It has long been understood that certain psychological disorders run in families. This early observation inspired family and twin research designs, which provided foundational support for the heritability of psychological disorders, including posttraumatic stress disorder (PTSD) and other trauma-related disorders. Thanks to the revolutionary efforts of the Human Genome Project (Lander et al., 2001), which mapped the entire human genome, advances in molecular genetic techniques have become increasingly refined, catapulting our understanding of how genes affect human behavior. This has, in turn, informed the increasing integration of two historically siloed fields, psychiatry and genetics. This growing appreciation for psychiatric and behavioral genetics is exemplified by our very own ISTSS annual meeting program year to year. As such, I and my fellow SIG chairs aim to provide a brief overview of some of the key concepts related to genomics and trauma that we hope will prove helpful and interesting to those less versed in behavioral genetics. We aim to highlight the utility of genomic approaches to studying trauma for even the most cynical reader (after all, who cares about small effect sizes anyway, right?; Sullivan et al., 2018).
To start, there is ample evidence to suggest that our genetic make-up influences trauma-related responses (Sheerin et al., 2017). Research exploring the effect of genetics on PTSD and other trauma-related outcomes has typically centered around single nucleotide polymorphisms (SNPs). SNPs (pronounced “snips”) are the most common form of variation in the DNA sequence. Often caused by single-point mutations along the DNA, SNPs produce biological variation that help to explain differences between members of the same species (e.g., humans). These differences range from eye color to complex traits, like PTSD. In other words, examining the influence of genetics (via SNPs) on adverse trauma responses can help to answer the million-dollar question (or, courtesy of NIH, the multimillion-dollar question) of multifinality: how is it that two people with similar trauma histories can experience completely different outcomes (e.g., adaptive versus maladaptive)?
To answer this question, numerous molecular approaches have been applied to investigate how genes influence trauma-related outcomes. One of these approaches is called a candidate gene study. This a priori approach tests risk (e.g., for PTSD) associated with one or multiple hypothesized SNPs within a given gene. Although useful as a starting point or for replication, candidate gene research is characterized by multiple limitations, most notably inconsistent findings across studies (Tabor et al., 2002) and the fact that it does not account for the small, combined effects of multiple genes on an outcome (i.e., “polygeneicity”; Martin et al., 2019). Strictly speaking, there is no one “PTSD gene.”
Advances in genomic sequencing, coupled with increasing sample sizes due largely to the collaborative efforts of the Psychiatric Genomics Consortium (PGC; Sullivan et al., 2018), have led to a shift in recent decades away from candidate gene research and towards the application of agnostic approaches, such as genome wide association studies (GWAS; Wilkening et al., 2009). GWAS allow for the simultaneous examination of millions of SNPs across the genome to identify potential SNPs that increase risk for a certain outcome, like PTSD. Simply put, a genome wide association analysis is a multiple regression of one trait (e.g., PTSD) on millions of independent variables (SNPs), correcting for extensive multiple testing. GWAS have helped identify specific SNPs that confer risk for PTSD (Nievergelt et al., 2019) and other trauma-related outcomes (e.g., problematic alcohol use; Sanchez-Roige et al., 2019), though these effects have been quite small (due to polygenicity) and difficult to replicate.
Despite evidence from GWAS that the risk for PTSD associated with any given SNP is small, the cumulative effects of SNPs can much more meaningfully predict whether someone is at risk for developing PTSD or other trauma-related outcomes. Aggregate molecular approaches (e.g., genome-wide complex trait analysis (GCTA; Yang et al., 2011), linkage disequilibrium score regression (LDSC; Bulik-Sullivan et al., 2015), polygenic risk scores (PRS; International Schizophrenia Consortium et al., 2009), account for polygenicity by approximating the additive genetic risk across all available SNPs. These methods not only have the potential to improve prediction of PTSD by looking at overall genetic risk, but they also offer a refined approach to testing gene-by-environment interactions (GxE), which are particularly relevant to the study of trauma. For example, PRS x environment analyses give us a leg up in our efforts to answer the aforementioned multimillion-dollar question of multifinality by providing a sophisticated test of the diathesis stress model: do those with a greater genetic predisposition (i.e., higher polygenic risk) for PTSD experience PTSD at lower levels of trauma exposure (or certain types of trauma) compared to those with low polygenic risk?
