The Collaborative Study on the Genetics of Alcoholism: Overview.

Alcohol use disorders (AUD) are commonly occurring, heritable and polygenic disorders with etiological origins in the brain and the environment. To outline the causes and consequences of alcohol-related milestones, including AUD, and their related psychiatric comorbidities, the Collaborative Study on the Genetics of Alcoholism (COGA) was launched in 1989 with a gene-brain-behavior framework. COGA is a family based, diverse (~25% self-identified African American, ~52% female) sample, including data on 17,878 individuals, ages 7-97 years, in 2246 families of which a proportion are densely affected for AUD. All participants responded to questionnaires (e.g., personality) and the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) which gathers information on psychiatric diagnoses, conditions and related behaviors (e.g., parental monitoring). In addition, 9871 individuals have brain function data from electroencephalogram (EEG) recordings while 12,009 individuals have been genotyped on genome-wide association study (GWAS) arrays. A series of functional genomics studies examine the specific cellular and molecular mechanisms underlying AUD. This overview provides the framework for the development of COGA as a scientific resource in the past three decades, with individual reviews providing in-depth descriptions of data on and discoveries from behavioral and clinical, brain function, genetic and functional genomics data. The value of COGA also resides in its data sharing policies, its efforts to communicate scientific findings to the broader community via a project website and its potential to nurture early career investigators and to generate independent research that has broadened the impact of gene-brain-behavior research into AUD.

The collaborative study on the genetics of alcoholism: Genetics.

This review describes the genetic approaches and results from the family-based Collaborative Study on the Genetics of Alcoholism (COGA). COGA was designed during the linkage era to identify genes affecting the risk for alcohol use disorder (AUD) and related problems, and was among the first AUD-focused studies to subsequently adopt a genome-wide association (GWAS) approach. COGA's family-based structure, multimodal assessment with gold-standard clinical and neurophysiological data, and the availability of prospective longitudinal phenotyping continues to provide insights into the etiology of AUD and related disorders. These include investigations of genetic risk and trajectories of substance use and use disorders, phenome-wide association studies of loci of interest, and investigations of pleiotropy, social genomics, genetic nurture, and within-family comparisons. COGA is one of the few AUD genetics projects that includes a substantial number of participants of African ancestry. The sharing of data and biospecimens has been a cornerstone of the COGA project, and COGA is a key contributor to large-scale GWAS consortia. COGA's wealth of publicly available genetic and extensive phenotyping data continues to provide a unique and adaptable resource for our understanding of the genetic etiology of AUD and related traits.

Stability of polygenic scores across discovery genome-wide association studies.

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.

What genes are differentially expressed in individuals with schizophrenia? A systematic review.

Schizophrenia is a severe, complex mental disorder characterized by a combination of positive symptoms, negative symptoms, and impaired cognitive function. Schizophrenia is highly heritable (~80%) with multifactorial etiology and complex polygenic genetic architecture. Despite the large number of genetic variants associated with schizophrenia, few causal variants have been established. Gaining insight into the mechanistic influences of these genetic variants may facilitate our ability to apply these findings to prevention and treatment. Though there have been more than 300 studies of gene expression in schizophrenia over the past 15 years, none of the studies have yielded consistent evidence for specific genes that contribute to schizophrenia risk. The aim of this work is to conduct a systematic review and synthesis of case-control studies of genome-wide gene expression in schizophrenia. Comprehensive literature searches were completed in PubMed, EmBase, and Web of Science, and after a systematic review of the studies, data were extracted from those that met the following inclusion criteria: human case-control studies comparing the genome-wide transcriptome of individuals diagnosed with schizophrenia to healthy controls published between January 1, 2000 and June 30, 2020 in the English language. Genes differentially expressed in cases were extracted from these studies, and overlapping genes were compared to previous research findings from the genome-wide association, structural variation, and tissue-expression studies. The transcriptome-wide analysis identified different genes than those previously reported in genome-wide association, exome sequencing, and structural variation studies of schizophrenia. Only one gene, GBP2, was replicated in five studies. Previous work has shown that this gene may play a role in immune function in the etiology of schizophrenia, which in turn could have implications for risk profiling, prevention, and treatment. This review highlights the methodological inconsistencies that impede valid meta-analyses and synthesis across studies. Standardization of the use of covariates, gene nomenclature, and methods for reporting results could enhance our understanding of the potential mechanisms through which genes exert their influence on the etiology of schizophrenia. Although these results are promising, collaborative efforts with harmonization of methodology will facilitate the identification of the role of genes underlying schizophrenia.

Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals.

Polygenic risk scores (PRS) represent an individual's summed genetic risk for a trait and can serve as biomarkers for disease. Less is known about the utility of PRS as a means to quantify genetic risk for substance use disorders (SUDs) than for many other traits. Nonetheless, the growth of large, electronic health record-based biobanks makes it possible to evaluate the association of SUD PRS with other traits. We calculated PRS for smoking initiation, alcohol use disorder (AUD), and opioid use disorder (OUD) using summary statistics from the Million Veteran Program sample. We then tested the association of each PRS with its primary phenotype in the Penn Medicine BioBank (PMBB) using all available genotyped participants of African or European ancestry (AFR and EUR, respectively) (N = 18,612). Finally, we conducted phenome-wide association analyses (PheWAS) separately by ancestry and sex to test for associations across disease categories. Tobacco use disorder was the most common SUD in the PMBB, followed by AUD and OUD, consistent with the population prevalence of these disorders. All PRS were associated with their primary phenotype in both ancestry groups. PheWAS results yielded cross-trait associations across multiple domains, including psychiatric disorders and medical conditions. SUD PRS were associated with their primary phenotypes; however, they are not yet predictive enough to be useful diagnostically. The cross-trait associations of the SUD PRS are indicative of a broader genetic liability. Future work should extend findings to additional population groups and for other substances of abuse.

Polygenic Risk of Psychiatric Disorders Exhibits Cross-trait Associations in Electronic Health Record Data From European Ancestry Individuals.

BACKGROUND: Prediction of disease risk is a key component of precision medicine. Common traits such as psychiatric disorders have a complex polygenic architecture, making the identification of a single risk predictor difficult. Polygenic risk scores (PRSs) denoting the sum of an individual's genetic liability for a disorder are a promising biomarker for psychiatric disorders, but they require evaluation in a clinical setting. METHODS: We developed PRSs for 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, cross disorder, attention-deficit/hyperactivity disorder, and anorexia nervosa) and 17 nonpsychiatric traits in more than 10,000 individuals from the Penn Medicine Biobank with accompanying electronic health records. We performed phenome-wide association analyses to test their association across disease categories. RESULTS: Four of the 6 psychiatric PRSs were associated with their primary phenotypes (odds ratios from 1.2 to 1.6). Cross-trait associations were identified both within the psychiatric domain and across trait domains. PRSs for coronary artery disease and years of education were significantly associated with psychiatric disorders, largely driven by an association with tobacco use disorder. CONCLUSIONS: We demonstrated that the genetic architecture of electronic health record-derived psychiatric diagnoses is similar to ascertained research cohorts from large consortia. Psychiatric PRSs are moderately associated with psychiatric diagnoses but are not yet clinically predictive in naïve patients. Cross-trait associations for these PRSs suggest a broader effect of genetic liability beyond traditional diagnostic boundaries. As identification of genetic markers increases, including PRSs alongside other clinical risk factors may enhance prediction of psychiatric disorders and associated conditions in clinical registries.

Using the tools of genetic epidemiology to understand sex differences in neuropsychiatric disorders.

Many neuropsychiatric disorders exhibit differences in prevalence, age of onset, symptoms or course of illness between males and females. For the most part, the origins of these differences are not well understood. In this article, we provide an overview of sex differences in psychiatric disorders including autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), anxiety, depression, alcohol and substance abuse, schizophrenia, eating disorders and risk of suicide. We discuss both genetic and nongenetic mechanisms that have been hypothesized to underlie these differences, including ascertainment bias, environmental stressors, X- or Y-linked risk loci, and differential liability thresholds in males and females. We then review the use of twin, family and genome-wide association approaches to study potential genetic mechanisms of sex differences and the extent to which these designs have been employed in studies of psychiatric disorders. We describe the utility of genetic epidemiologic study designs, including classical twin and family studies, large-scale studies of population registries, derived recurrence risks, and molecular genetic analyses of genome-wide variation that may enhance our understanding sex differences in neuropsychiatric disorders.

