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National Research Council (US) Committee on Advances in Collecting and Utilizing Biological Indicators and Genetic Information in Social Science Surveys; Weinstein M, Vaupel JW, Wachter KW, editors. Biosocial Surveys. Washington (DC): National Academies Press (US); 2008.

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Biosocial Surveys.

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18Genomics and Beyond: Improving Understanding and Analysis of Human (Social, Economic, and Demographic) Behavior


Genes exert their influence by encoding proteins. The level of such gene activity, however, is a regulated process. As molecules, genes are subject to regulation by intracellular factors that, in turn, are a reflection of environmental factors. Neither genes nor environment dominates development; rather there is a continual interaction between genes and the environment. Phenotype emerges as a function of this constant dialogue, and any effort to ascribe percentage values to isolated variables is likely to be biologically meaningless. (National Research Council, 2001b, pp. 63-64)

The genetics of behaviour is much too important a topic to be left to geneticists! (Plomin, 2001, p. 1104)

There is increasing recognition that real progress in understanding human behavior (or health) requires an integrative approach that explores the interplays of pathways within the person and processes whereby the environment influences the person. There is also recognition that developmental progressions through the life course involving these continuous feedbacks and interplays are an essential ingredient. At a minimum, these concerns involve interplays among genes, brains, the phenotype, experience, other persons, and structural contexts. This shift of emphasis is reflected in this volume, and also indicated by a growing number of attempts to provide synthetic frameworks that point toward this integrated approach for a variety of fields, including early child development (Granger and Kivlighan, 2003; National Research Council and Institute of Medicine, 2000); demographic behavior (National Research Council and Institute of Medicine, 2000; National Research Council, 2003; Hobcraft, 2006; Seltzer et al., 2005); social neuroscience (Cacioppo, Berntson, and Adolphs, 2002; Cacioppo, Berntson, Sheridan, and McLintock, 2000; see also Chapter 17 in this volume); health (Johnson and Crow, 2005; National Research Council, 2001b); human bonding (Miller and Rodgers, 2001); resilience (Curtis and Cicchetti, 2003); well-being (Davidson, 2004; Huppert and Bayliss, 2004); and economics (Chapter 15 in this volume).

Thus far, most real progress toward implementing integrative approaches has occurred for health, as reviewed in several chapters of this volume (see also National Research Council, 2001b; Johnson and Crow, 2005), and in psychology, especially psychopathology (see Plomin, DeFries, Craig, and McGuffin, 2003; Rutter, 2006). Bridging the gap between the biological and the social sciences has taken more time, although important developments have occurred at the intersections of economics and psychology in behavioral economics (Brocas and Carillo, 2003, 2004; Camerer, Loewenstein, and Rabin, 2004). However, an increasing number of important prospective national population surveys are collecting (or considering) DNA samples, with the intention of enabling social science researchers, subject to suitable disclosure controls, to access information on a significant range of genetic markers (e.g., in the United States, the National Longitudinal Study of Adolescent Health and the Fragile Families Survey). Although the importance of such large and often nationally representative samples for genetic research is recognized, the longer term potential lies in the ability to explore the interplays between genes and behavior over the life course in response to experiences.

One of the great challenges of an integrative biosocial life-course approach to the study of behavior is the very complexity involved and to find the means of avoiding drowning in the multiple levels and interplays. One of my favorite relevant aphorisms, from the different context of population projections, is John Hajnal's (1955, p. 321) plea for “less computation and more cogitation.” At the meeting during the preparation of this volume, Jim Vaupel summed this up differently as “model simple; think complex.” My own preference would be to extend this somewhat to “model sufficiently complex, not simplistically; think more, both to complexify and to simplify.” The key here is that we need to have more and better theory and conceptualization, including attention to mechanisms and pathways or processes (a theme developed at greater length in Hobcraft, 2006). The problems of data dredging or gene hunting are well recognized, and the need for replication and ever larger samples to avoid false positives is often stressed (e.g., Plomin, 2005).

