News and Commentary Archive

Explore recent scientific discoveries and news as well as CLBB events, commentary, and press.


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COMT Val158Met Genotype is Associated with Reward Learning: A Replication Study and Meta-Analysis

By N. S. Corral-Frías, D. A. Pizzagalli, J. M. Carré, L. J. Michalski, Y. S. Nikolova, R. H. Perlis, J. Fagerness, M. R. Lee, E. Drabant Conley, T. M. Lancaster, S. Haddad, A. Wolf, J. W. Smoller, A. R. Hariri, and R. Bogdan | Genes, Brain, and Behavior | June 1, 2016


Identifying mechanisms through which individual differences in reward learning emerge offers an opportunity to understand both a fundamental form of adaptive responding as well as etiological pathways through which aberrant reward learning may contribute to maladaptive behaviors and psychopathology. One candidate mechanism through which individual differences in reward learning may emerge is variability in dopaminergic reinforcement signaling. A common functional polymorphism within the catechol-O-methyl transferase gene (COMT; rs4680, Val158Met) has been linked to reward learning, where homozygosity for the Met allele (linked to heightened prefrontal dopamine function and decreased dopamine synthesis in the midbrain) has been associated with relatively increased reward learning. Here, we used a probabilistic reward learning task to asses response bias, a behavioral form of reward learning, across three separate samples that were combined for analyses (age: 21.80 ± 3.95; n = 392; 268 female; European-American: n = 208). We replicate prior reports that COMTrs4680 Met allele homozygosity is associated with increased reward learning in European-American participants (β = 0.20, t = 2.75, P < 0.01; ΔR2 = 0.04). Moreover, a meta-analysis of 4 studies, including the current one, confirmed the association between COMT rs4680 genotype and reward learning (95% CI −0.11 to −0.03; z = 3.2; P < 0.01). These results suggest that variability in dopamine signaling associated withCOMT rs4680 influences individual differences in reward which may potentially contribute to psychopathology characterized by reward dysfunction.

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Genome-Wide Association Studies of Posttraumatic Stress Disorder in 2 Cohorts of US Army Soldiers

By Murray B. Stein, Chia-Yen Chen, Robert J. Ursano, Tianxi Cai, Joel Gelernter, Steven G. Heeringa, Sonia Jain, Kevin P. Jensen, Adam X. Maihofer, Colter Mitchell, Caroline M. Nievergelt, Matthew K. Nock, Benjamin M. Neale, Renato Polimanti, Stephan Ripke, Xiaoying Sun, Michael L. Thomas, Qian Wang, Erin B. Ware, Susan Borja, Ronald C. Kessler, and Jordan W. Smoller | JAMA Psychiatry | May 11, 2016


Importance —  Posttraumatic stress disorder (PTSD) is a prevalent, serious public health concern, particularly in the military. The identification of genetic risk factors for PTSD may provide important insights into the biological foundation of vulnerability and comorbidity.

Objective —  To discover genetic loci associated with the lifetime risk for PTSD in 2 cohorts from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

Design, Setting, and Participants —  Two coordinated genome-wide association studies of mental health in the US military contributed participants. The New Soldier Study (NSS) included 3167 unique patients with PTSD and 4607 trauma-exposed control individuals; the Pre/Post Deployment Study (PPDS) included 947 unique patients with PTSD and 4969 trauma-exposed controls. The NSS data were collected from February 1, 2011, to November 30, 2012; the PDDS data, from January 9 to April 30, 2012. The primary analysis compared lifetime DSM-IV PTSD cases with trauma-exposed controls without lifetime PTSD. Data were analyzed from March 18 to December 27, 2015.

Main Outcomes and Measures —  Association analyses for PTSD used logistic regression models within each of 3 ancestral groups (European, African, and Latino American) by study, followed by meta-analysis. Heritability and genetic correlation and pleiotropy with other psychiatric and immune-related disorders were estimated.

Results —  The NSS population was 80.7% male (6277 of 7774 participants; mean [SD] age, 20.9 [3.3] years); the PPDS population, 94.4% male (5583 of 5916 participants; mean [SD] age, 26.5 [6.0] years). A genome-wide significant locus was found in ANKRD55 on chromosome 5 (rs159572; odds ratio [OR], 1.62; 95% CI, 1.37-1.92; P = 2.34 × 10−8) and persisted after adjustment for cumulative trauma exposure (adjusted OR, 1.64; 95% CI, 1.39-1.95; P = 1.18 × 10−8) in the African American samples from the NSS. A genome-wide significant locus was also found in or near ZNF626 on chromosome 19 (rs11085374; OR, 0.77; 95% CI, 0.70-0.85; P = 4.59 × 10−8) in the European American samples from the NSS. Similar results were not found for either single-nucleotide polymorphism in the corresponding ancestry group from the PPDS sample, in other ancestral groups, or in transancestral meta-analyses. Single-nucleotide polymorphism–based heritability was nonsignificant, and no significant genetic correlations were observed between PTSD and 6 mental disorders or 9 immune-related disorders. Significant evidence of pleiotropy was observed between PTSD and rheumatoid arthritis and, to a lesser extent, psoriasis.

Conclusions and Relevance —  In the largest genome-wide association study of PTSD to date, involving a US military sample, limited evidence of association for specific loci was found. Further efforts are needed to replicate the genome-wide significant association with ANKRD55—associated in prior research with several autoimmune and inflammatory disorders—and to clarify the nature of the genetic overlap observed between PTSD and rheumatoid arthritis and psoriasis.

