Arden Moscati

Losing faith and finding religion: Religiosity over the life course and substance use and abuse

BACKGROUND Religion has only come into the light of scientific inquiry as a factor influencing health and behavior in the last few decades. While religiosity is a protective factor for contemporaneous substance misuse, the relationship between longitudinal changes in religiosity and substance use outcomes is understudied. METHODS Using data from the National Comorbidity Study - Replication (N=6203), we examined how changes in religiosity from childhood to adulthood are related to use and abuse/dependence of licit (alcohol and tobacco) and illicit drugs. Multivariable logistic regression was used to account for potential confounders including demographic characteristics, familial disruption during childhood, and comorbid major depression. RESULTS Religiosity was inversely associated with use and misuse of both licit and illicit substances; however this relationship varied by level of childhood religiosity. Relative to stable levels of religiosity from childhood to adulthood, a 2-unit decrease in religiosity from childhood was associated with increased likelihood of illicit drug use in the past year (odds ratio (OR): 2.43, 95% confidence interval (CI): 1.39-4.25). However, a 2-unit increase in religiosity was also associated with past-year illicit drug use (OR: 1.85, 95% CI: 1.09-3.13). Comparable associations were found with a range of recent and lifetime measures of alcohol, tobacco, and illicit drugs. CONCLUSIONS Substantial gains or losses in religiosity from childhood to adulthood are associated with substance use and misuse. Findings support the use of a life course approach to understanding the relationship between religiosity and substance use outcomes.

CLASSIFICATION OF ANXIETY DISORDERS COMORBID WITH MAJOR DEPRESSION: COMMON OR DISTINCT INFLUENCES ON RISK?

Anxiety and depression display frequent comorbidity. Individuals with comorbid disorders also often have more extreme symptomatology than those with single disorders. This correlation between comorbidity and severity poses an interesting question: Are comorbid forms of anxiety and depression essentially just more severe versions of the pure disorders?

SNP-based heritability estimates of the personality dimensions and polygenic prediction of both neuroticism and major depression: findings from CONVERGE.

Biometrical genetic studies suggest that the personality dimensions, including neuroticism, are moderately heritable (~0.4 to 0.6). Quantitative analyses that aggregate the effects of many common variants have recently further informed genetic research on European samples. However, there has been limited research to date on non-European populations. This study examined the personality dimensions in a large sample of Han Chinese descent (N=10 064) from the China, Oxford, and VCU Experimental Research on Genetic Epidemiology study, aimed at identifying genetic risk factors for recurrent major depression among a rigorously ascertained cohort. Heritability of neuroticism as measured by the Eysenck Personality Questionnaire (EPQ) was estimated to be low but statistically significant at 10% (s.e.=0.03, P=0.0001). In addition to EPQ, neuroticism based on a three-factor model, data for the Big Five (BF) personality dimensions (neuroticism, openness, conscientiousness, extraversion and agreeableness) measured by the Big Five Inventory were available for controls (n=5596). Heritability estimates of the BF were not statistically significant despite high power (>0.85) to detect heritabilities of 0.10. Polygenic risk scores constructed by best linear unbiased prediction weights applied to split-half samples failed to significantly predict any of the personality traits, but polygenic risk for neuroticism, calculated with LDpred and based on predictive variants previously identified from European populations (N=171 911), significantly predicted major depressive disorder case–control status (P=0.0004) after false discovery rate correction. The scores also significantly predicted EPQ neuroticism (P=6.3 × 10−6). Factor analytic results of the measures indicated that any differences in heritabilities across samples may be due to genetic variation or variation in haplotype structure between samples, rather than measurement non-invariance. Findings demonstrate that neuroticism can be significantly predicted across ancestry, and highlight the importance of studying polygenic contributions to personality in non-European populations.

Evaluating the dopamine hypothesis of schizophrenia in a large-scale genome-wide association study

BACKGROUND The dopamine hypothesis, which posits that dysregulation of the dopaminergic system is etiologic for schizophrenia, is among the most enduring biological theories in psychiatry. Although variation within genes related to dopaminergic functioning has been associated with schizophrenia, an aggregate test of variation, using the largest publicly available schizophrenia dataset, has not previously been conducted. METHODS We first identified a core set of 11 genes involved in the synthesis, metabolism, and neurotransmission of dopamine. We then extracted summary statistics of markers falling within, or flanking, these genes from the Psychiatric Genomics Consortiums most recent schizophrenia mega-analysis results. We conducted aggregate tests for enrichment of dopamine-related pathways for association with schizophrenia. RESULTS We did not detect significant enrichment of signals across the core set of dopamine-related genes. However, we did observe modest to strong enrichment of genetic signals within the DRD2 locus. CONCLUSIONS Within the limits of available power, common sequence variation within core genes of the dopaminergic system is not related to risk of schizophrenia. This does not preclude a role of dopamine, or dopamine-related genes, in the clinical presentation of schizophrenia or in treatment response. However, it does suggest that the genetic risk for schizophrenia is not substantially affected by common variation in those genes which, collectively, critically impact dopaminergic functioning.

