Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability.

In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.

Methylome-wide association study of early life stressors and adult mental health.

The environment and events that we are exposed to in utero, during birth and in early childhood influence our future physical and mental health. The underlying mechanisms that lead to these outcomes are unclear, but long-term changes in epigenetic marks, such as DNA methylation, could act as a mediating factor or biomarker. DNA methylation data were assayed at 713 522 CpG sites from 9537 participants of the Generation Scotland: Scottish Family Health Study, a family-based cohort with extensive genetic, medical, family history and lifestyle information. Methylome-wide association studies of eight early life environment phenotypes and two adult mental health phenotypes (major depressive disorder and brief resilience scale) were conducted using DNA methylation data collected from adult whole blood samples. Two genes involved with different developmental pathways (PRICKLE2, Prickle Planar Cell Polarity Protein 2 and ABI1, Abl-Interactor-1) were annotated to CpG sites associated with preterm birth (P < 1.27 × 10-9). A further two genes important to the development of sensory pathways (SOBP, Sine Oculis Binding Protein Homolog and RPGRIP1, Retinitis Pigmentosa GTPase Regulator Interacting Protein) were annotated to sites associated with low birth weight (P < 4.35 × 10-8). The examination of methylation profile scores and genes and gene-sets annotated from associated CpGs sites found no evidence of overlap between the early life environment and mental health conditions. Birth date was associated with a significant difference in estimated lymphocyte and neutrophil counts. Previous studies have shown that early life environments influence the risk of developing mental health disorders later in life; however, this study found no evidence that this is mediated by stable changes to the methylome detectable in peripheral blood.

General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning.

Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|ß| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples.

BACKGROUND: The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. METHODS: We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. RESULTS: In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. CONCLUSION: Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Evaluation of putative signatures of consciousness using specific definitions of responsiveness, connectedness, and consciousness.

BACKGROUND: Understanding the neural correlates of consciousness has important ramifications for the theoretical understanding of consciousness and for clinical anaesthesia. A major limitation of prior studies is the use of responsiveness as an index of consciousness. We identified a collection of measures derived from unresponsive subjects and more specifically their association with consciousness (any subjective experience) or connectedness (specific experience of environmental stimuli). METHODS: Using published data generated through the UNderstanding Consciousness Connectedness and Intra-Operative Unresponsiveness Study (NCT03284307), we evaluated 10 previously published resting-state EEG-based measures that were derived using unresponsiveness as a proxy for unconsciousness. Measures were tested across dexmedetomidine and propofol sedation and natural sleep. These markers represent the complexity, connectivity, cross-frequency coupling, graph theory, and power spectrum measures. RESULTS: Although many of the proposed markers were associated with consciousness per se (reported subjective experience), none were specific to consciousness alone; rather, each was also associated with connectedness (i.e. awareness of the environment). In addition, multiple markers showed no association with consciousness and were associated only with connectedness. Of the markers tested, loss of normalised-symbolic transfer entropy (front to back) was associated with connectedness across all three experimental conditions, whereas the transition from disconnected consciousness to unconsciousness was associated with significant decreases in permutation entropy and spectral exponent (P<0.05 for all conditions). CONCLUSIONS: None of the proposed EEG-based neural correlates of unresponsiveness corresponded solely to consciousness, highlighting the need for a more conservative use of the term (un)consciousness when assessing unresponsive participants. CLINICAL TRIAL REGISTRATION: NCT03284307.

Genome-wide methylation data improves dissection of the effect of smoking on body mass index.

Variation in obesity-related traits has a genetic basis with heritabilities between 40 and 70%. While the global obesity pandemic is usually associated with environmental changes related to lifestyle and socioeconomic changes, most genetic studies do not include all relevant environmental covariates, so the genetic contribution to variation in obesity-related traits cannot be accurately assessed. Some studies have described interactions between a few individual genes linked to obesity and environmental variables but there is no agreement on their total contribution to differences between individuals. Here we compared self-reported smoking data and a methylation-based proxy to explore the effect of smoking and genome-by-smoking interactions on obesity related traits from a genome-wide perspective to estimate the amount of variance they explain. Our results indicate that exploiting omic measures can improve models for complex traits such as obesity and can be used as a substitute for, or jointly with, environmental records to better understand causes of disease.

