Using Primate Behavior to Draw Conclusions About Hominins

Abstract

The complexity and diversity of primate behavior have long attracted the attention of ethologists, psychologists, behavioral ecologists, and neuroscientists. Recent studies have advanced our understanding of the nature of genetic influences on differences in behavior among individuals within species. A number of analyses have focused on the genetic analysis of behavioral reactions to specific experimental tests, providing estimates of the degree of genetic control over reactivity, and beginning to identify the genes involved. Substantial progress is also being made in identifying genetic factors that influence the structure and function of the primate brain. Most of the published studies on these topics have examined either cercopithecines or chimpanzees, though a few studies have addressed these questions in other primate species. One potentially important line of research is beginning to identify the epigenetic processes that influence primate behavior, thus revealing specific cellular and molecular mechanisms by which environmental experiences can influence gene expression or gene function relevant to behavior. This review summarizes many of these studies of non-human primate behavioral genetics. The primary focus is on analyses that address the nature of the genes and genetic processes that affect differences in behavior among individuals within non-human primate species. Analyses of between species differences and potential avenues for future research are also discussed.

1 INTRODUCTION

The complexity and diversity of behaviors expressed by non-human primates (NHPs) is one of the hallmark features of their biology. The fascination that primates inspire in professional researchers and the general public alike derives in no small part from the ability of individual NHPs to display sophisticated sequences of behaviors, which are expressed flexibly in the face of environmental and/or social change, and are often taken to indicate complex cognitive abilities. The cognitive and behavioral capabilities of NHP stem, of course, from their underlying neurobiological complexity. That neurobiology is in turn the consequence of developmental genetic processes that govern, or possibly simply guide and predispose, the ontogenetic development of the intricate central nervous systems, neural circuitry, and behavioral outcomes that have attracted so much scientific attention.

Taking my lead from Niko Tinbergen, whose "four questions" profoundly influenced the growth of ethology (Tinbergen, 1963), I suggest (with no pretensions to similar impact) that there are four distinct topics that could be considered when investigating the behavioral genetics of NHP. First, many neuroscientists are interested in the developmental genetic processes that produce the complex brain structures and functions characteristic of NHPs, including those developmental processes that make primate brains, especially hominoid brains, different from other mammals. Second, researchers may address the genetic differences that produce the neurobiological and behavioral differences among species: why do some primate species do X while others do Y, answered from the perspective of genetic causes? Third, there is significant interest in the amount and type of behavioral variation observed among individuals within species that can be attributed to genetic differences. The goal here is to identify the specific genes and genetic variants that drive individual differences within a species or closely related clade of species. And finally, although given significantly less attention than the other questions, researchers may attempt to define the developmental genetic and/or functional genetic processes that are involved in generating the neurobiological features that facilitate the behavioral flexibility of individual primates. Specifically, are the changes in behavior that occur when an individual finds itself in altered circumstances, such as a change in social relationships within a group, dependent on changes in underlying gene expression?

Each of those four topics has its own scientific interest. This review will focus primarily on the third of those four topics—inter-individual differences in behavior—with brief discussions of the second and fourth. Individual differences in behavior within species are certainly a compelling aspect of primate biology, and have drawn the attention and committed efforts of many primatologists. Significant progress has been made over the past 20–25 years in quantifying genetic effects on individual differences in behavior, and in some cases identifying specific genes and genetic variants that are correlated with such differences. The first two topics above, genetic causes of uniquely primate neurobiological traits and genetic determinants of inter-species differences among primate taxa, are also making progress. I would suggest, however, that progress in those areas has been slower than for analyses of the factors governing variation within species, probably because the theory and methods for uncovering genetic causes of within species variation are better developed. On the topic of genetic factors underlying between-species differences, the contrasts between humans and NHP have attracted substantially more attention than differences among NHP (e.g., Bakken et al., 2016; Bozek et al., 2014; Charrier et al., 2012; O'Bleness, Searles, Varki, Gagneux, & Sikela, 2012).

This review will discuss current evidence for behavioral genetic variation within primate species, but unfortunately it cannot cover all relevant and important studies in that area. Reflecting the literature as a whole, much of the work discussed deals with either particular cercopithecine primates (macaques, baboons, and vervets) or chimpanzees. These are the primates that have received the vast majority of attention in this field, though a few studies of other species have been reported and will be mentioned. Furthermore, most published studies describe data from captive primates, and there are obvious practical reasons for this emphasis. In the final section, I will present some ideas about future directions for the field of NHP behavioral genetics, suggesting that greater emphasis on the analysis of natural populations should be encouraged.

Although I hope that the short summaries presented here will highlight the progress we have made in the past ∼20 years, there is much more that we do not know, including some very basic questions for which we have essentially no information. One fundamental question is this: How many independent genetic changes are required to make a biologically meaningful alteration in an adaptively significant behavior, such as the quality of social relationships within groups, or the demographic pattern of inter-group dispersal? In other words, how many genes are involved in determining the difference between rhesus macaques (Macaca mulatta) and crested macaques (Macaca nigra) in the style and quality of social interactions (Duboscq et al., 2013; Thierry, 2007), or the difference between olive baboons (Papio anubis) and hamadryas baboons (Papio hamadryas) in sex-specific patterns of dispersal and philopatry (Swedell, 2011)? Although there is as yet little information from NHP to address these questions, we can draw on two non-primate examples that may illustrate why we should not assume that any specific behavioral phenotype has either a simple or a complex underlying genetic basis. One perspective comes from now classic work on monogamous versus polygynous voles, in which a simple change in one neurotransmitter accounts for significant differences in what might be taken as a complex, multifaceted change in social relationships (Hammock, 2007; Young, Winslow, Nilsen, & Insel, 1997). The other perspective concerns the genetics of human height (stature). Thousands of common genetic variants have been identified that influence this phenotype (Wood et al., 2014), but the trait is also well-known to be dramatically affected by damaging single mutations in specific genes.

