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9
The Challenge of Social Genomics:
A Commentary on “Social Genomics and the Life Course:
Opportunities and Challenges for Multilevel Population Research”
Jason Schnittker
The preceding chapter by Michael J. Shanahan provides an excellent review of social
genomics as it relates to health. In my comments, I would like to entertain the challenges
embedded in the perspective. The chapter is comprehensive and forward-looking, but the
implications of social genomics for research on socioeconomic status (SES) and health are
mixed. On the one hand, the chapter is, in many respects, aspirational, with Shanahan outlining
three stages for moving the debate forward from where the field is now. The positive tone is
entirely appropriate given the very high upside of social genomics: In principle, social genomics
will circumvent the silos created by disciplines, encourage a robust multilevel approach, and
allow population scientists to explain health-related phenomena rather than merely describe
them. No less important is the possibility of new discoveries, which the framework has, in my
opinion, already delivered on (including in Shanahan’s excellent empirical work). On the other
hand, despite expanding on several fronts simultaneously, social genomics as Shanahan
articulates it does not put the influences it catalogues on the same conceptual plane. The chapter
is more detailed, specific, and directive with respect to the genomic aspects of social genomics
than with the social aspects. Furthermore, as a matter of emphasis, some influences are elevated
above others. Some of the topics Shanahan encourages more research on can (and should) be
explored fruitfully without considering genetic transcription, inflammatory processes, or other
aspects of social genomics. It is worth thinking seriously about what direction aging research
will move should it fully embrace a social genomic agenda.
In Shanahan’s chapter, the appeal of social genomics is cast in terms of identifying
mechanisms and strengthening causal effects, which, early in his chapter, Shanahan describes as
the “major payoff” for population research. He emphasizes the potential evolution of the
literature in noting that after transcriptional activity is established, scientists can begin to
understand how social experiences affect health. These ideas have a great deal of resonance: so
long as social scientists are unable to identify how social factors get under the skin, the argument
goes, strong claims regarding causality cannot be made, especially in the context of
nonexperimental data (Taylor, Repetti, and Seeman, 1997). Causality is also crucial for
prevention and intervention, as Shanahan notes in the conclusion of his chapter.
Yet there is a distinction between identifying effects and providing explanations. There
can be a strong sense of the former without the latter, regardless of the level of a putative effect.
Effective intervention can occur before an understanding of what connects an actionable “lever”
with an outcome. There are many examples of such levers, and they are arrayed across multiple
levels of analysis. Doctors used aspirin, for example, long before they understood how it worked,
and, of course, the effects of aspirin were no less real when they were poorly conceptualized.
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(Digitalis provides another example.) The same logic applies to causes further upstream. Social
scientists recognize that socioeconomic status, the focus of Shanahan’s chapter, has causal
effects on health even without being able to explain all of those effects in their entirety. To be
sure, the absence of a chained mechanistic explanation might be unsatisfying from some
perspectives. Furthermore, the fact that intervention can take place with incomplete knowledge
does not mean researchers should not try to find mechanisms—they can and should seek
mechanisms. Yet there are risks to demanding a full account that proceeds from the macro to the
micro or to thinking that social effects are weak or superficial in absence of a complete
biological explanation.
For one, the provisionality Shanahan identifies with respect to causal inference in the
social sciences applies as much to biological processes as social ones, resulting in uncertainty at
all levels. Shanahan points to the widespread use of nonexperimental survey data in population
research, but nonexperimental data are used in social genomics as well and there, too, this usage
has serious implications. The observational methods employed for studying gene-environment
interactions, for example, are often contaminated by gene-gene interactions, undermining
confidence regarding the main effects of genes (Conley and Rauscher, 2010). Furthermore, at
least in the social sciences, methods suitable for disambiguating correlation and causation (e.g.,
the use of compulsory education laws to identify the effects of education on mortality) are often
ill suited for illuminating mechanisms. More generally, the goals of social scientific explanation
can diverge in meaningful ways from those of biological explanation. Along these lines,
Shanahan notes that social genomic research demonstrates the relevance of socioeconomic status
for gene transcription, but a full explanation of the relationship between socioeconomic status
and health requires rigorous explanation both up and down the line. As Shanahan notes, this
research reveals little regarding what it is about socioeconomic status that matters, often relying
on single indicators. The same is true at other junctures in the chain of connections between
socioeconomic status and health. Chronic stress and the activation of the sympathetic nervous
system provides one potential mechanism linking socioeconomic status to transcription processes
and ultimately to health (as established in the case of HIV-1). Yet appealing to stress merely
invites further speculation regarding why and how socioeconomic status is related to chronic
stress. This issue is further complicated by the apparent resilience of those of low socioeconomic
status. Shanahan notes this, but scholars understand very little about the topic and, on its own, it
is worthy of focused attention. Some of these gaps and complications likely explain why
interactions between genes and features of socioeconomic status otherwise united in their
relationship to “resources” are occasionally inconsistent even in their direction (e.g., Pescosolido
et al., 2008). In short, there is much to be done simply in terms of characterizing the
environment.
