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Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings.

Childhood adversity is a potent determinant of health across development and is associated with altered DNA methylation signatures, which might be more common in children exposed during sensitive periods in development. However, it remains unclear whether adversity has persistent epigenetic associations across childhood and adolescence. We aimed to examine the relationship between time-varying adversity (defined through sensitive period, accumulation of risk, and recency life course hypotheses) and genome-wide DNA methylation, measured three times from birth to adolescence, using data from a prospective, longitudinal cohort study.

Childhood psychiatric symptoms are often diffuse but can coalesce into discrete mental illnesses during late adolescence. We leveraged polygenic scores (PGSs) to parse genomic risk for childhood symptoms and to uncover related neurodevelopmental mechanisms with transcriptomic and neuroimaging data. In independent samples (Adolescent Brain Cognitive Development, Generation R) a narrow cross-disorder neurodevelopmental PGS, reflecting risk for attention deficit hyperactivity disorder, autism, depression and Tourette syndrome, predicted psychiatric symptoms through early adolescence with greater sensitivity than broad cross-disorder PGSs reflecting shared risk across eight psychiatric disorders, the disorder-specific PGS individually or two other narrow cross-disorder (Compulsive, Mood-Psychotic) scores. Neurodevelopmental PGS-associated genes were preferentially expressed in the cerebellum, where their expression peaked prenatally. Further, lower gray matter volumes in cerebellum and functionally coupled cortical regions associated with psychiatric symptoms in mid-childhood. These findings demonstrate that the genetic underpinnings of pediatric psychiatric symptoms differ from those of adult illness, and implicate fetal cerebellar developmental processes that endure through childhood.

Variation in the mental health of people who have experienced childhood maltreatment is substantial. One hypothesis is that this variation is attributable, in part, to the timing of maltreatment—specifically, whether maltreatment occurs during sensitive periods in development when the brain is maximally sensitive to particular types of environmental input. To determine whether there is scientific consensus around when periods of peak sensitivity occur, we did a systematic review of human observational studies. Although 89 (75%) of the 118 unique cross-sectional or longitudinal cohort studies we identified reported timing effects, no consistent sensitive periods were identified for any of the most studied outcomes. Thus, observational research on childhood maltreatment has yet to converge on a single period (or set of periods) of increased vulnerability. We identified study characteristics that might contribute to these between-study differences and used observations from our Review to suggest a comprehensive set of recommendations for future research.

ABSTRACTBiomedical research has grown increasingly cooperative through the sharing of consortia-level epigenetic data. Since consortia preprocess data prior to distribution, new processing pipelines can lead to different versions of the same dataset. Similarly, analytic frameworks evolve to incorporate cutting-edge methods and best practices. However, it remains unknown how different data and analytic versions alter the results of epigenome-wide analyses, which could influence the replicability of epigenetic associations. Thus, we assessed the impact of these changes using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. We analysed DNA methylation from two data versions, processed using separate preprocessing and analytic pipelines, examining associations between seven childhood adversities or prenatal smoking exposure and DNA methylation at age 7. We performed two sets of analyses: (1) epigenome-wide association studies (EWAS); (2) Structured Life Course Modelling Approach (SLCMA), a two-stage method that models time-dependent effects. SLCMA results were also compared across two analytic versions. Data version changes impacted both EWAS and SLCMA analyses, yielding different associations at conventional p-value thresholds. However, the magnitude and direction of associations was generally consistent between data versions, regardless of p-values. Differences were especially apparent in analyses of childhood adversity, while smoking associations were more consistent using significance thresholds. SLCMA analytic versions similarly altered top associations, but time-dependent effects remained concordant. Alterations to data and analytic versions influenced the results of epigenome-wide analyses. Our findings highlight that magnitude and direction are better measures for replication and stability than p-value thresholds.

The Harvard Youth Violence Prevention Center has partnered with the city of Boston, Massachusetts to create a data system to track youth violence. The system provides continuing information about fighting, [...]