Such examples force the conclusion that the apparent simplicity or complexity of a phenotype provides little indication of the complexity of the underlying genetic influences. Further, I would suggest that this notion shares some similarity with the argument put forward by Barrett, Henzi and Rendall (Barrett, 2009; Barrett, Henzi, & Rendall, 2007) that the apparent complexity of behaviors observed among primates does not necessarily require that those animals are engaging in complex, internal, and declarative cognitive processes of the sort that humans experience. In parallel with Barrett's (2009) argument for behavior, we should also acknowledge that genotype–phenotype relationships with seemingly complex outcomes (i.e., multi-faceted changes in behavior resulting from genetic changes) may arise from quite simple underlying cellular mechanisms.

2 METHODS FOR DETECTING GENETIC EFFECTS ON BEHAVIORAL VARIATION WITHIN SPECIES

One fundamental assumption of evolutionary primatology is that behavioral differences among species evolve at least partly, although more likely primarily, through natural selection. The operation of natural selection depends on the presence of genetic differences among individuals within a species that predispose them to behave differently from one another. Thus, the inference that between-species behavioral diversity results from selection necessitates the additional assumption that significant within-species genetic variation has influenced behavior in the evolutionary past, and presumably is also present in species today. There are two commonly used strategies to determine whether genetic influences affect between-individual variation within a single species or population. The first approach is to use the methods of quantitative genetics (Anholt and Mackay, 2010; Falconer and Mackay 1996). Pedigree information is employed to determine the kinship or genealogical relatedness among all pairs of individuals within a target population, and pairwise phenotypic differences (in the case of behavior, pairwise differences in some quantitative measure of behavioral variation) are correlated with these kinship values. Closely related individuals share more genes in common than do more distantly related pairs. Thus, a significant negative correlation across all pairs of individuals in the dataset, such that higher pairwise kinship (greater relatedness) between two individuals is associated with lower pairwise phenotypic difference, indicates an overall effect of genetic similarity on the trait of interest (Anholt and Mackay, 2010; Falconer and Mackay 1996).

The relationship between phenotypic variation and genetic differences can be summarized as the heritability of a phenotype (more specifically the narrow sense heritability: see Anholt & MacKay, 2010). Narrow sense heritability is the ratio between the variance in the population due to additive genetic differences among individuals (σ² _A) and the total phenotypic variance in the population (σ² _P). Heritability is abbreviated as h ², so h ² = σ² _A/σ² _P (Falconer and Mackay 1996). Additive genetic variance (σ² _A) is that component of genetic variance that is transmitted from parent to offspring, that is, the effect of allelic variation directly inherited at individual loci. (Readers interested in a more thorough discussion of narrow sense heritability versus broad sense heritability, which includes variance due to dominance, epistasis, and other genetic processes, are referred to Anholt & MacKay, 2010 or Falconer and Mackay 1996). Because heritability is the ratio of additive genetic variation to total phenotypic variation, the value of h ² estimated for any given population is relevant only for that population assessed in a given environment using a specific protocol. If the same population of animals were tested in the same way, but in a different environmental context that generated greater environmental variance, this would increase the total phenotypic variance among the test subjects. As a result, the estimated h ² would be reduced, as the same amount of additive genetic variance would be divided by a larger total variance.

The second approach commonly employed in studies of primate behavior is genetic association. In this case, investigators test for a statistical relationship between genotypes at a specific genetic locus and the phenotypic values for the trait of interest. Individual animals are assayed for the genetic polymorphism under study, and a statistical test for differences between genotypic means in a quantitative trait (or phenotype frequencies for a qualitative measure of behavioral differences) is performed. The standard tests for genetic association assume that the individuals in the dataset are all unrelated to each other. This is necessary because related animals will share genotypes at many loci across the genome, including both any genes that influence the phenotype under study, and many other genes that do not. Thus, spurious correlations between the phenotype and one or more genotypes that have no true effect on that trait can occur when related pairs of animals are included in a simple genetic association test. However, when genotypes at many polymorphic loci are known for the study animals, methods are available that use the extensive genotype data to estimate kinship among individuals, and then account for those kinship relationships when assessing genetic association (e.g., Lippert et al., 2011). These methods allow researchers to analyze genetic association in datasets that include related animals. Other methods for detecting genetic effects on primate behavior are available, but are less commonly employed. For example, genetic linkage has been used in a few studies of primate neurobiology and behavior (Atkinson, Rogers, Mahaney, Cox, & Cheverud, 2015; Freimer et al., 2007; Johnson et al., 2015). Linkage analysis again requires data from related animals, and uses the known kinship among subjects to test the hypothesis that causative genetic variants are transmitted through the pedigree via Mendelian inheritance. Methods that combine quantitative genetic analysis of heritability with linkage and association in a single analysis are available and can be very powerful (Almasy et al., 2005; Blangero Blangero, Williams, & Almasy, 2001), but require that study subjects can be connected into extended pedigrees.

3 GENETIC EFFECTS ON RESPONSE TO NOVEL OBJECTS

Investigating possible genetic influences on behavior requires repeatable and reliable assays of behavioral variation. One of the simplest and most direct strategies is to present inanimate novel objects to a series of individual primates, and record the animals' reactions. Although this constitutes an artificial test, this paradigm can nevertheless reveal significant behavioral differences that have relevance beyond the test itself. In addition, identifying genes that affect response to a test situation points to candidate genes that can subsequently be tested under more natural circumstances. The ability to completely control the test environment and evaluate large numbers of individuals quickly are also strengths of this approach.