Characteristics of youth with reported family history of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort.

Little is known about the impact of family history of psychosis on youth from community samples. To fill this gap, we compared youth with a first-degree relative with psychosis spectrum symptoms (i.e. family history of psychosis spectrum symptoms, FHPS) to youth without FHPS in a cross-sectional analysis of the Philadelphia Neurodevelopmental Cohort (PNC). The PNC is a racially diverse community sample of 9498 youth ages 8-21 years old, of whom 8928 completed the Family Interview for Genetic Studies to determine FHPS status. Polygenic risk score for schizophrenia (PRSS) was available for a subsample of 4433 European Americans. FHPS youth (n = 489) constituted 5.5% of the analytic sample. After adjusting for environmental risk factors (sociodemographic variables and traumatic stressful events), FHPS youth had lower functioning on the Children's Global Assessment Scale and elevated psychosis spectrum, mood, externalizing, and fear symptoms compared to non-FHPS youth (all p < .001). In the European-American subsample, FHPS status was associated with poorer functioning and greater symptom burden in all four psychopathology domains (all p < .001), even after covarying for PRSS. Thus, ascertaining FHPS is important because it is uniquely associated with symptoms and functional impairment in community youth beyond PRS-S and the environmental risk factors we investigated. Future research identifying environmental causes of FHPS-associated impairment could inform the development of interventions for the broad array of symptoms observed in FHPS youth.

Harnessing Progress in Psychiatric Genetics to Advance Population Mental Health.

Advances in genomics and neuroscience have ushered in unprecedented opportunities to increase our understanding of the biological underpinnings of mental disorders, yet there has been limited progress in translating knowledge on genetic risk factors to reduce the burden of these conditions in the population. We describe the challenges and opportunities afforded by the growth of large-scale population health databases, progress in genomics, and collaborative efforts in epidemiology and neuroscience to develop informed population-wide interventions for mental disorders. Future progress is likely to benefit from the following efforts: expansion of large collaborative studies of mental disorders to include more systematically ascertained multiethnic samples from biobanks and registries, harmonization of phenotypic characterization in registry and population samples to extend clinical diagnosis to transdiagnostic concepts, systematic investigation of the influences of both specific and nonspecific environmental factors that may combine with genetic susceptibility to confer increased risk of specific mental disorders, and implementation of study designs that can inform gene-environment interactions. Such data can ultimately be combined to develop comprehensive models of risks of, interventions for, and outcomes of mental disorders. With its focus on phenotypic characterization, sampling, study designs, and analytic methods, epidemiology will be central to progress in translating genomics to public health.

Real-time Mobile Monitoring of the Dynamic Associations Among Motor Activity, Energy, Mood, and Sleep in Adults With Bipolar Disorder.

