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Frontoparietal connections underlie key executive cognitive functions. Abnormalities in the frontoparietal network have been observed in chronic alcoholics and associated with alcohol-related cognitive deficits. It remains unclear whether neurobiological differences in frontoparietal circuitry exist in substance-naïve youth who are at-risk for alcohol use disorders. This study used functional connectivity magnetic resonance imaging and diffusion tensor imaging to examine frontoparietal connectivity and underlying white matter microstructure in 20 substance-naïve youth with a family history of alcohol dependence and 20 well-matched controls without familial substance use disorders. Youth with a family history of alcohol dependence showed significantly less functional connectivity between posterior parietal and dorsolateral prefrontal seed regions (ps < .05), as compared to family history negative controls; however, they did not show differences in white matter architecture within tracts subserving frontoparietal circuitry (ps > .34). Substance-naïve youth with a family history of alcohol dependence show less frontoparietal functional connectivity in the absence of white matter microstructural abnormalities as compared to youth with no familial risk. This may suggest a potential neurobiological marker for the development of substance use disorders.

Background In the era of Big Data, genome-wide association studies (GWASs) have been the main instrument to better our understanding of the genetics behind human traits. The schizophrenia GWAS by the psychiatric genomics consortium (PGC) identified many loci plausibly involved in the etiology of the disorder [Ripke et al., 2014]. However, the picture appears to be far from complete [Lichtenstein, 2009; Polderman, 2015; Gejman, 2010; Lee, 2012]. Judging by the number of non-coding variants identified by GWASs of schizophrenia and other complex traits, many causal variants are likely to affect the regulation of the genes involved rather than severely disrupt their function. Expression quantitative trait loci (eQTLs) are genomic loci that contribute to variation in levels of mRNA. It is known [Bacanu, 2014] that many eQTLs are associated with schizophrenia, as well as other phenotypes [Nicolae, 2010]. Methods Given known pleiotropy between schizophrenia and other traits, we looked for association enrichment of eQTLs in tissues not obviously relevant to psychiatric disorders (blood, adipose, LCL, and skin [Buil, 2015]). In order to consolidate any evidence of tissue-specificity, we further looked for differential enrichment in different ENCODE functional annotation categories. We used summary p-value statistics from schizophrenia as well as body mass index, Crohn's disease, height, systolic blood pressure and type II diabetes GWASs. We matched the eQTLs to SNP sets with similar minor allele frequencies (MAF) and distances from their respective transcription start sites (TSS), and no evidence of association with expression levels of any genes. We further subdivided eQTLs into proximal and distal eQTLs, according to their position relative to the TSS or to their affiliation to promoters, and we annotated them according to their ENCODE designation. We visualized the enrichment in fold enrichment plots, screened it using Fisher tests and assessed its extent by regressing the squared association z-score against SNP affiliations and LD-score. Results Significant enrichment was seen in adipose (beta=0.24, p=2.2E-06), LCL (beta=0.27, p=4.0E-08) and skin (beta=0.23, p=2.7E-06) tissues. The enrichment was as widespread as that observed in another highly polygenic trait such as height, and more widespread than in traits such as body mass index (adipose and skin) or blood pressure (adipose). The schizophrenia association extent does not appear to depend on the eQTLs' distance from the TSS nor from their explicit affiliation to specific functional elements, as defined by ENCODE, suggesting that risk alleles for schizophrenia could frequently act in many different tissue contexts. Discussion Our results are in line with the notion that schizophrenia is a disorder involving many tissues and known pleiotropy with cardiovascular and immune diseases back our findings. The statistical power of the underlying GWAS does play a role in the detection of association enrichment of the sort we sought. Polygenicity, however, is also a major factor, as exemplified by the results for height, body mass index and blood pressure. In summary, we find that looking for functional correlates of schizophrenia risk loci in non-obvious tissues is productive, and this could be because of either pleiotropy or increased effectiveness of variants that work in many different environmental contexts. This suggests the utility of large, single tissue eQTL experiments in the study of schizophrenia in addition to smaller, multiple tissue approaches.

Background: There is currently a lack of consensus on the definition of successful aging (SA) and existing implementations have omitted constructs associated with SA. We used empirical methods to develop a dimensional model of SA that incorporates a wider range of associated variables, and we examined the relationship among these components using factor analysis and Bayesian Belief Nets.Methods: We administered a successful aging questionnaire comprising several standardized measures related to SA to a sample of 1948 older women enrolled in the San Diego site of the Women's Health Initiative study. The SA-related variables we included in the model were self-rated successful aging, depression severity, physical and emotional functioning, optimism, resilience, attitude towards own aging, self-efficacy, and cognitive ability. After adjusting for age, education and income, we fitted an exploratory factor analysis model to the SA-related variables and then, in order to address relationships among these factors, we computed a Bayesian Belief Net (BBN) using rotated factor scores.Results: The SA-related variables loaded onto five factors. Based on the loading, we labeled the factors as follows: self-rated successful aging, cognition, psychosocial protective factors, physical functioning, and emotional functioning. In the BBN, self-rated successful aging emerged as the primary downstream factor and exhibited significant partial correlations with psychosocial protective factors, physical/general status and mental/emotional status but not with cognitive ability.Conclusions: Our study represents a step forward in developing a dimensional model of SA. Our findings also point to a potential role for psychiatry in improving successful aging by managing depressive symptoms and developing psychosocial interventions to improve self-efficacy, resilience, and optimism.

This study used a new cell phone ecological momentary assessment approach to investigate daily emotional dynamics in 47 youths with major depressive disorder (MDD) and 32 no-psychopathology controls (CON) (ages 7-17 years). Information about emotional experience in the natural environment was obtained using answer-only cell phones, while MDD youths received an 8-week course of cognitive behavioral therapy and/or psychopharmacological treatment. Compared with CON youths, MDD youths reported more intense and labile global negative affect; greater sadness, anger, and nervousness; and a lower ratio of positive to negative affect. These differences increased with pubertal maturation. MDD youths spent more time alone and less time with their families than CON youths. Although differences in emotional experiences were found across social contexts, MDD youths were more negative than CON youths in all contexts examined. As the MDD participants progressed through treatment, diagnostic group differences in the intensity and lability of negative affect decreased, but there were no changes in the ratio of positive to negative affect or in measures of social context. We discuss methodological innovations and advantages of this approach, including improved ecological validity and access to information about variability in emotions, change in emotions over time, the balance of positive and negative emotions, and the social context of emotional experience.

Future clinical trials of neuroprotec- tion in prodromal Huntington's (known as preHD) will require sensitive in vivo imaging biomarkers to track disease progression over the shortest period. Since ba- sal ganglia atrophy is the most prominent structural characteristic of Huntington's pathology, systematic assessment of longitudinal subcortical atrophy holds great potential for future biomarker development. We studied 36 preHD and 22 age-matched controls using a novel method to quantify regional change from T1- weighted structural images acquired 1 year apart. We assessed cross-sectional volume differences and longi- tudinal volumetric change in 7 subcortical structures— the accumbens, amygdala, caudate, hippocampus, pal- lidum, putamen, and thalamus. At baseline, accumbens, caudate, pallidum, and putamen volumes were reduced in preHD versus controls (all P

OBJECTIVES: To determine whether measures of successful aging are associated with sexual activity, satisfaction, and function in older postmenopausal women. DESIGN: Cross-sectional study using self-report surveys; analyses included chi-square and t-tests and multiple linear regression analyses. SETTING: Community-dwelling older postmenopausal women in the greater San Diego region. PARTICIPANTS: One thousand two hundred thirty-five community-dwelling women aged 60 to 89 participating at the San Diego site of the Women's Health Initiative. MEASUREMENTS: Demographic information and self-reported measures of sexual activity, function, and satisfaction and successful aging. RESULTS: Sexual activity and functioning (desire, arousal, vaginal tightness, use of lubricants, and ability to climax) were negatively associated with age, as were physical and mental health. In contrast, sexual satisfaction and self-rated successful aging and quality of life remained unchanged across age groups. Successful aging measures were positively associated with sexual measures, especially self-rated quality of life and sexual satisfaction. CONCLUSION: Self-rated successful aging, quality of life, and sexual satisfaction appear to be stable in the face of declines in physical health, some cognitive abilities, and sexual activity and function and positively associated with each other from age 60 to 89.

