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William Calabrese, Maria Martin Lopez, Jacqueline Trumbull, Sarah B. Rutter, and M. Mercedes Perez-Rodriguez
Jang and Choi’s chapter “Issues and New Directions in Personality Disorder Genetics” (this volume) provides a cogent review of research in personality disorder genetics, heritability, endophenotypes, and assessment measures for personality disorders (PDs), while also offering guidance for how to shift our perspective on the field such that future research is unencumbered by “top-down” nosologies. The following commentary highlights four issues in the field. First, future research should focus on harnessing advances in PD assessment instead of relying on politically derived diagnostic systems. This focus includes growing knowledge on the hierarchical structure of PD traits and psychopathology, as well as research in moment-to-moment, in vivo, personality dynamics. Second, research needs to advance our understanding of the role of the environment and account for common pathway models, critical periods, development, and better defined environmental exposures. Third, more research and data are clearly needed in PD genetics to arrive at conclusions similar to what has been drawn from schizophrenia and mood disorder genetics research. Fourth, this research ought to have an eye towards refining treatment targets for PDs.
We wholeheartedly agree that research in PD genetics should not wait for a revised diagnostic system and instead we argue for quite the opposite. Our diagnostic systems need to better reflect the state of the science. Markon (2013) takes this point further in his review of how scientific developments have actually been impeded by “authoritative nosologies” and he argues that the scientific literature should “speak for itself” in order to better reflect the “epistemological pluralism” inherent to the field of psychopathology research. The chapter by Jang and Choi (this volume) effectively represents this “bottom-up” pluralistic discourse for identifying the most valid and useful foci of study in PD genetics. Specifically, the authors do well to highlight the importance of “personality nuances” in order to get away from the overly broad PD categories which are too distal from genetics and fraught with measurement issues (e.g., low stability, high comorbidity, high heterogeneity). Their review of research echoes the sentiment of NIMH’s Research Domain Criteria (RDoC) to understand how genes impact patterns of behavior by elucidating all of the mechanisms in between, like how mRNA affects neuropeptide development, which affects cellular function, which affects neurobiological functioning (i.e., endophenotypes), which affects personality nuances and dynamics (i.e., intermediate endophenotypes), which then lead to broader traits, trait constellations, and disorders. Fortunately, PD genetics research does not need to wait for an updated RDoC or DSM as advancements in personality research are progressing independent of these systems and offer guidance for how to direct translational research from genetics to behavior.
The Need to Harness Advances in Psychopathology Structure and Personality Dynamics
As an example of “bottom-up” pluralistic discourse, the Hierarchical Taxonomy of Psychopathology (HiTOP; Kotov et al., 2017) consortium has recently integrated and synthesized the quantitative research on the organization of psychopathology, which is a more comprehensive model compared to RDoC and offers “clearer phenotypes” for basic research. The consortium proposes six primary spectra: internalizing (or negative affectivity), thought disorder (or psychoticism), disinhibited externalizing, antagonistic externalizing, detachment, and somatoform. Research in this area has shown that these spectra underlie both personality and general psychopathology, which helps to explain their frequent co-occurrence. This model is akin to what is presented in DSM-5’s Section III Alternative Model of Personality Disorder (AMPD). A measure of this model, the Personality Inventory for DSM-5 (PID-5; Krueger, Derringer, Markon, Watson, & Skodol, 2012) has been constructed and well tested and offers exciting opportunities for standardized assessment of trait facets or “nuances” (e.g., deceitfulness vs. manipulativeness). Researchers examining the neurobiology of PD have begun to integrate this model, a noteworthy example being Mancke, Herpertz, and Bertsch’s (2015) multidimensional model of reactive aggression in borderline personality disorder (BPD), which includes five biobehavioral dimensions which they link to multiple levels of analysis, from neurochemistry to the DSM-5 AMPD. One example is their dimension of threat hypersensitivity, which is associated with enhanced perception of anger to ambiguous faces, enhanced P100 amplitude to facial emotions, prefrontal-limbic imbalance, low oxytocinergic activity, and can be linked to the trait facet of hostility in the DSM-5 AMPD. A natural next step in this body of research would be to examine the role of genetic factors in this chain from neurochemistry to hostility and BPD reactive aggression, more broadly.
