Search

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain through well-replicated mapping of abilities to specific structures (for example, lesion studies) and functions1–3 (for example, task functional MRI (fMRI)). Mental health research and care have yet to realize similar advances from MRI. A primary challenge has been replicating associations between inter-individual differences in brain structure or function and complex cognitive or mental health phenotypes (brain-wide association studies (BWAS)). Such BWAS have typically relied on sample sizes appropriate for classical brain mapping4 (the median neuroimaging study sample size is about 25), but potentially too small for capturing reproducible brain–behavioural phenotype associations5,6. Here we used three of the largest neuroimaging datasets currently available—with a total sample size of around 50,000 individuals—to quantify BWAS effect sizes and reproducibility as a function of sample size. BWAS associations were smaller than previously thought, resulting in statistically underpowered studies, inflated effect sizes and replication failures at typical sample sizes. As sample sizes grew into the thousands, replication rates began to improve and effect size inflation decreased. More robust BWAS effects were detected for functional MRI (versus structural), cognitive tests (versus mental health questionnaires) and multivariate methods (versus univariate). Smaller than expected brain–phenotype associations and variability across population subsamples can explain widespread BWAS replication failures. In contrast to non-BWAS approaches with larger effects (for example, lesions, interventions and within-person), BWAS reproducibility requires samples with thousands of individuals.

Semi-continuous data present challenges in both model fitting and interpretation. Parametric distributions may be inappropriate for extreme long right tails of the data. Mean effects of covariates, susceptible to extreme values, may fail to capture relevant information for most of the sample. We propose a two-component semi-parametric Bayesian mixture model, with the discrete component captured by a probability mass (typically at zero) and the continuous component of the density modeled by a mixture of B-spline densities that can be flexibly fit to any data distribution. The model includes random effects of subjects to allow for application to longitudinal data. We specify prior distributions on parameters and perform model inference using a Markov Chain Monte Carlo (MCMC) Gibbs-sampling algorithm programmed in R. Statistical inference can be made for multiple quantiles of the covariate effects simultaneously providing a comprehensive view. Various MCMC sampling techniques are used to facilitate convergence. We demonstrate the performance and the interpretability of the model via simulations and analyses on the National Consortium on Alcohol and Neurodevelopment in Adolescence study (NCANDA) data on alcohol binge drinking.

The human toll of disasters extends beyond death, injury and loss. Post-traumatic stress (PTS) can be common among directly exposed individuals, and children are particularly vulnerable. Even children far removed from harm’s way report PTS, and media-based exposure may partially account for this phenomenon. In this study, we examine this issue using data from nearly 400 9- to 11-year-old children collected before and after Hurricane Irma, evaluating whether pre-existing neural patterns moderate associations between hurricane experiences and later PTS. The ‘dose’ of both self-reported objective exposure and media exposure predicted PTS, the latter even among children far from the hurricane. Furthermore, neural responses in brain regions associated with anxiety and stress conferred particular vulnerability. For example, heightened amygdala reactivity to fearful stimuli moderated the association between self-reported media exposure and PTS. Collectively, these findings show that for some youth with measurable vulnerability, consuming extensive disaster-related media may offer an alternative pathway to disaster exposure that transcends geography and objective risk. Dick et al. show that in youth, post-traumatic stress related to Hurricane Irma was predicted by self-reported direct and media exposure. Furthermore, neural responses in brain regions associated with anxiety conferred particular vulnerability to media exposure.

Purpose Evaluate changes in early adolescent substance use during the coronavirus disease 2019 (COVID-19) pandemic using a prospective, longitudinal, nationwide cohort. Methods Participants were enrolled in the Adolescent Brain Cognitive Development Study. A total of 7,842 youth (mean age = 12.4 years, range = 10.5–14.6) at 21 study sites across the U.S. completed a three-wave assessment of substance use between May and August 2020. Youth reported whether they had used alcohol, nicotine, cannabis, or other substances in the past 30 days. Data were linked to prepandemic surveys that the same youth had completed in the years 2018–2020, before the advent of the COVID-19 pandemic. Results Past-30-day substance use remained stable in the 6 months since stay-at-home orders were first issued in U.S. states/counties; was primarily episodic (1–2 days in the past month); and was typically limited to a single substance. Using pretest/posttest and age-period designs, we found that compared to before the pandemic, fewer youth were using alcohol and more youth were using nicotine or misusing prescription drugs. During the pandemic, youth were more likely to use substances when they were more stressed by pandemic-related uncertainty; their family experienced material hardship; their parents used alcohol or drugs; or they experienced greater depression or anxiety. Neither engagement in social distancing nor worry about COVID-19 infection was associated with substance use. Several risk factors were stronger among older (vs. younger) adolescents. Conclusions Among youth in early adolescence, advent of the COVID-19 pandemic was associated with decreased use of alcohol and increased use of nicotine and misuse of prescription drugs.

This study examined how resting-state functional magnetic resonance imaging (rs-fMRI) data quality and availability relate to clinical and sociodemographic variables within the Adolescent Brain Cognitive Development Study. A sample of participants with an adequate sample of quality baseline rs-fMRI data containing low average motion (framewise displacement≤0.15; low-noise; n = 4,356) was compared to a sample of participants without an adequate sample of quality data and/or containing high average motion (higher-noise; n = 7,437) using Chi-squared analyses and t-tests. A linear mixed model examined relationships between clinical and sociodemographic characteristics and average head motion in the sample with low-noise data. Relative to the sample with higher-noise data, the low-noise sample included more females, youth identified by parents as non-Hispanic white, and youth with married parents, higher parent education, and greater household incomes (ORs = 1.32-1.42). Youth in the low-noise sample were also older and had higher neurocognitive skills, lower BMIs, and fewer externalizing and neurodevelopmental problems (ds = 0.12-0.30). Within the low-noise sample, several clinical and demographic characteristics related to motion. Thus, participants with low-noise rs-fMRI data may be less representative of the general population and motion may remain a confound in this sample. Future rs-fMRI studies of youth should consider these limitations in the design and analysis stages in order to optimize the representativeness and clinical relevance of analyses and results.