Intense efforts are underway to eval- uate neuroimaging measures as biomarkers for neuro- degeneration in premanifest Huntington's disease (preHD). We used a completely automated longitudinal analysis method to compare structural scans in preHD individuals and controls. Using a 1-year longitudinal design, we analyzed T1-weighted structural scans in 35 preHD individuals and 22 age-matched controls. We used the SIENA (Structural Image Evaluation, using Nor- malization, of Atrophy) software tool to yield overall per- centage brain volume change (PBVC) and voxel-level changes in atrophy. We calculated sample sizes for a hypothetical disease-modifying (neuroprotection) study. We found significantly greater yearly atrophy in preHD individuals versus controls (mean PBVC controls, 20.149%; preHD, 20.388%; P 5 .031, Cohen's d 5 .617). For a preHD subgroup closest to disease onset, yearly atrophy was more than 3 times that of controls (mean PBVC close-to-onset preHD, 20.510%; P 5 .019, Cohen's d 5 .920). This atrophy was evident at the voxel level in periventricular regions, consistent with well-established preHD basal ganglia atrophy. We esti- mated that a neuroprotection study using SIENA would only need 74 close-to-onset individuals in each arm (treatment vs placebo) to detect a 50% slowing in yearly atrophy with 80% power. Automated whole-brain analysis of structural MRI can reliably detect preHD dis- ease progression in 1 year. These results were attained with a readily available imaging analysis tool, SIENA, which is observer independent, automated, and robust with respect to image quality, slice thickness, and different pulse sequences. This MRI biomarker approach could be used to evaluate neuroprotection in preHD. V C 2011 Movement Disorder Society

INTRODUCTION: Attitudes toward own aging (ATOA) refers to expectations about the personal experience of aging. As of now, there is limited literature that addresses the impact of ATOA on indicators of psychological, physical, and social health. In this study, we examine associations between ATOA and several measures associated with successful aging. METHODS: A detailed cross-sectional survey questionnaire on successful aging was completed by 1,973 older women enrolled in the San Diego site of the Women's Health Initiative study. ATOA was measured using the Philadelphia Geriatric Morale Scale (PGMS) RESULTS: The final sample consisted of 1151 women. The mean ATOA score was 3.8 indicating generally positive ATOA. Positive ATOA score was significantly associated with younger age, lower income, being married, higher SF-36 Physical Composite scores, higher SF-36 Mental composite scores, lower depression scores, and higher resilience scores. Approximately 40% of variance in ATOA scores was explained by successful aging-related domain scores. CONCLUSIONS: Better physical and emotional functioning, greater resilience and lower depression are associated with more positive ATOA. Associations with sociodemographic traits are complex. Modifying ATOA may have potential to impact a broad range of health and successful aging related outcomes.

The anti-cancer immune response against mutated peptides of potential immunological relevance (neoantigens) is primarily attributed to MHC-I-restricted cytotoxic CD8+ Tcell responses. MHC-II-restricted CD4+ Tcells also drive anti-tumor responses, but their relation to neoantigen selection and tumor evolution has not been systematically studied. Modeling the potential of an individual's MHC-II genotype to present 1,018 driver mutations in 5,942 tumors, we demonstrate that the MHC-II genotype constrains the mutational landscape during tumorigenesis in a manner complementary to MHC-I. Mutations poorly bound to MHC-II are positively selected during tumorigenesis, even more than mutations poorly bound to MHC-I. This emphasizes the importance of CD4+ Tcells in anti-tumor immunity. In addition, we observed less inter-patient variation in mutation presentation for MHC-II than for MHC-I. These differences were reflected by age at diagnosis, which was correlated with presentation by MHC-I only. Collectively, ourresults emphasize the central role of MHC-II presentation in tumor evolution.

The role of spirituality in the context of mental health and successful aging is not well understood. In a sample of community-dwelling older women enrolled at the San Diego site of the Women's Health Initiative study, we examined the association between spirituality and a range of variables associated with successful cognitive and emotional aging, including optimism, resilience, depression, and health-related quality of life (HRQoL).A detailed cross-sectional survey questionnaire on successful aging was completed by 1973 older women. It included multiple self-reported measures of positive psychological functioning (e.g., resilience and optimism), as well as depression and HRQoL. Spirituality was measured using a five-item self-report scale constructed using two items from the Brief Multidimensional Measure of Religiosity/Spirituality and three items from Hoge's Intrinsic Religious Motivation Scale.Overall, 40% women reported regular attendance in organized religious practice, and 53% reported engaging in private spiritual practices. Several variables were significantly related to spirituality in bivariate associations; however, using model testing, spirituality was significantly associated only with higher resilience, lower income, lower education, and lower likelihood of being in a marital or committed relationship.Our findings point to a role for spirituality in promoting resilience to stressors, possibly to a greater degree in persons with lower income and education level. Future longitudinal studies are needed to confirm these associations.

The major histocompatibility complex (MHC) class I and class II molecules present peptide antigens to T cells to enable immune detection of foreign or mutated peptides. Mutant peptides presented by MHC-I on the cell surface have the potential to activate a cytotoxic CD8+ T-cell response, imposing selective pressure on tumor cells harboring neoantigens. Recently, we demonstrated that patient MHC-I genotype results in individual differences in which cancer-causing mutations can be effectively presented on the cell surface to stimulate CD8+ T cells and is a determinant of the probability of observing a particular mutation (Marty et al., Cell 2017). However, since cytotoxic CD8+ T cell responses require CD4+ T cells that are activated by MHC-II-based antigen presentation, we hypothesized that individual variation in MHC-II genotype must also be an important determinant of immune selection of tumor cells. To investigate the role of MHC-II restriction in shaping the mutational landscape of tumors, we first developed a score that represents the ability of an individual MHC-II genotype to present a mutation on the cell surface. The score maps affinities of mutated peptides for different MHC-II molecules into a single value and was able to distinguish peptides found in complex with MHC-II in vitro by mass spectrometry from random peptides with an AUC of 0.69. Next, we assessed the relationship between MHC-II presentation of 1,018 cancer mutations and their occurence in 7,137 tumors across 30 tissues types from The Cancer Genome Atlas (TCGA). Across all MHC-II genotypes in TCGA, we observed an unexpected bias for recurrent cancer mutations to be less well presented than random mutations (p < 2.2e-16), while foreign pathogenic sequences were more likely to be presented (p < 2.2e-16). Moreover, we found that higher mutation frequency among tumors correlated with overall poorer presentation across cancer patients. Comparing mutation presentation by MHC-II with our previous results for MHC-I, we found only a modest correlation in presentation scores across mutations (rho=0.28). Combining MHC-I and MHC-II genotype-derived presentation scores into a single model increased overall performance for predicting occurrence of mutation. Notably, MHC-II genotype had an even stronger influence over the probability for specific oncogenic mutations to arise than MHC-I genotype (OR 2.12 vs. OR 1.43). We also observed differences in the extent to which MHC-based presentation could predict driver mutation status in different tumor types, with the best performance observed in thyroid cancer (AUC=0.91). These results reveal that immunoediting during tumor development is dependent on MHC-II as well as MHC-I genotype, further implicating the inherited immune system as a key risk factor for cancer. Citation Format: Rachel Marty, Wesley Thompson, Trey Ideker, Maurizio Zanetti, Hannah Carter. MHC II complements MHC I in shaping the mutational landscape of tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5727.

Background - Amygdala-orbitofrontal cortical (OFC) functional connectivity (FC) to emotional stimuli and relationships with white matter remain little examined in bipolar disorder individuals (BD). Methods - Thirty-one BD (type I; n = 17 remitted; n = 14 depressed) and 24 age- and gender-ratio-matched healthy individuals (HC) viewed neutral, mild, and intense happy or sad emotional faces in two experiments. The FC was computed as linear and nonlinear dependence measures between amygdala and OFC time series. Effects of group, laterality, and emotion intensity upon amygdala-OFC FC and amygdala-OFC FC white matter fractional anisotropy (FA) relationships were examined. Results - The BD versus HC showed significantly greater right amygdala-OFC FC (p = .001) in the sad experiment and significantly reduced bilateral amygdala-OFC FC (p = .007) in the happy experiment. Depressed but not remitted female BD versus female HC showed significantly greater left amygdala-OFC FC (p = .001) to all faces in the sad experiment and reduced bilateral amygdala-OFC FC to intense happy faces (p = .01). There was a significant nonlinear relationship (p = .001) between left amygdala-OFC FC to sad faces and FA in HC. In BD, antidepressants were associated with significantly reduced left amygdala-OFC FC to mild sad faces (p = .001). Conclusions - In BD, abnormally elevated right amygdala-OFC FC to sad stimuli might represent a trait vulnerability for depression, whereas abnormally elevated left amygdala-OFC FC to sad stimuli and abnormally reduced amygdala-OFC FC to intense happy stimuli might represent a depression state marker. Abnormal FC measures might normalize with antidepressant medications in BD. Nonlinear amygdala-OFC FC–FA relationships in BD and HC require further study.

