Personalized mood prediction from patterns of behavior collected with smartphones.

Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R2 ≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

Geospatial investigations in Colombia reveal variations in the distribution of mood and psychotic disorders.

BACKGROUND: Geographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas. METHODS: We constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters. RESULTS: We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval [0.71, 0.89], p = 5.67E-05). We find nine clusters/hotspots of inpatients. CONCLUSIONS: Our results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.

The frequencies of mental disorders vary by geographic region. Investigating such variations may lead to more equitable access to mental healthcare and to scientific discoveries that reveal specific localized factors that contribute to the causes of mental illness. This study examined the frequency of three disorders with a major impact on public health ­ schizophrenia, bipolar disorder, and major depressive disorder ­ by analyzing electronic health records from a hospital providing comprehensive mental health care for a large region in Colombia. We show that individuals receiving outpatient care mainly live relatively near the facility. Those receiving inpatient care live throughout the region, but cluster in a few scattered locations. Future research could lead to strategies for more equitable provision of mental healthcare in Colombia and identify environmental or genetic factors that affect the likelihood that someone will develop one of these disorders.

Predicting diagnostic conversion from major depressive disorder to bipolar disorder: an EHR based study from Colombia.

Bipolar Disorder (BD) is a severe and chronic disorder characterized by recurrent episodes of depression, mania, and/or hypomania. Most BD patients initially present with depressive symptoms, resulting in a delayed diagnosis of BD and poor clinical outcomes. This study leverages electronic health record (EHR) data from the Clínica San Juan de Dios Manizales in Colombia to identify features predictive of the transition from Major Depressive Disorder (MDD) to BD. Analyzing EHR data from 13,607 patients diagnosed with MDD over 15 years, we identified 1,610 cases of conversion to BD. Using a multivariate Cox regression model, we identified severity of the initial MDD episode, the presence of psychosis and hospitalization at first episode, family history of mood or psychotic disorders, female gender to be predictive of the conversion to BD. Additionally, we observed associations with medication classes (prescriptions of mood stabilizers, antipsychotics, and antidepressants) and clinical features (delusions, suicide attempt, suicidal ideation, use of marijuana and alcohol use/abuse) derived from natural language processing (NLP) of clinical notes. Together, these risk factors predicted BD conversion within five years of the initial MDD diagnosis, with a recall of 72% and a precision of 38%. Our study confirms many previously identified risk factors identified through registry-based studies (such as female gender and psychotic depression at the index MDD episode), and identifies novel ones (specifically, suicidal ideation and suicide attempt extracted from clinical notes). These results simultaneously demonstrate the validity of using EHR data for predicting BD conversion as well as underscore its potential for the identification of novel risk factors and improving early diagnosis.

A Novel and Integrated Digitally Supported System of Care for Depression and Anxiety: Findings From an Open Trial.

BACKGROUND: The global burden of anxiety and depression has created an urgent need for scalable approaches to increase access to evidence-based mental health care. The Screening and Treatment for Anxiety and Depression (STAND) system of care was developed to meet this need through the use of internet-connected devices for assessment and provision of treatment. STAND triages to level of care (monitoring only, digital therapy with coaches, digital therapy assisted by clinicians in training, and clinical care) and then continuously monitors symptoms to adapt level of care. Triaging and adaptation are based on symptom severity and suicide risk scores obtained from computerized adaptive testing administered remotely. OBJECTIVE: This article discusses how the STAND system of care improves upon current clinical paradigms, and presents preliminary data on feasibility, acceptability, and effectiveness of STAND in a sample of US-based university students. METHODS: US-based university students were recruited and enrolled in an open trial of the STAND system of care. Participants were triaged based on initial symptom severity derived from a computerized adaptive test and monitored over 40 weeks on anxiety, depression, and suicide risk to inform treatment adaptation and evaluate preliminary effectiveness. RESULTS: Nearly 5000 students were screened and 516 received care. Depression and anxiety severity scores improved across all tiers (P<.001 in all cases). Suicide risk severity improved in the highest tier (ie, clinical care; P<.001). Acceptability and feasibility were demonstrated. CONCLUSIONS: STAND is a feasible and acceptable model of care that can reach large numbers of individuals. STAND showed preliminary effectiveness on all primary outcome measures. Current directions to improve STAND are described.

