic variation would be apparent in a Because the expected covariances for the three social struc- traditional QG analysis, the contribution of IGEs would be tures have different contributions from DGEs (Gff), IGEs (GSS), missed or confounded with DGEs, depending on the social and their covariance (GfS), they can be used to solve for each of structure used (Table 1). The presence of a significant IGE these components (after ref. 41). There are multiple ways to variance (GSS) indicates that a considerable portion of the solve for each component, but only results from a single ap- genetic variation for body size results from interactions among proach are presented here (all approaches yield similar results). individuals. In these interactions, individuals are in part deter- The three sire terms were used, because they are more likely to mining each other’s size. Because these effects have a genetic conform to the model (i.e., they contain no potential contribu- basis, they result in IGEs. This component of genetic variation tion of dominance or maternal effects). Covariance terms were is hidden to ordinary genetic analyses that are focused exclusively solved for as follows: on direct effects. This is significant, because the IGE component represents real heritable variation in a population that can ϭ ͑␴2 Ϫ ␴2 ͒ GfS 2 SireFull SireUnrel , [6a] contribute to evolutionary changes (6, 9, 44). The presence of ϭ ͑␴2 Ϫ ␴2 ͒ IGEs means that the genetic architecture of any phenotype GSS 4 SireHalf SireFull , [6b] influenced by social environments may be even more complex G ϭ 4͑␴2 Ϫ ␴2 ϩ ␴2 ͒. [6c] than previously thought (45), because genetic influences can ff SireUnrel SireHalf SireFull arise from the genes of the individual as well as from genes in Separate models were fitted for each block of the experiment, other individuals. As a result, a thorough understanding of providing 15 independent estimates of each covariance param- genetic architecture requires an understanding of the various eter. Means and confidence intervals for all covariances were pathways, including indirect ones, through which genetic varia- estimated from these replicated estimates. tion leads to phenotypic variation in populations. Perhaps the most significant finding in this study is the very Results large negative genetic correlation between direct and indirect ϭϪ The partial correlation between pupa length and dry weight, genetic effects (RfS 0.85), indicating that the direct effects corrected for sex differences in these two characters, is 0.71, P Ͻ that genes have on body size are negatively associated with 0.0001, indicating that length is a very good substitute for total indirect effects on body size. This antagonistic relationship is body size. likely due to pleiotropy, where genes that make individuals larger The parameter estimates and confidence intervals of the direct create an environment that makes other individuals smaller due additive genetic variance (Gff), the additive indirect genetic to social competition for limited resources. In this scenario, variance (GSS), and the additive direct–indirect genetic covari- genetic variation for competitive ability appears as genetic ance (GfS) are given in Table 2. The largest component of variation for body size, because differential competitive success variance, direct additive genetic variance, accounts for 34.4% of leads to different resource accumulation, which is reflected in phenotypic variation. This value corresponds to the narrow sense body size. As a result, loci affecting competitiveness should heritability, h2. The component attributed to additive IGEs appear as part of the genetic architecture of body size. However,

4658 ͉ www.pnas.org͞cgi͞doi͞10.1073͞pnas.0635741100 Wolf Downloaded by guest on September 29, 2021 the impact of this sort of genetic variation on evolution is very This form of pleiotropy does not manifest at the individual level. different from other types of variation; the influence on the Rather, it is a population-level phenomenon where the same phenotype depends on the context provided by social interac- genes that make individuals larger through direct mapping also SEE COMMENTARY tions. Because these loci influence body size through their effect make them smaller via indirect mapping. Because these two on competitiveness, they can also affect the size of individuals’ effects add together to determine the effects of genes on the social partners that are competing for those same limited evolution of the mean phenotype in a population, the IGE–DGE resources. Thus, social competition necessarily makes the direct antagonism constrains the overall response to selection. Thus, and indirect effects of genotypes negatively correlated. Because even though a single individual does not manifest these pleio- competition is widespread and likely to influence trait expres- tropic effects (i.e., effects of genes are on the same trait sion, it seems probable that this sort of antagonistic relationship expressed in two different individuals), they still affect how a between DGEs and IGEs is common. trait is expected to