We develop new statistical methods for estimating functional connectivity between components of a multivariate time series and for testing differences in functional connectivity across experimental conditions. Here, we characterize functional connectivity by partial coherence, which identifies the frequency band (or bands) that drives the direct linear association between any pair of components of a multivariate time series after removing the linear effects of the other components. Partial coherence can be efficiently estimated using the inverse of the spectral density matrix. However, when the number of components is large and the components of the multivariate time series are highly correlated, the spectral density matrix estimate may be numerically unstable and consequently gives partial coherence estimates that are highly variable. To address the problem of numerical instability, we propose a shrinkage-based estimator which is a weighted average of a smoothed periodogram estimator and a scaled identity matrix with frequency-specific weight computed objectively so that the resulting shrinkage estimator minimizes the mean-squared error criterion. Compared to typical smoothing-based estimators, the shrinkage estimator is more computationally stable and gives a lower mean squared error. In addition, we develop a randomization method for testing differences in functional connectivity networks between experimental conditions. Finally, we report results from numerical experiments and analyze an EEG data set recorded during a visually-guided hand movement task.

In this paper we present a model for the analysis of multivariate functional data with unequally spaced observation times that may differ among subjects. Our method is formulated as a Bayesian mixed-effects model in which the fixed part corresponds to the mean functions, and the random part corresponds to individual deviations from these mean functions. Covariates can be incorporated into both the fixed and the random effects. The random error term of the model is assumed to follow a multivariate Ornstein–Uhlenbeck process. For each of the response variables, both the mean and the subject-specific deviations are estimated via low-rank cubic splines using radial basis functions. Inference is performed via Markov chain Monte Carlo methods.

Neuroscientists have become increasingly interested in exploring dynamic relationships among brain regions. Such a relationship, when directed from one region toward another, is denoted by “effective connectivity.” An fMRI experimental paradigm which is well-suited for examination of effective connectivity is the slow event-related design. This design presents stimuli at sufficient temporal spacing for determining within-trial trajectories of BOLD activation, allowing for the analysis of stimulus-locked temporal covariation of brain responses in multiple regions. This may be especially important for emotional stimuli processing, which can evolve over the course of several seconds, if not longer. However, while several methods have been devised for determining fMRI effective connectivity, few are adapted to event-related designs, which include nonstationary BOLD responses and multiple levels of nesting. We propose a model tailored for exploring effective connectivity of multiple brain regions in event-related fMRI designs — a semi-parametric adaptation of vector autoregressive (VAR) models, termed “stimulus-locked VAR” (SloVAR). Connectivity coefficients vary as a function of time relative to stimulus onset, are regularized via basis expansions, and vary randomly across subjects. SloVAR obtains flexible, data-driven estimates of effective connectivity and hence is useful for building connectivity models when prior information on dynamic regional relationships is sparse. Indices derived from the coefficient estimates can also be used to relate effective connectivity estimates to behavioral or clinical measures. We demonstrate the SloVAR model on a sample of clinically depressed and normal controls, showing that early but not late cortico–amygdala connectivity appears crucial to emotional control and early but not late cortico–cortico connectivity predicts depression severity in the depressed group, relationships that would have been missed in a more traditional VAR analysis.

Although psychological stress has been associated with disturbed sleep in younger populations, little is known about the stress-sleep relationship in late life. In the present study, we evaluated relationships among a chronic stressor, ongoing financial strain, and sleep in a heterogenous sample (n = 75) of community-dwelling elders (mean age = 74.0 years). Self-report measures included ongoing financial strain, mental health, physical health and subjective sleep quality. Sleep duration, continuity, and architecture were measured by polysomnography (PSG). Analysis of variance and regression were used to test the hypothesis that ongoing financial strain is a significant correlate of disturbed sleep in the elderly. Covariates included age, sex, mental health and physical health. Analyses revealed that ongoing financial strain is a significant correlate of PSG-assessed sleep latency, wakefulness after sleep onset, and sleep efficiency. After adjusting for the effects of age, sex, mental health, and physical health on sleep, ongoing financial strain was associated with lower sleep efficiency (p

A laboratory study of sleep and circadian rhythms was undertaken in 28 spousally bereaved seniors (> or =60 yrs) at least four months after the loss event. Measures taken included two nights of polysomnography (second night used), approximately 36 h of continuous core body temperature monitoring, and four assessments of mood and alertness throughout a day. Preceding the laboratory study, two-week diaries were completed, allowing the assessment of lifestyle regularity using the 17-item Social Rhythm Metric (SRM) and the timing of sleep using the Pittsburgh Sleep Diary (PghSD). Also completed were questionnaires assessing level of grief (Texas Revised Inventory of Grief [TRIG] and Index of Complicated Grief [ICG]), subjective sleep quality (Pittsburgh Sleep Quality Index [PSQI]), morningness-eveningness (Composite Scale of Morningness [CSM]), and clinical interview yielding a Hamilton Depression Rating Scale (HDRS) score. Grief was still present, as indicated by an average TRIG score of about 60. On average, the bereaved seniors habitually slept between approximately 23:00 and approximately 06:40 h, achieving approximately 6 h of sleep with a sleep efficiency of approximately 80%. They took about 30 min to fall asleep, and had their first REM episode after 75 min. About 20% of their sleep was in Stage REM, and about 3% in Stages 3 or 4 (slow wave sleep). Their mean PSQI score was 6.4. Their circadian temperature rhythms showed the usual classic shape with a trough at approximately 01:00 h, a fairly steep rise through the morning hours, and a more gradual rise to mid-evening, with an amplitude of approximately 0.8 degrees C. In terms of lifestyle regularity, the mean regularity (SRM) score was 3.65 (slightly lower than that usually seen in seniors). Mood and alertness showed time-of-day variation with peak alertness in the late morning and peak mood in the afternoon. Correlations between outcome sleep/circadian variables and level of grief (TRIG score) were calculated; there was a slight trend for higher grief to be associated with less time spent asleep (p=0.07) and reduced alertness at 20:00 h (p=0.05). Depression score was not correlated with TRIG score (p>0.20). When subjects were divided into groups by the nature of their late spouse's death (expected/after a long-term chronic illness [n=18] versus unexpected [n=10]), no differences emerged in any of the variables. In conclusion, when studied at least four months after the loss event, there appears to be some sleep disruption in spousally bereaved seniors. However, this disruption does not appear to be due to bereavement-related disruptions in the circadian system.

Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn’s disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of pervasive small but replicating effects in CD and SZ on genomic control and power. Finally, we conclude that, despite having very similar estimates of variance explained by genotyped SNPs, CD and SZ have a broadly dissimilar genetic architecture, due to differing mean effect size and proportion of non-null loci., Author Summary We describe in detail the implications of a particular mixture model (a scale mixture of two normals) for effect size distributions from genome-wide genotyping data. Parameters from this model can be used for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, power for detecting non-null loci, and proportion of variance explained from additive effects. Here, we fit this model by minimizing discrepancies with nonparametric estimates from a resampling-based algorithm. We examine the effects of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We validate this approach using meta-analysis test statistics (“z-scores”) from two large GWAS, one for Crohn’s disease and the other for schizophrenia. We demonstrate that for these studies a scale mixture of two normal distributions generally fits empirical replication effect sizes well, providing an excellent fit for the schizophrenia effect sizes but underestimating the tails of the distribution for Crohn’s disease.

© 2015, Alcohol Research Documentation Inc. All rights reserved. Objective: During adolescence, neurobiological maturation occurs concurrently with social and interpersonal changes, including the initiation of alcohol and other substance use. The National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) is designed to disentangle the complex relationships between onset, escalation, and desistance of alcohol use and changes in neurocognitive functioning and neuromaturation. Method: A sample of 831 youth, ages 12-21 years, was recruited at five sites across the United States, oversampling those at risk for alcohol use problems. Most (83%) had limited or no history of alcohol or other drug use, and a smaller portion (17%) exceeded drinking thresholds. A comprehensive assessment of biological development, family background, psychiatric symptomatology, and neuropsychological functioning-in addition to anatomical, diffusion, and functional brain magnetic resonance imaging-was completed at baseline. Results: The NCANDA sample of youth is nationally representative of sex and racial/ethnic groups. More than 50% have at least one risk characteristic for subsequent heavy drinking (e.g., family history, internalizing or externalizing symptoms). As expected, those who exceeded drinking thresholds (n = 139) differ from those who did not (n = 692) on identified factors associated with early alcohol use and problems. Conclusions: NCANDA successfully recruited a large sample of adolescents and comprehensively assessed psychosocial functioning across multiple domains. Based on the sample’s risk profile, NCANDA is well positioned to capture the transition into drinking and alcohol problems in a large portion of the cohort, as well as to help disentangle the associations between alcohol use, neurobiological maturation, and neurocognitive development and functioning.