Various studies have used exposure to novel environments, novel social partners or novel inanimate objects to investigate individual variation in behavioral reactions (Kinnally, Whiteman, Mason, Mendoza, & Capitanio, 2008; Mason, Capitanio, Machado, Mendoza, & Amaral, 2006; Miller, Bard, Juno, & Nadler, 1986; Roma, Champoux, & Suomi, 2006). Furthermore, when multiple researchers use similar methods to elicit behavioral variation, it is possible to build up a wider picture of the relationships among genetics, behavior and downstream consequences. For example, Kinnally et al. (2008) showed through factor analysis of multiple behavioral phenotypes that the responses of young rhesus macaques to novel inanimate objects were correlated with their social behavior in familiar social groups, and were also marginally correlated with behavior toward unfamiliar conspecifics. This study did not investigate possible genetic effects, but does indicate that "artificial" tests of response to novel objects can be predictive of social behaviors expressed under different circumstances. Subsequently, Fawcett et al. (2014) investigated a population of 428 young rhesus macaques with known pedigree relationships and calculated the (narrow sense) heritability of specific behaviors in a novel environment. Latency to break contact with their mothers and explore the novel room was estimated to have a heritability of h ² = 0.26 in this population. That is, additive genetic differences among these infants account for an estimated 26% of the total phenotypic variance observed. Together these studies suggest that genetic differences among young rhesus macaques are predictive of responses to novelty, and that those responses are predictive of social behavior in groups.

Johnson et al. (2015) tested 578 pedigreed baboons (Papio anubis, Papio cynocephalus, and hybrids) using two novel objects (plastic toys) and found that a variety of behaviors exhibited significant heritability (Table 1). Factor analysis of a selected series of heritable behaviors among these baboons produced two major factors: one that was considered to reflect variation in arousal or fear, with an estimated h ² = 0.59, and a second factor that reflected variables measuring interaction with the novel object, with estimated h ² = 0.33. Genetic linkage analysis in this baboon pedigree identified preliminary evidence for a quantitative trait locus (QTL) on baboon chromosome 10 (PHA10) in a region that is homologous to human chromosome 20. This baboon QTL showed probable influence on the object interaction factor, as well as on other correlated phenotypes (e.g., cerebrospinal fluid levels of dopamine metabolites). Thus, results from these baboons indicate that a substantial fraction of the measured behavioral variation in this population is attributable to genetic differences among the animals. Furthermore, this may provide insight relevant to baboon behavior outside the test condition. Johnson et al. (2015) showed that individual baboons that exhibited elevated aggression to the toys under test conditions also displayed elevated aggression to cage mates when those individuals were later observed in their home cage and social group. Bergman and Kitchen (Bergman & Kitchen, 2009) found that on average, wild Papio baboons interacted more with novel objects and explored them for longer periods of time than did wild geladas (Theropithecus gelada) exposed to the same objects. Bergman and Kitchen suggest the generalist diet of baboons may have selected for a higher level of interest in novel food items than among the more specialized geladas, who mainly consume grass. Bergman and Kitchin suggest that this dietary difference may therefore explain the greater interest in novel objects among the baboons.

Table 1. Heritable behaviors in baboons in response to novel objects

Duration measures of behavior		Frequency measures of behavior
Heritability of reaction to toy no. 1 (Truck)		Heritability of reaction to toy no. 1 (Truck)
Locomotion	0.27	Aggression	0.24
Latency to touch	0.29	Mantle shake	0.14
Cage slap	0.39	Object interaction	0.19
Watch object	0.16	Self scratch	0.13
Heritability of reaction to toy no. 2 (Bear)		Heritability of reaction to toy no. 2 (Bear)
Locomotion	0.51	Locomotion	0.50
Aggression	0.19	Cage slap	0.37
Object interaction	0.17	Submissive	0.19
Self scratch	0.27	Abnormal behavior	0.29

For methods, description of novel objects and additional results see Johnson et al. (2015).

Various other aspects of NHP behavior have also been studied using similar methods. Although not a test of response to novelty per se, the motor skills and tool use performed by chimpanzees seem to be influenced by genetic differences among individuals. Hopkins, Reamer, Mareno, and Schapiro (2015) tested 243 captive chimpanzees on a battery of motor tasks, and found that both the quality of performance in tool use, and the preference for left- or right-hand dominance shows significant heritability. Importantly, Hopkins has extended this work to a study of generalized intelligence (Hopkins, Russell, & Schaeffer, 2014b). Though the concept of "intelligence" has its complexities and detractors, Hopkins et al., (2014b) used a series of 13 cognitive tests to evaluate variation in chimpanzee intelligence. Using principal components analysis to generate an overall "g" score, the researchers found that the heritability of "g" is significant (h ²=0.53) and that sub-components related to object permanence, spatial memory, attention and communication were also heritable in their study population.

These results demonstrate that there is substantial genetic variability underlying individual differences in behavior within species. Thus, our assumption that natural selection on behavioral phenotypes can effectively alter mean phenotypes over time is supported. In addition, although individuals obviously have some measure of flexibility in their behavior, we find that genetics also places constraints on behavior that can be detected as consistent statistical similarity among genealogical relatives (close kin) that cannot be explained in other ways (see also below).

4 RESPONSE TO NOVEL CONSPECIFICS AND OTHER SOCIAL CHALLENGES

Given the social complexity of primates, the diversity of primate social systems and the flexibility of social relationships within species, there is substantial interest in the genetic mechanisms that influence primate social behavior (Figure 1). In one of the first controlled tests of genetic effects on social behavior, Fairbanks et al. (2004) tested 352 pedigreed vervet monkeys (Chlorocebus aethiops) using a conspecific intruder challenge paradigm. Fairbanks developed this test to investigate individual variation in the reactions of vervets to social strangers (Fairbanks, 2001). Study subjects were tested in their home cages by placing another vervet monkey, unfamiliar to the test subjects, in a small cage immediately outside the home cage. Behavioral reactions were recorded for 30 min. Fairbanks et al. (2004) found that social impulsivity (a measure incorporating both assertive and explicitly aggressive behaviors) had a heritability of h ² = 0.35, meaning that about one-third of variation in social aggression among individual vervets could be attributed to genetic differences. The aggression subscale (a smaller set of clearly aggressive actions) provided even stronger evidence of genetic effects, with h ² = 0.61. In addition, the authors conducted an explicit test for maternal effects (learning or passive transmission) separate from inherited genetic effects, and found no evidence for such an effect on any of the behaviors in this population. As discussed above, these results apply only to this vervet population under these particular environmental circumstances, and cannot be taken as evidence against maternal effects on other behaviors or in other environmental contexts. It is the case however, that other studies of other phenotypes in other species have also failed to detect maternal effects (Fawcett et al., 2014).