Country-level gender inequality is associated with structural differences in the brains of women and men.

Gender inequality across the world has been associated with a higher risk to mental health problems and lower academic achievement in women compared to men. We also know that the brain is shaped by nurturing and adverse socio-environmental experiences. Therefore, unequal exposure to harsher conditions for women compared to men in gender-unequal countries might be reflected in differences in their brain structure, and this could be the neural mechanism partly explaining women's worse outcomes in gender-unequal countries. We examined this through a random-effects meta-analysis on cortical thickness and surface area differences between adult healthy men and women, including a meta-regression in which country-level gender inequality acted as an explanatory variable for the observed differences. A total of 139 samples from 29 different countries, totaling 7,876 MRI scans, were included. Thickness of the right hemisphere, and particularly the right caudal anterior cingulate, right medial orbitofrontal, and left lateral occipital cortex, presented no differences or even thicker regional cortices in women compared to men in gender-equal countries, reversing to thinner cortices in countries with greater gender inequality. These results point to the potentially hazardous effect of gender inequality on women's brains and provide initial evidence for neuroscience-informed policies for gender equality.

Reducing policing in mental health crises: A vision for university campuses.

University campuses could become leaders in developing alternatives to policing for managing public health and safety, yet, nearly all campuses rely on campus or local police to respond to mental health emergencies. Herein, we present the available evidence for campus mobile crisis intervention teams (MCITs) as an alternative to policing, consider what colleges and universities can learn from existing community MCIT models, and propose initial steps for the development and implementation of a campus MCIT.

Evidence of selection in the uncoupling protein 1 gene region suggests local adaptation to solar irradiance in savannah monkeys (Chlorocebus spp.).

In the last 300 thousand years, the genus Chlorocebus expanded from equatorial Africa into the southernmost latitudes of the continent, where colder climate was a probable driver of natural selection. We investigated population-level genetic variation in the mitochondrial uncoupling protein 1 (UCP1) gene region-implicated in non-shivering thermogenesis (NST)-in 73 wild savannah monkeys from three taxa representing this southern expansion (Chlorocebus pygerythrus hilgerti, Chlorocebus cynosuros and Chlorocebus pygerythrus pygerythrus) ranging from Kenya to South Africa. We found 17 single nucleotide polymorphisms with extended haplotype homozygosity consistent with positive selective sweeps, 10 of which show no significant linkage disequilibrium with each other. Phylogenetic generalized least-squares modelling with ecological covariates suggest that most derived allele frequencies are significantly associated with solar irradiance and winter precipitation, rather than overall low temperatures. This selection and association with irradiance is demonstrated by a relatively isolated population in the southern coastal belt of South Africa. We suggest that sunbathing behaviours common to savannah monkeys, in combination with the strength of solar irradiance, may mediate adaptations to thermal stress via NST among savannah monkeys. The variants we discovered all lie in non-coding regions, some with previously documented regulatory functions, calling for further validation and research.

Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk.

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

The enduring gap in educational attainment in schizophrenia according to the past 50 years of published research: a systematic review and meta-analysis.

