Social Selection and Indirect Genetic Effects in Structured populations

Social selection and indirect genetic effects (IGEs) are established concepts in both behavioural ecology and evolutionary genetics. While IGEs describe effects of an individual’s genotype on phenotypes of social partners (and may thus affect their fitness indirectly), the concept of social selection assumes that a given phenotype in one individual affects the fitness of other individuals directly. Although different frameworks, both have been used to investigate the evolution of social traits, such as cooperative behaviour. Despite their similarities (both concepts consider interactions among individuals), they differ in the type of interaction. It remains unclear whether the two concepts make the same predictions about evolutionary trajectories or not. To address this question, we investigate four possible scenarios of social interactions and compare the effects of IGEs and social selection for trait evolution in a multi-trait multi-member model. We show that the two mechanisms can yield similar evolutionary outcomes and that both can create selection pressure at the group level. However, the effect of IGEs can be stronger due to the possibility of feedback loops. Finally, we demonstrate that IGEs, but not social selection gradients, may lead to differences in the direction of evolutionary response between genotypes and phenotypes.     

The Afterlife of Interspecific Indirect Genetic Effects: Genotype Interactions Alter Litter Quality with Consequences for Decomposition and Nutrient Dynamics

Aboveground-belowground linkages are recognized as divers of community dynamics and ecosystem processes, but the impacts of plant-neighbor interactions on these linkages are virtually unknown. Plant-neighbor interactions are a type of interspecific indirect genetic effect (IIGE) if the focal plant’s phenotype is altered by the expression of genes in a neighboring heterospecific plant, and IIGEs could persist after plant senescence to affect ecosystem processes. This perspective can provide insight into how plant-neighbor interactions affect evolution, as IIGEs are capable of altering species interactions and community composition over time. Utilizing genotypes of Solidago altissima and Solidago gigantea, we experimentally tested whether IIGEs that had affected living focal plants would affect litter decomposition rate, as well as nitrogen (N) and phosphorous (P) dynamics after the focal plant senesced. We found that species interactions affected N release and genotype interactions affected P immobilization. From a previous study we knew that neighbor genotype influenced patterns of biomass allocation for focal plants. Here we extend those previous results to show that these changes in biomass allocation altered litter quality, that then altered rates of decomposition and nutrient cycling. Our results provide insights into above- and belowground linkages by showing that, through their effects on plant litter quality (e.g., litter lignin:N), IIGEs can have afterlife effects, tying plant-neighbor interactions to ecosystem processes. This holistic approach advances our understanding of decomposition and nutrient cycling by showing that evolutionary processes (i.e., IIGEs) can influence ecosystem functioning after plant senescence. Because plant traits are determined by the combined effects of genetic and environmental influences, and because these traits are known to affect decomposition and nutrient cycling, we suggest that ecosystem processes can be described as gene-less products of genetic interactions among the species comprising ecological communities.     

Phenotypic and Evolutionary Consequences of Social Behaviours: Interactions among Individuals Affect Direct Genetic Effects

Traditional quantitative genetics assumes that an individual''s phenotype is determined by both genetic and environmental factors. For many animals, part of the environment is social and provided by parents and other interacting partners. When expression of genes in social partners affects trait expression in a focal individual, indirect genetic effects occur. In this study, we explore the effects of indirect genetic effects on the magnitude and range of phenotypic values in a focal individual in a multi-member model analyzing three possible classes of interactions between individuals. We show that social interactions may not only cause indirect genetic effects but can also modify direct genetic effects. Furthermore, we demonstrate that both direct and indirect genetic effects substantially alter the range of phenotypic values, particularly when a focal trait can influence its own expression via interactions with traits in other individuals. We derive a function predicting the relative importance of direct versus indirect genetic effects. Our model reveals that both direct and indirect genetic effects can depend to a large extent on both group size and interaction strength, altering group mean phenotype and variance. This may lead to scenarios where between group variation is much higher than within group variation despite similar underlying genetic properties, potentially affecting the level of selection. Our analysis highlights key properties of indirect genetic effects with important consequences for trait evolution, the level of selection and potentially speciation.     