Individual MHC genotype constrains the mutational landscape during tumorigenesis. Immune checkpoint inhibition reactivates immunity against tumors that escaped immune surveillance in approximately 30% of cases. Recent studies demonstrated poorer response rates in female and younger patients. Although immune responses differ with sex and age, the role of MHC-based immune selection in this context is unknown. We find that tumors in younger and female individuals accumulate more poorly presented driver mutations than those in older and male patients, despite no differences in MHC genotype. Younger patients show the strongest effects of MHC-based driver mutation selection, with younger females showing compounded effects and nearly twice as much MHC-II based selection. This study presents evidence that strength of immune selection during tumor development varies with sex and age, and may influence the availability of mutant peptides capable of driving effective response to immune checkpoint inhibitor therapy., Here the authors show that stronger immune selection and immune editing in females and younger patients lead to the accumulation of poorly presented driver mutations in tumors. These results may explain why young and female patients are characterized by lower response rates to immune checkpoint blockade therapies.

OBJECTIVE Executive control continues to develop throughout adolescence and is vulnerable to alcohol use. Although longitudinal assessment is ideal for tracking executive function development and onset of alcohol use, prior testing experience must be distinguished from developmental trajectories. METHOD We used the Stroop Match-to-Sample task to examine the improvement of processing speed and specific cognitive and motor control over 4 years in 445 adolescents. The twice-minus-once-tested method was used and expanded to four test sessions to delineate prior experience (i.e., learning) from development. A General Additive Model evaluated the predictive value of age and sex on executive function development and potential influences of alcohol use on development. RESULTS Results revealed strong learning between the first two assessments. Adolescents significantly improved their speed processing over 4 years. Compared with boys, girls enhanced ability to control cognitive interference and motor reactions. Finally, the influence of alcohol use initiation was tested over 4 years for development in 110 no/low, 110 moderate/heavy age- and sex-matched drinkers; alcohol effects were not detected in the matched groups. CONCLUSIONS Estimation of learning effects is crucial for examining developmental changes longitudinally. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N = 35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study (R). This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic.MethodsParticipants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms.ResultsThe prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms.ConclusionsThe sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.

Trait stability of measures is an essential requirement for individual differences research. Functional MRI has been increasingly used in studies that rely on the assumption of trait stability, such as attempts to relate task related brain activation to individual differences in behavior and psychopathology. However, recent research using adult samples has questioned the trait stability of task-fMRI measures, as assessed by test-retest correlations. To date, little is known about trait stability of task fMRI in children. Here, we examined within-session reliability and longitudinal stability of task-fMRI using data from the Adolescent Brain Cognitive Development (ABCD) Study using its tasks focused on reward processing, response inhibition, and working memory. We also evaluated the effects of factors potentially affecting reliability and stability. Reliability and stability [quantified via an intraclass correlation (ICC) that focuses on rank consistency] was poor in virtually all brain regions, with an average ICC of .078 and .054 for short (within-session) and long-term (between-session) ICCs, respectively, in regions of interest (ROIs) historically-recruited by the tasks. ICC values in ROIs did not exceed the ‘poor’ cut-off of .4, and in fact rarely exceeded .2 (only 5.9%). Motion had a pronounced effect on estimated ICCs, with the lowest motion quartile of participants having a mean reliability/stability three times higher (albeit still ‘poor’) than the highest motion quartile. Regions with stronger activation tended to show higher ICCs, with the absolute value of activity and reliability/stability correlating at .53. Across regions, the magnitude of age-related longitudinal (between-session) changes positively correlated with the longitudinal stability of individual differences, which suggests developmental change was not necessarily responsible for poor stability. Poor reliability and stability of task-fMRI, particularly in children, diminishes potential utility of fMRI data due to a drastic reduction of effect sizes and, consequently, statistical power for the detection of brain-behavior associations. This essential issue needs to be addressed through optimization of preprocessing pipelines and data denoising methods.

Early microbiota perturbations are associated with disorders that involve immunological underpinnings. Cesarean section (CS)-born babies show altered microbiota development in relation to babies born vaginally. Here we present the first statistically powered longitudinal study to determine the effect of restoring exposure to maternal vaginal fluids after CS birth., This work was partially supported by the C&D Research Fund, the Emch Fund for Human Microbiome Studies, and CIFAR FS20-078 #125869 (to M.G.D.-B.); Chilean CONICYT PIA/ANILLO grant ACT172097 and Chilean SOCHIPE Project 022019 (to P.R.H.); and the Norwegian Institute of Public Health (2019-0350), the Emerald Foundation, an NIH Pioneer award (1DP1AT010885), the National Institute of Justice (2016-DN-BX-4194), the San Diego Digestive Diseases Research Center (NIDDK 1P30DK120515), Janssen Pharmaceuticals (20175015)

Background Neuroticism is a heritable trait that contributes to the vulnerability to depression. We used polygenic risk scores (PRS) to examine genetic vulnerability to neuroticism and its associations with reward/punishment processing in a clinical sample with mood, anxiety, and substance use disorders. It was hypothesized that higher PRS for neuroticism is associated with attenuated neural responses to reward/punishment. Method Four hundred sixty-nine participants were genotyped and their PRSs for neuroticism were computed. Associations between PRS for neuroticism and anticipatory processing of monetary incentives were examined using functional magnetic resonance imaging. Results Individuals with higher PRS for neuroticism showed less anticipatory activation in the left amygdala and caudate region to incentives regardless of incentive valence. Further, these individuals exhibited altered sensitivity to gain/loss processing in the right anterior insula. Higher PRSs for neuroticism were also associated with reduced processing of gains in the precuneus. Limitations The study population consisted of a transdiagnostic sample with dysfunctions in positive and negative valence processing. PRS for neuroticism may be correlated with current clinical symptoms due to the vulnerability to psychiatric disorders. Conclusions Greater genetic loading for neuroticism was associated with attenuated anticipatory responsiveness in reward/punishment processing with altered sensitivity to valences. Thus, a higher genetic risk for neuroticism may limit the degree to which positive and/or negative outcomes influence the current mood state, which may contribute to the development of positive and negative affective dysfunctions in individuals with mood, anxiety, and addictive disorders.

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. Here we report a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 is associated with gestational duration; the association is replicated in 9,291 additional infants (combined P = 3.96 × 10−14). Analysis of 15,588 mother-child pairs shows that the association is driven by fetal rather than maternal genotype. Functional experiments show that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality., Gestational duration depends on both maternal and fetal genetic influences. Here, the authors perform a fetal genome-wide association meta-analysis and find that a locus on 2q13 is associated with pregnancy duration and further show that the lead SNP rs7594852 changes the binding properties of transcriptional repressor HIC1.

Difficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2013 9-10 year olds in the first data release from the Adolescent Brain Cognitive Development 21-site consortium study in order to identify resting state functional connectivity patterns that predict individual-differences in three domains of higher-order cognitive functions: General Ability, Speed/Flexibility, and Learning/Memory. For General Ability scores in particular, we observed consistent cross-site generalizability, with statistically significant predictions in 14 out of 15 held-out sites. These results survived several tests for robustness including replication in split-half analysis and in a low head motion subsample. We additionally found that connectivity patterns involving task control networks and default mode network were prominently implicated in predicting differences in General Ability across participants. These findings demonstrate that resting state connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network interconnections as a major locus of individual differences in cognitive functioning in early adolescence.

Learning a second language in childhood is inherently advantageous for communication. However, parents, educators and scientists have been interested in determining whether there are additional cognitive advantages. One of the most exciting yet controversial1 findings about bilinguals is a reported advantage for executive function. That is, several studies suggest that bilinguals perform better than monolinguals on tasks assessing cognitive abilities that are central to the voluntary control of thoughts and behaviours—the so-called ‘executive functions’ (for example, attention, inhibitory control, task switching and resolving conflict). Although a number of small-2–4 and large-sample5,6 studies have reported a bilingual executive function advantage (see refs. 7–9 for a review), there have been several failures to replicate these findings10–15, and recent meta-analyses have called into question the reliability of the original empirical claims8,9. Here we show, in a very large sample (n = 4,524) of 9- to 10-year-olds across the United States, that there is little evidence for a bilingual advantage for inhibitory control, attention and task switching, or cognitive flexibility, which are key aspects of executive function. We also replicate previously reported disadvantages in English vocabulary in bilinguals7,16,17. However, these English vocabulary differences are substantially mitigated when we account for individual differences in socioeconomic status or intelligence. In summary, notwithstanding the inherently positive benefits of learning a second language in childhood18, we found little evidence that it engenders additional benefits to executive function development. By analysing data from more than 4,500 9- to 10-year-olds, Dick et al. found no evidence that bilingual children have an advantage in executive functions, the cognitive abilities that are central to the voluntary control of thoughts and behaviours.

Background: Clinical variables—age, family history, genetics—are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death. Methods: Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n=3,279; 2,163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests. Results: Median age at last follow-up/prostate cancer death were 78.0 (IQR: 72.3–84.1) and 81.4 (75.4–86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95%CI 2.78–4.17]), family history (HR 1.72 [1.46–2.03]), alcohol (HR 1.74 [1.40–2.15]), diabetes (HR 0.53 [0.37–0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99–2.97]), family history (HR 1.73 [1.48–2.03]), alcohol (HR 1.45 [1.19–1.76]), diabetes (HR 0.62 [0.42–0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p, A polygenic hazard score (PHS1) improves prostate cancer screening accuracy in European patients. Here, the authors test the performance of a version compatible with OncoArray genotypes (PHS2) in a multi-ethnic dataset and find that it risk-stratifies men for any, aggressive, and fatal prostate cancer.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children’s health in the United States. A cohort of n= 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

IntroductionPolygenic hazard score (PHS) models are associated with age at diagnosis of prostate cancer. Our model developed in Europeans (PHS46), showed reduced performance in men with African genetic ancestry. We used a cross-validated search to identify SNPs that might improve performance in this population.Material and MethodsAnonymized genotypic data were obtained from the PRACTICAL consortium for 6,253 men with African genetic ancestry. Ten iterations of a ten-fold cross-validation search were conducted, to select SNPs that would be included in the final PHS46+African model. The coefficients of PHS46+African were estimated in a Cox proportional hazards framework using age at diagnosis as the dependent variable and PHS46, and selected SNPs as predictors. The performance of PHS46 and PHS46+African were compared using the same cross-validated approach.ResultsThree SNPs (rs76229939, rs74421890, and rs5013678) were selected for inclusion in PHS46+African. All three SNPs are located on chromosome 8q24. PHS46+African showed substantial improvements in all performance metrics measured, including a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47 to 4.34) and a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65 to 0.53).ConclusionsWe identified three SNPs that substantially improved the association of PHS46 with age at diagnosis of prostate cancer in men with African genetic ancestry to levels comparable to Europeans and Asians. A strategy of building on established statistical models might benefit ancestral groups generally under-represented in genome-wide association studies.

Schizophrenia is a heterogeneous disorder, exhibiting variability in presentation and outcomes that complicate treatment and recovery. To explore this heterogeneity, we leverage the comprehensive Danish health registries to conduct a prospective, longitudinal study from birth of 5432 individuals who would ultimately be diagnosed with schizophrenia, building individual trajectories that represent sequences of comorbid diagnoses, and describing patterns in the individual-level variability. We show that psychiatric comorbidity is prevalent among individuals with schizophrenia (82%) and multi-morbidity occur more frequently in specific, time-ordered pairs. Three latent factors capture 79% of variation in longitudinal comorbidity and broadly relate to the number of co-occurring diagnoses, the presence of child versus adult comorbidities and substance abuse. Clustering of the factor scores revealed five stable clusters of individuals, associated with specific risk factors and outcomes. The presentation and course of schizophrenia may be associated with heterogeneity in etiological factors including family history of mental disorders., Schizophrenia is a complex disorder where individuals experience different symptoms and outcomes that can be captured by patterns in other diagnoses. Here the authors use computational approaches to summarize these patterns and suggest they are associated with genetic and environmental exposure.

Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10−8, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation., Regional brain morphology has a complex genetic architecture. Here the authors present MOSTest, a multivariate statistical framework, apply it to UK Biobank data, and discover hundreds of loci associated with regional brain morphology.