Yet some of the most influential findings on gene-environment interactions for behavior have emerged from the Dunedin study, which has a total sample of around 1,000 individuals (Caspi et al., 2002, 2003, 2005). Moreover, the best known of these studies, linking the serotonin transporter short allele as a moderator of life stress on depression, has been replicated several times. To give a flavor of these findings, 10 percent of sample members who did not experience stressful events between ages 21 and 26 met diagnostic criteria for depression at age 26, regardless of whether they had two long alleles or one or both alleles short on the 5-HTTLPR gene; however, among those experiencing four or more stressful life events, 17 percent with both alleles long were depressed, compared with 33 percent among those with one or both alleles short. The strategy through which the key investigators approached this research, based on careful evaluation of theory and knowledge, is well laid out in a series of papers (T.E. Moffitt, 2005; Moffitt, Caspi, and Rutter, 2005, 2006; Rutter, Moffitt, and Caspi, 2006). I return to this theme later in this chapter.

A related key topic is the approach to understanding behavior and the challenging issues around causation. Econometricians have devised a range of sophisticated approaches to dealing with unmeasured variables (e.g., Wooldridge, 2002), although when the unmeasured variables are a key contingent part of the process (e.g., as moderators or interactions), these approaches fail. As one of the major contributors to these approaches has put it: “there is no mechanical algorithm for producing a set of ‘assumption free’ facts or causal estimates based on those facts” (Heckman, 2000, p. 91). Randomized controlled trials have become the gold standard in the health sciences and have also penetrated some other areas, yet “randomized trials can never estimate channels for the effects of treatment” (Moffitt, 2003, p. 453). Once the importance of interplays of genes and experience (and other levels, too) over time is acknowledged, other approaches are needed that emphasize contingent relationships and pathways or progressions. A perceptive account of the wide range of issues involved in gaining understanding from prospective studies is provided by Rutter (1994). Caspi (2004) provides a useful and well-illustrated account of the importance of both self-selection (effects of persons on their environments, whether genetic, from experience, or from choice) and social causation (effects of the environment or contexts on the person, including gene-environment interplays). He argues convincingly that selection is not just a nuisance factor to be controlled away, but that the processes involved are ubiquitous and consequential, pose challenges for policy, have compositional consequences, and the mechanisms must be understood. New and more sophisticated approaches to multilevel life-course model formulation, analysis, and testing are needed, ones that can both achieve some of the requisite rigor in avoiding misattribution of causality to correlation but, perhaps more importantly, also enable serious exploration of pathways, processes, and progressions (Hobcraft, 2006).


Until recently most knowledge concerning behavioral genetics came from “quantitative” studies that relied on genetically informative designs (Rutter, Pickles, Murray, and Eaves, 2001), mainly of twins or adoptees. Such studies can provide only indications that there is a genetic component to the variance, which includes any gene-environment interactions and correlations. This black-box approach to partitioning variance relies on a number of contestable assumptions (see Rutter, 2006, pp. 41-54), including reared-together twins experiencing equal environments and twins or adoptees being nonselective of the general population.1 Moreover, such approaches face additional challenges in dealing with dyads (e.g., both partners in a marriage) or adult environments. Nevertheless, some clues about possible interplays of genes and environment can be obtained, as exemplified by the apparent differential heritability of IQ for groups with different socioeconomic status (SES) (Guo and Stearns, 2002; Rowe, Jacobsen, and Van den Oord, 1999; Turkheimer, Haley, Waldron, D'Onofrio, and Gottesman, 2003), although this may also have arisen from differential variances in experiences by SES group. Moreover, there have been substantial advances in exploring the covariation of multiple characteristics, in order to establish shared components or comorbidities of genetic or environmental variance (Kendler, 2005, refers to this as “advanced genetic epidemiology”).

With recent advances in genomics, there has been a major shift toward behavioral genomics, in which specific genetic markers are linked to specific attributes, behaviors, or experiences. Techniques for identifying large numbers of single-nucleotide polymorphisms (SNPs) through gene “chips” or microarrays are association analysis (see the recent series of review articles on genetic epidemiology in Lancet: Burton, Tobin, and Hopper, 2005; Teare and Barrett, 2005; Cordell and Clayton, 2005; Palmer and Cardon, 2005; Hattersley and McCarthy, 2005; Hopper, Bishop, and Easton, 2005; Davey Smith et al., 2005; see also reviews on genome-wide scans in Nature Reviews Genetics: Hirschhorn and Daly, 2005; Wang, Barratt, Clayton, and Todd, 2005).