Social interaction among adult baboons. Three adult male yellow baboons (*P. cynocephalus*) engaged in an episode of aggression, submissive behavior and communication. The individual at the upper right is threatening the individual at the far left, using head bobs, a raised eyebrow display and by slapping the ground. The individual at the far left is soliciting support from the third individual, including lipsmacking and presenting to his desired supporter (Photo by J. Rogers)

A number of studies have investigated genetic influences on sociality in chimpanzees. Hopkins et al. (2014a) reported an innovative study of joint attention (one individual shifting its visual attention in a targeted way in response to signals of attention given by a second individual) capitalizing on the ability of this species to react appropriately to social signals and to share joint attention with a human investigator. Hopkins et al. (2014a) tested 232 pedigreed chimpanzees from two captive populations by assessing their reactions to gazing or pointing by a human experimenter. The rapidity and facility with which the chimpanzees responded to the human signaling was evaluated by observing whether the chimpanzee shifted their visual attention in response to human gazing or pointing, and the analyses revealed a heritability of h ² = 0.25.

In addition, the authors tested the arginine vasopressin receptor 1A gene (AVPR1A) for association with joint attention scores and found that AVPR1A genotype was significantly associated with the chimpanzees' behavioral responses. Furthermore, they found that the genetic effect was possibly stronger among male chimpanzees than females. In another study, researchers (Anestis et al., 2014) found that chimpanzees with different AVPR1A genotypes differed in their use of social coalitions, the rate at which they received grooming and other measures of social interaction. Staes et al. (2015) also found a significant genetic association between AVPR1A genotype and sociality measured using behavioral observational data. This type of replication, consisting of multiple studies using similar but not identical behavioral assessments that find similar associations for the same gene, provide strong evidence in favor of gene-phenotype relationships.

Finally, Latzman, Freeman, Schapiro, and Hopkins (2015) used ratings made by colony care staff to compare personality among 178 chimpanzees. They found that factors which the researchers considered to reflect "extraversion" and "dominance" had heritability values of 0.38 and 0.20, respectively. Importantly, in this case the heritability appears to be valid for chimpanzees reared by their mothers in social groups, but the phenotypes show much less genetic impact when nursery-reared animals were analyzed separately. This raises the possibility that the atypical developmental environment of the nursery may have lasting effects on social behavior that mask the typical genetic variance present among the chimpanzees (see discussion of rearing effects on rhesus macaques below).

Researchers have also identified genetic effects on social reactivity in baboons. In the same study that tested for reactions to novel objects (toys), Johnson et al. (2015) also tested the baboons with a mirror. The broad consensus is that while many chimpanzees can recognize that a mirror presents them with their own reflection (self-recognition), baboons and other Old World monkeys generally fail the mirror test, and do not recognize a mirror image as their own image. Instead, Old World monkeys react as if they are seeing an unfamiliar conspecific (but see Toda and Platt, 2015). Johnson et al. (2015) found that the baboons exhibited a range of responses to the mirror, varying from very submissive to quite aggressive. Heritability values for the frequency of specific behaviors indicate that genetic differences do influence this individual variation: Aggression to the mirror showed h ² = 0.24; cage slapping in response to the mirror produced a value of h ² = 0.27; and yawn frequency showed h ² = 0.22. Other locomotor behaviors, and the position of the baboons within their cage relative to the mirror, were also heritable.

These results parallel those of (Golub, Hogrefe, & Unger, 2012) who showed that among young rhesus macaques, monoamine oxidase A (MAOA) genotype is associated with various measures of social behavior, including responses to videos showing macaque behavior. Golub et al. (2012) also found genetic effects on the responses of macaques to a conspecific social intruder. Coyne et al. (2015) report that genotypes at the dopamine D4 receptor locus are associated with variation in proximity of Cayo Santiago rhesus macaques to their mothers, and to avoidance of other macaques. And further, Schwandt et al. (2010) found that genotype at the serotonin transporter promoter repeat (5HTTLPR) interacts with rearing condition (whether an individual was reared by their natural mother or reared in a peer group without the mother) to influence the level of high-risk aggression displayed toward an unfamiliar conspecific intruder.

Significant genotype-by-environment interactions have been reported for a variety of test situations, especially involving the serotonin transporter gene which controls reuptake of serotonin by pre-synaptic neurons (Barr et al., 2004a, 2004b; Howell et al., 2014). This genetic effect, observed in both humans and rhesus macaques, has received substantial attention (Barr et al., 2003a; Bethea et al., 2004; Brown and Harris, 2006, 2008; Chakraborty et al., 2010; Daniele et al., 2011; Howell et al., 2014; Kalin et al., 2008; Vallender et al., 2008). Gene × environment interactions like that reported for the 5HTTLPR variants may be an important aspect of primate behavioral genetics, with broad implications. Other researchers have reported simple genetic associations or gene × environment interactions involving other genes: MAOA (Newman et al., 2005), TPH2 (Chen et al., 2010), COMT (Gutleb, Roos, Noll, Ostner, & Schulke, 2017; Pfluger et al., 2016), DAT (Rajala et al., 2014), and MECP2 (Liu et al., 2016). We may find that early developmental experience can alter the mean phenotype associated with particular genotypes at various loci.

The majority of studies investigating social behavior have involved testing primates in controlled circumstances. However, Trefilov, Berard, Krawczak, and Schmidtke (2000) compared the age at which free-ranging male rhesus macaques on Cayo Santiago disperse from their natal social group and attempt to immigrate into another group. These researchers found that genotype at the serotonin transporter promoter repeat locus (5HTTLPR) was predictive of age at first dispersal. This is obviously a demographic and social outcome that may impact various aspects of the life history of those males. The semi-naturalistic setting of this study provides opportunities to address such important life-history variables, and illustrates the type of work that could be performed in fully natural populations, but is impossible in most captive populations.