BACKGROUND: Life events experienced during adolescence are associated with risk and resilience to heavy episodic drinking (HED; i.e., binge drinking). The current study builds on prior research using latent class analysis (LCA) to examine heterogeneity in patterns of adolescent life events at baseline to HED over the course of three years (4 timepoints) as part of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA). METHODS: Life event classes were modeled using LCA that characterized NCANDA participants based upon their responses to the Life Events Questionnaire (N = 467, age: M = 14.98, SD = 1.69, 49.7% female). These baseline latent life event classes were then compared to HED at baseline and years 1, 2 and 3 using multinomial logistic regression. RESULTS: At baseline, the LCA characterized four classes of adolescents based on endorsement of life events: negative-relational conflict (n = 65, 13.9%), negative-financial problems (n = 49, 10.5%), low life events (n = 130, 27.8%), and positive life events (n = 223, 47.8%). Life event trajectories differed for the negative life event classes compared to the other two classes, with greater odds of HED in the negative-financial problems class at year 1. CONCLUSION: The four latent classes derived from the life events of NCANDA youth yielded a characterization of adolescents that could aid in understanding HED over the subsequent three years, suggesting that everyday life events may inform adolescent binge drinking.

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened\ud risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified\ud 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and\ud implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved\ud in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression\ud with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were\ud putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry\ud lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression\ud and imply that a continuous measure of risk underlies the clinical phenotype.

Biospecimen collection in the Adolescent Brain Cognitive Development (ABCD) study – of hair samples, shed deciduous (baby) teeth, and body fluids – will serve dual functions of screening for study eligibility, and providing measures of biological processes thought to predict or correlate with key study outcomes on brain and cognitive development. Biosamples are being collected annually to screen for recency of drug use prior to the neuroimaging or cognitive testing visit, and to store for the following future studies: (1) on the effects of exposure to illicit and recreational drugs (including alcohol and nicotine); (2) of pubertal hormones on brain and cognitive developmental trajectories; (3) on the contribution of genomics and epigenomics to child and adolescent development and behavioral outcomes; and (4) with pre- and post-natal exposure to environmental neurotoxicants and drugs of abuse measured from novel tooth analyses. The present manuscript describes the rationales for inclusion and selection of the specific biospecimens, methodological considerations for each measure, future plans for assessment of biospecimens during follow-up visits, and preliminary ABCD data to illustrate methodological considerations. Keywords: Biospecimens, ABCD study, Substance use, Gonadal hormones, Genetics, Environmental exposures

Parkinson’s disease is the second most common neurological disease and affects about 1% of persons over the age of 60 years. Due to the lack of approved surrogate markers, confirmation of the disease still requires postmortem examination. Identifying and validating biomarkers are essential steps toward improving clinical diagnosis and accelerating the search for therapeutic drugs to ameliorate disease symptoms. Until recently, statistical analysis of multicohort longitudinal studies of neurodegenerative diseases has usually been restricted to a single analysis per outcome with simple comparisons between diagnostic groups. However, an important methodological consideration is to allow the modeling framework to handle multiple outcomes simultaneously and consider the transitions between diagnostic groups. This enables researchers to monitor multiple trajectories, correctly account for the correlation among biomarkers, and assess how these associations may jointly change over the long-term course of disease. In this study, we apply a latent time joint mixed-effects model to study biomarker progression and disease dynamics in the Parkinson’s Progression Markers Initiative (PPMI) and examine which markers might be most informative in the earliest phases of disease. The results reveal that, even though diagnostic category was not included in the model, it seems to accurately reflect the temporal ordering of the disease state consistent with diagnosis categorization at baseline. In addition, results indicated that the specific binding ratio on striatum and the total Unified Parkinson’s Disease Rating Scale (UPDRS) show high discriminability between disease stages. An extended latent time joint mixed-effects model with heterogeneous latent time variance also showed improvement in model fit in a simulation study and when applied to real data.

Many psychosocial factors have been implicated in the onset and escalation of substance use in adolescence and young adulthood. Typically, each factor explains a small amount of the variance in substance use outcomes, and effects are typically applied across a broad range of ages or computed from cross-sectional data. The current study evaluated the association of factors including social influence (e.g., peer substance use), cognitive features (e.g., alcohol expectancies), and personality and emotional characteristics (e.g., impulsivity and typical responses to stress) in substance use throughout adolescence and emerging adulthood (ages 13–25; N = 798). Mixed-effects models tailored for the accelerated longitudinal design employed in this study were constructed with psychosocial and developmental factors predicting alcohol and cannabis use. As most participants in the sample exhibited little or no substance use at baseline by design, we excluded baseline assessments and examined data from follow-up years 1, 2, 3, and 4. Interactions between age cohort, change in age, and psychosocial predictors of substance use revealed differing associations over the developmental window for alcohol and cannabis use. For example, positive alcohol expectancies and sensation seeking were most strongly associated with greater drinking after age 18, whereas sensation seeking was associated with increased cannabis use as early as age 15. Higher emotion regulation skills led to less cannabis use in younger ages (i.e., shallower slopes below age 17), but this protective effect diminished after age 17. Results highlight developmentally important factors that differentially contribute to substance use in adolescence and young adulthood. We also demonstrate the importance of developmentally sensitive analyses that maximize the value of data from accelerated longitudinal designs.

Background Differentiating whether an action leads to an outcome by chance or by an underlying statistical regularity that signals environmental change profoundly affects adaptive behavior. Previous studies have shown that anxious individuals may not appropriately differentiate between these situations. This investigation aims to precisely quantify the process deficit in anxious individuals and determine the degree to which these process dysfunctions are specific to anxiety. Methods One hundred twenty-two subjects recruited as part of an ongoing large clinical population study completed a change point detection task. Reinforcement learning models were used to explicate observed behavioral differences in low anxiety (Overall Anxiety Severity and Impairment Scale score ≤ 8) and high anxiety (Overall Anxiety Severity and Impairment Scale score ≥ 9) groups. Results High anxiety individuals used a suboptimal decision strategy characterized by a higher lose-shift rate. Computational models and simulations revealed that this difference was related to a higher base learning rate. These findings are better explained in a context-dependent reinforcement learning model. Conclusions Anxious subjects’ exaggerated response to uncertainty leads to a suboptimal decision strategy that makes it difficult for these individuals to determine whether an action is associated with an outcome by chance or by some statistical regularity. These findings have important implications for developing new behavioral intervention strategies using learning models.