A number of approaches have been developed for “gene hunting,” a search for markers on the genome that are associated with a particular life-course outcome. Out of such research a wide range of links to areas of the genome, SNPs, haplotypes, etc., have been identified for many outcomes: attention deficit hyperactivity disorder (ADHD) has received enough attention to warrant a special issue of Biological Psychiatry (see Faraone et al., 2005; Sklar, 2005, on haplotype mapping; also Mill et al., 2005). Some progress has also been made in identifying genetic markers for personality traits (Munafò et al., 2003; Van Gestel and Van Broeckhoven, 2003), affective and anxiety disorders (Leonardo and Hen, 2006), gambling (Comings et al., 2001), and intelligence (Harlaar et al., 2005; Plomin, Kennedy, and Craig, 2006). However, many of the identified associations are small and are often not replicated in other studies.

A good example of some of these challenges is the study of general cognitive ability, in which quantitative behavioral genetic studies consistently suggest a high heritability, but few genetic markers have been identified and those that have account for quite small fractions of the variation observed (see Plomin, 2003; Plomin and Spinath, 2004; and Plomin, Kennedy, and Craig, 2006, for good accounts of the methods used and problems encountered). When large numbers of genetic markers are screened (and gene arrays that can identify allelic variation on 900K SNPs are available), there is a huge risk of false positive associations, unless sample sizes are extremely large (Zonderman and Cardon, 2004). However, such studies are one approach to identifying “candidate” genes (see Munafò, 2006) that can be explored in more detail in other studies, if replicable (see also Craig and Plomin, 2006).

A further set of concerns arises in deciding which genetic markers to screen. The human haplotype project is one attempt to systematize such work, by identifying small parts of a chromosome for which there is little or no evidence of recombination (haplotype blocks), such that linkage to areas of the chromosome is relatively robust (e.g., Van den Oord and Neale, 2004; Conrad et al., 2006). Identification of ever increasing numbers of regularly spaced SNPs is another approach to increasing precision in identification of areas of the genome. Both haplotype mapping and SNP identification point to areas on the genome that show a significant association with a characteristic, but neither points to the specific gene that is implicated, and further detailed mapping of identified segments is required. For example, there is increasing evidence for the importance of specific “microsatellite” or “simple sequence repeats” in the context of social behaviors, for which differences in the number of repeats matter (e.g., Bachner-Melman et al., 2005; Hammock and Young, 2005; Kashi and King, 2006).

Even further complexities are introduced as soon as interplays are considered. Gene-environment interactions involve the response to a stimulus varying for different allelic combinations, whereby genes can be seen as moderators of the response (e.g., Caspi et al., 2003; T.E. Moffitt, 2005; Rutter, 2006). Moreover, there is increasing evidence of epistatic effects, in which combinations of genetic markers interact together (including regulatory chains or cascades) rather than singly, and examples have been found in which there is no main effect for single markers but the combination matters (e.g., Brodie, 2000; Grigorenko, 2003; Marchini, Donnelly, and Cardon, 2005; Templeton, 2000). There is also increasing evidence for epigenetic effects, whereby gene expression is altered on a lasting (and possibly heritable) basis through external influences that may operate through DNA methylation (e.g., Cordell, 2002; Eaves, Silberg, and Erklani, 2003; Jaenisch and Bird, 2003; Pastinen et al., 2003; Weaver et al., 2004). A further complexity arises from genes that have many different effects, known as pleiotropy.

All of these complexities have parallels in social science: the relatively rare exploration of pathways or interactions arises in part because of overreliance on statistical models with only main effects. The occurrence of multiple small effects (polygenic for genes) is not at all unusual: for example, associations of differing contexts with early adolescent development are small, but the total variance accounted for by contexts is quite large (Cook, Herman, Phillips, and Settersten, 2002). For several years I have been grappling with the issues involved in the life-course links of experience of disadvantage, in which multiple childhood origins all matter for multiple adult outcomes (e.g., Hobcraft, 2004): such complexities provide major analytic and interpretational challenges, a theme returned to below.