Indirect Interactions in the High Arctic

Indirect interactions as mediated by higher and lower trophic levels have been advanced as key forces structuring herbivorous arthropod communities around the globe. Here, we present a first quantification of the interaction structure of a herbivore-centered food web from the High Arctic. Targeting the Lepidoptera of Northeast Greenland, we introduce generalized overlap indices as a novel tool for comparing different types of indirect interactions. First, we quantify the scope for top-down-up interactions as the probability that a herbivore attacking plant species i itself fed as a larva on species j. Second, we gauge this herbivore overlap against the potential for bottom-up-down interactions, quantified as the probability that a parasitoid attacking herbivore species i itself developed as a larva on species j. Third, we assess the impact of interactions with other food web modules, by extending the core web around the key herbivore Sympistis nigrita to other predator guilds (birds and spiders). We find the host specificity of both herbivores and parasitoids to be variable, with broad generalists occurring in both trophic layers. Indirect links through shared resources and through shared natural enemies both emerge as forces with a potential for shaping the herbivore community. The structure of the host-parasitoid submodule of the food web suggests scope for classic apparent competition. Yet, based on predation experiments, we estimate that birds kill as many (8%) larvae of S. nigrita as do parasitoids (8%), and that spiders kill many more (38%). Interactions between these predator guilds may result in further complexities. Our results caution against broad generalizations from studies of limited food web modules, and show the potential for interactions within and between guilds of extended webs. They also add a data point from the northernmost insect communities on Earth, and describe the baseline structure of a food web facing imminent climate change.     

Social Effects of Genetic Counselling

In a follow-up study of 104 subjects referred for genetic counselling between 1965 and 1969 all were at risk of having children with a variety of serious genetic disorders. Most subjects were in social classes III and IV, were married, in their late 20s, and already had an affected child. Sixty-three per cent. were referred by hospital consultants, 27% by their general practitioners, and 10% were self-referrals. All of those counselled appeared to have appreciated the genetic implications, although four overestimated the risks and 11 underestimated the risks.Of those at high risk (greater than 1 in 10) of having an affected child 10 out of 55 couples “planned” further pregnancies despite the risks. In two this was because they had been unable to adopt a child, in four because they had no living children and the disorders in question usually resulted in stillbirth or death in infancy so that the “burden” of an affected child would be of relatively short duration, and one mother had had antenatal diagnosis and selective abortion. Most of the couples in the low-risk group (less than 1 in 20) were reassured and planned further pregnancies.     

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Play fighting in the rat involves attack and defense of the nape of the neck, which if contacted, is gently nuzzled with the snout. Because the movements of one animal are countered by the actions of its partner, play fighting is a complex, dynamic interaction. This dynamic complexity raises methodological problems about what to score for experimental studies. We present a scoring schema that is sensitive to the correlated nature of the actions performed. The frequency of play fighting can be measured by counting the number of playful nape attacks occurring per unit time. However, playful defense, as it can only occur in response to attack, is necessarily a contingent measure that is best measured as a percentage (#attacks defended/total # attacks X 100%). How a particular attack is defended against can involve one of several tactics, and these are contingent on defense having taken place; consequently, the type of defense is also best expressed contingently as a percentage. Two experiments illustrate how these measurements can be used to detect the effect of brain damage on play fighting even when there is no effect on overall playfulness. That is, the schema presented here is designed to detect and evaluate changes in the content of play following an experimental treatment.     

Dynamics and Interactions of Viroids

Abstract

Viroids are single stranded circular RNA molecules of 120 000 dal tons which are pathogens of certain higher plants and replicate autonomously in the host cell. Virusoids are similar to viroids in respect to size and circularity but do replicate only as a part of a larger plant virus. The structure and structural transitions have been investigated by thermodynamic, kinetic and hydrodynamic methods and have been compared to results from calculations of the most favorable native structures and the denaturation process. The algorithm of Zuker et al. was modified for the application to circular nucleic acids.

For viroids the calculations confirm our earlier theoretical and experimental results about the extended native structure and the highly cooperative transition into a branched structure. Virusoids, although described in the literature as viroid-like, show less base pairing, branching in the native secondary structure, and only low cooperativity during denaturation. They resemble more closely the properties of random sequences with length, G:C content, and circularity as in viroids but sequences generated by a computer. The comparison of viroids, virusoids and circular RNA of random sequences underlines the uniqueness of viroid structure.

The interactions of viroids with dye and oligonucleotide-ligands and with RNA-polymerase II from wheat germ, which enzyme replicates viroids in vitro, has been studied in order to correlate viroid structure and its ability for specific interactions. Specificity of the interactions may be interpreted on the basis of the neighbourhood of double stranded and single stranded regions. In the host cell viroids are localized in the cell nucleus; they may be detected as free nucleic acids and in high molecular weight complexes together with other RNA and proteins.     

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.     