BACKGROUND: Educational attainment is associated with wellbeing and health, but patients with schizophrenia achieve lower levels of education than people without. Several effective interventions can ameliorate this situation. However, the magnitude of the education gap in schizophrenia and its change over time are unclear. We aimed to reconstruct the trajectories of educational attainment in patients with schizophrenia and, if reported, their healthy comparator controls. METHODS: We did a systematic review and meta-analysis including all studies reporting on patients with schizophrenia (of mean age ≥18 years) and describing the number of years of education of the participants, with or without healthy controls. There were no other design constraints on studies. We excluded studies that included only patients with other schizophrenia spectrum disorders and studies that did not specify the number of years of education of the participants. 22 reviewers participated in retrieving data from a search in PubMed and PsycINFO (Jan 1, 1970, to Nov 24, 2020). We estimated the birth date of participants from their mean age and publication date, and meta-analysed these data using random-effects models, focusing on educational attainment, the education gap, and changes over time. The primary outcome was years of education. The protocol was registered on PROSPERO (CRD42020220546). FINDINGS: From 32 593 initial references, we included 3321 studies reporting on 318 632 patients alongside 138 675 healthy controls (170 941 women and 275 821 men from studies describing sex or gender; data on ethnicity were not collected). Patients' educational attainment increased over time, mirroring that of controls. However, patients with schizophrenia in high-income countries had 19 months less education than controls (-1·59 years, 95% CI -1·66 to -1·53; p<0·0001), which is equivalent to a Cohen's d of -0·56 (95% CI -0·58 to -0·54) and implies an odds ratio of 2·58 for not completing 12 years of education (ie, not completing secondary education) for patients compared with controls. This gap remained stable throughout the decades; the rate of change in number of total years of education in time was not significant (annual change: 0·0047 years, 95% CI -0·0005 to 0·0099; p=0·078). For patients in low-income and middle-income countries, the education gap was significantly smaller than in high-income countries (smaller by 0·72 years, 0·85 to 0·59; p<0·0001), yet there was evidence that this gap was widening over the years, approaching that of high-income countries (annual change: -0·024 years, -0·037 to -0·011; p=0·0002). INTERPRETATION: Patients with schizophrenia have faced persistent inequality in educational attainment in the last century, despite advances in psychosocial and pharmacological treatment. Reducing this gap should become a priority to improve their functional outcomes. FUNDING: Ciencia y Tecnología para el Desarrollo (CYTED) to the Latin American Network for the Study of Early Psychosis (ANDES).

Bruins-in-Genomics: Evaluation of the impact of a UCLA undergraduate summer program in computational biology on participating students.

Recruiting, training and retaining scientists in computational biology is necessary to develop a workforce that can lead the quantitative biology revolution. Yet, African-American/Black, Hispanic/Latinx, Native Americans, and women are severely underrepresented in computational biosciences. We established the UCLA Bruins-in-Genomics Summer Research Program to provide training and research experiences in quantitative biology and bioinformatics to undergraduate students with an emphasis on students from backgrounds underrepresented in computational biology. Program assessment was based on number of applicants, alumni surveys and comparison of post-graduate educational choices for participants and a control group of students who were accepted but declined to participate. We hypothesized that participation in the Bruins-in-Genomics program would increase the likelihood that students would pursue post-graduate education in a related field. Our surveys revealed that 75% of Bruins-in-Genomics Summer participants were enrolled in graduate school. Logistic regression analysis revealed that women who participated in the program were significantly more likely to pursue a Ph.D. than a matched control group (group x woman interaction term of p = 0.005). The Bruins-in-Genomics Summer program represents an example of how a combined didactic-research program structure can make computational biology accessible to a wide range of undergraduates and increase participation in quantitative biosciences.

Exome sequencing in bipolar disorder identifies AKAP11 as a risk gene shared with schizophrenia.

We report results from the Bipolar Exome (BipEx) collaboration analysis of whole-exome sequencing of 13,933 patients with bipolar disorder (BD) matched with 14,422 controls. We find an excess of ultra-rare protein-truncating variants (PTVs) in patients with BD among genes under strong evolutionary constraint in both major BD subtypes. We find enrichment of ultra-rare PTVs within genes implicated from a recent schizophrenia exome meta-analysis (SCHEMA; 24,248 cases and 97,322 controls) and among binding targets of CHD8. Genes implicated from genome-wide association studies (GWASs) of BD, however, are not significantly enriched for ultra-rare PTVs. Combining gene-level results with SCHEMA, AKAP11 emerges as a definitive risk gene (odds ratio (OR) = 7.06, P = 2.83 × 10-9). At the protein level, AKAP-11 interacts with GSK3B, the hypothesized target of lithium, a primary treatment for BD. Our results lend support to BD's polygenicity, demonstrating a role for rare coding variation as a significant risk factor in BD etiology.

Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci.

Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.

Epigenetic clock and methylation studies in vervet monkeys.