Ultimately, what is required is not just more sophisticated statistical techniques but instead building a much better understanding of pathways and mechanisms involved. For social scientific behavior, many such insights have to come from social scientists. Among other things, we have a large body of evidence relevant to the search for pleiotropic associations, knowing much about how a single stimulus can affect many responses, although the pathways could prove different. But there is also a need for greater interplays among geneticists, neuroscientists, and social scientists to help refine and target what gene-brain-behavior interplays should be prioritized for exploration.

Valuable insights into the processes involved in identifying gene-environment interactions are provided by Moffit et al. (2005), who identify seven steps needed to identify measured gene-environment interactions: (1) consulting quantitative behavioral genetic studies; (2) identifying a candidate environmental factor; (3) optimizing environmental risk measurement; (4) identifying candidate susceptibility genes; (5) testing for an interaction; (6) evaluating whether the interaction extends beyond the gene-environment-outcome triad; and (7) replication and meta-analysis. Moreover, they emphasize the great care required in choosing the candidate environmental and genetic indicators and having strong reasons for expecting the pathway to be plausible.

At a fairly general level, it is hardly surprising that most successful attempts to identify gene-environment interactions relating to behavior have linked to genetic markers for neurotransmitters, since the brain is vital in most behaviors and the ability to cross the blood-brain barrier is likely to be important to responses to external stimuli. For example, Caspi et al. (2003) were able to draw on a body of evidence that suggested that serotonin was involved in pathways to depression. Their choice of stressful events as a likely trigger mechanism was also entirely plausible and evidence-based. Such evidence-based, theoretically informed approaches are more likely to be successful in identifying complex pathways than gene-hunting (although this is one route to identifying candidate genes)—see Rutter et al. (2006). In this area, bringing together social scientists with both geneticists and neuroscientists (and other relevant disciplines) is essential to real progress.

A further example, still being explored, begins in neuroscience. Over a period of several years, the key roles of oxytocin and vasopressin2 brain receptors in pair bonding have been explored, particularly for voles (Hammock and Young, 2002; Lim, Hammock, and Young, 2004; Young, 2003). Prairie voles bond for life, whereas montane voles do not, and this difference has been linked to a single polymorphism. Other studies of rats, sheep, and hamsters also suggest important roles for oxytocin and vasopressin in maternal imprinting (Insel and Fernald, 2004; Numan and Insel, 2003). For humans, some clues come from functional magnetic resonance imaging (fMRI) scans that suggest that “romantic” and maternal attachments both overlap with sites that are rich in oxytocin and vasopressin receptors (Bartels and Zeki, 2000, 2004). There is further evidence that the dopamine D2 receptor (part of the reward system) plays a part in such bonding (Insel and Fernald, 2004; Insel and Young, 2001; Young, Wang, and Insel, 2002). The well-established release of oxytocin during breastfeeding is probably also linked to mother-child bonding. Moreover, recent experimental economic research has shown a link between oxytocin (administered as an intranasal spray) and increased levels of trust in humans (Kosfeld, Heinrichs, Zak, Fischbacher, and Fehr, 2005). All of this body of research points strongly toward genetic markers for oxytocin, vasopressin, and dopamine receptors being candidate genes for human bonding (and possibly partnership breakdown, too?), and exploration has begun (Hammock and Young, 2005).

There is clearly scope for social scientific input into the research on pair bonding and partnership breakdown. We have accumulated a body of knowledge about the personality traits and experiences that are associated with partnership formation and breakdown, and serious work on gene-environment interactions requires identification of the candidate stimuli. Such work could also draw on the integrative synthesis on human bonding of Miller and Rodgers (2001). Do these genetic markers help to explain the greater union fragility of young partnerships? If so, is this linked to late teenage brain development? Several animal studies link the receptors to the olfactory bulb, suggesting that smell (or pheromones) plays an important part, yet the human brain scan studies relied solely on a visual (photographic) stimulus. The neurotransmitters and genes for the receptors involved may be common across species, but the brain areas involved differ, raising unanswered questions about evolution. There are also unanswered questions as to what mechanisms inside the brain result in lasting pair bonding. Is lasting pair bonding a result of an epigenetic effect, involving a lasting change in gene expression? Or is long-term memory involved, and how? And what are the triggers and pathways to generate the oxytocin, vasopressin, and dopamine that the receptors respond to?