Importance: Biologic systems involved in the regulation of motor activity are intricately linked with other homeostatic systems such as sleep, feeding behavior, energy, and mood. Mobile monitoring technology (eg, actigraphy and ecological momentary assessment devices) allows the assessment of these multiple systems in real time. However, most clinical studies of mental disorders that use mobile devices have not focused on the dynamic associations between these systems. Objectives: To examine the directional associations among motor activity, energy, mood, and sleep using mobile monitoring in a community-identified sample, and to evaluate whether these within-day associations differ between people with a history of bipolar or other mood disorders and controls without mood disorders. Design, Setting, and Participants: This study used a nested case-control design of 242 adults, a subsample of a community-based sample of adults. Probands were recruited by mail from the greater Washington, DC, metropolitan area from January 2005 to June 2013. Enrichment of the sample for mood disorders was provided by volunteers or referrals from the National Institutes of Health Clinical Center or by participants in the National Institute of Mental Health Mood and Anxiety Disorders Program. The inclusion criteria were the ability to speak English, availability to participate, and consent to contact at least 2 living first-degree relatives. Data analysis was performed from June 2013 through July 2018. Main Outcomes and Measures: Motor activity and sleep duration data were obtained from minute-to-minute activity counts from an actigraphy device worn on the nondominant wrist for 2 weeks. Mood and energy levels were assessed by subjective analogue ratings on the ecological momentary assessment (using a personal digital assistant) by participants 4 times per day for 2 weeks. Results: Of the total 242 participants, 92 (38.1%) were men and 150 (61.9%) were women, with a mean (SD) age of 48 (16.9) years. Among the participants, 54 (22.3%) had bipolar disorder (25 with bipolar I; 29 with bipolar II), 91 (37.6%) had major depressive disorder, and 97 (40.1%) were controls with no history of mood disorders. A unidirectional association was found between motor activity and subjective mood level (ß = -0.018, P = .04). Bidirectional associations were observed between motor activity (ß = 0.176; P = .03) and subjective energy level (ß = 0.027; P = .03) as well as between motor activity (ß = -0.027; P = .04) and sleep duration (ß = -0.154; P = .04). Greater cross-domain reactivity was observed in bipolar disorder across all outcomes, including motor activity, sleep, mood, and energy. Conclusions and Relevance: These findings suggest that interventions focused on motor activity and energy may have greater efficacy than current approaches that target depressed mood; both active and passive tracking of multiple regulatory systems are important in designing therapeutic targets.

Sex differences in estimated brain metabolism in relation to body growth through adolescence.

The human brain consumes a disproportionate amount of the body's overall metabolic resources, and evidence suggests that brain and body may compete for substrate during development. Using perfusion MRI from a large cross-sectional cohort, we examined developmental changes of MRI-derived estimates of brain metabolism, in relation to weight change. Nonlinear models demonstrated that, in childhood, changes in body weight were inversely related to developmental age-related changes in brain metabolism. This inverse relationship persisted through early adolescence, after which body and brain metabolism began to decline. Females achieved maximum body growth approximately two years earlier than males, with a correspondingly earlier stabilization of brain metabolism to adult levels. These findings confirm prior findings with positron emission tomography performed in a much smaller cohort, demonstrate that relative brain metabolism can be inferred from noninvasive MRI data, and extend observations on the associations between body growth and brain metabolism to sex differences through adolescence.

Parental Age and Offspring Psychopathology in the Philadelphia Neurodevelopmental Cohort.

OBJECTIVE: Increasing evidence implicates advanced paternal age at offspring birth in neuropsychiatric disorders. Advanced maternal age has also been associated with schizophrenia and other neurodevelopmental disorders, whereas younger maternal age has been linked with behavioral disorders. Few studies have considered the specificity of the associations with respect to comorbidity. In addition, most prior studies have been conducted in clinical samples or registries that may reflect more severe forms of psychopathology. The aim of this research is to examine the independent and joint associations of maternal and paternal age with specific subtypes of psychopathology in offspring in a pediatric sample of adolescents with emergent psychiatric syndromes. METHOD: A total of 8,725 youths (aged 8-21 years) from the Philadelphia Neurodevelopmental Cohort were included in the analyses. Logistic regression models with parental age predicting offspring psychopathology were adjusted for sociodemographic factors and comorbid disorders. RESULTS: We found that younger parental ages were generally associated with increased rates of offspring psychopathology. After controlling for sociodemographic characteristics and comorbidity, both younger maternal and paternal ages were associated with behavior syndromes and psychosis in youth, whereas advanced paternal age was associated with pervasive developmental disorders/autism spectrum disorder (PDD/ASD). CONCLUSION: These findings suggest that both younger and older parental age at birth are associated with specific forms of psychopathology in offspring. The persistence of the influence of parental age after control for demographic factors and an index of social environment suggests that additional explanations for these findings should be examined in future studies.

Neurocognitive performance as an endophenotype for mood disorder subgroups.