Several lines of evidence suggest that genome-wide association studies (GWAS) have the potential to explain more of the “missing heritability” of common complex phenotypes. However, reliable methods to identify a larger proportion of single nucleotide polymorphisms (SNPs) that impact disease risk are currently lacking. Here, we use a genetic pleiotropy-informed conditional false discovery rate (FDR) method on GWAS summary statistics data to identify new loci associated with schizophrenia (SCZ) and bipolar disorders (BD), two highly heritable disorders with significant missing heritability. Epidemiological and clinical evidence suggest similar disease characteristics and overlapping genes between SCZ and BD. Here, we computed conditional Q–Q curves of data from the Psychiatric Genome Consortium (SCZ; n = 9,379 cases and n = 7,736 controls; BD: n = 6,990 cases and n = 4,820 controls) to show enrichment of SNPs associated with SCZ as a function of association with BD and vice versa with a corresponding reduction in FDR. Applying the conditional FDR method, we identified 58 loci associated with SCZ and 35 loci associated with BD below the conditional FDR level of 0.05. Of these, 14 loci were associated with both SCZ and BD (conjunction FDR). Together, these findings show the feasibility of genetic pleiotropy-informed methods to improve gene discovery in SCZ and BD and indicate overlapping genetic mechanisms between these two disorders., Author Summary Genome-wide association studies (GWAS) have thus far identified only a small fraction of the heritability of common complex disorders, such as severe mental disorders. We used a conditional false discovery rate approach for analysis of GWAS data, exploiting “genetic pleiotropy” to increase discovery of common gene variants associated with schizophrenia and bipolar disorders. Leveraging the increased power from combining GWAS of two associated phenotypes, we found a striking overlap in polygenic signals, allowing for the discovery of several new common gene variants associated with bipolar disorder and schizophrenia that were not identified in the original analysis using traditional GWAS methods. Some of the gene variants have been identified in other studies with large targeted replication samples, validating the present findings. Our pleiotropy-informed method may be of significant importance for detecting effects that are below the traditional genome-wide significance level in GWAS, particularly in highly polygenic, complex phenotypes, such as schizophrenia and bipolar disorder, where most of the genetic signal is missing (i.e., “missing heritability”). The findings also offer insights into mechanistic relationships between bipolar disorder and schizophrenia pathogenesis.

Rationale: Coronary artery disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes. Objective: We aimed to improve discovery of CAD genes and inform the pathogenic relationship between CAD and several cardiovascular disease risk factors using a shared polygenic signal–informed statistical framework. Methods and Results: Using genome-wide association studies summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate methodology, we systematically investigated genetic overlap between CAD and 8 traits related to cardiovascular disease risk factors: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. We found significant enrichment of single-nucleotide polymorphisms associated with CAD as a function of their association with low-density lipoprotein, high-density lipoprotein, triglycerides, type 2 diabetes mellitus, C-reactive protein, body mass index, systolic blood pressure, and type 1 diabetes mellitus. Applying the conditional false discovery rate method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional false discovery rate Conclusions: The observed polygenic overlap between CAD and cardiometabolic risk factors indicates a pathogenic relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD.

© 2016 Elsevier Inc. Neurodevelopment continues through adolescence, with notable maturation of white matter tracts comprising regional fiber systems progressing at different rates. To identify factors that could contribute to regional differences in white matter microstructure development, large samples of youth spanning adolescence to young adulthood are essential to parse these factors. Recruitment of adequate samples generally relies on multi-site consortia but comes with the challenge of merging data acquired on different platforms. In the current study, diffusion tensor imaging (DTI) data were acquired on GE and Siemens systems through the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a multi-site study designed to track the trajectories of regional brain development during a time of high risk for initiating alcohol consumption. This cross-sectional analysis reports baseline Tract-Based Spatial Statistic (TBSS) of regional fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (L1), and radial diffusivity (LT) from the five consortium sites on 671 adolescents who met no/low alcohol or drug consumption criteria and 132 adolescents with a history of exceeding consumption criteria. Harmonization of DTI metrics across manufacturers entailed the use of human-phantom data, acquired multiple times on each of three non-NCANDA participants at each site's MR system, to determine a manufacturer-specific correction factor. Application of the correction factor derived from human phantom data measured on MR systems from different manufacturers reduced the standard deviation of the DTI metrics for FA by almost a half, enabling harmonization of data that would have otherwise carried systematic error. Permutation testing supported the hypothesis of higher FA and lower diffusivity measures in older adolescents and indicated that, overall, the FA, MD, and L1 of the boys were higher than those of the girls, suggesting continued microstructural development notable in the boys. The contribution of demographic and clinical differences to DTI metrics was assessed with General Additive Models (GAM) testing for age, sex, and ethnicity differences in regional skeleton mean values. The results supported the primary study hypothesis that FA skeleton mean values in the no/low-drinking group were highest at different ages. When differences in intracranial volume were covaried, FA skeleton mean reached a maximum at younger ages in girls than boys and varied in magnitude with ethnicity. Our results, however, did not support the hypothesis that youth who exceeded exposure criteria would have lower FA or higher diffusivity measures than the no/low-drinking group; detecting the effects of excessive alcohol consumption during adolescence on DTI metrics may require longitudinal study.

© 2015, Springer-Verlag Berlin Heidelberg. Anxiety is a risk factor for many adverse neuropsychiatric and socioeconomic outcomes, and has been linked to functional and structural changes in the ventromedial prefrontal cortex (VMPFC). However, the nature of these differences, as well as how they develop in children and adolescents, remains poorly understood. More effective interventions to minimize the negative consequences of anxiety require better understanding of its neurobiology in children. Recent research suggests that structural imaging studies may benefit from clearly delineating between cortical surface area and thickness when examining these associations, as these distinct cortical phenotypes are influenced by different cellular mechanisms and genetic factors. The present study examined relationships between cortical surface area and thickness of the VMPFC and a self-report measure of anxiety (SCARED-R) in 287 youths aged 7–20 years from the Pediatric Imaging, Neurocognition, and Genetics (PING) study. Age and gender interactions were examined for significant associations in order to test for developmental differences. Cortical surface area and thickness were also examined simultaneously to determine whether they contribute independently to the prediction of anxiety. Anxiety was negatively associated with relative cortical surface area of the VMPFC as well as with global cortical thickness, but these associations diminished with age. The two cortical phenotypes contributed additively to the prediction of anxiety. These findings suggest that higher anxiety in children may be characterized by both delayed expansion of the VMPFC and an altered trajectory of global cortical thinning. Further longitudinal studies will be needed to confirm these findings.

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity.

© 2015 Andreassen et al. Epidemiological studies suggest a relationship between blood lipids and immune-mediated diseases, but the nature of these associations is not well understood. We used genome-wide association studies (GWAS) to investigate shared single nucleotide polymorphisms (SNPs) between blood lipids and immune-mediated diseases. We analyzed data fromGWAS (n∼200,000 individuals), applying new False Discovery Rate (FDR) methods, to investigate genetic overlap between blood lipid levels [triglycerides (TG), low density lipoproteins (LDL), high density lipoproteins (HDL)] and a selection of archetypal immune-mediated diseases (Crohn's disease, ulcerative colitis, rheumatoid arthritis, type 1 diabetes, celiac disease, psoriasis and sarcoidosis). We found significant polygenic pleiotropy between the blood lipids and all the investigated immune-mediated diseases.We discovered several shared risk loci between the immune-mediated diseases and TG (n = 88), LDL (n = 87) and HDL (n = 52). Three-way analyses differentiated the pattern of pleiotropy among the immune-mediated diseases. The new pleiotropic loci increased the number of functional gene network nodes representing blood lipid loci by 40%. Pathway analyses implicated several novel shared mechanisms for immune pathogenesis and lipid biology, including glycosphingolipid synthesis (e.g. FUT2) and intestinal host-microbe interactions (e.g. ATG16L1). We demonstrate a shared genetic basis for blood lipids and immune-mediated diseases independent of environmental factors. Our findings provide novel mechanistic insights into dyslipidemia and im-mune-mediated diseases and may have implications for therapeutic trials involving lipidlowering and anti-inflammatory agents.