The balance between sufficient complexity and judicious simplification is needed for real progress to be made in understanding the complexities of human behavior. We have explored some of these issues for behavioral genomics and, fleetingly, neuroscience. But some real progress is also required in the social sciences. Treading the delicate path between mindless empiricism (with parallels to gene hunting) and overblown social theorization (with parallels to some evolutionary theorization) is vital for progress in the science of social science. This requires building (partial) analytic (not grand) theories or frameworks, exploring pathways and mechanisms, finding means of exploring interlinked processes (rather than throwing out any endogenous element), and careful and thorough empirical work, including models that are just complex enough. In this section, some of the complexities that are all too often ignored in oversimplified models are discussed.

A good example of progress toward these goals comes from the recent developments in behavioral economics, in which the realities of departures from a simple rational choice framework are being explored (Brocas and Carillo, 2003, 2004; Camerer et al., 2004). Key to such progress has been the engagement of economics and psychology (e.g., Kahneman, 2003). This section also urges a much greater interaction with psychology, since I think we have much to learn from the underlying approach of some of the best practice there. Moreover, the concerns of this chapter to emphasize the need for an integrated approach to alleles, brains, and contexts and their interplays in understanding human behavior (see also Hobcraft, 2006) also point toward greater connections to or borrowing from psychology, since this is one of the few disciplines that engages across this broad range. Biology is another useful model in this respect— see Lieberson and Lynn (2002) for a thoughtful discussion of the value of some aspects of biology for sociological research.

Development over the life course is complex (e.g., Mortimer and Shanahan, 2003). We have emphasized the role of interplays (and likely inseparability) of pathways within the person, both genetic and neuroendocrine, and processes, whereby contexts and experiences affect the person, over the life course. This developmental perspective is important: genes, brain, mind, the person, other persons, and structures all interplay over time (see the quotation from the National Research Council, 2001b, at the head of this chapter and Hobcraft, 2006). Multiple dynamic processes thus need to be considered and modeled. We should expect and look for complex feedbacks and interactions, developmental chains involving interlinked sequences, possible key trigger events or experiences, and packages or groups of experiences that together make more than the sum of the parts. Endowments and experiences, both within the person and external, shape the person, who also reacts to and selects contexts and experiences. Refusal to consider intimately interlinked elements of these progressions because of possible biases arising from endogeneity is akin to throwing out the baby with the bathwater.

This perspective raises huge analytic challenges, in addition to the theoretical and conceptual ones. The economist James Duesenbury quipped that “economics is all about how people make choices; sociology is all about how they don't have any choices to make.” But the tendency to regard choice (or agency) and structures or constraints (I prefer the broader term contexts) as in opposition should be consigned to the same dustbin of history as the opposition of nature and nurture: both matter a great deal, and they interplay. Neither agency nor structure is a nuisance element that can be discarded from models, and separating the two may not be possible, although disentangling how they interplay matters for understanding.


Several modern approaches to analysis are relevant to the research agenda of trying to improve understanding of human behavior and are increasingly being used. The intrinsically multilevel nature of the gene-brain-person-context interplays virtually demands multilevel models (e.g., Goldstein, 2003), and such models have been adapted for behavioral genetic research (see Guo and Wang, 2002). However, the subtle and likely contingent interplays and feedbacks across these levels are rarely thought through or captured in actual model specification (e.g., Cacioppo et al., 2000; and Chapter 17 in this volume). Relatedly, there have been important developments in multiprocess models that enable the exploration of correlated (endogenous) processes over time (Aassve, Burgess, Propper, and Dickson, 2004; Steele, Joshi, Kallis, and Goldstein, 2006; Steele, Kallis, Goldstein, and Joshi, 2005), although again there is a need to conceptualize and specify the nature of interplays carefully. More use could also be made of structural equation models as used by psychologists, which include such useful features as combining measures in latent constructs and carefully specifying theoretically informed pathways (e.g., Kaplan, 2000). There are also clear links to behavioral genetics here, since most quantitative behavioral genetics analysis is done using structural equation models. There has been a explosion of innovative statistical methods used in genetics in recent years (e.g., Thomas 2004). Given the complexities of these different types of modeling, it is essential that more substantial research teams working on understanding human behavior have integrated statisticians, as happens in much genetic and medical research. Moreover, the next stage should probably involve much greater attention to integrating the different approaches, since we badly need sensible approaches that enable the modeling of complex interplays among genes and environments (note the deliberate use of plurals here).