5 THE GENERATOR OF BEHAVIOR: GENETICS OF BRAIN STRUCTURE AND FUNCTION

The studies summarized earlier investigated possible relationships between genetic variation and expressed behavior. Another significant line of research explores genetic effects on the underlying neural systems. Obviously, in order for genes to influence behavioral differences, those genes must alter the function of the nervous system in one way or another. Various approaches have been used to explore genetic effects on within-species variation in the structure, metabolism or neurochemistry of the primate brain.

Table 2 presents a series of analyses that have quantified genetic influences on brain structure. A few studies (Atkinson et al., 2015; Cheverud et al., 1990) have used indirect measures of brain size and structure, such as estimates of brain volume obtained from measuring cranial vault volume, or endocast impressions representing the surface of the cerebral hemispheres that form on the inner surface of the skull. Most of the remaining studies employ magnetic resonance or other imaging techniques to directly quantify elements of brain structure.

Table 2. Genetic effects on brain morphology and structure

Species	Phenotype	Heritability	References
RhesusMacaque	Total brain size	0.60	Cheverud et al. (1990)
RhesusMacaque	Measured of sulcal length	0.34–0.77	Cheverud et al. (1990)
SquirrelMonkey	Volume of hippocampus	0.54	Lyons, Yang, Sawyer-Glover, Moseley, and Schatzberg (2001)
Vervet	Total brain volume	0.99	Fears et al. (2009)
Vervet	Cerebral volume	0.98	Fears et al. (2009)
Vervet	Hippocampal volume	0.95	Fears et al. (2009)
Baboon	Total brain volume	0.82	Rogers et al. (2007)
Baboon	Total cerebral volume	0.85	Rogers et al. (2010)
Baboon	Gyrification index	0.71	Rogers et al. (2010)
Baboon	Cerebral surface area	0.76	Rogers et al. (2010)
Baboon	Genetic correlaton between gyrification index and cerebral volume	−0.77	Rogers et al. (2010)
Baboon	11 primary sulci, measures of sulcal length, depth, area	Various	Kochunov et al. (2010)
Baboon	Corpus callosum area	0.42	Phillips et al. (2012)
Baboon	Metric and non-metric traits describing sulcal length and position, QTL for sulcal phenotypes	Various	Atkinson et al. (2015)
Chimpanzee	Total brain size	0.53	Gomez-Robles, Hopkins, Schapiro, and Sherwood (2015)
Chimpanzee	Cortical organization, size of cerebral regions	Various	Gomez-Robles et al. (2015)

There is an extensive literature documenting the general observation that NHPs have larger total brain volume and larger cerebral volumes relative to body size than is typical for other mammals (Martin, 1981; Preuss, 2007). Recent genetic analyses have examined brain structure in more detail, asking questions about specific components of the central nervous system. Among anthropoid primates, the correlation between brain volume and brain surface area is not consistent across species. Surface area is a useful comparative measure of brain structure across species because the surface layers or cortex of the cerebral hemispheres contain the majority of neuronal cell bodies. Greater surface area correlates with larger numbers of neurons and greater cerebral differentiation (Sousa, Meyer, Santpere, Gulden, & Sestan, 2017). Gyrification, the degree to which the cerebral cortex folds to form grooves and crests known as cortical sulci and gyri, provides additional information about brain structure beyond that obtained by measuring cerebral volume. For example, the degree of gyrification is significantly greater in the human brain than in macaque or marmoset brains (Rogers et al., 2010). That same study found that the volume of the cerebral hemispheres, the surface area of the cerebral hemispheres and the degree of gyrification are all significantly heritable in both Papio baboons and humans (Rogers et al., 2010). The length and depth of particular sulci provide information about the relative size of particular cortical regions that may be specialized for different functions (Atkinson et al., 2015; Van Essen 1997) and among baboons are also significantly influenced by genetic variation (Kochunov et al., 2010).

For a number of years, Ned Kalin and his colleagues have investigated a model of anxious temperament (AT) that uses juvenile rhesus macaques as subjects (Fox, Shelton, Oakes, Davidson, & Kalin, 2008; Kalin and Shelton, 2000, 2003). This work has demonstrated that there are a number of significant parallels between the anxiety and AT expressed by some juvenile rhesus macaques and the anxiety experienced by children with extreme behavioral inhibition (Fox et al., 2008). Kalin and colleagues have used this model to explore genetic and non-genetic risk factors for juvenile and adolescent anxiety, and have identified both specific genotypes that are associated with AT and specific patterns of gene expression in relevant structures within the brain. Working with this model, (Oler et al., 2009) have shown that the level of expression of the serotonin transporter gene (SLC6A4) in the amygdala, bed nucleus of the stria terminalis and hippocampus is correlated with AT, as quantified using both behavioral observations and cortisol levels in a human intruder test. Oler et al. (2009) also found that serotonin transporter gene expression is correlated with activity (metabolism) within the neural circuit that includes those brain structures, thereby connecting gene expression, brain activity and expressed behavior. In a subsequent study, Oler et al. (2010) showed that the metabolic activity of specific brain regions (e.g., anterior hippocampus) is heritable and correlated with AT.

More recently, Roseboom et al. (2014) showed that expression of the NPY1R and NPY5R genes in the amygdala are also correlated with AT, and Rogers et al. (2013) found a genetic association between polymorphisms in the corticotrophin releasing hormone receptor 1 (CRHR1) locus and AT. Sequence variants in CRHR1 were also associated with neural circuit activity in the amygdala and hippocampus. Each of these three genes (NPY1R, NPY5R, and CRHR1) code for receptor proteins that are expressed in the brain, and in other tissues such as fat or endometrium. In the brain, these receptors bind neuropeptides and transmit downstream signals in neural circuits known to affect emotionality and related behaviors. Alisch et al. (2014) reported that the level of methylation of two genes in the amygdala (BCL11A, which encodes a zinc-finger protein that is involved in neurogenesis and JAG1, which encodes a protein that binds to the notch receptor and is involved in several developmental processes) is correlated with AT. This suggests that gene regulation through epigenetic mechanisms (see below), rather than just the gene sequences themselves, can affect these particular anxiety-related behaviors. Consistent with the studies by Oler et al. (2009), Rogers et al. (2013), and Roseboom et al. (2014), the work by Alisch et al. (2014) highlights the importance of quantitative levels of gene expression as well as DNA sequence variation in affecting behavioral and neurobiological phenotypes.