短季棉早熟性的遗传效应及其与环境互作研究

以熟期不同的9个棉花品种为亲本,按部分双列杂交配制46个组合的F1、F2,在3个不同生态环境条件下,研究了7个早熟相关性状的遗传效应及其与环境互作。结果表明:短季棉7个早熟相关性状的遗传均以加性效应为主,同时存在着显性效应,对于播种-现蕾、播种-开花和现蕾-开花还存在着上位性效应;短季棉各早熟性状的遗传效应与环境互作显著。生育期、播种-开花的狭义遗传率均较高,分别为66.1%和49.1%,且与环境互作效应较小,而果枝始节和播种-现蕾的遗传率最低,分别为19.8%和18.8%,且与环境互作达到极显著水平,现蕾-开花、开花-吐絮和株高这3个性状的遗传率及其与环境互作居中。由此说明:早熟性的遗传受环境影响较大,在生态条件差异较大的育种地点,以果枝始节和播种-现蕾作为早熟性指标进行异地选择是不可靠的,而以生育期、开花期为早熟性选择指标是比较可行的。     

Current Applications of Models of Genetic Effects with Interactions Across the Genome

Models of genetic effects integrate the action of genes, regulatory regions and interactions among alleles across the genome. Such theoretical frameworks are critical for applied studies in at least two ways. First, discovering genetic networks with specific effects underlying traits in populations requires the development of models that implement those effects as parameters—adjusting the implementation of epistasis parameters in genetic models has for instance been a requirement for properly testing for epistasis in gene-mapping studies. Second, studying the properties and implications of models of genetic effects that involve complex genetic networks has proven to be valuable, whether those networks have been revealed for particular organisms or inferred to be of interest from theoretical works and simulations. Here I review the current state of development and recent applications of models of genetic effects. I focus on general models aiming to depict complex genotype-to-phenotype maps and on applications of them to networks of interacting loci.     

Joint Modeling of Multiple Social Networks to Elucidate Primate Social Dynamics: I. Maximum Entropy Principle and Network-Based Interactions

In a complex behavioral system, such as an animal society, the dynamics of the system as a whole represent the synergistic interaction among multiple aspects of the society. We constructed multiple single-behavior social networks for the purpose of approximating from multiple aspects a single complex behavioral system of interest: rhesus macaque society. Instead of analyzing these networks individually, we describe a new method for jointly analyzing them in order to gain comprehensive understanding about the system dynamics as a whole. This method of jointly modeling multiple networks becomes valuable analytical tool for studying the complex nature of the interaction among multiple aspects of any system. Here we develop a bottom-up, iterative modeling approach based upon the maximum entropy principle. This principle is applied to a multi-dimensional link-based distributional framework, which is derived by jointly transforming the multiple directed behavioral social network data, for extracting patterns of synergistic inter-behavioral relationships. Using a rhesus macaque group as a model system, we jointly modeled and analyzed four different social behavioral networks at two different time points (one stable and one unstable) from a rhesus macaque group housed at the California National Primate Research Center (CNPRC). We report and discuss the inter-behavioral dynamics uncovered by our joint modeling approach with respect to social stability.     

Indirect Genetic Effects and Housing Conditions in Relation to Aggressive Behaviour in Pigs

Indirect Genetic Effects (IGEs), also known as associative effects, are the heritable effects that an individual has on the phenotype of its social partners. Selection for IGEs has been proposed as a method to reduce harmful behaviours, in particular aggression, in livestock and aquaculture. The mechanisms behind IGEs, however, have rarely been studied. The objective was therefore to assess aggression in pigs which were divergently selected for IGEs on growth (IGEg). In a one generation selection experiment, we studied 480 offspring of pigs (Sus scrofa) that were selected for relatively high or low IGEg and housed in homogeneous IGEg groups in either barren or enriched environments. Skin lesion scores, a proxy measure of aggression, and aggressive behaviours were recorded. The two distinct IGEg groups did not differ in number of skin lesions, or in amount of reciprocal fighting, both under stable social conditions and in confrontation with unfamiliar pigs in a 24 h regrouping test. Pigs selected for a positive effect on the growth of their group members, however, performed less non-reciprocal biting and showed considerably less aggression at reunion with familiar group members after they had been separated during a 24 h regrouping test. The enriched environment was associated with more skin lesions but less non-reciprocal biting under stable social conditions. Changes in aggression between pigs selected for IGEg were not influenced by G×E interactions with regard to the level of environmental enrichment. It is likely that selection on IGEg targets a behavioural strategy, rather than a single behavioural trait such as aggressiveness.     