Sex-stratified genome-wide association study of multisite chronic pain in UK Biobank.

Chronic pain is highly prevalent worldwide and imparts a significant socioeconomic and public health burden. Factors influencing susceptibility to, and mechanisms of, chronic pain development, are not fully understood, but sex is thought to play a significant role, and chronic pain is more prevalent in women than in men. To investigate sex differences in chronic pain, we carried out a sex-stratified genome-wide association study of Multisite Chronic Pain (MCP), a derived chronic pain phenotype, in UK Biobank on 178,556 men and 209,093 women, as well as investigating sex-specific genetic correlations with a range of psychiatric, autoimmune and anthropometric phenotypes and the relationship between sex-specific polygenic risk scores for MCP and chronic widespread pain. We also assessed whether MCP-associated genes showed expression pattern enrichment across tissues. A total of 123 SNPs at five independent loci were significantly associated with MCP in men. In women, a total of 286 genome-wide significant SNPs at ten independent loci were discovered. Meta-analysis of sex-stratified GWAS outputs revealed a further 87 independent associated SNPs. Gene-level analyses revealed sex-specific MCP associations, with 31 genes significantly associated in females, 37 genes associated in males, and a single gene, DCC, associated in both sexes. We found evidence for sex-specific pleiotropy and risk for MCP was found to be associated with chronic widespread pain in a sex-differential manner. Male and female MCP were highly genetically correlated, but at an rg of significantly less than 1 (0.92). All 37 male MCP-associated genes and all but one of 31 female MCP-associated genes were found to be expressed in the dorsal root ganglion, and there was a degree of enrichment for expression in sex-specific tissues. Overall, the findings indicate that sex differences in chronic pain exist at the SNP, gene and transcript abundance level, and highlight possible sex-specific pleiotropy for MCP. Results support the proposition of a strong central nervous-system component to chronic pain in both sexes, additionally highlighting a potential role for the DRG and nociception.

Blood-based epigenome-wide analyses of 19 common disease states: A longitudinal, population-based linked cohort study of 18,413 Scottish individuals.

BACKGROUND: DNA methylation is a dynamic epigenetic mechanism that occurs at cytosine-phosphate-guanine dinucleotide (CpG) sites. Epigenome-wide association studies (EWAS) investigate the strength of association between methylation at individual CpG sites and health outcomes. Although blood methylation may act as a peripheral marker of common disease states, previous EWAS have typically focused only on individual conditions and have had limited power to discover disease-associated loci. This study examined the association of blood DNA methylation with the prevalence of 14 disease states and the incidence of 19 disease states in a single population of over 18,000 Scottish individuals. METHODS AND FINDINGS: DNA methylation was assayed at 752,722 CpG sites in whole-blood samples from 18,413 volunteers in the family-structured, population-based cohort study Generation Scotland (age range 18 to 99 years). EWAS tested for cross-sectional associations between baseline CpG methylation and 14 prevalent disease states, and for longitudinal associations between baseline CpG methylation and 19 incident disease states. Prevalent cases were self-reported on health questionnaires at the baseline. Incident cases were identified using linkage to Scottish primary (Read 2) and secondary (ICD-10) care records, and the censoring date was set to October 2020. The mean time-to-diagnosis ranged from 5.0 years (for chronic pain) to 11.7 years (for Coronavirus Disease 2019 (COVID-19) hospitalisation). The 19 disease states considered in this study were selected if they were present on the World Health Organisation's 10 leading causes of death and disease burden or included in baseline self-report questionnaires. EWAS models were adjusted for age at methylation typing, sex, estimated white blood cell composition, population structure, and 5 common lifestyle risk factors. A structured literature review was also conducted to identify existing EWAS for all 19 disease states tested. The MEDLINE, Embase, Web of Science, and preprint servers were searched to retrieve relevant articles indexed as of March 27, 2023. Fifty-four of approximately 2,000 indexed articles met our inclusion criteria: assayed blood-based DNA methylation, had >20 individuals in each comparison group, and examined one of the 19 conditions considered. First, we assessed whether the associations identified in our study were reported in previous studies. We identified 69 associations between CpGs and the prevalence of 4 conditions, of which 58 were newly described. The conditions were breast cancer, chronic kidney disease, ischemic heart disease, and type 2 diabetes mellitus. We also uncovered 64 CpGs that associated with the incidence of 2 disease states (COPD and type 2 diabetes), of which 56 were not reported in the surveyed literature. Second, we assessed replication across existing studies, which was defined as the reporting of at least 1 common site in >2 studies that examined the same condition. Only 6/19 disease states had evidence of such replication. The limitations of this study include the nonconsideration of medication data and a potential lack of generalizability to individuals that are not of Scottish and European ancestry. CONCLUSIONS: We discovered over 100 associations between blood methylation sites and common disease states, independently of major confounding risk factors, and a need for greater standardisation among EWAS on human disease.