evolve. The genetic covariance parameters from the QG analysis In the case of body size in Drosophila, we predict that selection (Table 2) can be combined with the evolutionary model pre- for increased size would be constrained, because selection for sented above to examine how IGEs modify the predicted evo- size leads to an increase in the average competitiveness of the lutionary dynamics of body size. The genetic parameters can be population. The change in average competitiveness would ap- substituted into Eq. 3 to predict response to selection as a pear as a degradation of the social environment, which would function of strength of selection. The resulting equation tells us partially counter the positive changes in DGEs affecting the trait. how the genetic system translates selection into evolutionary Although the phenotype is not expected to evolve at the rate change. Starting with the assumption that interactions are among predicted from the level of genetic variation and strength of selection, evolution at the genetic level (i.e., change in allele unrelated individuals, we would predict response of body size ⌬៮ (pupa length in micrometers) to selection by using the equation: frequencies as measured by af) continues. However, this ge- netic evolution is not matched by an equivalent magnitude of ⌬៮ ϭ ͑ Ϫ ͒␤ ϭ ␤ zf 11,290 6,955 f 4,335 f . [7] change at the phenotypic level. For example, if we were to select the largest individuals in a generation, they would on average In contrast, if we ignore the contribution of IGEs and use the also be the most competitive individuals. These individuals ⌬៮ ϭ ␤ standard QG model ( zf Gff f), we predict: would have a set of genotypes that, under the current social conditions, make individuals large, and selection would there- ⌬៮ ϭ ␤ EVOLUTION zf 11,290 f . [8] fore produce a genetic change in the population. However, the progeny of these individuals would find themselves in a more Thus, under the IGE model, the predicted response to selection competitive environment, because they all inherited genes from is less than half that expected under a traditional model. the most competitive individuals in the previous generation. When individuals that develop together are related, the ex- Thus, this new generation would not be as large as we would have pected response to selection is further diminished due to kin expected based on the size of their parents, because they are effects. Adding relatedness to Eq. 8 yields experiencing a different social environment. As an analogy, we can view body size evolving on a treadmill, ⌬៮ ϭ ␤ Ϫ ␤ zf 4,335 f r1,090 f . [9] where every step forward is accompanied by movement back- ward due to the associated negative changes in the environment. Eq. 9 demonstrates that the more closely individuals are related The result is that, depending on the speed of the treadmill, the to their competitors, the lower the expected response to selection trait either remains where it started or does not move as far as on body size. For example, if individuals develop with their full expected [Dickerson (48) referred to this as ‘‘slippage’’ on the sibs, a situation that would arise when singly inseminated females treadmill]. When describing his fundamental theorem of natural lay multiple eggs on a food source, the expected response to ␤ selection, Fisher (47) recognized this process as the critical selection would be 3,790 f. This reduction in the expected rate reason why populations do not continue to evolve to higher states of evolution was predicted by Hamilton (see part c of section 3 of fitness (or character values) despite widespread recurrent in ref. 11) in his consideration of negative kin effects (see also directional selection. However, his intuition that these effects ref. 46). Because the DGE–IGE covariance (GfS) is larger than would exist and be potentially important had not been previously the IGE variance (GSS), social performance cannot evolve in an demonstrated. In addition, recurrent directional selection has ‘‘altruistic’’ direction, because there is no level of relatedness that been predicted to be a consequence of social selection, where the can satisfy the conditions expressed in Eq. 5. treadmill of social competition leads to a relentless force of The diminished response to selection caused by the antago- selection (32). nistic counterevolution of IGEs (Eq. 7) and the further dimi- This type of antagonistic evolution of the social environment nution expected when interactions are among relatives (Eq. 9), has also been considered important in cases of evolution by can be viewed as a constraint on phenotypic evolution. The sexual selection, where each sex provides an environment for the constraint arises because evolution of body size is determined by other. Antagonistic interactions of males and females (sexual the sum of changes in the mean contribution of both DGEs and conflict) create a situation where the adaptation of one sex is ⌬៮ ϩ⌬៮ IGEs (i.e., we can view the response to selection as af aS), perceived as a degradation of the environment experienced by ⌬៮ ⌬៮ which show antagonistic