Background Why schizophrenia has accompanied humans throughout our history despite its negative effect on fitness remains an evolutionary enigma. It is proposed that schizophrenia is a by-product of the complex evolution of the human brain and a compromise for humans' language, creative thinking, and cognitive abilities. Methods We analyzed recent large genome-wide association studies of schizophrenia and a range of other human phenotypes (anthropometric measures, cardiovascular disease risk factors, immune-mediated diseases) using a statistical framework that draws on polygenic architecture and ancillary information on genetic variants. We used information from the evolutionary proxy measure called the Neanderthal selective sweep (NSS) score. Results Gene loci associated with schizophrenia are significantly ( p = 7.30 × 10 −9 ) more prevalent in genomic regions that are likely to have undergone recent positive selection in humans (i.e., with a low NSS score). Variants in brain-related genes with a low NSS score confer significantly higher susceptibility than variants in other brain-related genes. The enrichment is strongest for schizophrenia, but we cannot rule out enrichment for other phenotypes. The false discovery rate conditional on the evolutionary proxy points to 27 candidate schizophrenia susceptibility loci, 12 of which are associated with schizophrenia and other psychiatric disorders or linked to brain development. Conclusions Our results suggest that there is a polygenic overlap between schizophrenia and NSS score, a marker of human evolution, which is in line with the hypothesis that the persistence of schizophrenia is related to the evolutionary process of becoming human.

© 2014 Elsevier Inc. The phenotypic and genetic relationship between global cortical size and general cognitive ability (GCA) appears to be driven by surface area (SA) and not cortical thickness (CT). Gyrification (cortical folding) is an important property of the cortex that helps to increase SA within a finite space, and may also improve connectivity by reducing distance between regions. Hence, gyrification may be what underlies the SA-GCA relationship. In previous phenotypic studies, a 3-dimensional gyrification index (3DGI) has been positively associated with cognitive ability and negatively associated with mild cognitive impairment, Alzheimer's disease, and psychiatric disorders affecting cognition. However, the differential genetic associations of 3DGI and SA with GCA are still unclear. We examined the heritability of 3DGI, and the phenotypic, genetic, and environmental associations of 3DGI with SA and GCA in a large sample of adult male twins (N. = 512). Nearly 85% of the variance in 3DGI was due to genes, and 3DGI had a strong phenotypic and genetic association with SA. Both 3DGI and total SA had positive phenotypic correlations with GCA. However, the SA-GCA correlation remained significant after controlling for 3DGI, but not the other way around. There was also significant genetic covariance between SA and GCA, but not between 3DGI and GCA. Thus, despite the phenotypic and genetic associations between 3DGI and SA, our results do not support the hypothesis that gyrification underlies the association between SA and GCA.

Background— Epidemiological findings suggest a relationship between Alzheimer disease (AD), inflammation, and dyslipidemia, although the nature of this relationship is not well understood. We investigated whether this phenotypic association arises from a shared genetic basis. Methods and Results— Using summary statistics ( P values and odds ratios) from genome-wide association studies of >200 000 individuals, we investigated overlap in single-nucleotide polymorphisms associated with clinically diagnosed AD and C-reactive protein (CRP), triglycerides, and high- and low-density lipoprotein levels. We found up to 50-fold enrichment of AD single-nucleotide polymorphisms for different levels of association with C-reactive protein, low-density lipoprotein, high-density lipoprotein, and triglyceride single-nucleotide polymorphisms using a false discovery rate threshold HS3ST1 ; odds ratio=1.07; 95% confidence interval=1.05–1.11; P =2.86×10 −8 ) and chromosome 10 (rs7920721; closest gene, ECHDC3 ; odds ratio=1.07; 95% confidence interval=1.04–1.11; P =3.38×10 −8 ). We also found that gene expression of HS3ST1 and ECHDC3 was altered in AD brains compared with control brains. Conclusions— We demonstrate genetic overlap between AD, C-reactive protein, and plasma lipids. By conditioning on the genetic association with the cardiovascular phenotypes, we identify novel AD susceptibility loci, including 2 genome-wide significant variants conferring increased risk for AD.

There is a growing realization that early life influences have lasting impact on brain function and structure. Recent research has demon- strated that genetic relationships in adults can be used to parcellate the cortex into regions of maximal shared genetic influence, and a major hypothesis is that genetically progr ammed neurodevel opmental events cause a lasting impact on the organization of the cerebral cortex observable decades later. Here we tested how developmental and lifespan changes in cortical thickness fit the underlying genetic organi- zational principles of co rtical thickness in a longitudinal sample of 974 participants between 4.1 and 88.5 y of age with a total of 1,633 scans, including 773 scans from children below 12 y. Genetic clustering of cortical thickness was based on an independent dataset of 406 adult twins. Developmental and adult age- related changes in cortical thick- ness followed closely the genetic organization of the cerebral cortex, with change rates varying as a functi on of genetic similarity between regions. Cortical regions with overlapping genetic architecture showed correlated development al and adult age change trajectories and vice versa for regions with low genetic overlap. Thus, effects of genes on regional variations in cortical thickness in middle age can be traced to regional differences in neurodevelopmental change rates and extrap- olated to further adult aging-related cortical thinning. This finding suggests that genetic factors contribute to cortical changes through life and calls for a lifespan perspective i n research aimed at identifying the genetic and environmental determinants of cortical development and aging. © National Academy of Sciences. This is the authors’ accepted and refereed manuscript to the article.

The aim of this study was to evaluate the role of transabdominal amnioinfusion in relieving oligohydramnios and improving pregnancy outcome.Pregnant women with oligohydramnios amniotic fluid index (AFI5) and premature rupture of membranes (PROM) from27 weeks gestation were managed with serial transabdominal amnioinfusions. Under ultrasonic guidance, a 20-gauge needle was instilled in the uterine cavity and normal saline was infused until the AFI was normal. Repeat amnioinfusion was done weekly if oligohydramnios recurred. Amnioinfused cases were compared to cases with pPROM and oligohydramnios who had standard management.The mean gestational age at first procedure was 22 weeks. The mean pre-procedure AFI was 1.1cm and post-procedure was 12 cm. The mean number of infusions was 2.4. The mean first infusion to delivery interval was 33 days. The amnioinfused group when compared to the control group had decreased perinatal mortality of 33% versus 83% (P=0.036, OR=0.4, 95% CI=0.17-0.93), neonatal mortality of 17% versus 71% (P=0.049, OR=0.26, 95% CI=0.07-0.97) and neonatal sepsis 86% versus 27%(P=0.049, OR=0.32, 95% CI=0.12-0.87) with no statistical difference in gestational age at rupture and delivery nor birthweight. Babies discharged home compared to non-survivors had a significant increase in gestational age at delivery, birthweight, NICU days and transabdominal amnioinfusions. Logistic regression showed that only transabdominal amnioinfusion and gestational age correlated with survival.In selected cases of oligohydramnios with pPROM, transabdominal amnioinfusions may be associated with fluid retention and improved neonatal survival.

© 2017 American Medical Association. All rights reserved. IMPORTANCE: Schizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction. OBJECTIVE: To identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains. DESIGN, SETTING, AND PARTICIPANTS: Combining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888). MAIN OUTCOMES AND MEASURES: Genetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined. RESULTS: Among the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.2), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10−7), general cognitive function (z score, –4.43; P = 9.42 × 10−6), and verbal-numerical reasoning (z score, –5.43; P = 5.64 × 10−8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain. CONCLUSIONS AND RELEVANCE: The discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.

© 2017 Srinivasan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Schizophrenia is suggested to be a by-product of the evolution in humans, a compromise for our language, creative thinking and cognitive abilities, and thus, essentially, a human disorder. The time of its origin during the course of human evolution remains unclear. Here we investigate several markers of early human evolution and their relationship to the genetic risk of schizophrenia. We tested the schizophrenia evolutionary hypothesis by analyzing genome-wide association studies of schizophrenia and other human phenotypes in a statistical framework suited for polygenic architectures. We analyzed evolutionary proxy measures: human accelerated regions, segmental duplications, and ohnologs, representing various time periods of human evolution for overlap with the human genomic loci associated with schizophrenia. Polygenic enrichment plots suggest a higher prevalence of schizophrenia associations in human accelerated regions, segmental duplications and ohnologs. However, the enrichment is mostly accounted for by linkage disequilibrium, especially with functional elements like introns and untranslated regions. Our results did not provide clear evidence that markers of early human evolution are more likely associated with schizophrenia. While SNPs associated with schizophrenia are enriched in HAR, Ohno and SD regions, the enrichment seems to be mediated by affiliation to known genomic enrichment categories. Taken together with previous results, these findings suggest that schizophrenia risk may have mainly developed more recently in human evolution.