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes.

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

Pathological and Clinical Outcomes in a Large Surveillance and Intervention Cohort of Radiographically Cystic Renal Masses.

PURPOSE: We evaluated oncologic risks in a large cohort of patients with radiographic cystic renal masses who underwent active surveillance or intervention. MATERIALS AND METHODS: A single-institutional database of 4,340 kidney lesions managed with either active surveillance or intervention between 2000-2020 was queried for radiographically cystic renal masses. Association of radiographic tumor characteristics and high-grade pathology was evaluated. RESULTS: We identified 387 radiographically confirmed cystic lesions in 367 patients. Of these, 247 were resected (n=240) or ablated (n=7; n=247, 203 immediate vs 44 delayed intervention). Pathologically, 23% (n=56) demonstrated high-grade pathology. Cystic features were explicitly described by pathology in only 18% (n=33) of all lesions and in 7% (n=4) of high-grade lesions. Of the intervention cohort, African American race, male gender, and Bosniak score were associated with high-grade pathology (P < .05). On active surveillance (n=184), Bosniak IV lesions demonstrated faster growth rates than IIF and III lesions (2.7 vs 0.6 and 0.5 mm/y, P ≤ .001); however, growth rates were not associated with high-grade pathology (P = .5). No difference in cancer-specific survival was identified when comparing intervention vs active surveillance at 5 years (99% vs 100%, P = .2). No difference in recurrence was observed between immediate intervention vs delayed intervention (P > .9). CONCLUSIONS: A disconnect between "cystic" designation on imaging and pathology exists for renal lesions. Over 80% of radiographic Bosniak cystic lesions are not described as "cystic" on pathology reports. More than 1 in 5 resected cystic renal lesions demonstrated high-grade disease. Despite this finding, judiciously managed active surveillance ± delayed intervention is a safe and effective management option for most radiographic cystic renal masses.

Associations of negative affective biases and depressive symptoms in a community-based sample.

BACKGROUND: Major depressive disorder (MDD) was previously associated with negative affective biases. Evidence from larger population-based studies, however, is lacking, including whether biases normalise with remission. We investigated associations between affective bias measures and depressive symptom severity across a large community-based sample, followed by examining differences between remitted individuals and controls. METHODS: Participants from Generation Scotland (N = 1109) completed the: (i) Bristol Emotion Recognition Task (BERT), (ii) Face Affective Go/No-go (FAGN), and (iii) Cambridge Gambling Task (CGT). Individuals were classified as MDD-current (n = 43), MDD-remitted (n = 282), or controls (n = 784). Analyses included using affective bias summary measures (primary analyses), followed by detailed emotion/condition analyses of BERT and FAGN (secondary analyses). RESULTS: For summary measures, the only significant finding was an association between greater symptoms and lower risk adjustment for CGT across the sample (individuals with greater symptoms were less likely to bet more, despite increasingly favourable conditions). This was no longer significant when controlling for non-affective cognition. No differences were found for remitted-MDD v. controls. Detailed analysis of BERT and FAGN indicated subtle negative biases across multiple measures of affective cognition with increasing symptom severity, that were independent of non-effective cognition [e.g. greater tendency to rate faces as angry (BERT), and lower accuracy for happy/neutral conditions (FAGN)]. Results for remitted-MDD were inconsistent. CONCLUSIONS: This suggests the presence of subtle negative affective biases at the level of emotion/condition in association with depressive symptoms across the sample, over and above those accounted for by non-affective cognition, with no evidence for affective biases in remitted individuals.