Author(s): Sullivan, EV; Brumback, T; Tapert, SF; Prouty, D; Fama, R; Thompson, WK; Brown, SA; Cummins, K; Colrain, IM; Baker, FC; Clark, DB; Chung, T; De Bellis, MD; Hooper, SR; Nagel, BJ; Nichols, BN; Chu, W; Kwon, D; Pohl, KM; Pfefferbaum, A | Abstract: © 2017 The Authors Longitudinal study provides a robust method for tracking developmental trajectories. Yet inherent problems of retesting pose challenges in distinguishing biological developmental change from prior testing experience. We examined factors potentially influencing change scores on 16 neuropsychological test composites over 1 year in 568 adolescents in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) project. The twice-minus-once-tested method revealed that performance gain was mainly attributable to testing experience (practice) with little contribution from predicted developmental effects. Group mean practice slopes for 13 composites indicated that 60% to ∼100% variance was attributable to test experience; General Ability accuracy showed the least practice effect (29%). Lower baseline performance, especially in younger participants, was a strong predictor of greater gain. Contributions from age, sex, ethnicity, examination site, socioeconomic status, or family history of alcohol/substance abuse were nil to small, even where statistically significant. Recognizing that a substantial proportion of change in longitudinal testing, even over 1-year, is attributable to testing experience indicates caution against assuming that performance gain observed during periods of maturation necessarily reflects development. Estimates of testing experience, a form of learning, may be a relevant metric for detecting interim influences, such as alcohol use or traumatic episodes, on behavior.

There are substantial gaps in understanding near-term precursors of suicidal ideation in bipolar II disorder. We evaluated whether repeated patient-reported mood and energy ratings predicted subsequent near-term increases in suicide ideation.Secondary data were used from 86 depressed adults with bipolar II disorder enrolled in one of 3 clinical trials evaluating Interpersonal and Social Rhythm Therapy and/or pharmacotherapy as treatments for depression. Twenty weeks of daily mood and energy ratings and weekly Hamilton Depression Rating Scale (HDRS) were obtained. Penalized regression was used to model trajectories of daily mood and energy ratings in the 3 week window prior to HDRS Suicide Item ratings.Participants completed an average of 68.6 (sd=52) days of mood and energy ratings. Aggregated across the sample, 22% of the 1675 HDRS Suicide Item ratings were non-zero, indicating presence of at least some suicidal thoughts. A cross-validated model with longitudinal ratings of energy and depressed mood within the three weeks prior to HDRS ratings resulted in an AUC of 0.91 for HDRS Suicide item2, accounting for twice the variation when compared to baseline HDRS ratings. Energy, both at low and high levels, was an earlier predictor than mood.Data derived from a heterogeneous treated sample may not generalize to naturalistic samples. Identified suicidal behavior was absent from the sample so it could not be predicted.Prediction models coupled with intensively gathered longitudinal data may shed light on the dynamic course of near-term risk factors for suicidal ideation in bipolar II disorder.

Infections and mental disorders are two of the major global disease burdens. While correlations between mental disorders and infections have been reported, the possible genetic links between them have not been assessed in large-scale studies. Moreover, the genetic basis of susceptibility to infection is largely unknown, as large-scale genome-wide association studies of susceptibility to infection have been lacking. We utilized a large Danish population-based sample (N = 65,534) linked to nationwide population-based registers to investigate the genetic architecture of susceptibility to infection (heritability estimation, polygenic risk analysis, and a genome-wide association study (GWAS)) and examined its association with mental disorders (comorbidity analysis and genetic correlation). We found strong links between having at least one psychiatric diagnosis and the occurrence of infection (P = 2.16 × 10−208, OR = 1.72). The SNP heritability of susceptibility to infection ranged from ~2 to ~7% in samples of differing psychiatric diagnosis statuses (suggesting the environment as a major contributor to susceptibility), and polygenic risk scores moderately but significantly explained infection status in an independent sample. We observed a genetic correlation of 0.496 (P = 2.17 × 10−17) between a diagnosis of infection and a psychiatric diagnosis. While our GWAS did not identify genome-wide significant associations, we found 90 suggestive (P ≤ 10−5) associations for susceptibility to infection. Our findings suggest a genetic component in susceptibility to infection and indicate that the occurrence of infections in individuals with mental illness may be in part genetically driven.

The risk of posttraumatic stress disorder (PTSD) following trauma is heritable, but robust common variants have yet to be identified. In a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls we conduct a genome-wide association study of PTSD. We demonstrate SNP-based heritability estimates of 5–20%, varying by sex. Three genome-wide significant loci are identified, 2 in European and 1 in African-ancestry analyses. Analyses stratified by sex implicate 3 additional loci in men. Along with other novel genes and non-coding RNAs, a Parkinson’s disease gene involved in dopamine regulation, PARK2, is associated with PTSD. Finally, we demonstrate that polygenic risk for PTSD is significantly predictive of re-experiencing symptoms in the Million Veteran Program dataset, although specific loci did not replicate. These results demonstrate the role of genetic variation in the biology of risk for PTSD and highlight the necessity of conducting sex-stratified analyses and expanding GWAS beyond European ancestry populations., Post-traumatic stress disorder (PTSD) is a common mental health problem. Here, the authors report a GWAS from the Psychiatric Genomics Consortium in which they identify two risk loci in European ancestry and one locus in African ancestry individuals and find that PTSD is genetically correlated with several other psychiatric traits.

Human leukocyte antigen (HLA) genes encode proteins with important roles in the regulation of the immune system. Many studies have also implicated HLA genes in psychiatric and neurodevelopmental disorders. However, these studies usually focus on one disorder and/or on one HLA candidate gene, often with small samples. Here, we access a large dataset of 65,534 genotyped individuals consisting of controls (N = 19,645) and cases having one or more of autism spectrum disorder (N = 12,331), attention deficit hyperactivity disorder (N = 14,397), schizophrenia (N = 2401), bipolar disorder (N = 1391), depression (N = 18,511), anorexia (N = 2551) or intellectual disability (N = 3175). We imputed participants’ HLA alleles to investigate the involvement of HLA genes in these disorders using regression models. We found a pronounced protective effect of DPB1*1501 on susceptibility to autism (p = 0.0094, OR = 0.72) and intellectual disability (p = 0.00099, OR = 0.41), with an increased protective effect on a comorbid diagnosis of both disorders (p = 0.003, OR = 0.29). We also identified a risk allele for intellectual disability, B*5701 (p = 0.00016, OR = 1.33). Associations with both alleles survived FDR correction and a permutation procedure. We did not find significant evidence for replication of previously-reported associations for autism or schizophrenia. Our results support an implication of HLA genes in autism and intellectual disability, which requires replication by other studies. Our study also highlights the importance of large sample sizes in HLA association studies.