BACKGROUND: There is growing evidence that neurocognitive function may be an endophenotype for mood disorders. The goal of this study is to examine the specificity and familiality of neurocognitive functioning across the full range of mood disorder subgroups, including Bipolar I (BP-I), Bipolar II (BP-II), Major Depressive Disorders (MDD), and controls in a community-based family study. METHODS: A total of 310 participants from 137 families with mood spectrum disorders (n=151) and controls (n=159) completed the University of Pennsylvania's Computerized Neurocognitive Battery (CNB) that assessed the accuracy and speed of task performance across five domains. Mixed effects regression models tested association and familiality. RESULTS: Compared to those without mood disorders, participants with BP-I had increased accuracy in complex cognition, while participants with MDD were more accurate in emotion recognition. There was also a significant familial association for accuracy of complex cognition. Mood disorder subgroups did not differ in performance speed in any of the domains. LIMITATIONS: The small number of BP-I cases, and family size limited the statistical power of these analyses, and the cross-sectional assessment of neurocognitive function precluded our ability to determine whether performance precedes or post dates onset of disorder. CONCLUSIONS: This is one of the few community-based family studies of potential neurocognitive endophenotypes that includes the full range of mood disorder subgroups. There were few differences in neurocognitive function except enhanced accuracy in specific domains among those with BP-I and MDD. The differential findings across specific mood disorder subgroups substantiate their heterogeneity in other biologic and endophenotypic domains.

The phenotypic manifestations of rare CNVs in schizophrenia.

There is compelling evidence for the role of copy number variants (CNVs) in schizophrenia susceptibility, and it has been estimated that up to 2-3% of schizophrenia cases may carry rare CNVs. Despite evidence that these events are associated with an increased risk across categorical neurodevelopmental disorders, there is limited understanding of the impact of CNVs on the core features of disorders like schizophrenia. Our objective was to evaluate associations between rare CNVs in differentially brain expressed (BE) genes and the core features and clinical correlates of schizophrenia. The sample included 386 cases of Irish ancestry with a diagnosis of schizophrenia, at least one rare CNV impacting any gene, and a core set of phenotypic measures. Statistically significant associations between deletions in differentially BE genes were found for family history of mental illness (decreased prevalence of all CNVs and deletions, unadjusted and adjusted) and for paternal age (increase in deletions only, unadjusted, among those with later ages at birth of patient). The strong effect of a lack of a family history on BE genes suggests that CNVs may comprise one pathway to schizophrenia, whereas a positive family history could index other genetic mechanisms that increase schizophrenia vulnerability. To our knowledge, this is the first investigation of the association between genome-wide CNVs and risk factors and sub-phenotypic features of schizophrenia beyond cognitive function.

The association between major depressive disorder and obesity in US adolescents: results from the 2001-2004 National Health and Nutrition Examination Survey.

The association between major depressive disorder (MDD) and obesity was assessed in 4,150 US adolescents aged 12-19 years from the 2001-2004 National Health and Nutrition Examination Survey. Weight and height were measured by health professionals and MDD was based on a structured diagnostic interview. The prevalence of MDD in the past year among US adolescents was 3.2% and 16.8% of US adolescents were obese. After adjustment for sex, age, race/ethnicity and poverty, MDD was not significantly associated with obesity among adolescents overall (adjusted odds ratio (adjOR) = 1.6, 95% confidence interval (CI) = 0.9-2.9), but an increased odds of obesity was observed among males (adjOR = 2.7, 95% CI = 1.1-7.1) and non-Hispanic blacks (adjOR = 3.1, 95% CI = 1.1-8.3) with MDD. Future research on strategies that might reduce the risk of obesity in males and non-Hispanic black adolescents with MDD may be warranted.

Copy-number variants in neurodevelopmental disorders: promises and challenges.

Copy-number variation (CNV) is the most prevalent type of structural variation in the human genome. There is emerging evidence that copy-number variants (CNVs) provide a new vista on understanding susceptibility to neuropsychiatric disorders. Some challenges in the interpretation of current CNV studies include the use of overlapping samples, differing phenotypic definitions, an absence of population norms for CNVs and a lack of consensus in methods for CNV detection and analysis. Here, we review current CNV association study methods and results in autism spectrum disorders (ASD) and schizophrenia, and provide suggestions for design approaches to future studies that might maximize the translation of this work to etiological understanding.