Alcohol use disorder is associated with DNA methylation-based shortening of telomere length and regulated by TESPA1: implications for aging.

Chronic heavy alcohol consumption is associated with increased mortality and morbidity and often leads to premature aging; however, the mechanisms of alcohol-associated cellular aging are not well understood. In this study, we used DNA methylation derived telomere length (DNAmTL) as a novel approach to investigate the role of alcohol use on the aging process. DNAmTL was estimated by 140 cytosine phosphate guanines (CpG) sites in 372 individuals with alcohol use disorder (AUD) and 243 healthy controls (HC) and assessed using various endophenotypes and clinical biomarkers. Validation in an independent sample of DNAmTL on alcohol consumption was performed (N = 4219). Exploratory genome-wide association studies (GWAS) on DNAmTL were also performed to identify genetic variants contributing to DNAmTL shortening. Top GWAS findings were analyzed using in-silico expression quantitative trait loci analyses and related to structural MRI hippocampus volumes of individuals with AUD. DNAmTL was 0.11-kilobases shorter per year in AUD compared to HC after adjustment for age, sex, race, and blood cell composition (p = 4.0 × 10-12). This association was partially attenuated but remained significant after additionally adjusting for BMI, and smoking status (0.06 kilobases shorter per year, p = 0.002). DNAmTL shortening was strongly associated with chronic heavy alcohol use (ps < 0.001), elevated gamma-glutamyl transferase (GGT), and aspartate aminotransferase (AST) (ps < 0.004). Comparison of DNAmTL with PCR-based methods of assessing TL revealed positive correlations (R = 0.3, p = 2.2 × 10-5), highlighting the accuracy of DNAmTL as a biomarker. The GWAS meta-analysis identified a single nucleotide polymorphism (SNP), rs4374022 and 18 imputed ones in Thymocyte Expressed, Positive Selection Associated 1(TESPA1), at the genome-wide level (p = 3.75 × 10-8). The allele C of rs4374022 was associated with DNAmTL shortening, lower hippocampus volume (p < 0.01), and decreased mRNA expression in hippocampus tissue (p = 0.04). Our study demonstrates DNAmTL-related aging acceleration in AUD and suggests a functional role for TESPA1 in regulating DNAmTL length, possibly via the immune system with subsequent biological effects on brain regions negatively affected by alcohol and implicated in aging.

Epigenome-wide association study of alcohol consumption in N = 8161 individuals and relevance to alcohol use disorder pathophysiology: identification of the cystine/glutamate transporter SLC7A11 as a top target.