Background In Genome-wide association studies (GWASs), biologically determined subsets of the single nucleotide polymorphisms (SNPs) can exhibit an increased proportion of extreme statistics compared to the whole set of SNPs. The excess of extreme statistics—which we refer to as "enrichment"—provides a means to improve gene discovery. This approach has proven valuable for polygenic traits like schizophrenia, for which pleiotropy [O. Andreassen et al. PLoS Genet. 9.4, e1003455 (2013)] and annotation categories in the genome [A. Schork et al. PLoS Genet. 9.4, e1003449 (2013)], successfully have been used as selection criteria. The degree of enrichment is, however, challenging to quantify; current enrichment claims are mainly based on visual inspection of QQ plots or fold enrichment plots. Methods We outline here a Bayesian method to quantify the degree of enrichment, based on a mixture model where each SNP is either null or non-null. What sets this apart from previous work, is that we incorporate the z-score covariance matrix for the multivariate (global) null distribution into the model, thereby accounting for the linkage disequilibrium dependence. By assuming that the z-scores with low absolute value are dominated by the null distribution, we can express the posterior distribution for the proportion of non-null SNPs in terms of the multivariate null distribution only, avoiding more specific assumptions about the unknown non-null distribution. We calculate this posterior distribution for the selected subsets of SNPs and its compliment, and quantify the level of enrichment by comparing the two. Results The method is tested on synthetic data, and shows promising results. We further compare the method to other approaches, like permutation tests. Finally, we show how this method can be applied to map out enrichment of SNP subsets for different psychiatric phenotypes. Discussion The proposed method seems to be a promising tool for quantifying enrichment in the context of GWAS summary statistics.

Background Psychosocial interventions for bipolar disorder are frequently unavailable and resource intensive. Mobile technology may improve access to evidence-based interventions and may increase their efficacy. We evaluated the feasibility, acceptability and efficacy of an augmentative mobile ecological momentary intervention targeting self-management of mood symptoms. Methods This was a randomized single-blind controlled trial with 82 consumers diagnosed with bipolar disorder who completed a four-session psychoeducational intervention and were assigned to 10 weeks of either: 1) mobile device delivered interactive intervention linking patient-reported mood states with personalized self-management strategies, or 2) paper-and-pencil mood monitoring. Participants were assessed at baseline, 6 weeks (mid-point), 12 weeks (post-treatment), and 24 weeks (follow up) with clinician-rated depression and mania scales and self-reported functioning. Results Retention at 12 weeks was 93% and both conditions were associated with high satisfaction. Compared to the paper-and-pencil condition, participants in the augmented mobile intervention condition showed significantly greater reductions in depressive symptoms at 6 and 12 weeks (Cohen׳s d for both were d =0.48). However, these effects were not maintained at 24-weeks follow up. Conditions did not differ significantly in the impact on manic symptoms or functional impairment. Limitations This was not a definitive trial and was not powered to detect moderators and mediators. Conclusions Automated mobile-phone intervention is feasible, acceptable, and may enhance the impact of brief psychoeducation on depressive symptoms in bipolar disorder. However, sustainment of gains from symptom self-management mobile interventions, once stopped, may be limited.

Psychological and psychosocial resources, including resilience and social support, have traditionally been studied in the context of the stress paradigm and, more recently, in the context of successful aging. This study used moderated mediation analyses to examine the role of perceived stress in the relationships between physical and mental health functioning and self-rated successful aging (SRSA) and whether differences between people in level of resilience and social support changes the role of perceived stress in these relationships. A cross-sectional study of 1,006 older adults (mean age: 77 years) completed scales addressing SRSA, physical and mental health functioning, perceived stress, resilience, and social support. Results indicated that the strength of relationships between both physical and mental health functioning and SRSA were reduced after accounting for variation in level of perceived stress. The role of perceived stress in the association between mental health functioning and SRSA was found to be stronger among participants with the highest levels of resilience, and the influence of perceived stress on the degree of relationship between physical health functioning and SRSA was stronger among those with greatest social support. These findings suggest that interventions to reduce perceived stress may help break the link between disability and poor well-being in older adults. The findings further suggest that the impact of such interventions might differ depending on psychological resources (i.e., resilience) for mental health disabilities and external resources (i.e., social support) for those with physical health problems. The complex interplay of these factors should be taken into account in clinical settings.

Motivation: Genome-wide association studies (GWAS) have largely failed to identify most of the genetic basis of highly heritable diseases and complex traits. Recent work has suggested this could be because many genetic variants, each with individually small effects, compose their genetic architecture, limiting the power of GWAS, given currently obtainable sample sizes. In this scenario, Bonferroni-derived thresholds are severely underpowered to detect the vast majority of associations. Local false discovery rate (fdr) methods provide more power to detect non-null associations, but implicit assumptions about the exchangeability of single nucleotide polymorphisms (SNPs) limit their ability to discover non-null loci. Methods: We propose a novel covariate-modulated local false discovery rate (cmfdr) that incorporates prior information about gene element–based functional annotations of SNPs, so that SNPs from categories enriched for non-null associations have a lower fdr for a given value of a test statistic than SNPs in unenriched categories. This readjustment of fdr based on functional annotations is achieved empirically by fitting a covariate-modulated parametric two-group mixture model. The proposed cmfdr methodology is applied to a large Crohn’s disease GWAS. Results: Use of cmfdr dramatically improves power, e.g. increasing the number of loci declared significant at the 0.05 fdr level by a factor of 5.4. We also demonstrate that SNPs were declared significant using cmfdr compared with usual fdr replicate in much higher numbers, while maintaining similar replication rates for a given fdr cutoff in de novo samples, using the eight Crohn’s disease substudies as independent training and test datasets. Availability an implementation: https://sites.google.com/site/covmodfdr/ Contact : wes.stat@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

High frequency data collection in a participant's usual environment [ecological momentary assessment (EMA)] offers a promising approach for understanding, and potentially intervening on, precursors of clinically important states and behaviors in psychiatric disorders such as suicide (1–3). An estimated 25–50% of those with bipolar disorder will attempt suicide in their lifetime (4), and evidence suggests that suicide attempts of individuals with bipolar disorder are also associated with greater lethality or suicide potential compared to unipolar depression (5). Suicidal ideation is a reliable risk factor for death by suicide and suicide attempt incidence across psychiatric disorders (6). However, the temporal course of precursors to suicidal ideation in individuals with bipolar disorder is poorly understood (7). In particular, it is unclear what symptoms or states may place persons at elevated risk for suicidal ideation and the earliest that meaningful changes in these precursors may be detected. Although some surges in suicidal ideation may be impulsive and without precedent, it is likely that, for many patients with bipolar disorder, changes in mood states or comorbidities predict later emergence of suicidal ideation (8). A goal of some recent studies employing EMA data collection methodology is to intensively assess changes in potential risk factors in psychiatric disorders to determine when and how accurately they predict suicidal ideation (9, 10). Statistical prediction models are well suited for application to intensively sampled risk factors obtained from EMA data (11–13) because of their utility in discovering patterns of temporal variation antecedent to and predictive of clinical outcomes. An important criterion for evaluating EMA-based prediction models is robust sensitivity and specificity of predictions across heterogeneous samples. Moreover, prediction models should ideally identify risk states (e.g., mood changes) in advance of the observed outcome (e.g., suicidal ideation) for the application of timely preventative interventions. The greater temporal resolution of EMA data and associated prediction models should also provide for greater accuracy and/or earlier detection than would data obtainable from routine intervals, such as in clinic visits or typical longitudinal clinical research studies. There have been a number of studies utilizing EMA data for the prediction of suicidal behaviors over the short term (9, 14–16); however, none have examined prediction of risk in individuals with bipolar disorder. Prediction of suicide risk in patients with bipolar disorder may be particularly challenging; long-term prospective studies indicate substantial heterogeneity in both symptoms (17, 18) and precursors to exacerbations (19, 20). However, negative affect appears to be a common precursor to symptom worsening in mood disorders (21), providing a rationale for using EMA negative affect ratings in statistical prediction models to detect early signs of suicide risk within bipolar disorder specifically. In the present report, we evaluated EMA-derived negative affect from a pilot study of individuals with inter-episode bipolar disorder across an eight-week assessment period. Applying statistical prediction models, we sought to use EMA data to prospectively predict study-emergent suicidal ideation prior to in-person assessments. Specifically, we employed functional linear models (FLMs) (22, 23) which utilize intensively sampled longitudinal data to generate empirically derived, updatable predictions. We hypothesized that use of individual daily affect ratings as predictors in FLMs would provide more accurate individualized predictions of suicidal ideation compared to data from more infrequently scheduled in-person assessments alone.