Cross-Disorder Psychiatric Genomics

Purpose of ReviewThe following review provides some description of the movement in cross-disorder psychiatric genomics toward addressing both comorbidity and polygenicity.Recent FindingsWe attempt to show how dimensional approaches to the phenotype have led to further addressing the problem of comorbidity of psychiatric diagnoses. And we also attempt to show how a dimensional approach to the genome, with different statistical methods from traditional genome-wide association analyses, has begun to resolve the problem of massive polygenicity.SummaryCross-disorder research, of any area in psychiatry, arguably has the most potential to inform clinical diagnosis, early detection and prevention strategies, and pharmacological treatment research. Future research might leverage what we now know to inform developmental studies of risk and resilience.

Cross-Lagged Analysis of Interplay Between Differential Traits in Sibling Pairs: Validation and Application to Parenting Behavior and ADHD Symptomatology

Understanding the factors that contribute to behavioral traits is a complex task, and partitioning variance into latent genetic and environmental components is a useful beginning, but it should not also be the end. Many constructs are influenced by their contextual milieu, and accounting for background effects (such as gene–environment correlation) is necessary to avoid bias. This study introduces a method for examining the interplay between traits, in a longitudinal design using differential items in sibling pairs. The model is validated via simulation and power analysis, and we conclude with an application to paternal praise and ADHD symptoms in a twin sample. The model can help identify what type of genetic and environmental interplay may contribute to the dynamic relationship between traits using a cross-lagged panel framework. Overall, it presents a way to estimate and explicate the developmental interplay between a set of traits, free from many common sources of bias.

Pathway-based polygenic risk implicates GO: 17144 drug metabolism in recurrent depressive disorder

The Psychiatric Genomics Consortium (PGC) has made major advances in the molecular etiology of MDD, confirming that MDD is highly polygenic, with any top risk loci conferring a very small proportion of variance in case-control status (1). Pathway enrichment results from PGC meta-analyses can also be used to help inform molecular drug targets. Prior to any knowledge of molecular biomarkers for MDD, drugs targeting molecular pathways have proved successful in treating MDD. However, it is possible that with information from PGC analyses, examining specific molecular pathway(s) implicated in MDD can further inform our study of molecular drug targets. Using a large case-control GWAS based on low-coverage whole genome sequencing (N = 10,640), we derived polygenic risk scores for MDD and for MDD specific to each of over 300 molecular pathways. We then used these data to identify sets of scores significantly predictive of case status, accounting for critical covariates. Over and above global polygenic risk for MDD, polygenic risk within the GO: 17144 drug metabolism pathway significantly predicted recurrent depression. In transcriptomic analyses, two pathway genes yielded suggestive signals at FDR q-values = .054: CYP2C19 (family of Cytochrome P450) and CBR1 (Carbonyl Reductase 1). Because the neuroleptic carbamazepine is a known inducer of CYP2C19, future research might examine whether drug metabolism PRS has any influence on clinical presentation and treatment response. Overall, results indicate that pathway-based risk might inform treatment of severe depression. We discuss limitations to the generalizability of these preliminary findings, and urge replication in future research.

Proof of concept: Molecular prediction of schizophrenia risk

Key Points Question To what extent do global polygenic risk scores (PRS), molecular pathway-specific PRS, complement component (C4) gene expression, MHC loci, sex, and ancestry jointly contribute to risk for schizophrenia-spectrum disorders (SZ)? Findings Global polygenic risk for schizophrenia, sex, and their interaction most robustly predict risk in a classification and regression tree model, with highest risk groups having 50/50 chance of SZ. Meaning Psychometric risk indicators, such as prodromal symptom assessments, may be enhanced by the examination of genetic risk metrics. Preliminary results suggest that of genetic risk metrics, global polygenic information has the most potential to significantly aide in the prediction of SZ. Abstract Importance Schizophrenia (SZ) has a complex, heterogeneous symptom presentation with limited established associations between biological markers and illness onset. Many (gene) molecular pathways (MPs) are enriched for SZ signal, but it is still unclear how these MPs, global PRS, major histocompatibility complex (MHC) complement component (C4) gene expression, and MHC loci might jointly contribute to SZ and its clinical presentation. It is also unclear whether sex or ancestry interacts with these metrics to increase risk in certain individuals. Objective To examine multiple genetic metrics, sex, and their interactions as possible predictors of SZ risk. Genetic information could aid in the clinical prediction of risk, but it is still unclear which genetic metrics are most promising, and how sex interacts with genetic risk metrics. Design, Setting, and Participants To examine molecular risk in a proof-of-concept study, we used the Wellcome Trust case-control cohort and classified cases as a function of 1) polygenic risk score (PRS) for both whole genome and for 345 implicated molecular pathways, 2) predicted C4 expression, 3) SZ-relevant MHC loci, 4) sex, and 5) ancestry. Main Outcomes and Measures PRSs, C4 expression, SZ-relevant MHC loci, sex, and ancestry as joint risk factors for SZ. Results Recursive partitioning yielded 15 molecular risk classes and retained as significant psychosis classifiers only sex, genome-wide SZ polygenic risk, and one MP PRS. Sex was the most robust classifier in a stepwise regression, and there was a significant interaction of sex with SZ PRS on case status, suggesting males have a lower polygenic risk threshold. By down-sampling case proportion to 1% and 1.4% population base rates in males and females, respectively, high-risk subtypes defined by this model had roughly a 52% odds of developing SZ (individuals with SZ PRS elevated by 2.6 SDs; incidence = 51.8%). Conclusions and Relevance This proof-of-concept suggests that global SZ PRS, sex, and their interaction are robust predictors of risk and that males have a lower PRS threshold for onset. Implications for the integration of these metrics with psychometrically-identified risk are discussed.