Alcohol misuse is common in many societies worldwide and is associated with extensive morbidity and mortality, often leading to alcohol use disorders (AUD) and alcohol-related end-organ damage. The underlying mechanisms contributing to the development of AUD are largely unknown; however, growing evidence suggests that alcohol consumption is strongly associated with alterations in DNA methylation. Identification of alcohol-associated methylomic variation might provide novel insights into pathophysiology and novel treatment targets for AUD. Here we performed the largest single-cohort epigenome-wide association study (EWAS) of alcohol consumption to date (N = 8161) and cross-validated findings in AUD populations with relevant endophenotypes, as well as alcohol-related animal models. Results showed 2504 CpGs significantly associated with alcohol consumption (Bonferroni p value < 6.8 × 10-8) with the five leading probes located in SLC7A11 (p = 7.75 × 10-108), JDP2 (p = 1.44 × 10-56), GAS5 (p = 2.71 × 10-47), TRA2B (p = 3.54 × 10-42), and SLC43A1 (p = 1.18 × 10-40). Genes annotated to associated CpG sites are implicated in liver and brain function, the cellular response to alcohol and alcohol-associated diseases, including hypertension and Alzheimer's disease. Two-sample Mendelian randomization confirmed the causal relationship of consumption on AUD risk (inverse variance weighted (IVW) p = 5.37 × 10-09). A methylation-based predictor of alcohol consumption was able to discriminate AUD cases in two independent cohorts (p = 6.32 × 10-38 and p = 5.41 × 10-14). The top EWAS probe cg06690548, located in the cystine/glutamate transporter SLC7A11, was replicated in an independent cohort of AUD and control participants (N = 615) and showed strong hypomethylation in AUD (p < 10-17). Decreased CpG methylation at this probe was consistently associated with clinical measures including increased heavy drinking days (p < 10-4), increased liver function enzymes (GGT (p = 1.03 × 10-21), ALT (p = 1.29 × 10-6), and AST (p = 1.97 × 10-8)) in individuals with AUD. Postmortem brain analyses documented increased SLC7A11 expression in the frontal cortex of individuals with AUD and animal models showed marked increased expression in liver, suggesting a mechanism by which alcohol leads to hypomethylation-induced overexpression of SLC7A11. Taken together, our EWAS discovery sample and subsequent validation of the top probe in AUD suggest a strong role of abnormal glutamate signaling mediated by methylomic variation in SLC7A11. Our data are intriguing given the prominent role of glutamate signaling in brain and liver and might provide an important target for therapeutic intervention.

Methylome-wide association study of antidepressant use in Generation Scotland and the Netherlands Twin Register implicates the innate immune system.

Antidepressants are an effective treatment for major depressive disorder (MDD), although individual response is unpredictable and highly variable. Whilst the mode of action of antidepressants is incompletely understood, many medications are associated with changes in DNA methylation in genes that are plausibly linked to their mechanisms. Studies of DNA methylation may therefore reveal the biological processes underpinning the efficacy and side effects of antidepressants. We performed a methylome-wide association study (MWAS) of self-reported antidepressant use accounting for lifestyle factors and MDD in Generation Scotland (GS:SFHS, N = 6428, EPIC array) and the Netherlands Twin Register (NTR, N = 2449, 450 K array) and ran a meta-analysis of antidepressant use across these two cohorts. We found ten CpG sites significantly associated with self-reported antidepressant use in GS:SFHS, with the top CpG located within a gene previously associated with mental health disorders, ATP6V1B2 (ß = -0.055, pcorrected = 0.005). Other top loci were annotated to genes including CASP10, TMBIM1, MAPKAPK3, and HEBP2, which have previously been implicated in the innate immune response. Next, using penalised regression, we trained a methylation-based score of self-reported antidepressant use in a subset of 3799 GS:SFHS individuals that predicted antidepressant use in a second subset of GS:SFHS (N = 3360, ß = 0.377, p = 3.12 × 10-11, R2 = 2.12%). In an MWAS analysis of prescribed selective serotonin reuptake inhibitors, we showed convergent findings with those based on self-report. In NTR, we did not find any CpGs significantly associated with antidepressant use. The meta-analysis identified the two CpGs of the ten above that were common to the two arrays used as being significantly associated with antidepressant use, although the effect was in the opposite direction for one of them. Antidepressants were associated with epigenetic alterations in loci previously associated with mental health disorders and the innate immune system. These changes predicted self-reported antidepressant use in a subset of GS:SFHS and identified processes that may be relevant to our mechanistic understanding of clinically relevant antidepressant drug actions and side effects.

Genome-wide interaction study with major depression identifies novel variants associated with cognitive function.