Shanahan is sensitive to these issues and concludes his chapter by discussing several
topics for future research, including how possibly to refine measures of social risk factors, the
specificity of causes and effects, and the value of comparative studies. It is notable, however,
that these discussions become increasingly speculative and Shanahan has fewer empirical
examples the further he moves away from influences that lie beneath the skin. The point is not
that these influences are less relevant. As Shanahan argues, research should explore, for
example, the effects of the welfare state, the network of correlations among stressors, and self-
regulation processes. Yet studying how these influences are related to health does not require the
collection of gene expression data, biomarkers, or neuroimaging and it is not necessary to wait
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until such data are produced to investigate them further. These influences also probably implicate
far more than stress or other biological processes that inform much of the transcription research.
Understanding the role of the political economy in health, for example, will require more than an
understanding of transcription.
Indeed, insisting on collecting transcription-relevant data as a matter of emphasis may, in
some instances, lead investigations astray. One risk in demanding a biological mechanism lies in
prematurely foreclosing on a generative sense of uncertainty, especially when mediating
pathways are dynamic. Shanahan provides some illustrative specific examples of mediation. For
instance, he reviews how socioeconomic status may be linked to HIV-1 progression through
activation of the sympathetic nervous system and, in turn, HIV’s transcription and replication.
Yet, in general, socioeconomic status is linked to health through a variety of proximate
mechanisms that change over time and, therefore, the link will involve different biological
pathways at different times. The relationship between socioeconomic status and cholesterol, for
instance, switched direction with the introduction of statins (Chang and Lauderdale, 2009). In
this case, the identification of a mediating mechanism does not settle the debate regarding
causality any more than the use of statins ensures the elimination of the association between
socioeconomic status and heart disease. Provisionality, in this sense, is part of the effect itself,
not a reflection of scientific naiveté.
A related complication concerns the scope of social genomics as it applies the literature
on socioeconomic status and health. The perspective Shanahan articulates is capacious,
implicating multilevel processes in an integrated life course framework. But when it comes to the
specific results of the studies he reviews, much of it is quite focused. For example, Shanahan
focuses much of his attention on gene-environment interactions—how do social factors affect
gene transcription—which he identifies as the core agenda of social genomics. This work has
immediate appeal to social scientists (e.g., Caspi et al., 2003). Among other things, studies of this
sort move the literature away from simply gene or environment questions to gene and
environment questions. Yet focusing on interactions can constrain the scientific imagination
nearly as much as focusing on main effects. It also leaves scientists ill prepared to take the null
hypothesis (no interaction) seriously, which is increasingly required given the state of the gene-
environment literature. The literature also suffers from well-known replication problems, with
interactions significant in one study often insignificant in another (e.g., Risch et al., 2009).
Shanahan ultimately emphasizes the relatively robust patterns found in research on
transcription processes and, in turn, inflammatory processes. But if this is the area with the most
scientific confidence, the relevance of social genomics for socioeconomic status and health is
suddenly smaller than it once seemed. The set of disease outcomes related to socioeconomic
status exceeds the set related to the inflammatory response. If stress has a prominent
transcriptional fingerprint, as Shanahan notes, socioeconomic status has an enormous mortality
footprint. Lest researchers disregard genes or environments altogether, motivating interaction
effects should perhaps take a back-seat to motivating main effects. Surely genes are worth
exploring even if social processes have little bearing on how they are expressed, just as social
processes are worth exploring even if they sometimes operate independently of genetic
transcription or inflammation.