Functional neurological (conversion) disorder (FND) is a neuropsychiatric condition whereby individuals present with sensorimotor symptoms incompatible with other neurological disorders. Early-life maltreatment (ELM) is a risk factor for developing FND, yet few studies have investigated brain network–trauma relationships in this population. In this neuroimaging–gene expression study, we used two graph theory approaches to elucidate ELM subtype effects on resting-state functional connectivity architecture in 30 patients with motor FND. Twenty-one individuals with comparable depression, anxiety, and ELM scores were used as psychiatric controls. Thereafter, we compared trauma endophenotypes in FND with regional differences in transcriptional gene expression as measured by the Allen Human Brain Atlas (AHBA). In FND patients only, we found that early-life physical abuse severity, and to a lesser extent physical neglect, correlated with corticolimbic weighted-degree functional connectivity. Connectivity profiles influenced by physical abuse occurred in limbic (amygdalar–hippocampal), paralimbic (cingulo-insular and ventromedial prefrontal), and cognitive control (ventrolateral prefrontal) areas, as well as in sensorimotor and visual cortices. These findings held adjusting for individual differences in depression/anxiety, PTSD, and motor phenotypes. In FND, physical abuse also correlated with amygdala and insula coupling to motor cortices. In exploratory analyses, physical abuse correlated connectivity maps overlapped with the AHBA spatial expression of three gene clusters: (i) neuronal morphogenesisand synaptic transmissiongenes in limbic/paralimbic areas; (ii) locomotory behaviorand neuronal generationgenes in left-lateralized structures; and (iii) nervous system developmentand cell motilitygenes in right-lateralized structures. These circuit-specific architectural profiles related to individual differences in childhood physical abuse burden advance our understanding of the pathophysiology of FND.

Animal and human studies have documented the existence of developmental windows (or sensitive periods) when experience can have lasting effects on brain structure or function, behavior, and disease. Although sensitive periods for depression likely arise through a complex interplay of genes and experience, this possibility has not yet been explored in humans. We examined the effect of genetic pathways regulating sensitive periods, alone and in interaction with common childhood adversities, on depression risk. Guided by a translational approach, we: (1) performed association analyses of three gene sets (60 genes) shown in animal studies to regulate sensitive periods using summary data from a genome-wide association study of depression (n= 807,553); (2) evaluated the developmental expression patterns of these genes using data from BrainSpan (n= 31), a transcriptional atlas of postmortem brain samples; and (3) tested gene-by-development interplay (dGxE) by analyzing the combined effect of common variants in sensitive period genes and time-varying exposure to two types of childhood adversity within a population-based birth cohort (n= 6254). The gene set regulating sensitive period opening associated with increased depression risk. Notably, 6 of the 15 genes in this set showed developmentally regulated gene-level expression. We also identified a statistical interaction between caregiver physical or emotional abuse during ages 1–5 years and genetic risk for depression conferred by the opening genes. Genes involved in regulating sensitive periods are differentially expressed across the life course and may be implicated in depression vulnerability. Our findings about gene-by-development interplay motivate further research in large, more diverse samples to further unravel the complexity of depression etiology through a sensitive period lens.

Depression is more frequent among individuals exposed to traumatic events. Both trauma exposure and depression are heritable. However, the relationship between these traits, including the role of genetic risk factors, is complex and poorly understood. When modelling trauma exposure as an environmental influence on depression, both gene-environment correlations and gene-environment interactions have been observed. The UK Biobank concurrently assessed Major Depressive Disorder (MDD) and self-reported lifetime exposure to traumatic events in 126,522 genotyped individuals of European ancestry. We contrasted genetic influences on MDD stratified by reported trauma exposure (final sample size range: 24,094–92,957). The SNP-based heritability of MDD with reported trauma exposure (24%) was greater than MDD without reported trauma exposure (12%). Simulations showed that this is not confounded by the strong, positive genetic correlation observed between MDD and reported trauma exposure. We also observed that the genetic correlation between MDD and waist circumference was only significant in individuals reporting trauma exposure (rg= 0.24, p= 1.8 × 10−7versus rg= −0.05, p= 0.39 in individuals not reporting trauma exposure, difference p= 2.3 × 10−4). Our results suggest that the genetic contribution to MDD is greater when reported trauma is present, and that a complex relationship exists between reported trauma exposure, body composition, and MDD.

Although exposure to adversity increases risk for poor mental health outcomes, many people exposed to adversity do not develop such outcomes. Psychological resilience, defined broadly as positive emotional and/or behavioral adaptation to adversity, may be influenced by genetic factors that have remained largely unexplored in the era of large-scale genome-wide studies. In this perspective, we provide an integrative framework for studying human genome-wide variation underlying resilience. We first outline three complementary working definitions of psychological resilience—as a capacity, process, and outcome. For each definition, we review emerging empirical evidence, including findings from positive psychology, to illustrate how a resilience-based framework can guide novel and fruitful directions for the field of psychiatric genomics, distinct from the ongoing study of psychiatric risk and related traits. Finally, we provide practical recommendations for future genomic research on resilience, highlighting a need to augment cross-sectional findings with prospective designs that include detailed measurement of adversities and outcomes. A research framework that explicitly addresses resilience could help us to probe biological mechanisms of stress adaptation, identify individuals who may benefit the most from prevention and early intervention, and ascertain modifiable protective factors that mitigate negative outcomes even for those at high genetic risk.

Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure.

Objective:Non-healing lower extremity ulcers (NHLU) are a common podiatric complication of diabetes, with poor glycaemic control as a risk factor for development. Glycaemic indices, such as haemoglobin A1c (HbA1c) and fasting plasma glucose (FPG), are used to diagnose and to monitor diabetes. Using a population-based, nationally representative sample, we evaluate the relationship between glycaemic indices and NHLU (as defined by the patient) to propose glycaemic thresholds for clinical suspicion of patient NHLU status.Method:Using data from the 1999–2004 National Health and Nutrition Examination Surveys (NHANES), a total of 9769 adults (≥40 years old) with available self-reported diabetes and NHLU status were analysed. Glycaemic index markers, including FPG and HbA1c, were assessed via laboratory analysis from serum blood samples. Logistic regression models were fitted to determine optimal thresholds for FPG and HbA1c to predict NHLU status.Results:Compared with those without NHLU, NHLU patients were older, male, had higher rates of diabetes, were more likely to take insulin, and had lower total cholesterol. Youden's Index for NHLU identified the optimal FPG threshold as 117.7mg/dl (sensitivity: 33.5%; specificity: 82.6%). The optimal HbA1c threshold was 5.9% (sensitivity: 43.2%; specificity: 77.3%). HbA1c (Odds ratio (OR) 2.44, 95% Confidence Interval (CI) 1.96–3.05; Area under curve (AUC) 0.62) was a stronger discriminator of NHLU compared to FPG (OR 2.19; 95%CI 1.57–3.05; AUC 0.60).Conclusion:This study identified glycaemic thresholds for suspicion of NHLU development that are lower than the glucose goal levels recommended as optimal by the American Diabetes Association. Health professionals should be aware of these glycaemic indices when screening patients with diabetes for NHLU. Future longitudinal and validation studies are necessary to better discern the ideal glycaemic index thresholds to identify NHLU.

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Gene by environment (GxE) interaction studies have investigated the influence of a number of candidate genes and variants for major depressive disorder (MDD) on the association between childhood trauma and MDD. Most of these studies are hypothesis driven and investigate only a limited number of SNPs in relevant pathways using differing methodological approaches. Here (1) we identified 27 genes and 268 SNPs previously associated with MDD or with GxE interaction in MDD and (2) analyzed their impact on GxE in MDD using a common approach in 3944 subjects of European ancestry from the Psychiatric Genomics Consortium who had completed the Childhood Trauma Questionnaire. (3) We subsequently used the genome‐wide SNP data for a genome‐wide case‐control GxE model and GxE case‐only analyses testing for an enrichment of associated SNPs. No genome‐wide significant hits and no consistency among the signals of the different analytic approaches could be observed. This is the largest study for systematic GxE interaction analysis in MDD in subjects of European ancestry to date. Most of the known candidate genes/variants could not be supported. Thus, their impact on GxE interaction in MDD may be questionable. Our results underscore the need for larger samples, more extensive assessment of environmental exposures, and greater efforts to investigate new methodological approaches in GxE models for MDD.

Although generalized anxiety disorder (GAD) is heritable and aggregates in families, no genomic loci associated with GAD have been reported. We aimed to discover potential loci by conducting a genome‐wide analysis of GAD symptoms in a large, population‐based sample of Hispanic/Latino adults. Data came from 12,282 participants (aged 18–74) in the Hispanic Community Health Study/Study of Latinos. Using a shortened Spielberger Trait Anxiety measure, we analyzed the following: (i) a GAD symptoms score restricted to the three items tapping diagnostic features of GAD as defined by DSM‐V; and (ii) a total trait anxiety score based on summing responses to all ten items. We first calculated the heritability due to common variants (h2SNP) and then conducted a genome‐wide association study (GWAS) of GAD symptoms. Replication was attempted in three independent Hispanic cohorts (Multi‐Ethnic Study of Atherosclerosis, Women's Health Initiative, Army STARRS). The GAD symptoms score showed evidence of modest heritability (7.2%; P= 0.03), while the total trait anxiety score did not (4.97%; P= 0.20). One genotyped SNP (rs78602344) intronic to thrombospondin 2 (THBS2) was nominally associated (P= 5.28 × 10−8) in the primary analysis adjusting for psychiatric medication use and significantly associated with the GAD symptoms score in the analysis excluding medication users (P= 4.18 × 10−8). However, meta‐analysis of the replication samples did not support this association. Although we identified a genome‐wide significant locus in this sample, we were unable to replicate this finding. Evidence for heritability was also only detected for GAD symptoms, and not the trait anxiety measure, suggesting differential genetic influences within the domain of trait anxiety. © 2016 Wiley Periodicals, Inc.