Others advocate moving away from statistical models (and the probabilistic nature of risks) altogether: in psychology this has become known as a “person-centered” approach, which involves distinguishing groups of individuals who are “alike.” However, most cluster analytic approaches allocate individuals on a model-based probabilistic basis, making the claims of dealing with groups of real people hard to justify. Alternative approaches use recursive trees, which do genuinely divide the population into groups, but, again using only other statistical algorithms; the advantage of such recursive tree approaches in part lies in the greater emphasis on interactions (e.g., Hobcraft and Sigle-Rushton, 2005; Zhang and Singer, 1999) and an extension (to “recursive forests”) has been used in identifying candidate genes (Zhang, Yu, and Singer, 2003).

A further problem that bedevils much modeling is that many simplifcations may be virtually equally likely. Sometimes overly rigid theoretical straitjackets hide such model uncertainty, but more use could be made of Bayesian approaches to model uncertainty in exploring candidate genes or candidate environmental indicators and their interplays (see Sorensen and Gianola, 2006).

Conceptual Issues

Successful models require strong conceptual underpinnings or, put another way, statistical models are simply a tool to help inference and the crucial issues are involved in the formulation and underpinning of the models. In this section I touch on a range of issues that should be in mind when exploring the understanding of behavior over the life course, drawing particularly on some of the concerns of psychologists.

O'Connor (2003) provides a useful discussion of different conceptual models for early experience, although many of the issues are of broader life-course relevance. He distinguishes three different models of development: sensitive periods, experience-adaptive or developmental programming, and cumulative models. Each has different implications for how to study the process in question, and they are sometimes harder to separate analytically than conceptually. Sensitive periods or experience-expectant models mean that an input is necessary for development to proceed within a particular window of time. The clearest example is probably the development of the visual cortex, for which external stimulus is essential during a critical period. Experience-adaptive or developmental programming involves biological systems adapting to environmental stimuli in lasting ways: an example would be epigenetic effects, in which gene expression is changed in lasting ways as a result of some experience. The third route is through cumulative or chain models, in which lasting effects occur only if reinforced over time or early experiences set longer term pathways in train. Clearly distinguishing between these three is not always simple, and the first two can have longer term repercussions through the third.

Path dependence through the life course, whereby origins, endowments, and previous experiences shape current behavioral responses to current circumstances, is a crucial component in understanding human behaviors. The role of sequences or packages of experiences over time through developmental chains or cascades is an essential component. Intermediate elements in a causal chain are referred to as “mediators” in the psychological literature, whereas “moderators” alter the impact of another risk factor and involve contingent relationships (or interactions in statistical terminology). Gene-environment interactions are but one example of contingent relationships of this type. The consequences of a specific life experience for an outcome are extremely likely to vary among individuals: some will be more vulnerable to the impact of shocks than others, depending for example on their past experiences, their personality traits, or their cognitive style (see Rutter, 2002, 2004). Previous experience of adversity may steel the individual against a repetition or may sensitize them further (e.g., Rutter, 2004); related examples include the lasting legacies of childhood poverty or behavior and of early unemployment (among other factors, sometimes referred to as “scarring”) for a wide range of adult outcomes (see Hobcraft, 2004). Rutter (2004) persuasively argues that many moderators only accentuate a pathway rather than cause an abrupt change, although there are sometimes claims that turning points exist, too. Sampson and Laub (1993) identify a good marriage or the discipline brought about by armed service as being critical turning points in the careers of criminals; however, a good marriage is hardly likely to be an exogenous shock and is at least as likely to be a marker in a process of some duration.

Just as there is a huge range of genetic or other biomarkers that might be considered in the context of understanding human behavior, there is also a wide range of environmental or contextual influences that can affect behavior or experience. In an extended discussion of contexts for understanding demographic behavior, I distinguished two classes: interplays of the person with other persons and with structures or institutions (Hobcraft, 2006). Examples of potentially important interplays with other persons include the partnership dyad, the mother-father-child triad, family networks (both kin and partner kin), peer groups, friendship or support networks, care or service providers, employers and workmates, and the local community. Examples of structural contexts include welfare regimes; labor markets; education and training systems; housing markets and neighborhoods; health systems; policy and benefit environments; norms and laws; economic, cultural, religious, and political institutions; and gender structures.