RATIONALE: The impact of neuroscience-based approaches for psychiatry on pragmatic clinical decision-making has been limited. Although neuroscience has provided insights into basic mechanisms of neural function, these insights have not improved the ability to generate better assessments, prognoses, diagnoses, or treatment of psychiatric conditions. OBJECTIVES: To integrate the emerging findings in machine learning and computational psychiatry to address the question: what measures that are not derived from the patient’s self-assessment or the assessment by a trained professional can be used to make more precise predictions about the individual’s current state, the individual’s future disease trajectory, or the probability to respond to a particular intervention? RESULTS: Currently, the ability to use individual differences to predict differential outcomes is very modest possibly related to the fact that the effect sizes of interventions are small. There is emerging evidence of genetic and neuroimaging-based heterogeneity of psychiatric disorders, which contributes to imprecise predictions. Although the use of machine learning tools to generate clinically actionable predictions is still in its infancy, these approaches may identify subgroups enabling more precise predictions. In addition, computational psychiatry might provide explanatory disease models based on faulty updating of internal values or beliefs. CONCLUSIONS: There is a need for larger studies, clinical trials using machine learning or computational psychiatry model parameters predictions as actionable outcomes, comparing alternative explanatory computational models, and using translational approaches that apply similar paradigms and models in humans and animals.

There is mounting evidence that seemingly diverse psychiatric disorders share genetic etiology, but the biological substrates mediating this overlap are not well characterized. Here we leverage the unique Integrative Psychiatric Research Consortium (iPSYCH) study, a nationally representative cohort ascertained through clinical psychiatric diagnoses indicated in Danish national health registers. We confirm previous reports of individual and cross-disorder single-nucleotide polymorphism heritability for major psychiatric disorders and perform a cross-disorder genome-wide association study. We identify four novel genome-wide significant loci encompassing variants predicted to regulate genes expressed in radial glia and interneurons in the developing neocortex during mid-gestation. This epoch is supported by partitioning cross-disorder single-nucleotide polymorphism heritability, which is enriched at regulatory chromatin active during fetal neurodevelopment. These findings suggest that dysregulation of genes that direct neurodevelopment by common genetic variants may result in general liability for many later psychiatric outcomes.

The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen's d effect size (ES) 0.06-0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08-0.1) and fluid intelligence (ES: 0.04-0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.

Author(s): Ajmera, Veeral; Thompson, Wesley K; Smith, Davey M; Malhotra, Atul; Mehta, Ravindra L; Tolia, Vaishal; Yin, Jeffrey; Sriram, Krishna; Insel, Paul A; Collier, Summer; Richards, Lisa; Loomba, Rohit | Abstract: Background and aimsRetrospective studies have shown that angiotensin-converting-enzyme (ACE) inhibitors are associated with a reduced risk of complications and mortality in persons with novel coronavirus disease 2019 (COVID-19). Thus, we aimed to examine the efficacy of ramipril, an ACE-inhibitor, in preventing ICU admission, mechanical ventilation and/or mortality while also minimizing the risk of transmission and use of personal protective equipment (PPE).MethodsRAMIC is a multicenter, randomized, double-blind, allocation-concealed, placebo-controlled trial comparing the efficacy of treatment with ramipril 2.5nmg orally daily compared to placebo for 14ndays. The study population includes adult patients with COVID-19 who were admitted to a hospital or assessed in an emergency department or ambulatory clinic. Key exclusion criteria include ICU admission or need for mechanical ventilation at screening, use of an ACE inhibitor or angiotensin-receptor-II blocker within 7ndays, glomerular filtration ratenln40nmL/min or a systolic blood pressure (BP)nln100nmmHg or diastolic BPnln65nmmHg. Patients are randomized 2:1 to receive ramipril (2.5nmg) or placebo daily. Informed consent and study visits occur virtually to minimize the risk of SARS-CoV-2 transmission and preserve PPE. The primary composite endpoint of ICU admission, invasive mechanical ventilation and death are adjudicated virtually.ConclusionsRAMIC is designed to assess the efficacy of treatment with ramipril for 14ndays to decrease ICU admission, mechanical ventilator use and mortality in patients with COVID-19 and leverages virtual study visits and endpoint adjudication to mitigate risk of infection and to preserve PPE (ClinicalTrials.gov, NCT04366050).

Schizophrenia is a complex disorder with high heritability. Recent findings from several large genetic studies suggest a large number of risk variants are involved (i.e., schizophrenia is a polygenic disorder) and analytic approaches could be tailored for this scenario. Novel statistical approaches for analyzing GWAS data have recently been developed to be more sensitive to polygenic traits. These approaches have provided intriguing new insights into neurobiological pathways and support for the involvement of regulatory mechanisms, neurotransmission (glutamate, dopamine, GABA), and immune and neurodevelopmental pathways. Integrating the emerging statistical genetics evidence with sound neurobiological experiments will be a critical, and challenging, next step in deciphering the specific disease mechanisms of schizophrenia.

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders., Simone de Jong et al. examine the balance of common and rare risk for psychiatric disorders in a large family with high incidence of Bipolar Disorder and Major Depressive Disorder. They find that increased polygenic risk over generations could be partially due to assortative mating, which may explain the observation of anticipation in mood disorders.

Higher cognitive functions are regarded as one of the main distinctive traits of humans. Evidence for the cognitive evolution of human beings is mainly based on fossil records of an expanding cranium and an increasing complexity of material culture artefacts. However, the molecular genetic factors involved in the evolution are still relatively unexplored. Here, we investigated whether genomic regions that underwent positive selection in humans after divergence from Neanderthals are enriched for genetic association with phenotypes related to cognitive functions. We used genome wide association data from a study of college completion (N = 111,114), one of educational attainment (N = 293,623) and two different studies of general cognitive ability (N = 269,867 and 53,949). We found nominally significant polygenic enrichment of associations with college completion (p = 0.025), educational attainment (p = 0.043) and general cognitive ability (p = 0.015 and 0.025, respectively), suggesting that variants influencing these phenotypes are more prevalent in evolutionarily salient regions. The enrichment remained significant after controlling for other known genetic enrichment factors, and for affiliation to genes highly expressed in the brain. These findings support the notion that phenotypes related to higher order cognitive skills typical of humans have a recent genetic component that originated after the separation of the human and Neanderthal lineages.