correlated evolution (i.e., af and aS the other. Empirical evidence of this process has come from the are of opposite sign) due to the negative IGE–DGE covariance. evolution of seminal proteins in Drosophila, where males and These antagonistic effects partially cancel each other out and, as females continually adapt to the environment provided by the a result, body size is expected to show much less of a response other sex (49). This process has been referred to as the intraspe- to selection than predicted by traditional QG theory (44). This cific Red Queen to reflect the fact that the evolving environment type of constraint is fundamentally different from the usually provides a constant force of selection (7). considered genetic constraint on evolution arising from antag- The results of this study, and the general phenomenon of onistic pleiotropy (where genetic multicollinearity restricts mul- antagonism between IGEs and DGEs, may also help explain a tivariate evolution). The constraint arises from ‘‘socially antag- number of interesting but unexplained patterns of variation in onistic pleiotropy,’’ where genes that positively affect a character heritability found in other studies. For example, Scheiner and (via their effect on competitiveness) also negatively impact the Lyman (50) found that the realized heritability of body size social environment experienced by conspecifics (see ref. 47). (measured as thorax size) was consistently smaller than the

Wolf PNAS ͉ April 15, 2003 ͉ vol. 100 ͉ no. 8 ͉ 4659 Downloaded by guest on September 29, 2021 heritability value estimated by using full- or half-sib correlations. itive environment, even if the direct narrow-sense They estimated narrow-sense heritabilities both before and after were similar in the two environments. artificial selection on body size and found that the two herita- Because interactions between individuals are ubiquitous, the bility estimates were very similar, indicating that selection did opportunity for phenotypic effects of these interactions, and thus not significantly diminish genetic variance. However, realized IGEs, is considerable. This is particularly true for phenotypes heritabilities, estimated from the replicated responses to selec- like social behaviors, whose expression often depends on the social environment [or the ‘‘behavioral environment’’ (4, 52)], or tion over 16 generations, were considerably smaller than the for traits affected by social competition or other interactions narrow–sense heritability estimates from the sib analysis (the Ϸ during trait development. In addition, in many populations and realized heritabilities averaged 64% of the narrow-sense her- in most laboratory analyses of quantitative genetics, interacting͞ itability values). This result would be expected if their population competing individuals are related (e.g., ref. 53), providing the has a genetic architecture similar to the population analyzed opportunity for kin effects and selection (54). Thus, the data here. Antagonistic evolution of IGEs would be expected to presented here from Drosophila are expected to have significant diminish the response to selection (as in Eq. 9), making the implications for genetic analysis of a variety of traits in a diversity realized heritability smaller than the narrow-sense heritability. of taxa. These data suggest that the traditional paradigm, focused Given that they used a standard group-rearing protocol, this is exclusively on direct effects of genes, is inadequate. To develop likely to be the case. Another example comes from a study of an accurate picture of genetic architecture that describes how realized heritabilities of body size (fresh weight) in both rich and genetic variation leads to phenotypic variation in a population, poor larval environments (51). In this study, Hillesheim and information on both direct and indirect effects of genes will be required whenever individuals interact. Stearns found that nearly all realized heritability estimates were larger when larvae were reared in the richer environment. I thank E. D. Brodie III, J. M. Cheverud, P. X. Kover, A. J. Moore, and Assuming that the poor environment is also more competitive, M. J. Wade for insightful discussions during the development this work; we would expect the realized heritabilities to be smaller in that A. J. Moore, M. Pigliucci, M. J. West-Eberhard, and an anonymous environment due to stronger antagonistic IGEs. In other words, reviewer for thoughtful comments on the manuscript; A. Epps and P. X. because competition enforces the negative covariance between Kover for help with the conducting of the experiments; and A. K. Chippindale for generously sharing the LHM population. This work was IGEs and DGEs, one would expect that the covariance would be supported by a grant from Sigma Xi, a Postdoctoral Fellowship in larger (i.e., more negative) when competition is stronger. This Bioinformatics from the National Science Foundation (NSF), and NSF would result in the smaller response to selection in the compet- IBN-9896116 to E. D. Brodie III.

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