Major Depressive Disorder (MDD) often is associated with significant cognitive dysfunction. We conducted a meta-analysis of genome-wide interaction of MDD and cognitive function using data from four large European cohorts in a total of 3510 MDD cases and 6057 controls. In addition, we conducted analyses using polygenic risk scores (PRS) based on data from the Psychiatric Genomics Consortium (PGC) on the traits of MDD, Bipolar disorder (BD), Schizophrenia (SCZ), and mood instability (MIN). Functional exploration contained gene expression analyses and Ingenuity Pathway Analysis (IPA®). We identified a set of significantly interacting single nucleotide polymorphisms (SNPs) between MDD and the genome-wide association study (GWAS) of cognitive domains of executive function, processing speed, and global cognition. Several of these SNPs are located in genes expressed in brain, with important roles such as neuronal development (REST), oligodendrocyte maturation (TNFRSF21), and myelination (ARFGEF1). IPA® identified a set of core genes from our dataset that mapped to a wide range of canonical pathways and biological functions (MPO, FOXO1, PDE3A, TSLP, NLRP9, ADAMTS5, ROBO1, REST). Furthermore, IPA® identified upstream regulator molecules and causal networks impacting on the expression of dataset genes, providing a genetic basis for further clinical exploration (vitamin D receptor, beta-estradiol, tadalafil). PRS of MIN and meta-PRS of MDD, MIN and SCZ were significantly associated with all cognitive domains. Our results suggest several genes involved in physiological processes for the development and maintenance of cognition in MDD, as well as potential novel therapeutic agents that could be explored in patients with MDD associated cognitive dysfunction.

Risk and protective factors for new-onset binge eating, low weight, and self-harm symptoms in >35,000 individuals in the UK during the COVID-19 pandemic.

OBJECTIVE: The disruption caused by the COVID-19 pandemic has been associated with poor mental health, including increases in eating disorders and self-harm symptoms. We investigated risk and protective factors for the new onset of these symptoms during the pandemic. METHOD: Data were from the COVID-19 Psychiatry and Neurological Genetics study and the Repeated Assessment of Mental health in Pandemics Study (n = 36,715). Exposures were socio-demographic characteristics, lifetime psychiatric disorder, and COVID-related variables, including SARS-CoV-2 infection/illness with COVID-19. We identified four subsamples of participants without pre-pandemic experience of our outcomes: binge eating (n = 24,211), low weight (n = 24,364), suicidal and/or self-harm ideation (n = 18,040), and self-harm (n = 29,948). Participants reported on our outcomes at frequent intervals (fortnightly to monthly). We fitted multiple logistic regression models to identify factors associated with the new onset of our outcomes. RESULTS: Within each subsample, new onset was reported by: 21% for binge eating, 10.8% for low weight, 23.5% for suicidal and/or self-harm ideation, and 3.5% for self-harm. Shared risk factors included having a lifetime psychiatric disorder, not being in paid employment, higher pandemic worry scores, and being racially minoritized. Conversely, infection with SARS-CoV-2/illness with COVID-19 was linked to lower odds of binge eating, low weight, and suicidal and/or self-harm ideation. DISCUSSION: Overall, we detected shared risk factors that may drive the comorbidity between eating disorders and self-harm. Subgroups of individuals with these risk factors may require more frequent monitoring during future pandemics. PUBLIC SIGNIFICANCE: In a sample of 35,000 UK residents, people who had a psychiatric disorder, identified as being part of a racially minoritized group, were not in paid employment, or were more worried about the pandemic were more likely to experience binge eating, low weight, suicidal and/or self-harm ideation, and self-harm for the first time during the pandemic. People with these risk factors may need particular attention during future pandemics to enable early identification of new psychiatric symptoms.

Sociodemographic, mental health, and physical health factors associated with participation within re-contactable mental health cohorts: an investigation of the GLAD Study.