The ABCD study is a new and ongoing project of very substantial size and scale involving 21 data acquisition sites. It aims to recruit 11,500 children and follow them for ten years with extensive assessments at multiple timepoints. To deliver on its potential to adequately describe adolescent development, it is essential that it adopt recruitment procedures that are efficient and effective and will yield a sample that reflects the nation’s diversity in an epidemiologically informed manner. Here, we describe the sampling plans and recruitment procedures of this study. Participants are largely recruited through the school systems with school selection informed by gender, race and ethnicity, socioeconomic status, and urbanicity. Procedures for school selection designed to mitigate selection biases, dynamic monitoring of the accumulating sample to correct deviations from recruitment targets, and a description of the recruitment procedures designed to foster a collaborative attitude between the researchers, the schools and the local communities, are provided. Keywords: Adolescent Brain Cognitive Development, Recruitment, Adolescence, Study design

The Adolescent Brain Cognitive Development (ABCD) study is poised to be the largest single-cohort long-term longitudinal study of neurodevelopment and child health in the United States. Baseline data on N= 4521 children aged 9–10 were released for public access on November 2, 2018. In this paper we performed principal component analyses of the neurocognitive assessments administered to the baseline sample. The neurocognitive battery included seven measures from the NIH Toolbox as well as five other tasks. We implemented a Bayesian Probabilistic Principal Components Analysis (BPPCA) model that incorporated nesting of subjects within families and within data collection sites. We extracted varimax-rotated component scores from a three-component model and associated these scores with parent-rated Child Behavior Checklist (CBCL) internalizing, externalizing, and stress reactivity. We found evidence for three broad components that encompass general cognitive ability, executive function, and learning/memory. These were significantly associated with CBCL scores in a differential manner but with small effect sizes. These findings set the stage for longitudinal analysis of neurocognitive and psychopathological data from the ABCD cohort as they age into the period of maximal adolescent risk-taking. Keywords: Adolescence, Neurocognition, NIH toolbox, Principal components analysis, Child behavior checklist, Externalizing, Internalizing, Stress reactivity

Spatial mapping is a promising strategy to investigate the mechanisms underlying the incidence of psychosis. We analyzed a case-cohort study (n = 24,028), drawn from the 1.47 million Danish persons born between 1981 and 2005, using a novel framework for decomposing the geospatial risk for schizophrenia based on locale of upbringing and polygenic scores. Upbringing in a high environmental risk locale increases the risk for schizophrenia by 122%. Individuals living in a high gene-by-environmental risk locale have a 78% increased risk compared to those who have the same genetic liability but live in a low-risk locale. Effects of specific locales vary substantially within the most densely populated city of Denmark, with hazard ratios ranging from 0.26 to 9.26 for environment and from 0.20 to 5.95 for gene-by-environment. These findings indicate the critical synergism of gene and environment on the etiology of schizophrenia and demonstrate the potential of incorporating geolocation in genetic studies., Schizophrenia (SCZ) risk is influenced by genetic and environmental factors. Here, the authors develop a statistical method for analyzing gene-by-environment effects in SCZ risk across Denmark with fine spatial resolution.

Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures., To better understand the phenotypic relationships of complex traits it is also important to understand their genetic overlap. Here, Frei et al. develop MiXeR which uses GWAS summary statistics to evaluate the polygenic overlap between two traits irrespective of their genetic correlation.

Personality change due to traumatic brain injury (PC) in children is an important psychiatric complication of injury and is a form of severe affective dysregulation. This study aimed to examine neurocognitive correlates of PC. The sample included 177 children 5-14 years old with traumatic brain injury who were enrolled from consecutive admissions to five trauma centers. Patients were followed up prospectively at baseline and at 6 months, and they were assessed with semistructured psychiatric interviews. Injury severity, socioeconomic status, and neurocognitive function (measures of attention, processing speed, verbal memory, IQ, verbal working memory, executive function, naming/reading, expressive language, motor speed, and motor inhibition) were assessed with standardized instruments. Unremitted PC was present in 26 (18%) of 141 participants assessed at 6 months postinjury. Attention, processing speed, verbal memory, IQ, and executive function were significantly associated with PC even after socioeconomic status, injury severity, and preinjury attention deficit hyperactivity disorder were controlled. These findings are a first step in characterizing concomitant cognitive impairments associated with PC. The results have implications beyond brain injury to potentially elucidate the neurocognitive symptom complex associated with mood instability regardless of etiology.

Objective In an effort to address earliest detection of mild cognitive impairment (MCI), we examined hippocampal volumes and atrophy in middle-aged men to explore neuroanatomical support for different neuropsychological definitions of MCI. Methods 460 men aged 51–60 years underwent neuropsychological testing and MRI. MCI was defined according to five criteria sets. MRI-derived hippocampal volume and hippocampal occupancy (HOC) were obtained via FreeSurfer. Statistical analyses were performed using linear mixed models. Results Differences in HOC between normal cognitive functioning, amnestic, and non-amnestic MCI were observed using MCI criteria that required one impaired (>1.5 SD) cognitive measure in a given cognitive domain or a cognitive composite score method with a cut-point 2 SD below the mean. Differences in standard hippocampal volume were only found between normal and amnestic presentations and only when using the composite score method. Conclusion Results provide empirical support for detection of pre-MCI in younger cohorts. Convergence of neuropsychological and neuroanatomical data, particularly HOC (as opposed to standard cross-sectional volume), supports early identification of MCI as defined by some neuropsychological criteria.