Physical activity is associated with mental health benefits in youths. Here, we used causal inference and triangulation with 2 levels of biology to substantiate relationships between sports participation and dimensional psychopathology in youths.

Most people with serious mental illness (SMI) experience difficulty in fulfilling a vocational role, with many being unemployed or underemployed. Given the profound social and economic costs of this level of work impairment, researchers have investigated ways to enhance "vocational recovery," or the processes through which people with SMI regain their role as workers and reintegrate into the workforce. Using data collected from a larger qualitative study of 23 individuals who had progressed to an advanced stage of recovery from SMI, this study explored respondents' perspectives on employment and its relationship to their vocational recovery. Text passages describing employment were analyzed inductively by a diverse team of researchers. Seven themes were identified as being important in helping participants return to work or remain employed following the onset of a serious psychiatric disability: having the confidence to work, having the motivation to work, possessing work-related skills, assessing person-job fit, creating work opportunities, receiving social support, and having access to consumer-oriented programs and services. Implications of these findings on the development of interventions and policies to improve the vocational outcomes of people with SMI are discussed.

We tested the hypothesis that an education program addressing breast cancer screening schedules and modalities coupled with a breast cancer risk assessment provided by community pharmacists can increase women's confidence in performing screening practices endorsed by the American Cancer Society (ACS). This randomized, paired, pre‐post study was conducted in six community pharmacies and two health‐screening fairs; subjects were 140 women over 18 years of age. The pharmacist‐administered program used the Breast Cancer Risk‐Assessment Tool (Gail model) software provided by the National Cancer Institute of the National Institutes of Health. In addition, pharmacists provided education and training on breast self‐examination (BSE), clinical breast examination (CBE), and mammography. Adherence to ACS guidelines for monthly BSE increased from 31% to 56% (p<0.001) for all women 6 months after the program. Performance of monthly BSE by women considered at high risk for developing breast cancer increased from 20% to 60% (p<0.005). The mean number of BSEs performed over 6 months increased from 2.69 to 4.09 (p<0.001). Women's confidence performing correct BSE improved from 6.41 to 7.04 (p<0.001) on a scale of 0–10. Adherence to ACS guidelines for CBE and mammography did not reveal statistically significant improvements except for better adherence to CBE in women aged 40–49 years (81% to 97%, p<0.025). The strength of the pharmacists' intervention may not appear as manipulation of high‐risk patients' behavior but as improvement of self‐directed behaviors, such as BSE, across all age groups.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.

Recognizing that health outcomes are influenced by and occur within multiple social and physical contexts, researchers have used multilevel modeling techniques for decades to analyze hierarchical or nested data. Cross-Classified Multilevel Models (CCMM) are a statistical technique proposed in the 1990s that extend standard multilevel modeling and enable the simultaneous analysis of non-nested multilevel data. Though use of CCMM in empirical health studies has become increasingly popular, there has not yet been a review summarizing how CCMM are used in the health literature. To address this gap, we performed a scoping review of empirical health studies using CCMM to: (a) evaluate the extent to which this statistical approach has been adopted; (b) assess the rationale and procedures for using CCMM; and (c) provide concrete recommendations for the future use of CCMM. We identified 118 CCMM papers published in English-language literature between 1994 and 2018. Our results reveal a steady growth in empirical health studies using CCMM to address a wide variety of health outcomes in clustered non-hierarchical data. Health researchers use CCMM primarily for five reasons: (1) to statistically account for non-independence in clustered data structures; out of substantive interest in the variance explained by (2) concurrent contexts, (3) contexts over time, and (4) age-period-cohort effects; and (5) to apply CCMM alongside other techniques within a joint model. We conclude by proposing a set of recommendations for use of CCMM with the aim of improved clarity and standardization of reporting in future research using this statistical approach.