We respond here to a commentary by Johnson1 on our article entitled ‘Early identification and heritability of mild cognitive impairment’.2 We believe that Johnson missed the point of the article. After criticizing much of the article, her own conclusions simply reiterate one of our main points, namely, that considerable study is still needed before we can be confident about a definition of mild cognitive impairment (MCI). Johnson is experiencing ‘discontent’ about the state of MCI diagnoses, which she says is ‘one messy concept’. She finds our results ‘difficult to interpret’, and wrote that our reported prevalence rates ranging from 2.57% to 64.74% are ‘epidemiologically useless’. She stated that the wide range is due to different thresholds in ‘very arbitrary’ definitions as well as several generic problems inherent in cognitive assessment, one of which is our use of general cognitive ability to gauge decline, because people differ in their specific cognitive strengths and weaknesses. She was also critical of our reported heritability estimates ranging from 0.0 to 0.56, again suggesting that this reflects the imprecision of measurement of MCI. Finally, she disagrees with our suggestion that MCI is an appropriate phenotype for genome-wide association studies. We think our approach and results were quite clear. The wide-ranging prevalence rates make one of the key points of our article, namely, that researchers and clinicians do not yet know what are the best cut points or the best set of tests. To model this lack of consensus on what constitutes impairment, our five criteria sets, based on published research,3 intentionally went from extremely liberal to extremely conservative to reflect the full range of possibilities. Moreover, as we noted, there are similarly wide prevalence ranges across studies of older adults. Given that all current definitions of MCI have high false-positive rates, we also emphasized the point that none of the cross-sectional findings could be sufficient. Rather, we pointed out that determining the optimal criteria set would require longitudinal trajectories, and that it would be valuable to assess people earlier (in midlife) than has been done in almost all previous studies. Regarding the point about using general cognitive ability to gauge decline, neuropsychologists are, of course, acutely aware of individual differences in cognitive profiles. Yet, routinely, estimates of general premorbid ability are used successfully to gauge whether or not a person’s current performance represents a decline. Johnson’s critique ignores the reality that this is the standard of care, and is currently the best that can be done in neuropsychiatric diagnosis and assessment. People still must be evaluated and decisions to treat or not still must be made even when we have less than perfect measurement tools and imprecise diagnoses. Regarding the heritability estimates, Johnson cites Turkheimer’s4 proposition that essentially all human traits measured with some accuracy will be heritable, but as we pointed out, our results challenged previous studies that showed no heritability. Despite the well-known caveats Johnson enumerated about state effects, cognitive tests do consistently have moderate heritability and reasonable test-retest reliability compared with many other phenotypes. Also, the identical set of tests was used in all cases, so measurement precision did not differ across MCI definitions. Rather, as we pointed out, the variability of heritability estimates likely stems from the substantial differences in definitions of impairment. We did not state explicitly that genome-wide association studies should probably wait until there is more of a consensus on optimal MCI definitions, but we do think it was strongly implied by our clear emphasis on the fact that we are some distance from that consensus. Johnson concludes with some recommendations. First she states that we need ‘to come to better agreement on what MCI should represent’, raising questions about how many cognitive domains should be included, how far down the distribution of functioning will meet the criteria, should it represent change in function rather than level, etc? The strongest message of our study, she says, is that MCI research will progress little until these questions are answered.We find it rather puzzling that she writes as if we ignored all of these issues in our article, and she questions the usefulness of our findings. We think that any reasonable reading of our article would indicate that we had already made many of the same points enumerated by Johnson in her commentary, and our analysis examining how heritability estimates vary as a function of different definitions of MCI is clearly germane to understanding what MCI represents.

Clinical research studies generate data that need to be shared and statistically analyzed by their participating institutions. The distributed nature of research and the different domains involved present major challenges to data sharing, exploration, and visualization. The Data Portal infrastructure was developed to support ongoing research in the areas of neurocognition, imaging, and genetics. Researchers benefit from the integration of data sources across domains, the explicit representation of knowledge from domain experts, and user interfaces providing convenient access to project specific data resources and algorithms. The system provides an interactive approach to statistical analysis, data mining, and hypothesis testing over the lifetime of a study and fulfills a mandate of public sharing by integrating data sharing into a system built for active data exploration. The web-based platform removes barriers for research and supports the ongoing exploration of data. © 2014 Bartsch, Thompson, Jernigan and Dale.

Attachment theory has become a key framework for understanding responses to and consequences of trauma across the life course. We predicted that more severe post-traumatic stress (PTS) symptoms at age 37 years would be associated with insecure attachment at age 55 and with worse PTS symptoms 24 years later at age 61, and that age 55 attachment would mediate the influence of earlier PTS symptoms on later symptoms.Data on PTS self-reported symptoms were available for 975 community-dwelling participants from the longitudinal Vietnam Era Twin Study of Aging at ages 37 and 61 years. At age 55, participants completed the Experiences in Close Relationships Inventory, a measure of adult attachment.PTS symptoms at ages 37 and 61 correlated (r = 0.43; p 0.0001). Multiple mediation models found significant direct effects of age 37 PTS symptoms on age 61 PTS symptoms (β = 0.26; 95% confidence interval: 0.19-0.33). Anxious and avoidant attachment at age 55 predicted PTS symptoms at age 61 (r = 0.34 and 0.25; ps 0.0001, respectively) and also significantly mediated PTS symptoms over time, showing that insecure attachment increased PTS severity. Participants with higher age 37 PTS symptoms were more likely to have a history of divorce; marital status did not mediate PTS.Analyses demonstrate the persistence of PTS symptoms from early midlife into early old age. Mediation analyses revealed that one path through which PTS symptoms persisted was indirect: through their influence on attachment insecurity. This study provides insight into ongoing interconnections between psychological and interpersonal responses to stress.

IMPORTANCE: Converging evidence indicates that clusterin, a chaperone glycoprotein, influences Alzheimer disease neurodegeneration. However, the precise role of clusterin in Alzheimer disease pathogenesis is still not well understood. OBJECTIVE: To elucidate the relationship between clusterin, amyloid-β (Aβ), phosphorylated tau (p-tau), and the rate of brain atrophy over time among nondemented older individuals. DESIGN, SETTING, AND PARTICIPANTS: This longitudinal cohort included cognitively normal older participants and individuals with mild cognitive impairment assessed with baseline lumbar puncture and longitudinal structural magnetic resonance imaging. We examined 241 nondemented older individuals from research centers across the United States and Canada (91 participants with a Clinical Dementia Rating score of 0 and 150 individuals with a Clinical Dementia Rating score of 0.5). MAINOUTCOMESANDMEASURES: Using linear mixed-effects models, we investigated interactions between cerebrospinal fluid (CSF) clusterin, CSF Aβ1-42, and CSF p-tau at threonine 181 (p-tau181p) on the atrophy rate of the entorhinal cortex and hippocampus. RESULTS: Across all participants, we found a significant interaction between CSF clusterin and CSF Aβ1-42 on the entorhinal cortex atrophy rate but not on the hippocampal atrophy rate. Cerebrospinal fluid clusterin was associated with the entorhinal cortex atrophy rate among CSF Aβ1-42-positive individuals but not among CSF Aβ1-42-negative individuals. In secondary analyses, we found significant interactions between CSF Aβ1-42 and CSF clusterin, as well as CSF Aβ1-42 and CSF p-tau 181p, on the entorhinal cortex atrophy rate. We found similar results in subgroup analyses within the mild cognitive impairment and cognitively normal cohorts. CONCLUSIONS AND RELEVANCE: In nondemented older individuals, Aβ-associated volume loss occurs in the presence of elevated clusterin. The effect of clusterin on Aβ-associated brain atrophy is not confounded or explained by p-tau. These findings implicate a potentially important role for clusterin in the earliest stages of the Alzheimer disease neurodegenerative process and suggest independent effects of clusterin and p-tau on Aβ-associated volume loss. © Copyright 2014 American Medical Association. All rights reserved.

Genome-wide association studies (GWAS) have identified a large number of gene variants associated with schizophrenia, but these variants explain only a small portion of the heritability. It is becoming increasingly clear that schizophrenia is influenced by many genes, most of which have effects too small to be identified using traditional GWAS statistical methods. By applying recently developed Empirical Bayes statistical approaches, we have demonstrated that functional genic elements show differential contribution to phenotypic variance, with some elements (regulatory regions and exons) showing strong enrichment for association with schizophrenia. Applying related methods, we also showed abundant genetic overlap (pleiotropy) between schizophrenia and other phenotypes, including bipolar disorder, cardiovascular disease risk factors, and multiple sclerosis. We estimated the number of gene variants with effects in schizophrenia and bipolar disorder to be approximately 1.2%. By applying our novel statistical framework, we dramatically improved gene discovery and detected a large number of new gene loci associated with schizophrenia that have not yet been identified with standard GWAS methods. Utilizing independent schizophrenia substudies, we showed that these new loci have high replication rates in de novo samples, indicating that they likely represent true schizophrenia risk genes. The new statistical tools provide a powerful approach for uncovering more of the missing heritability of schizophrenia and other complex disorders. In conclusion, the highly polygenic architecture of schizophrenia strongly suggests the utility of research approaches that recognize schizophrenia neuropathology as a complex dynamic system, with many small gene effects integrated in functional networks. © 2013 The Author.