Table 3 presents the results of additional studies of neurobiological traits. Monoamine neurotransmitters (dopamine, norepinephrine, and serotonin) play many important roles in central nervous system function (Kandel, Schwartz, Jessell, Siegelbaum, & Hudspeth, 2013), and have been associated with both normal variation in behavior and risk of human psychiatric illness (Balestri, Calati, Serretti, & De Ronchi, 2014; Kohler, Cierpinsky, Kronenberg, & Adli, 2016; Salatino-Oliveira, Rohde, & Hutz, 2017). Dopamine is critical to the biochemistry of reward and addiction, while norepinephrine is involved attention and reactivity to external stimuli. The studies in Table 3 address questions concerning the genetic control of monoamine metabolism and function in NHPs.

Table 3. Genetic effects on other neurobiological traits

Species	Phenotype	Heritability	References
Baboon	HVA (dopamine metabolite)	0.50	Rogers et al. (2004)
Baboon	5-HIAA (serotonin metabolite)	0.30	Rogers et al. (2004)
Baboon	MHPG (norepinephrine metabolite)	0.36	Rogers et al. (2004)
Vervet	HVA (dopamine metabolite)	0.52	Freimer et al. (2007)
Vervet	5-HIAA (serotonin metabolite)	0.41	Freimer et al. (2007)
Vervet	MHPG (norepinephrine metabolite)	0.39	Freimer et al. (2007)
Rhesus macaque	Maximum heritability: Hippocampal metabolism quantified through PET	0.76	Oler et al. (2010)
Rhesus macaque	Maximum heritability: Hippocampal voxels significantly associated with AT	0.52	Oler et al. (2010)

6 REVERSING THE ARROW OF CAUSALITY: BEHAVIOR ALTERS GENE FUNCTION

For decades researchers understood the relationship between genes and behavior to be unidirectional (Fairbanks et al., 2004; Flint, Greenspan, & Kendler, 2010; Freimer et al., 2007; Rogers et al., 2004). The accepted model held that DNA sequences are passed from parent to offspring, and except for the rare de novo mutation that alters the integrity of that information, the inherited gene sequences dictate, generation after generation, the sequences of resulting proteins. This model also presumed that the relevant regulatory sequences that govern when, in which cells, and at what quantitative level the encoded proteins are expressed, are similarly inherited essentially unchanged (again with the major exception of rare de novo mutations). However, new information has significantly altered, or more accurately expanded, our understanding of these genetic processes. Experiments that began decades ago exploring the impact of developmental experience (i.e., mother- vs. peer-rearing, or other environmental manipulations visited upon newborn offspring) have demonstrated that the behavior of individual primates can be profoundly, predictably, and durably affected by early psychosocial experience (Barr et al., 2003b; Barr et al., 2004b; Stevens, Leckman, Coplan, & Suomi, 2009). The field of epigenetics (Allis, Caparros, Jemnuwein and Reinberg, 2015) is now beginning to provide detailed descriptions of molecular and cellular mechanisms that help link the lived experience of developing individuals to the neurobiological and other physiological processes that are altered as a result of those experiences, and which can generate robust changes in behavioral function into adulthood.

Epigenetics is a broad, growing and intensely active field within genetics and genomics (Allis et al., 2015; Li & Zhang 2015). In brief, epigenetics is the study of specific cellular mechanisms that can influence the expression of genes through chemical modifications of DNA or the chromatin proteins that bind and interact with DNA. Unlike traditional regulatory processes that are dictated by either the amino acid sequences of transcription factors or the nucleotide sequences of gene regulatory elements (promoters or enhancers), epigenetic mechanisms can respond dynamically to environmental factors, and may then be passed from parent cell to daughter cell during mitosis as acquired traits. In some cases, epigenetic features can be transmitted from sire or dam to offspring, providing a molecular explanation for the observation that environmental experiences in one generation can, in some circumstances, lead to changes in gene function in descendant offspring.

Although researchers had begun to understand epigenetic mechanisms in the 1990s, groundbreaking studies by Michael Meaney, Moshe Szyf, and others showed that the quality of maternal behavior experienced by neonatal rats could produce changes in the epigenetic regulation of genes involved in stress response (Meaney & Szyf 2005a, 2005b). Furthermore, these epigenetic changes in rats can sustain altered gene expression into adulthood, and this pattern of gene regulation may be inherited from one generation to the next. Subsequent studies have shown that DNA methylation, one form of epigenetic regulation, can alter gene expression in infant macaques (Massart et al., 2014), similar to the earlier findings in rats.

In an important study that shows the potential for future studies in natural populations, Runcie et al. (2013) investigated gene expression in the blood cells of wild-caught baboons. Their results showed that the social dominance rank of individuals, as well as the size of social groups in which they lived, influenced both gene expression and gene regulation. This mechanism is therefore likely to be part of the explanation for the life-long behavioral effects of early adverse developmental environments (Stevens et al., 2009). The available evidence indicates that maternal behavior and the quality of the mother-infant relationship can influence gene function in NHP offspring, and have lasting consequences (Massart et al., 2014). Runcie et al. extend this to other types of behavioral interactions.