In addition to exploring additive genetic risk across SNPs, one increasingly popular approach to exploring the genetic architecture of PTSD and other trauma-related outcomes is genomic structural equation modeling (gSEM; Grotzinger et al., 2019). An extension of ordinary SEM, gSEM is used to estimate latent genetic risk for a trait of interest (e.g., PTSD) that can then be used in multivariate analyses to explore the joint genetic architecture (e.g., shared genetic risk) of complex traits. This timely approach aptly coincides with the rise of dimensional models of psychopathology (i.e., Hierarchical Taxonomy of Psychopathology; Kotov et al., 2021). Several recent studies have used gSEM to explore genetic relatedness, or shared risk, across commonly comorbid trauma-related outcomes (e.g., PTSD and AUD; Bountress et al., 2022).
The aforementioned approaches are techniques for exploring how genetics (i.e., the unmodifiable genetic material that we as humans are born with and that remains constant over the course of our lives) influence risk for trauma-related outcomes. However, there are additional approaches that build upon this understanding while inching us toward the more translational side of the bench. For example, although our genetic make-up remains stable from birth to death, how our genes function or how they are expressed (i.e., “gene expression”) can be influenced by factors in the environment. This process is commonly referred to as “epigenetics.” Understanding epigenetic processes related to trauma and PTSD is particularly relevant given the known GxE effects discussed earlier, whereby trauma exposure interacts with genetic factors to influence the development of PTSD. One’s biological response to trauma plays an important role in distinguishing maladaptive versus adaptive responses to trauma (Zannas et al., 2015). This reinforces the notion that trauma is a necessary but not sufficient criterion for PTSD.
Thus, as our understanding of behavioral genetics and epigenetics increases, the applicability of the age-old question of nature versus nurture becomes increasingly moot. Rather than asking whether nature or nurture plays a role in the development, maintenance, or course of PTSD, we as a field have begun to appropriately ask how nature interacts with environment (e.g., trauma) to engender differential responses to trauma.
The Genomics & Trauma SIG, co-chaired by myself, Leslie Brick (co-chair), and Anne Stevenson (student chair), has several goals. First, the intent of this group is to foster a network of collaboration, which is particularly important in genetic studies that often require collaborations across multiple international groups. Second, the group provides opportunities for clinicians and scientists interested in different aspects of genomic research, an inherently transdisciplinary field, to learn from one another. Third, the Genomics & Trauma SIG aims to provide opportunities to ISTSS members who are unfamiliar with genomic approaches to learn more about genomic methods and the implications of genomic findings for other areas of the study of traumatic stress.
We as a SIG hold a weekly writing group on Thursdays @ 2PM-4PM EST via Zoom. We publish a newsletter quarterly, and host at least one networking event for those interested in learning more about and/or collaborating on projects related to genomics and trauma. Please consider joining the Genomics and Trauma Special Interest Group if you’re interested in learning more!
References
Bountress, K. E., Brick, L. A., Sheerin, C., Grotzinger, A., Bustamante, D., Hawn, S. E., Gillespie, N., Kirkpatrick, R. M., Kranzler, H., Morey, R., Edenberg, H. J., Maihofer, A. X., Disner, S., Ashley-Koch, A., Peterson, R., Lori, A., Stein, D. J., Kimbrel, N., Nievergelt, C., Andreassen, O. A., … Amstadter, A. B. (2022). Alcohol use and alcohol use disorder differ in their genetic relationships with PTSD: A genomic structural equation modelling approach. Drug and Alcohol Dependence, 234, 109430.
Bulik-Sullivan, B. K., Loh, P-R, Finucane, H.K., Ripke, S., Yang, J., Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015). LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature Genetics, 47, 291-295.
Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., Ip, H. F., Marioni, R. E., McIntosh, A. M., Deary, I. J., Koellinger, P. D., Harden, K. P., Nivard, M. G., & Tucker-Drob, E. M. (2019). Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nature Human Behaviour, 3(5), 513–525.
International Schizophrenia Consortium, Purcell, S. M., Wray, N. R., Stone, J. L., Visscher, P. M., O'Donovan, M. C., Sullivan, P. F., & Sklar, P. (2009). Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature, 460(7256), 748–752.
Kotov, R., Krueger, R. F., Watson, D., Cicero, D. C., Conway, C. C., DeYoung, C. G., Eaton, N. R., Forbes, M. K., Hallquist, M. N., Latzman, R. D., Mullins-Sweatt, S. N., Ruggero, C. J., Simms, L. J., Waldman, I. D., Waszczuk, M. A., & Wright, A. G. C. (2021). The Hierarchical Taxonomy of Psychopathology (HiTOP): A Quantitative Nosology Based on Consensus of Evidence. Annual Review of Clinical Psychology, 17, 83–108.
Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., Funke, R., Gage, D., Harris, K., Heaford, A., Howland, J., Kann, L., Lehoczky, J., LeVine, R., McEwan, P., McKernan, K., … International Human Genome Sequencing Consortium (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822), 860–921.
Martin, A. R., Daly, M. J., Robinson, E. B., Hyman, S. E., & Neale, B. M. (2019). Predicting Polygenic Risk of Psychiatric Disorders. Biological psychiatry, 86(2), 97–109.
Nievergelt, C. M., Maihofer, A. X., Klengel, T., Atkinson, E. G., Chen, C. Y., Choi, K. W., Coleman, J. R. I., Dalvie, S., Duncan, L. E., Gelernter, J., Levey, D. F., Logue, M. W., Polimanti, R., Provost, A. C., Ratanatharathorn, A., Stein, M. B., Torres, K., Aiello, A. E., Almli, L. M., Amstadter, A. B., … Koenen, K. C. (2019). International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nature Communications, 10(1), 4558.
Sanchez-Roige, S., Palmer, A. A., Fontanillas, P., Elson, S. L., 23andMe Research Team, the Substance Use Disorder Working Group of the Psychiatric Genomics Consortium, Adams, M. J., Howard, D. M., Edenberg, H. J., Davies, G., Crist, R. C., Deary, I. J., McIntosh, A. M., & Clarke, T. K. (2019). Genome-Wide Association Study Meta-Analysis of the Alcohol Use Disorders Identification Test (AUDIT) in Two Population-Based Cohorts. The American Journal of Psychiatry, 176(2), 107–118.
Sheerin, C. M., Lind, M. J., Bountress, K., Nugent, N. R., & Amstadter, A. B. (2017). The Genetics and Epigenetics of PTSD: Overview, Recent Advances, and Future Directions. Current opinion in psychology, 14, 5–11.
Sullivan, P. F., Agrawal, A., Bulik, C. M., Andreassen, O. A., Børglum, A. D., Breen, G., Cichon, S., Edenberg, H. J., Faraone, S. V., Gelernter, J., Mathews, C. A., Nievergelt, C. M., Smoller, J. W., O'Donovan, M. C., & Psychiatric Genomics Consortium (2018). Psychiatric Genomics: An Update and an Agenda. The American Journal of Psychiatry, 175(1), 15–27.
Tabor, H. K., Risch, N. J., & Myers, R. M. (2002). Candidate-gene approaches for studying complex genetic traits: practical considerations. Nature Reviews. Genetics, 3(5), 391–397.
Wilkening, S., Chen, B., Bermejo, J. L., & Canzian, F. (2009). Is there still a need for candidate gene approaches in the era of genome-wide association studies? Genomics, 93(5), 415–419.
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011). GCTA: a tool for genome-wide complex trait analysis. American journal of human genetics, 88(1), 76–82.
Zannas, A. S., Provençal, N., & Binder, E. B. (2015). Epigenetics of Posttraumatic Stress Disorder: Current Evidence, Challenges, and Future Directions. Biological Psychiatry, 78(5), 327–335.