Objective: The NIH Toolbox Cognition Battery (NTCB) was designed to provide a brief, efficient computerized test of key neuropsychological functions appropriate for use in children as young as 3 years of age. This report describes the performance of a large group of typically developing children and adolescents and examines the impact of age and sociocultural variables on test performance. Method: The NTCB was administered to a sample of 1,020 typically developing males and females ranging in age from 3 to 20 years, diverse in terms of socioeconomic status (SES) and race/ethnicity, as part of the new publicly accessible Pediatric Imaging, Neurocognition, and Genetics (PING) data resource, at 9 sites across the United States. Results: General additive models of nonlinear age-functions were estimated from age-differences in test performance on the 8 NTCB subtests while controlling for family SES and genetic ancestry factors (GAFs). Age accounted for the majority of the variance across all NTCB scores, with additional significant contributions of gender on some measures, and of SES and race/ethnicity (GAFs) on all. After adjusting for age and gender, SES and GAFs explained a substantial proportion of the remaining unexplained variance in Picture Vocabulary scores. Conclusions: The results highlight the sensitivity to developmental effects and efficiency of this new computerized assessment battery for neurodevelopmental research. Limitations are observed in the form of some ceiling effects in older children, some floor effects, particularly on executive function tests in the youngest participants, and evidence for variable measurement sensitivity to cultural/socioeconomic factors. © 2013 American Psychological Association.

Blood pressure is a critical determinant of cardiovascular morbidity and mortality. It is affected by environmental factors, but has a strong heritable component. Despite recent large genome-wide association studies, few genetic risk factors for blood pressure have been identified. Epidemiological studies suggest associations between blood pressure and several diseases and traits, which may partly arise from a shared genetic basis (genetic pleiotropy). Using genome-wide association studies summary statistics and a genetic pleiotropy-informed conditional False Discovery Rate method, we systematically investigated genetic overlap between systolic blood pressure and 12 co-morbid traits and diseases. We found significant ‘enrichment’ of single nucleotide polymorphisms associated with systolic blood pressure as a function of their association with body mass index, low density lipoprotein, waist hip ratio, schizophrenia, bone mineral density, type 1 diabetes and celiac disease. In contrast, the magnitude of enrichment due to shared polygenic effects was smaller with the other phenotypes (triglycerides, high density lipoproteins, type 2 diabetes, rheumatoid arthritis, and height). Applying the conditional False Discovery Rate method to the enriched phenotypes, we identified 62 loci associated with systolic blood pressure (False Discovery Rate < 0.01), including 42 novel loci. The observed polygenic overlap between systolic blood pressure and several related disorders indicates that the epidemiological associations are not mediated solely via lifestyle factors, but also reflect an etiological relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to lipid biology and the immune system in systolic blood pressure.

Backgound: Identifying mild cognitive impairment (MCI) in midlife could improve early identification of Alzheimer's disease (AD). Also, AD is highly heritable, but the heritability of MCI has not been established. We estimated prevalence rates, association with premorbid general cognitive ability (GCA) and heritability for different definitions of neuropsychologically defined MCI in adults in their 50s. Method: We examined 1126 twins aged 51-59 years when recruited into the Vietnam Era Twin Study of Aging (VETSA). Six neurocognitive domains were assessed using tests designed to avoid ceiling effects. To differentiate MCI from low overall ability, criteria included adjustment for GCA measured at approximately age 20 years. Results: As in older adults, prevalence rates varied widely. Among the lower prevalence rates were some definitions of multiple-domain MCI and single-domain amnestic MCI, which may be less likely than other MCI categories to revert to normal on follow-up. Low prevalence rates in middle-aged adults are also more likely to be valid. MCI was also associated with lower premorbid GCA. Heritability estimates for any MCI and amnestic MCI averaged .40-.48. Conclusions: By testing multiple cognitive domains and avoiding ceiling effects, MCI can be identified before age 60 years. Premorbid GCA is a risk/protective factor, but deficits after adjusting for early adult GCA suggest additional processes leading to declining trajectories. Heritabilities were comparable to AD, suggesting MCI as an appropriate phenotype for genetic association studies. Full validation will require follow-up assessments (currently under way). Community-based studies are important for this early identification because adults of this age are unlikely to present in clinics. © The Author 2013; all rights reserved.

Right–left regional cerebral differences are a feature of the human brain linked to functional abilities, aging, and neurodevelopmental and mental disorders. The role of genetic factors in structural asymmetry has been incompletely studied. We analyzed data from 515 individuals (130 monozygotic twin pairs, 97 dizygotic pairs, and 61 unpaired twins) from the Vietnam Era Twin Study of Aging to answer three questions about genetic determinants of brain structural asymmetry: First, does the magnitude of heritability differ for homologous regions in each hemisphere? Despite adequate power to detect regional differences, heritability estimates were not significantly larger in one hemisphere versus the other, except left > right inferior lateral ventricle heritability. Second, do different genetic factors influence left and right hemisphere size in homologous regions? Interhemispheric genetic correlations were high and significant; in only two subcortical regions (pallidum and accumbens) did the estimate statistically differ from 1.0. Thus, there was little evidence for different genetic influences on left and right hemisphere regions. Third, to what extent do genetic factors influence variability in left–right size differences? There was no evidence that variation in asymmetry (i.e., the size difference) of left and right homologous regions was genetically determined, except in pallidum and accumbens. Our findings suggest that genetic factors do not play a significant role in determining individual variation in the degree of regional cortical size asymmetries measured with MRI, although they may do so for volume of some subcortical structures. Despite varying interpretations of existing data, we view the present results as consistent with previous findings.

Compassion is an important contributor to pro-social behavior and maintenance of interpersonal relationships, yet little is known about what factors influence compassion in late life. The aim of this study was to test theories about how past and current stressors and emotional functioning, resilience, and demographic indicators of life experiences are related to compassion among older adults.One thousand and six older adults (50-99 years) completed a comprehensive survey including self-report measures of compassion, resilience, past and present stress, and emotional functioning (i.e., stressful life events, perceived stress, and current and prior depression and anxiety), and demographic information. The sample was randomly split, and exploratory and confirmatory regression analyses were conducted testing hypothesized relationships with compassion.Exploratory stepwise regression analysis (n = 650) indicated that participants who reported higher levels of compassion were more likely to be female, not currently in a married/married-like relationship, reported higher resilience levels, and had experienced more significant life events. Age, income level, past and current mental distress, and interactions between resilience and other predictors were not significantly related to compassion. The associations between greater self-reported compassion and being female, having greater resilience, and having experienced more significant life events were supported by a confirmatory stepwise regression analysis (n = 356).Older women report more compassion than older men. Resilience and significant life events, independently, also appear to facilitate a desire to help others, while current stress and past and present emotional functioning are less relevant. Specificity of findings to older adults is not yet known.

Animal data show that cortical development is initially patterned by genetic gradients largely along three orthogonal axes. We previously reported differences in genetic influences on cortical surface area along an anterior-posterior axis using neuroimaging data of adult human twins. Here, we demonstrate differences in genetic influences on cortical thickness along a dorsal-ventral axis in the same cohort. The phenomenon of orthogonal gradations in cortical organization evident in different structural and functional properties may originate from genetic gradients. Another emerging theme of cortical patterning is that patterns of genetic influences recapitulate the spatial topography of the cortex within hemispheres. The genetic patterning of both cortical thickness and surface area corresponds to cortical functional specializations. Intriguingly, in contrast to broad similarities in genetic patterning, two sets of analyses distinguish cortical thickness and surface area genetically. First, genetic contributions to cortical thickness and surface area are largely distinct; there is very little genetic correlation (i.e., shared genetic influences) between them. Second, organizing principles among genetically defined regions differ between thickness and surface area. Examining the structure of the genetic similarity matrix among clusters revealed that, whereas surface area clusters showed great genetic proximity with clusters from the same lobe, thickness clusters appear to have close genetic relatedness with clusters that have similar maturational timing. The discrepancies are in line with evidence that the two traits follow different mechanisms in neurodevelopment. Our findings highlight the complexity of genetic influences on cortical morphology and provide a glimpse into emerging principles of genetic organization of the cortex.

This paper proposes a semiparametric methodology for modeling multivariate and conditional distributions. We first build a multivariate distribution whose dependence structure is induced by a Gaussian copula and whose marginal distributions are estimated nonparametrically via mixtures of B-spline densities. The conditional distribution of a given variable is obtained in closed form from this multivariate distribution. We take a Bayesian approach, using Markov chain Monte Carlo methods for inference. We study the frequentist properties of the proposed methodology via simulation and apply the method to estimation of conditional densities of summary statistics, used for computing conditional local false discovery rates, from genetic association studies of schizophrenia and cardiovascular disease risk factors.