In another remarkable study, Snyder-Mackler et al. (2016b) have shown that manipulating the social dominance rank of female rhesus macaques can alter gene expression in immune cells. Furthermore, these induced differences in immune function potentially influence cellular responses to pathogens. It is clear from this study that the social relationships experienced by a female macaque can have significant impact on the function of her genes. This line of research is highly significant as we now have well-defined molecular evidence for the influence of behavioral interactions on cellular processes that can affect life-history. Further studies will likely identify other physiological systems that can be similarly affected, and we should not be surprised when gene expression in the limbic system or cortical circuits of the brain is implicated. Baker et al. (2017) have shown that the quality of early development environment can alter epigenetic regulation (DNA methylation) of the oxytocin receptor gene, which numerous previous studies have shown can influence behavioral outcomes. Finally, an important study by Kinnally and Capitanio (2015) reports that infant macaques born to sires that were nursery-reared display increased emotionality and higher plasma cortisol levels at 3–4 months of age, relative to offspring sired by mother-reared males. The implication here is that, in addition to the effects of maternal behavior (or maternal deprivation) on gene expression in the offspring directly affected, sires can transmit altered patterns of gene regulation that were induced by the environmental experiences of the sire. Environmental consequences are transmitted from one generation to the next without direct effect on the offspring.

These various studies open new avenues for understanding behavioral differences and similarities among individuals within primate species. Both traditional DNA sequence variation, and these more recently uncovered mechanisms of epigenetic regulation, can affect the behavior and physiology of infants, juveniles and adults. Under some circumstances, these epigenetic factors can be passed from generation to generation, just as DNA sequences themselves are inherited.

7 COMPARISONS ACROSS SPECIES OR POPULATIONS

This review has focused primarily on studies of genetic variation within species. But a number of studies have compared populations within a species, or across closely related species, in order to either identify behavioral characteristics that may be influenced by genetic differences, or explore such genetic differences using natural inter-population variation. This approach can highlight particular behavioral traits that deserve additional attention. Some studies in this vein have compared Indian-origin rhesus macaques with either Chinese-origin rhesus or hybrids between these two populations (Champoux, Higley, & Suomi, 1997; Jiang, Kanthaswamy, & Capitanio, 2013). These studies have identified specific features of temperament or personality that differ on average between the populations, and therefore may be influenced by genetic differences. This strategy has also identified behavioral differences between long-tailed or cynomolgus macaques (Macaca fascicularis) originating from mainland Indochina and long-tailed macaques from island populations such as the Philippines, Mauritius, or Indonesia (Brent & Veira, 2002). Others have identified population differences among baboons in allele frequencies for genes previously associated with behavioral variation in other contexts (Kalbitzer et al., 2016).

A recent study of baboons has demonstrated a novel and powerful strategy for investigating genetic effects on behavior that takes advantage of hybridization between two species. The hybrid zone between olive and hamadryas baboons in Ethiopia has been the subject of many influential studies of behavioral variation and reproductive strategies (Bergman, Phillips-Conroy, & Jolly, 2008; Jolly, Phillips-Conroy, Kaplan, & Mann, 2008; Phillips-Conroy, Jolly, & Brett, 1991). Using genotypes for thousands of single nucleotide polymorphisms (SNPs) whose allele frequencies differ between olive and hamadryas baboons, Bergey, Phillips-Conroy, Disotell, and Jolly (2016) identified regions of the genome that show dramatic differences in genotype frequencies between the two species. They further showed that genetic pathways involved in dopamine metabolism are significantly over-represented among the genes showing substantial genetic differentiation between species. This strongly suggests that the genetic foundation of the dopamine neurotransmitter system has significantly diverged between these closely related species that differ so markedly in social behavior. This research group has also reported previously that dopamine metabolite levels differ in the adult males of these species (Jolly et al., 2008).

This strategy of using large-scale genomics to interrogate fundamental processes in natural populations has outstanding potential and will hopefully inspire similar studies in other populations. Given the decreasing cost of large-scale whole genome or exome sequencing (or even lower cost of broad-scale SNP genotyping using custom designed SNP arrays), the production of significant amounts of genetic information for wild populations is becoming more practical. In parallel, researchers are making progress in the development of methods for recovering DNA from non-invasively collected samples (Snyder-Mackler et al., 2016a). Obviously future progress in obtaining genomic material non-invasively from field populations, along with reduced costs for large-scale genomic analysis, has the potential to transform the behavioral genetic analysis of NHP. This would facilitate extensive microevolutionary analyses in which allele frequency differences among natural populations of a species (chimpanzees or rhesus macaques), or among populations within a recent radiation of species/superspecies (vervets; Svardal et al., 2017), could be compared with behavioral differences. This line of analysis will not be simple, because many genes will differ among populations, and naïve analyses would generate spurious associations. But it is possible that specific hypotheses concerning genotype–phenotype relationships could be tested if proper controls were used. Such a microevolutionary approach could exploit any number of recent rapid radiations of primates, genera such as Microcebus, Aotus, Papio, Macaca, Callithrix, and others.

We must also avoid the temptation to restrict our vision or be too parochial, considering data only from NHP. There is a growing and intriguing literature describing the behavioral genetics of dogs, rodents other than mice, and other mammals (von Holdt et al., 2017; Zapata, Serpell, & Alvarez, 2016). And of course, there is always that other primate: behavioral genetic studies of humans, including both psychiatric genetics and behavioral genetics of "normal" variation, have generated information about specific genes that have led to influential hypotheses about NHP behavior. The extensive, high impact literature on the serotonin transporter promoter repeat polymorphism (5HTTLPR) is just one example of how initial discoveries in humans can inspire extensive and valuable studies in NHP and other species. Recent attention to the oxytocin and vasopressin receptors is another example.

8 MANIPULATIONS OF PRIMATE GENOMES

Both the scientific literature and the popular press have given substantial attention to major advances in our ability to manipulate animal and human genomes. There are now several technologies, most significantly the CRISPR-Cas9 method (Hess, Tycko, Yao, & Bassik, 2017; Murugan, Babu, Sundaresan, Rajan, & Sashital, 2017; Sternberg and Doudna, 2015), that allow investigators to edit mammalian genomes more easily than was previously possible. These gene editing technologies are unlikely to be widely used in studies of NHP because the cost per experiment for primates remains high, and the lifespan of anthropoid primates makes it difficult to investigate adult behaviors in engineered animals.