Faced with such a wide range of possible influencing factors, there is again a need for judicious simplification. One approach that can be advocated is to concentrate initially on more proximate factors, whether linked by the outcome being considered (e.g., health systems for health outcomes), or by proximity in likely causal chains (e.g., parent-child interplays for child development). I have advocated such a proximal approach for linking values or attitudes to reproductive behavior (Hobcraft, 2002, 2003). Cacioppo (Chapter 17 in this volume) takes a similar position regarding the genetic linkages to prostate cancer, and a similar case can be made for greater specificity in the genetic pathways to cardiovascular disease and longevity (see also Chapter 1 in this volume and Christensen, Johnson, and Vaupel, 2006). Perhaps the search for “generalist genes” (e.g., Kovas and Plomin, 2006; Butcher, Kennedy, and Plomin, 2006, on intelligence) is doomed to come up with few consistent results for many quantitative trait loci, each with small average effects, and much more specificity about pathways, epistatic or epigenetic interplays, or gene-environment interplays is needed (e.g., Rutter, Moffitt, and Caspi, 2006; for a specific example on ADHD and links to reading disabilities, see Stevenson et al., 2005).

The study of contextual (and perhaps particularly interpersonal) influences on individual behavior needs to pay attention both to persons seeking contexts compatible with their own characteristics and persons evoking a response from the context (e.g., other person). See the related literature on active and reactive gene-environment correlations (e.g., Plomin, 1994; Rutter and Silberg, 2002; Rutter et al., 2006) and also recall the discussions on the difficulties in separating agency and constraint above.


There is no doubt that human behavior is complex. Improving understanding of these behaviors necessarily requires exploring the feedbacks and interplays among genes, brain, mind, the person, other persons, and structures. Emotions, personality, decision making, endowments, experiences and networks all play some part in the processes too. Such research can be carried out only by transdisciplinary teams (e.g., Institute of Medicine, 2006) that bring together a wide range of disciplinary skills: molecular biology, genetics, neuroscience, behavioral science, and social science in its relevant guises would constitute a fairly essential list that might be supplemented by epidemiology and health sciences as well as many intermediate skill groups, such as behavioral genetics. The reward structures also need to change, so as to enable young scholars to work in these interstices while also gaining tenure. Much training to raise cross-disciplinary understanding and communication is also needed.

I have made a strong case that many social scientists might benefit from thinking more about these interplays and then sifting knowledge and empirically exploring so as to achieve enough simplification to make the challenge practicable. Most studies will address only fragments of the multiple levels and interplays involved, but I would still urge greater attention to pathways within the individual and their interplays with the processes and progressions whereby the individual interplays with multiple contexts over the life course. A concentration on chains or sequences of events, greater awareness of contingent relationships (interactions in statistical language), and elaboration of partial midlevel frameworks or mechanisms is also required (see Hedström, 2005; Hobcraft, 2006). As a result, models will become more complex, but also more realistic and less simplistic.


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A similar problem of the selectivity of “switchers” bedevils most attempts to control for unobserved variation through fixed-effects models (e.g., restricting analysis to sibling pairs in which one experienced a teenage birth and one did not, or to those who changed their affiliation to a trade union, etc.).

Oxytocin and vasopressin are closely related neuropeptide hormones and have the property of neurotransmitters that enables them to cross the blood-brain barrier. They have been implicated in a wide range of pro- and antisocial behaviors (for a good overview, see Caldwell and Young, 2006).



A similar problem of the selectivity of “switchers” bedevils most attempts to control for unobserved variation through fixed-effects models (e.g., restricting analysis to sibling pairs in which one experienced a teenage birth and one did not, or to those who changed their affiliation to a trade union, etc.).


Oxytocin and vasopressin are closely related neuropeptide hormones and have the property of neurotransmitters that enables them to cross the blood-brain barrier. They have been implicated in a wide range of pro- and antisocial behaviors (for a good overview, see Caldwell and Young, 2006).

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