To explore the complex role of genetics in the development and maintenance of PDs, future research can harness advances in personality “dynamics” (i.e., how personality manifests in different situations). There are emerging models that describe moment-by-moment personality and interpersonal processes which can help researchers move beyond the broader PD traits and categories. We refer readers to a recent review by Hopwood (2018) where he presents an interpersonal scheme (Pincus, Hopwood, & Wright, in press) that describes how “recursive within‐situation” interpersonal patterns (e.g., motives, perceptions) lead up to the stable between‐situation patterns of personality traits and disorders. He also reviews emerging models that can help to explain how personality manifests in different situations and changes over time, which would be very beneficial in PD genetics research given the push to better understand how we, and our genome, interact with our environment over time to lead to disorder. Wrzus and Roberts’ (2017) TESSERA model describes how triggers lead to expectancies, which lead to states and state expressions, and reactions. It would be interesting to study how genes affect each part of this model, as well as the learning processes that occur as a result of reinforcing/punishing reactions that can eventually lead to pathological personality patterns. Wessels, Zimmermann, and Leising’s (2016) version of the SORKC model (i.e., stimuli, organism, response, and subsequent consequences) highlights the difference between our internal perceptions and the external world of situations, responses, and consequences. Other models include Back et al.’s (2011) PERSOC framework that describes the interplay between personality and social relationships, DeYoung’s (2015) theory of how cybernetic goals lead to actions, and Fleeson and Jayarwickreme’s (2015) Whole Trait Theory, which describes how social-cognitive mechanisms lead to personality states related to the Big Five model, which coincides well with the HiTOP spectra. All of these models can help to connect the “intermediate endophenotypes” (i.e., traits) with endophenotypes and the environment.
The Need to Harness Advances in Modeling and Measurement of the Environment
Jang and Choi (this volume) provide an excellent review of gene–environment interaction research in personality traits. To continue moving this literature forward, it is important to take into account that “the phenotype […] is more than the sum of the genetics and the environmental parts” (Derefinko & Widiger, 2016, p. 232; Hyde et al., 2016; Viding & McCrory, 2012). This does not alter the importance of gene–environment interaction, but merely ensures that we are clear on the nature of the phenotype so as to truly understand the gene expression being presented. As delineated by Franić et al. (2013), truly assessing the gene–environment to phenotype connection requires modeling of how genetic and environmental effects act on latent variables to cause differences in observed traits given that genetic and environmental latent variables themselves represent the effects of many unidentified influences (i.e., effect of unknown number of genes, environmental factors corresponding to unknown number of unmeasured environmental influences). In this regard, the common pathway model has proven valuable, which looks at influences of additive genetic (A), shared environmental (C), and individual-specific environmental (E) sources on item covariation mediated by a latent variable (Franić et al., 2013; Rosenström et al., 2017). Unlike independent pathway models of the past, the common pathway model assumes that genes and environment are affected and in turn influence an intermediate phenotype, which can further influence the criteria being studied.
The purpose of the common pathway model is to differentiate the contribution of genetic versus environmental effects between diagnostic items, and to provide an additional analysis of the independent pathway model which looks more deeply at the influence of multiple genetic and environmental factors on distinct sources of between-person variation (Rosenström et al., 2017). The common pathway model estimates A, C, and E components separately for each of the latent factors while the independent pathway model estimates separate latent factors for each of the modeled components. Despite the importance of explicitly comparing the two models, examining the common pathway model is crucial given that it makes an explicit assumption of the phenotypic latent variable model with regards to the sources of item covariation meaning “a latent variable model cannot hold unless the corresponding common pathway model holds” (Franić et al., 2013, p. 409). The common pathway model allows us to more explicitly delineate the nature of the intermediate endophenotypes and move towards incorporating modern measures and models of personality pathology which will dictate how we understand and conceptualize our genetic findings and may guide us in the right direction of uncovering the appropriate genes.