Nevertheless, some experiments have already been reported. The first such effort, using earlier, slower and less efficient technology involved the manipulation of marmoset genomes (Sasaki et al., 2009). More recently, the CRISPR-Cas9 approach was used to alter the PPARG and RAG1 genes of long-tailed macaques, M. fascicularis (Niu et al., 2014). In addition, two studies have manipulated a gene (MECP2) with probable neurobiological and behavioral effects, as this gene is associated with autism in humans (Chen et al., 2017; Liu et al., 2016). This technology makes it possible to produce primate models of single gene neurobiological disorders. But in my opinion, it is unlikely to contribute significantly to our understanding of the genetics of normal behavioral variation within and between primate species. This is because it will be difficult and prohibitively expensive to generate large enough populations of modified animals to generate the kinds of statistically adequate datasets that would permit typical genetic effects to be investigated, given that most genetic variants exert only small to moderate effects on expressed behavior. Indeed, the bulk of genetic variation segregating within natural populations of primates is likely to consist of alleles that do not individually produce dramatic effects (but exceptions will surely be discovered). Furthermore, the generation time of anthropoid primates makes large-scale studies of adult behavior in species-typical social groups of genetically modified primates problematic.

9 DISCUSSION AND CONCLUSIONS

The summary provided here demonstrates that the study of primate behavioral genetics is active and making significant progress. Both the quantitative genetic analysis of heritability, which identifies behavioral or neurobiological phenotypes that are significantly influenced by within-species genetic variation, and the analysis of genetic associations that identify specific genes influencing particular phenotypes, have produced substantial and important novel insights. Expansion of this work from almost exclusively studies of captive populations to both captive and field studies would be a welcome addition. Furthermore, the initial studies of epigenetic processes that affect primate behavior constitute a very exciting recent development.

As we learn more about behavioral genetics, we are also learning more about the overall extent and nature of genetic variation within primate populations. Whole genome and whole exome sequencing of NHPs has demonstrated that most primate species carry as much or more genetic variation than humans (Gazave et al., 2011; Prado-Martinez et al., 2013; Rogers and Gibbs 2014; Warren et al., 2015; Xue et al., 2016). Much of this variation is functionally significant, meaning that it is likely to have effects on cellular, physiological, morphological, or behavioral phenotypes. Furthermore, there is evidence that natural selection influences primate variation in substantial ways (Munch, Nam, Schierup, & Mailund, 2016; Rogers and Gibbs, 2014; Xue et al., 2016). Therefore, as we learn more about DNA sequence and epigenetic variation within NHP species, we are certain to gain insight into specific genetic variants that are candidates for causal effects on neurobiology and behavior (Bakken et al., 2016; Bergey et al., 2016; Rogers, 2013; Rogers et al., 2013).

However, as a research community we must also be aware of potential pitfalls. One source of concern is the way in which genetic association analyses are reported in the literature. As mentioned above, the standard genetic association tests used for many years assume that the individual study subjects in an analysis are unrelated, thus making all datapoints statistically independent. Related individuals share many genes in common, and therefore a genetic association analysis that uses related animals, and does not employ any of the available methods to adjust or account for that relatedness (Lippert et al., 2011), suffers increased risk of false positive results. The literature in primate behavioral genetics is mixed, in that not all authors report whether their study subjects are related, or whether they accounted for such relatedness in their statistical analysis. This creates uncertainty about the results.

A second potential problem stems from multiple testing. Authors do not always report the total number of genetic association tests using different genetic polymorphisms that were performed in the search for genetic effects on a given phenotype. Clearly, if one performs 25 association tests of one phenotype against 25 different genes, and finds one genetic association that is statistically significant at p = .045, we must be concerned about the meaning of this result. The p-value indicates that the results observed would occur by chance alone in roughly one test out of 20. Performing more than 20 tests to obtain a result that is expected about that often, even in the absence of any real genetic effect on the phenotype, should not inspire great confidence. But if authors do not report, and journal editors do not require, an explicit statement of the number of genes or genetic polymorphisms tested in pursuing the reported analysis, then readers cannot fully assess the scientific import of the result.

In several ways, the genomics research community has played a major role in the movement toward greater openness and transparency in scientific research. Genomics journals generally require that genome sequence or genotype data used in peer-reviewed publications are submitted to publicly accessible databases such as the NCBI Short Read Archive, dbSNP, dbGAP, or others. In many cases, journals require that data be made available as publicly accessible on-line supplementary information associated with a publication. Although there may be additional concerns involved with submission of NHP behavioral and physiological data to such readily accessible databases, there are several models available for controlled or vetted access that should allow some degree of control over data distribution, while still increasing data sharing. One can envision the implementation of NHP-specific guidelines or policies related to secondary use of data. In addition, software tools and analytical methods are critical elements of our science, and should also be included in policies for community access. As primate behavioral genetics advances and both the size of datasets and complexity of analyses increase, there may be substantial value in beginning to establish systems for data sharing and access that could become the expectation, if not the requirement, for publication in the primatology literature.

My own view is that the field of NHP behavioral genetics is a vibrant and productive aspect of primatology, and that it has outstanding potential for significant discoveries and advances in the near future. Progressively lower costs for genome sequencing, the greater availability and sophistication of functional brain imaging and the growing appreciation for epigenetic mechanisms all promise new insights that will have broad impact. Connecting the genetic basis of behavioral variation with our growing understanding of population-level behavioral variation, in parallel with conceptual advances in interpreting such variation, is likely to increase our knowledge of and fascination with NHP behavior. However, improved data reporting standards and journal policies for publishing results, including mechanisms for improved data sharing, would advance the field by providing greater opportunities for the research community to evaluate and expand upon significant new findings.

ACKNOWLEDGMENTS

I wish to thank Louise Barrett for valuable comments and feedback on a draft of this article. I also thank two anonymous reviewers for their constructive comments and recommendations. The author received support for this work from NIH grants R24-OD011173 to J.R. and U54-HG006484 (R. Gibbs, PI).

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