Discovering genetic variants associated with human brain structures is an on-going effort. The ENIGMA consortium conducted genome-wide association studies (GWAS) with standard multi-study analytical methodology and identified several significant single nucleotide polymorphisms (SNPs). Here we employ a novel analytical approach that incorporates functional genome annotations (e.g., exon or 5′UTR), total linkage disequilibrium (LD) scores and heterozygosity to construct enrichment scores for improved identification of relevant SNPs. The method provides increased power to detect associated SNPs by estimating stratum-specific false discovery rate (FDR), where strata are classified according to enrichment scores. Applying this approach to the GWAS summary statistics of putamen volume in the ENIGMA cohort, a total of 15 independent significant SNPs were identified (conditional FDR −8). These 11 novel loci include GATAD2B, ASCC3, DSCAML1, and HELZ, which are previously implicated in various neural related phenotypes. The current findings demonstrate the boost in power with the annotation-informed FDR method, and provide insight into the genetic architecture of the putamen.

Author(s): Xu, Selene; Thompson, Wesley; Ancoli-Israel, Sonia; Liu, Lianqi; Palmer, Barton; Natarajan, Loki | Abstract: OBJECTIVE:Breast cancer patients frequently complain of cognitive dysfunction during chemotherapy. Patients also report experiencing a cluster of sleep problems, fatigue, and depressive symptoms during chemotherapy. We aimed to understand the complex dynamic interrelationships of depression, fatigue, and sleep to ultimately elucidate their role in cognitive performance and quality of life amongst breast cancer survivors undergoing chemotherapy treatment. METHODS:Our study sample comprised 74 newly diagnosed stage I to III breast cancer patients scheduled to receive chemotherapy. An objective neuropsychological test battery and self-reported fatigue, mood, sleep quality, and quality of life were collected at 3 time points: before the start of chemotherapy (baseline: BL), at the end of cycle 4 chemotherapy (C4), and 1nyear after the start of chemotherapy (Y1). We applied novel Bayesian network methods to investigate the role of sleep/fatigue/mood on cognition and quality of life prior to, during, and after chemotherapy. RESULTS:The fitted network exhibited strong direct and indirect links between symptoms, cognitive performance, and quality of life. The only symptom directly linked to cognitive performance was C4 sleep quality; at C4, fatigue was directly linked to sleep and thus indirectly influenced cognitive performance. Mood strongly influenced concurrent quality of life at C4 and Y1. Regression estimates indicated that worse sleep quality, fatigue, and mood were negatively associated with cognitive performance or quality of life. CONCLUSIONS:The Bayesian network identified local structure (eg, fatigue-mood-QoL or sleep-cognition) and possible intervention targets (eg, a sleep intervention to reduce cognitive complaints during chemotherapy).

Traditional genome-wide association studies (GWAS) have successfully detected genetic variants associated with schizophrenia. However, only a small fraction of heritability can be explained. Gene-set/pathway-based methods can overcome limitations arising from single nucleotide polymorphism (SNP)-based analysis, but most of them place constraints on size which may exclude highly specific and functional sets, like macromolecules. Voltage-gated calcium (Cav ) channels, belonging to macromolecules, are composed of several subunits whose encoding genes are located far away or even on different chromosomes. We combined information about such molecules with GWAS data to investigate how functional channels associated with schizophrenia. We defined a biologically meaningful SNP-set based on channel structure and performed an association study by using a validated method: SNP-set (sequence) kernel association test. We identified eight subtypes of Cav channels significantly associated with schizophrenia from a subsample of published data (N = 56,605), including the L-type channels (Cav 1.1, Cav 1.2, Cav 1.3), P-/Q-type Cav 2.1, N-type Cav 2.2, R-type Cav 2.3, T-type Cav 3.1, and Cav 3.3. Only genes from Cav 1.2 and Cav 3.3 have been implicated by the largest GWAS (N = 82,315). Each subtype of Cav channels showed relatively high chip heritability, proportional to the size of its constituent gene regions. The results suggest that abnormalities of Cav channels may play an important role in the pathophysiology of schizophrenia and these channels may represent appropriate drug targets for therapeutics. Analyzing subunit-encoding genes of a macromolecule in aggregate is a complementary way to identify more genetic variants of polygenic diseases. This study offers the potential of power for discovery the biological mechanisms of schizophrenia.

© 2017 Macmillan Publishers Limited, part of Springer Nature. The most recent genome-wide association studies (GWAS) of schizophrenia (SCZ) identified hundreds of risk variants potentially implicated in the disease. Further, novel statistical methodology designed for polygenic architecture revealed more potential risk variants. This can provide a link between individual genetic factors and the mechanistic underpinnings of SCZ. Intriguingly, a large number of genes coding for ionotropic and metabotropic receptors for various neurotransmitters - glutamate, 3-aminobutyric acid (GABA), dopamine, serotonin, acetylcholine and opioids - and numerous ion channels were associated with SCZ. Here, we review these findings from the standpoint of classical neurobiological knowledge of neuronal synaptic transmission and regulation of electrical excitability. We show that a substantial proportion of the identified genes are involved in intracellular cascades known to integrate slow' (G-protein-coupled receptors) and fast' (ionotropic receptors) neurotransmission converging on the protein DARPP-32. Inspection of the Human Brain Transcriptome Project database confirms that that these genes are indeed expressed in the brain, with the expression profile following specific developmental trajectories, underscoring their relevance to brain organization and function. These findings extend the existing pathophysiology hypothesis by suggesting a unifying role of dysregulation in neuronal excitability and synaptic integration in SCZ. This emergent model supports the concept of SCZ as an associative' disorder - a breakdown in the communication across different slow and fast neurotransmitter systems through intracellular signaling pathways - and may unify a number of currently competing hypotheses of SCZ pathophysiology.

Background: Epidemiological findings suggest a relationship between multiple sclerosis (MS) and cardiovascular disease (CVD) risk factors, although the nature of this relationship is not well understood. Objective: We used genome-wide association study (GWAS) data to identify shared genetic factors (pleiotropy) between MS and CVD risk factors. Methods: Using summary statistics from a large, recent GWAS (total n > 250,000 individuals), we investigated overlap in single nucleotide polymorphisms (SNPs) associated with MS and a number of CVD risk factors including triglycerides (TG), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, body mass index, waist-to-hip ratio, type 2 diabetes, systolic blood pressure, and C-reactive protein level. Results and conclusion: Using conditional enrichment plots, we found 30-fold enrichment of MS SNPs for different levels of association with LDL and TG SNPs, with a corresponding reduction in conditional false discovery rate (FDR). We identified 133 pleiotropic loci outside the extended major histocompatibility complex with conditional FDR