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Common Measures for National Institute of Mental Health Funded Research

By Deanna M. Barch, Ian H. Gotlib, Robert M. Bilder, Daniel S. Pine, Jordan W. Smoller, C. Hendricks Brown, Wayne Huggins, Carol Hamilton, Adam Haim, and Gregory K. Farber | Biological Psychiatry | February 19, 2016


One of the most encouraging, but also the most challenging, aspects of current research on psychopathology is the diversity of measures used to assess constructs across research studies and programs. Clearly, this diversity reflects the creativity and generativity of our field and the continual growth of our science. At the same time, however, this diversity also makes data harmonization across studies difficult, if not sometimes impossible. The National Human Genome Research Institute recognized this conundrum in the field of genetics and started an initiative referred to as consensus measures for Phenotypes and eXposures (PhenX) to identify and recommend a small number of measures for each of 21 broad research domains that could be used as common assessments to facilitate integration across genome-wide association studies (1, 2, 3 and 4). These measures are made available to the scientific community, at no cost, in the PhenX Toolkit ( Subsequently, the PhenX consensus process was used to identify measures in support of substance abuse and addiction (SAA) research, adding depth to the toolkit in this area. This project was funded by the National Institute on Drug Abuse (NIDA) with the participation of the National Institute on Alcohol Abuse and Alcoholism. Perhaps due to a growing awareness of the need to share data across studies to increase statistical power and study impact, a number of other common data element programs have been underway, including the Patient-Reported Outcomes Measurement Information System (5), the National Institutes of Health (NIH) Toolbox (6), the Neurological Quality of Life (7), the National Institute of Neurological Disorders and Stroke Common Data Elements program (8 and 9), and the NIH Common Data Elements program ( The program staff at the National Institute of Mental Health (NIMH), as well as its funded researchers, have also recognized the challenges posed by a lack of common measures across studies. The NIMH has taken note of this recent emphasis on larger scale studies to address core questions about the mechanisms of psychopathology and recent attempts at data harmonization across studies of psychopathology that address similar issues. Accordingly, the NIMH felt that it was time to identify brief, low-burden measures that NIMH-funded researchers could include in their studies to increase cross-study data compatibility. The goal of the current report is to briefly describe the genesis and development of the PhenX project, to outline the process that the Mental Health Research Panel used to select a set of common measures, to describe the measures themselves, and to outline the goals associated with including these measures in future studies.

Continue reading the full report here.

Genetic Influences on Schizophrenia and Subcortical Brain Volumes: Large-Scale Proof of Concept

By Barbara FrankeJason L. SteinStephan RipkeVerneri AnttilaDerrek P. HibarKimm J. E. van HulzenAlejandro Arias-VasquezJordan W. SmollerThomas E. NicholsMichael C. NealeAndrew M. McIntoshPhil LeeFrancis J. McMahonAndreas Meyer-LindenbergManuel MattheisenOle A. AndreassenOliver GruberPerminder S. SachdevRoberto Roiz-SantiañezAndrew J. SaykinStefan EhrlichKaren A. MatherJessica A. TurnerEmanuel SchwarzAnbupalam Thalamuthu, et al. | Nature Neuroscience | February 1, 2016


Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.

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Socioeconomic Disadvantage and Neural Development from Infancy through Early Childhood

By Galen Chin-Lun HungJill HahnBibi AlamiriStephen L. BukaJill M. GoldsteinNan LairdCharles A. NelsonJordan W. Smoller, and Stephen E. Gilman | International Journal of Epidemiology | December 16, 2015


Background: Early social experiences are believed to shape neurodevelopment, with potentially lifelong consequences. Yet minimal evidence exists regarding the role of the social environment on children’s neural functioning, a core domain of neurodevelopment.

Methods: We analysed data from 36 443 participants in the United States Collaborative Perinatal Project, a socioeconomically diverse pregnancy cohort conducted between 1959 and 1974. Study outcomes included: physician (neurologist or paediatrician)-rated neurological abnormality neonatally and thereafter at 4 months and 1 and 7 years; indicators of neurological hard signs and soft signs; and indicators of autonomic nervous system function.

Results: Children born to socioeconomically disadvantaged parents were more likely to exhibit neurological abnormalities at 4 months [odds ratio (OR) = 1.20; 95% confidence interval (CI) = 1.06, 1.37], 1 year (OR = 1.35; CI = 1.17, 1.56), and 7 years (OR = 1.67; CI = 1.48, 1.89), and more likely to exhibit neurological hard signs (OR = 1.39; CI = 1.10, 1.76), soft signs (OR = 1.26; CI = 1.09, 1.45) and autonomic nervous system dysfunctions at 7 years. Pregnancy and delivery complications, themselves associated with socioeconomic disadvantage, did not account for the higher risks of neurological abnormalities among disadvantaged children.

Conclusions: Parental socioeconomic disadvantage was, independently from pregnancy and delivery complications, associated with abnormal child neural development during the first 7 years of life. These findings reinforce the importance of the early environment for neurodevelopment generally, and expand knowledge regarding the domains of neurodevelopment affected by environmental conditions. Further work is needed to determine the mechanisms linking socioeconomic disadvantage with children’s neural functioning, the timing of such mechanisms and their potential reversibility.

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