DNA methylation-based biomarkers of aging have been developed for many mammals but not yet for the vervet monkey (Chlorocebus sabaeus), which is a valuable non-human primate model for biomedical studies. We generated novel DNA methylation data from vervet cerebral cortex, blood, and liver using highly conserved mammalian CpGs represented on a custom array (HorvathMammalMethylChip40). We present six DNA methylation-based estimators of age: vervet multi-tissue epigenetic clock and tissue-specific clocks for brain cortex, blood, and liver. In addition, we developed two dual species clocks (human-vervet clocks) for measuring chronological age and relative age, respectively. Relative age was defined as ratio of chronological age to maximum lifespan to address the species differences in maximum lifespan. The high accuracy of the human-vervet clocks demonstrates that epigenetic aging processes are evolutionary conserved in primates. When applying these vervet clocks to tissue samples from another primate species, rhesus macaque, we observed high age correlations but strong offsets. We characterized CpGs that correlate significantly with age in the vervet. CpG probes that gain methylation with age across tissues were located near the targets of Polycomb proteins SUZ12 and EED and genes possessing the trimethylated H3K27 mark in their promoters. The epigenetic clocks are expected to be useful for anti-aging studies in vervets.

Mitochondrial genome copy number measured by DNA sequencing in human blood is strongly associated with metabolic traits via cell-type composition differences.

BACKGROUND: Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). RESULTS: We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10-8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10-8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10-21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. CONCLUSION: These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.

Identifying nootropic drug targets via large-scale cognitive GWAS and transcriptomics.

Broad-based cognitive deficits are an enduring and disabling symptom for many patients with severe mental illness, and these impairments are inadequately addressed by current medications. While novel drug targets for schizophrenia and depression have emerged from recent large-scale genome-wide association studies (GWAS) of these psychiatric disorders, GWAS of general cognitive ability can suggest potential targets for nootropic drug repurposing. Here, we (1) meta-analyze results from two recent cognitive GWAS to further enhance power for locus discovery; (2) employ several complementary transcriptomic methods to identify genes in these loci that are credibly associated with cognition; and (3) further annotate the resulting genes using multiple chemoinformatic databases to identify "druggable" targets. Using our meta-analytic data set (N = 373,617), we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging our transcriptomic and chemoinformatic databases, we identified 16 putative genes targeted by existing drugs potentially available for cognitive repurposing.

Association of structural variation with cardiometabolic traits in Finns.

The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10-54) and is also associated with increased levels of total cholesterol (p = 1.22 × 10-28) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10-21) and alanine (p = 6.14 × 10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10-10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.

Genetic analysis of activity, brain and behavioral associations in extended families with heavy genetic loading for bipolar disorder.

BACKGROUND: Disturbed sleep and activity are prominent features of bipolar disorder type I (BP-I). However, the relationship of sleep and activity characteristics to brain structure and behavior in euthymic BP-I patients and their non-BP-I relatives is unknown. Additionally, underlying genetic relationships between these traits have not been investigated. METHODS: Relationships between sleep and activity phenotypes, assessed using actigraphy, with structural neuroimaging (brain) and cognitive and temperament (behavior) phenotypes were investigated in 558 euthymic individuals from multi-generational pedigrees including at least one member with BP-I. Genetic correlations between actigraphy-brain and actigraphy-behavior associations were assessed, and bivariate linkage analysis was conducted for trait pairs with evidence of shared genetic influences. RESULTS: More physical activity and longer awake time were significantly associated with increased brain volumes and cortical thickness, better performance on neurocognitive measures of long-term memory and executive function, and less extreme scores on measures of temperament (impulsivity, cyclothymia). These associations did not differ between BP-I patients and their non-BP-I relatives. For nine activity-brain or activity-behavior pairs there was evidence for shared genetic influence (genetic correlations); of these pairs, a suggestive bivariate quantitative trait locus on chromosome 7 for wake duration and verbal working memory was identified. CONCLUSIONS: Our findings indicate that increased physical activity and more adequate sleep are associated with increased brain size, better cognitive function and more stable temperament in BP-I patients and their non-BP-I relatives. Additionally, we found evidence for pleiotropy of several actigraphy-behavior and actigraphy-brain phenotypes, suggesting a shared genetic basis for these traits.

Diversity matters: opportunities in the study of the genetics of psychotic disorders in low- and middle-income countries in Latin America.