Indirect Genetic Effects from Competition in the Clonal Herb Sedum album (Crassulaceae)

Recent years have seen increasing interest in indirect genetic effects, i.e. influences on the phenotype that depend on the genotype of other conspecific individuals; however, the empirical evidence for such effects is still limited, especially in wild plant species. The present study of the clonal herb Sedum album assessed direct and indirect genetic effects on performance-related traits in a 4-year experiment with clonally replicated genotypes, grown in pairs and differing in anthocyanin pigmentation to allow separation of individuals during data collection. In agreement with the existence of indirect genetic effects, the experimentally-paired plants not only expressed their own genotype but were also affected by the genotype of their pair mate. The effect of neighbour genotype explained up to one-fourth of the variation in performance and most likely resulted from competition, imposed by the close physical contact between paired individuals and the limiting conditions used in the garden environment. Indirect genetic effects from competition have the potential to enhance the efficacy of group-level selection relative to individual selection, given the nutrient-poor and spatially-confined substrate available to plants of S. album in the natural habitat.     

Dynamics of the Interactions of Basic Proteins with Polyribonucleotides

Pancreatic ribonuclease was irradiated in the dried state with electrons and then added to acetate buffer solutions that contained different concentrations of polyribonucleotides. Qualitatively similar results were obtained by adding a combination of unirradiated ribonuclease and lysozyme to such solutions. Such solutions scatter light strongly, and the intensity of the scattered light changes with time after mixing. The angular distribution of the scattered light was obtained as a function of time and compared with the rates at which hydrolysis products were formed. The turbidity of the solutions increases rapidly with time at the lower polyribonucleotide concentrations, and seems to result from a complex between inactive ribonuclease, or lysozyme, and oligonucleotides that appear during enzymic hydrolysis of the polynucleotides. The dissymmetry of the scattered light is approximately 5, indicating that the scattering centers are, if spherical, about 1500 A in diameter. The turbidities are remarkably high when one considers the low concentrations of protein and nucleic acid materials that are used.     

Social Interactions through the Eyes of Macaques and Humans

Group-living primates frequently interact with each other to maintain social bonds as well as to compete for valuable resources. Observing such social interactions between group members provides individuals with essential information (e.g. on the fighting ability or altruistic attitude of group companions) to guide their social tactics and choice of social partners. This process requires individuals to selectively attend to the most informative content within a social scene. It is unclear how non-human primates allocate attention to social interactions in different contexts, and whether they share similar patterns of social attention to humans. Here we compared the gaze behaviour of rhesus macaques and humans when free-viewing the same set of naturalistic images. The images contained positive or negative social interactions between two conspecifics of different phylogenetic distance from the observer; i.e. affiliation or aggression exchanged by two humans, rhesus macaques, Barbary macaques, baboons or lions. Monkeys directed a variable amount of gaze at the two conspecific individuals in the images according to their roles in the interaction (i.e. giver or receiver of affiliation/aggression). Their gaze distribution to non-conspecific individuals was systematically varied according to the viewed species and the nature of interactions, suggesting a contribution of both prior experience and innate bias in guiding social attention. Furthermore, the monkeys’ gaze behavior was qualitatively similar to that of humans, especially when viewing negative interactions. Detailed analysis revealed that both species directed more gaze at the face than the body region when inspecting individuals, and attended more to the body region in negative than in positive social interactions. Our study suggests that monkeys and humans share a similar pattern of role-sensitive, species- and context-dependent social attention, implying a homologous cognitive mechanism of social attention between rhesus macaques and humans.     

Identification of Direct and Indirect Social Network Effects in the Pathophysiology of Insulin Resistance in Obese Human Subjects

Objective

The aim of the present study was to examine to what extent different social network mechanisms are involved in the pathogenesis of obesity and insulin-resistance.

Design

We used nonparametric and parametric regression models to analyse whether individual BMI and HOMA-IR are determined by social network characteristics.

Subjects and Methods

A total of 677 probands (EGO) and 3033 social network partners (ALTER) were included in the study. Data gathered from the probands include anthropometric measures, HOMA-IR index, health attitudes, behavioural and socio-economic variables and social network data.

Results

We found significant treatment effects for ALTERs frequent dieting (p<0.001) and ALTERs health oriented nutritional attitudes (p<0.001) on EGO''s BMI, establishing a significant indirect network effect also on EGO''s insulin resistance. Most importantly, we also found significant direct social network effects on EGO''s insulin resistance, evidenced by an effect of ALTERs frequent dieting (p = 0.033) and ALTERs sport activities (p = 0.041) to decrease EGO''s HOMA-IR index independently of EGO''s BMI.

Conclusions

Social network phenomena appear not only to be relevant for the spread of obesity, but also for the spread of insulin resistance as the basis for type 2 diabetes. Attitudes and behaviour of peer groups influence EGO''s health status not only via social mechanisms, but also via socio-biological mechanisms, i.e. higher brain areas might be influenced not only by biological signals from the own organism, but also by behaviour and knowledge from different human individuals. Our approach allows the identification of peer group influence controlling for potential homophily even when using cross-sectional observational data.