BACKGROUND: The Genetic Links to Anxiety and Depression (GLAD) Study is a large cohort of individuals with lifetime anxiety and/or depression, designed to facilitate re-contact of participants for mental health research. At the start of the pandemic, participants from three cohorts, including the GLAD Study, were invited to join the COVID-19 Psychiatry and Neurological Genetics (COPING) study to monitor mental and neurological health. However, previous research suggests that participation in longitudinal studies follows a systematic, rather than random, process, which can ultimately bias results. Therefore, this study assessed participation biases following the re-contact of GLAD Study participants. METHODS: In April 2020, all current GLAD Study participants (N = 36,770) were invited to the COPING study. Using logistic regression, we investigated whether sociodemographic, mental, and physical health characteristics were associated with participation in the COPING baseline survey (aim one). Subsequently, we used a zero-inflated negative binomial regression to examine whether these factors were also related to participation in the COPING follow-up surveys (aim two). RESULTS: For aim one, older age, female gender identity, non-binary or self-defined gender identities, having one or more physical health disorders, and providing a saliva kit for the GLAD Study were associated with an increased odds of completing the COPING baseline survey. In contrast, lower educational attainment, Asian or Asian British ethnic identity, Black or Black British ethnic identity, higher alcohol consumption at the GLAD sign-up survey, and current or ex-smoking were associated with a reduced odds. For aim two, older age, female gender, and saliva kit provision were associated with greater COPING follow-up survey completion. Lower educational attainment, higher alcohol consumption at the GLAD Study sign-up, ex-smoking, and self-reported attention deficit hyperactivity disorder had negative relationships. CONCLUSIONS: Participation biases surrounding sociodemographic and physical health characteristics were particularly evident when re-contacting the GLAD Study volunteers. Factors associated with participation may vary depending on study design. Researchers should examine the barriers and mechanisms underlying participation bias in order to combat these issues and address recruitment biases in future studies.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Padded Headgear in Junior and Youth Australian Football: Player Insights From a National Survey.

OBJECTIVES: To investigate beliefs and factors associated with padded headgear (HG) use in junior (<13 years) and youth (≥13 years) Australian football. DESIGN: Online survey. SETTING: Junior and youth athletes in Australia. PARTICIPANTS: Australian football players aged U8 to U18. ASSESSMENT OF VARIABLES: Survey questions regarding demographics, HG use, concussion history, beliefs about HG, and risk-taking propensity. MAIN OUTCOME MEASURES: Rates of padded HG use, and beliefs associated with HG use. RESULTS: A total of 735 players (including 190, 25.9% female) representing 206 clubs participated. Headgear was worn by 315 players (42.9%; 95% CI: 39.3-46.4). Most (59.5%) HG users wore it for games only and wore it voluntarily (59.7%), as opposed to being mandated to do so. Junior players were more likely than youth players to agree to feeling safer ( P < 0.001) and being able to play harder while wearing HG ( P < 0.001). Median responses were "disagree" on preferring to risk an injury than wear HG, and on experienced players not needing to wear HG. Beliefs did not differ between males and females. Headgear use was associated with players belonging to a club where HG was mandated for other age groups (OR 16.10; 95% CI: 7.71-33.62, P < 0.001), youth players (OR 2.79; 95% CI: 1.93-3.93, P < 0.001), and female players (OR 1.57; 95% CI: 1.07-2.30, P = 0.019). CONCLUSIONS: Club HG culture, older age and being female were prominent variables associated with voluntary HG use. Players reported believing that HG offers protection. The rate of voluntary and mandated HG use identified is at odds with current scientific evidence that does not support HG as effective concussion prevention.

Genetic examination of the Mood Disorder Questionnaire and its relationship with bipolar disorder.

The Mood Disorder Questionnaire (MDQ) is a common screening tool for bipolar disorder that assesses manic symptoms. Its utility for genetic studies of mania or bipolar traits has not been fully examined. We psychometrically compared the MDQ to self-reported bipolar disorder in participants from the United Kingdom National Institute of Health and Care Research Mental Health BioResource. We conducted genome-wide association studies of manic symptom quantitative traits and symptom subgroups, derived from the MDQ items (N = 11,568-19,859). We calculated genetic correlations with bipolar disorder and other psychiatric and behavioral traits. The MDQ screener showed low positive predictive value (0.29) for self-reported bipolar disorder. Neither concurrent nor lifetime manic symptoms were genetically correlated with bipolar disorder. Lifetime manic symptoms had a highest genetic correlation (rg = 1.0) with posttraumatic stress disorder although this was not confirmed by within-cohort phenotypic correlations (rp = 0.41). Other significant genetic correlations included attention deficit hyperactivity disorder (rg = 0.69), insomnia (rg = 0.55), and major depressive disorder (rg = 0.42). Our study adds to existing literature questioning the MDQ's validity and suggests it may capture symptoms of general distress or psychopathology, rather than hypomania/mania specifically, in at-risk populations.