Genome-Wide Polygenic Atlas of the Phenome in Emerging Adulthood: Prediction of Behavioral and Health Outcomes

Background Identifying genetic relationships between complex traits in emerging adulthood can provide useful etiological insights into risk for psychopathology and other adverse outcomes. This study examined genomic data from a large sample of emerging adults (N = 5,947) to construct an atlas of polygenic risk that indexes diverse psychiatric, behavioral and health outcomes. Methods Genome-wide association studies of 34 diverse psychiatric phenotypes and health factors were used as discovery samples to calculate genome-wide polygenic scores (GPS), which were then used to predict 55 phenotypes in the emerging adults. Special emphasis was placed on replicating previously published phenotypic and genetic relationships. All analyses were tested separately in each ancestry group (based on 1000 Genomes super-populations) and corrected for multiple testing within group. Results The analyses resulted in over 1,800 associations between GPS and phenotype, with over 80 reaching significance. The majority of previously published hypotheses were replicated. A number of notable findings emerged beyond the expected within-trait prediction (GPS for height and body mass index predicted phenotypic height and BMI, respectively). The GPS for schizophrenia predicted depressive symptoms, anxiety symptoms, and nicotine use, as well as experiences of interpersonal trauma and family history of mental health problems. The neuroticism GPS significantly predicted general anxiety, phobia, insomnia, phenotypic neuroticism, and depressive symptoms. Conversely, the subjective well-being GPS predicted fewer depressive symptoms, fewer anxiety symptoms, decreased family history of mental health problems, as well as increased social support and relationship satisfaction. Many of these associations were consistent across ancestry groups. Discussion These results highlight the utility of a comprehensive polygenic modeling framework, and provide potential avenues for prediction of risk and resilience in emerging adulthood. While the variance explained by any of these GPSs is small, they provide easily accessible information to guide future prediction, prevention, and intervention efforts to improve health and quality of life outcomes. Furthermore, many distinct GPS displayed significant prediction of the same phenotypes, indicating that more cross-trait research is needed to better understand the complex pattern of relationships between psychiatric outcomes.

Genome-Wide Polygenic Atlas of The Phenome In Emerging Adulthood: Genetic Overlap of Risk Across Five Ancestries

Background Creating a network of the genetic relationships between multiple psychiatric and medical traits, during a critical developmental period, can enhance our understanding of risk for psychopathology. This study utilized genomic data from emerging adults (N = 5,947) to construct a comprehensive atlas of polygenic risk that indexes diverse psychiatric, behavioral and health outcomes. Methods In addition to testing the GPSs prediction of the phenotypes (Moscati & Docherty, et al., this meeting), GPSs were also examined for associations with each other in this sample, across five ethnicities..Genome-wide association studies of 34 diverse phenotypes were used as as discovery samples to calculate genome-wide polygenic scores (GPS), and these scores were then used to predict over 50 phenotypes in the emerging adults. We computed partial correlations adjusted for ancestry principle components in order to estimate genetic relationships between the phenotypes, and corrected for multiple testing. Based on the cross-disorder psychiatric genomics findings to date, we hypothesized significant GPS associations between five major psychiatric disorders across each of the ancestry groups. We also attempted to replicate the associations reported in a previously derived atlas by Bulik-Sullivan and colleagues. Results Several significant associations were observed in the European sample: SZ~BP (β = 0.73, p = 2.7*10-67), BP~MDD (β = 0.23, p = 4.6*10-33), and SZ~MDD (β = 0.43, p = 7.2*10-21). Significant associations were not observed for AUT~ADHD (β =-0.005, p = 0.04) or AUT~SZ (β = 0.005, p = 0.19). All of the GPS regression replications from the previous atlas were robustly significant and all were consistent with the sign of the previously published coefficients. Importantly, some unexpected but informative associations were observed: for example, significant positive associations of neuroticism GPS with GPSs for triglycerides and for coronary artery disease. We also present these regressions across the four additional ancestry groups, with a majority of the significant associations holding in these groups. Discussion The findings here present a wide-ranging and nuanced picture of major dimensions of vulnerability to psychopathology at a genetic level. Overall, results reflect relationships between anxiety, depressive, and schizophrenia-spectrum disorders that are largely consistent with our current conceptualizations of diagnostic classification and confirm the important involvement of a network of medical and risk phenotypes in genetic predisposition to these disorders. Findings relating genetic risk for neuroticism with genetic risk for cardiovascular phenotypes may have important implications for psychiatry and public health.