Beyond these concerns are some risks to organizing a debate according to the concept of
levels, at least as the idea is usually articulated. In any multilevel investigation, some levels
emerge as more important than others, even if the goal is to shed light on many different
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influences. Furthermore, the idea of a level implies a boundary, and, despite the ecumenical
nature of social genomics, some of these boundaries are becoming sharper, not softer. It is
possible to be sensitive to all the relevant influences, without attempting to reduce an outcome to
a narrow set of risk factors. A notable feature of the current multilevel approach, for example, is
a split between those moving toward higher levels of analysis, such as those interested in
different policy settings and political economies, and those moving toward more micro-level
processing, such as biomarkers, neurons, and genes. Shanahan encourages movement in both
directions and effectively reviews both arms of the literature. Yet it is important to be mindful of
the vast meso-level in between. This may be where much of the action is, and, indeed, much of
the research Shanahan reviews would seem to push in a meso direction. Shanahan highlights, for
example, the role of a sense of threat or heightened vigilance in mediating the link between
social stress and transcription profiles. He further highlights, by way of review, the role of
changes in corticolimbic circuitry in linking stress to vigilance. In this way, he links
socioeconomic status to stress to neurons to cognition to health, consistent with his mediational
focus. Psychological influences provide a crucial link in this chain, but it is useful to pause with
every link and, thus, to dwell on the independent relevance of psychological influences, apart
from any role they may play in explaining the relationship between socioeconomic status and
health.
For one, heightened vigilance is not entirely a reflection of socioeconomic status.
Furthermore, important psychological influences can be measured in survey instruments without
also collecting biological samples. (In Shanahan’s framework, the primary purpose of Stage
Three is to collect biological and genetic data in more representative samples in order to confirm
results from earlier stages.) It is important, for example, to study the relationship between social
context and sense of threat whether or not the relationship reflects the corticolimbic system. A
counterargument, articulated by Shanahan, is that social genomic research illuminates the
importance of psychology and, therefore, that the study of genes is a useful tool for the discovery
and refinement of social mechanisms. But if social genomics is fundamentally about illuminating
mediational pathways, it would do just as well to remind social scientists that the mind is
important as to remind them that genes are important, and the former can be considered without
the latter. Indeed, the literature risks misplaced specificity by focusing on genetic transcription,
and, in the process of further reduction over consecutive stages, risks overlooking other
important influences. Regardless of the route that leads researchers to psychological influences,
it is important to give them their full conceptual due, and a tight focus on transcription processes
might prevent this.
In the formidable sweep of Shanahan’s chapter, an emphasis on psychological factors
might appear modest. It might even appear regressive. There is no mistaking the cutting-edge
allure of genetic transcription and the inflammatory response. It is also impressive how many
social scientists, like Shanahan, have developed expertise in fields other than their own. Yet an
appreciation of human psychology alone addresses many of the themes discussed in Shanahan’s
chapter, including scientific integration, causality, contingency, and multilevel integration.
Psychology is concerned with how the environment is internalized and, in this sense,
psychological factors are no less “under the skin” than genes. The scope of psychological
influences is also appropriate to the task of explaining socioeconomic differences in health
insofar as they are related to multiple health outcomes through many different pathways.
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Furthermore, the reluctance of some disciplines to integrate genomics is very closely
related to the reluctance of the same disciplines to integrate psychology: In both cases, social
scientists fear explaining behavior in terms of individual attributes rather than social structures,
or in terms of dispositions rather than constraints. Yet the empirical foundation of psychology is
strong, long-standing, and sophisticated in ways that are not dissimilar to the foundation of social
genomics. Decades of research reveal that psychological factors are crucial to behavior and
intersect with the environment in interesting ways (Ross and Nisbett, 1991). Psychological
factors motivate behavior, and therefore can link environments to action. They are also central to
how the social environment is created, construed, and remembered, and therefore can speak to
person-environment interactions. The high-degree of contingency within social genomics—an
especially appealing aspect of the field—is echoed throughout contemporary personality
psychology (Mischel, 2004). If population research wishes to traverse levels while progressing
“inward” in some fashion, psychology provides an excellent avenue. And if the challenge of
integration rests as much with psychology as genes, the challenge is no less great.
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