While not specific to PD research, another key challenge of gene–environment studies is the need for standardization and optimization of measures of environmental influences (“the exposome”) (Miller & Jones, 2014). Although genetics and genomics researchers have made significant efforts to standardize their measures and analytic tools, the same cannot be said yet of studies focused on environmental exposures (Steckling et al., 2018), and environmental protective factors (i.e., “positive” environments) are particularly understudied. Several large initiatives are taking place in both the European Union and the USA to advance exposome research. For example, a major goal of the National Institute of Environmental Health Sciences (NIEHS) Strategic Plans is to “promote exposome research and create a blueprint for incorporating exposure science into human health studies.”
In terms of design, PD genetics research would do well to follow studies in schizophrenia and depression in adopting objective measures of environmental factors and looking at transdiagnostic features of personality disorders, such as impulsivity, aggression, and neuroticism (Bulbena-Cabre, Bassir Nia, & Perez-Rodriguez, 2018). Regarding the lack of measures on environmental factors, Rauthmann et al. (2014) addressed this issue and constructed a taxonomy of situations called the “Situational Eight DIAMONDS” model, which includes Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, and Sociality. This model could help to guide PD genetics researchers to the “psychologically important situations” that are most relevant in gene–environment interactions giving rise to pathological personality patterns.
The Need for Larger Samples
Another problem that has impeded progress in PD genetics research is the lack of samples that are sufficiently large to support finer-grained conclusions. Creating larger biobanks that would allow for some of these more complex analyses would be beneficial, as would designing studies with specific theoretical models in mind. For example, the Differential Susceptibility framework suggests that certain genes make an individual more susceptible to the environment and therefore have a for-better or for-worse effect (Assary, Vincent, Keers, & Pluess, 2018). Perez-Perez and colleagues (2018) recently used this model to examine the impact of positive and negative life events in neuroticism and FKBP5. FKBP5 encodes a binding protein for FK506, and promotes regulation of the hypothalamic-pituitary-adrenal (HPA) axis through inhibition of glucocorticoid receptor activity (Perez-Perez et al., 2018).
The Need for Treatment Targets
Our last piece of commentary pertains to the need to direct PD genetics research towards identifying appropriate treatment targets, which Jang and Choi (this volume) discuss in terms of the “raison d’être” of genetics research. This is particularly important since there are no FDA-approved pharmacological treatments for highly disabling PDs such as borderline personality disorder. Several key steps are needed to elucidate the genetic mechanisms of personality disorders. First, the genetic risk variants or loci need to be identified (e.g., through large-scale genome-wide association studies, GWAS, deep sequencing, etc.); second, the potential causal gene (or genes) need to be identified; third, studies need to elucidate the mechanism through which the causal genes exert their effect on susceptibility to PD, most likely at critical periods during development. These breakthroughs can lead to the discovery of therapeutic interventions grounded on the neurobiology of PD, which may be more efficacious than the currently available treatments, which target symptoms but not the core etiopathology of the disorders.
Ethical Issues
As technology in genetic modification progresses faster than ever (e.g., Clustered Regularly Interspaced Short Palindromic Repeats [CRISPR]), the scientific community and society, in general, will be faced with serious moral issues. For instance, if genetic testing determines that an infant could be at risk for patterns of affective instability, given a set of conditions, how do we proceed?
This commentary has aimed to highlight the complexity of how PD likely develops through interactions between variables across various levels of analysis (e.g., genes, endophenotypes, intermediate endophenotypes, environment) across time. Given this complexity, it seems clear that, in this instance, genetic modification would create a “ripple effect” that would have unfathomable consequences. However, advances in PD gene–environment research could help to identify how to influence human development to foster prevention of future PD. If a child tests as having a predisposition for affective instability, then caretakers and therapists can work to create an environment and a set of conditions that could either alter the development of related traits or mitigate the effects of these traits. Ideally, as research in PD genetics becomes more refined, so will our treatment recommendations. For example, more refined models may allow us to predict the cascade of effects and interactions that eventually lead to PD. This information could put greater demand on preemptive and disease-modifying treatment to hopefully lower the prevalence of PD and ameliorate the suffering of individuals diagnosed with a PD.
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The 2nd, 3rd, and 4th authors all contributed equally as 2nd author