Lack of diversity regarding genetic and environmental backgrounds weakens the generalization and clinical applicability of research findings on psychotic disorders. Notably, Latin Americans have been generally neglected in genetic studies, comprising less than 2% of genome-wide association study samples. But Latin American populations represent a unique opportunity for research, given the exceptionally high ethnic admixture of this group. Increasing genetic diversity is essential to improve the fine mapping of known regions associated with psychotic disorders, discover novel genetic associations, and replicate studies. Additionally, Latin America is characterized by massive social, political, and economic inequalities, all known risk factors for mental health issues, including psychotic disorders. This article aims to 1) discuss the challenges and advantages of studying Latin America's particular genetic makeup and environmental context; 2) review previous studies conducted in the region; and 3) describe three Latin American research initiatives in progress: the Neuropsychiatric Genetics of Psychosis in Mexican Populations (NeuroMEX), the Paisa, and the Latin American Network for the Study of Early Psychosis (ANDES) studies.

Genome-wide mapping of brain phenotypes in extended pedigrees with strong genetic loading for bipolar disorder.

Bipolar disorder is a highly heritable illness, associated with alterations of brain structure. As such, identification of genes influencing inter-individual differences in brain morphology may help elucidate the underlying pathophysiology of bipolar disorder (BP). To identify quantitative trait loci (QTL) that contribute to phenotypic variance of brain structure, structural neuroimages were acquired from family members (n = 527) of extended pedigrees heavily loaded for bipolar disorder ascertained from genetically isolated populations in Latin America. Genome-wide linkage and association analysis were conducted on the subset of heritable brain traits that showed significant evidence of association with bipolar disorder (n = 24) to map QTL influencing regional measures of brain volume and cortical thickness. Two chromosomal regions showed significant evidence of linkage; a QTL on chromosome 1p influencing corpus callosum volume and a region on chromosome 7p linked to cortical volume. Association analysis within the two QTLs identified three SNPs correlated with the brain measures.

Shifts in microbial diversity, composition, and functionality in the gut and genital microbiome during a natural SIV infection in vervet monkeys.

BACKGROUND: The microbiota plays an important role in HIV pathogenesis in humans. Microbiota can impact health through several pathways such as increasing inflammation in the gut, metabolites of bacterial origin, and microbial translocation from the gut to the periphery which contributes to systemic chronic inflammation and immune activation and the development of AIDS. Unlike HIV-infected humans, SIV-infected vervet monkeys do not experience gut dysfunction, microbial translocation, and chronic immune activation and do not progress to immunodeficiency. Here, we provide the first reported characterization of the microbial ecosystems of the gut and genital tract in a natural nonprogressing host of SIV, wild vervet monkeys from South Africa. RESULTS: We characterized fecal, rectal, vaginal, and penile microbiomes in vervets from populations heavily infected with SIV from diverse locations across South Africa. Geographic site, age, and sex affected the vervet microbiome across different body sites. Fecal and vaginal microbiome showed marked stratification with three enterotypes in fecal samples and two vagitypes, which were predicted functionally distinct within each body site. External bioclimatic factors, biome type, and environmental temperature influenced microbiomes locally associated with vaginal and rectal mucosa. Several fecal microbial taxa were linked to plasma levels of immune molecules, for example, MIG was positively correlated with Lactobacillus and Escherichia/Shigella and Helicobacter, and IL-10 was negatively associated with Erysipelotrichaceae, Anaerostipes, Prevotella, and Anaerovibrio, and positively correlated with Bacteroidetes and Succinivibrio. During the chronic phase of infection, we observed a significant increase in gut microbial diversity, alterations in community composition (including a decrease in Proteobacteria/Succinivibrio in the gut) and functionality (including a decrease in genes involved in bacterial invasion of epithelial cells in the gut), and partial reversibility of acute infection-related shifts in microbial abundance observed in the fecal microbiome. As part of our study, we also developed an accurate predictor of SIV infection using fecal samples. CONCLUSIONS: The vervets infected with SIV and humans infected with HIV differ in microbial responses to infection. These responses to SIV infection may aid in preventing microbial translocation and subsequent disease progression in vervets, and may represent host microbiome adaptations to the virus. Video Abstract.