William R. Reay

No widely accepted standard exists for analysing structural variants from short-read whole genome sequencing (WGS) data. We use comprehensive SV truth sets (HG002 T2T-Q100, Platinum Pedigree) and long-read sequencing databases (1KGP-ONT, CoLoRS) to benchmark SV breakpoints and sizes at base pair resolution. Using 30X Illumina WGS data, only five of sixteen individual tools tested reliably output deletions, insertions and duplications that perfectly match the truth sets. Ensemble pipelines (e.g. GATK-SV, MOPline, Parliament2), frequently used in population-scale datasets, reduced perfect matching accuracy by up to 98% compared to their highest-performing constituent tools. This inaccuracy propagates into a short-read database (gnomAD-SV) constructed from an ensemble pipeline (GATK-SV) and likely affects other databases (TOPMed, All of Us, Biobank Japan). As a proof-of-concept, we demonstrate an intra-sample merging strategy that generates the highest F1-scores for deletions and insertions on the HG002 sample, recovering more perfect matches than total matches from the best performing ensemble pipeline tested. Common HG002 truth SVs (allele frequency > 1%) showed extremely high concordance for exact matches between our ensemble callset and two orthogonal long-read sequencing databases (CoLoRS, ONT-1KGP). This study provides a vital step towards robust standards for SV analysis, guiding short-read WGS ensemble pipeline development and improving cross-study comparisons.

Major depressive disorder (MDD) is characterized by substantial comorbidity with medical conditions. To achieve better outcomes for patients with MDD, an improved understanding of the mechanisms underlying pervasive comorbidities is required. Here, to this end, we mapped patterns of pleiotropy by defining four clusters of physical diseases (cardiovascular, metabolic, gastrointestinal and immune) and analyzed their genetic relationships with MDD using genomic structural equation modeling. Three disease clusters exhibited independent associations with MDD and accounted for 47% of MDD h ² _SNP , with the gastrointestinal disease cluster having the strongest association ( β = 0.63, s.e. = 0.05, P = 3.04 × 10 ⁻³⁰ ). In addition, we identified independent loci associated with the shared genetic liability between each disease cluster and MDD, revealing different pleiotropic components. Characterization of these loci revealed previously unidentified associations with MDD and physical disease traits, along with unique biological pathways, drug groups, cell types and genes associated with each disease–MDD cluster. Our findings reveal genetic connections implicating the gut–brain axis as a key mechanism underlying the comorbidity of physical diseases in MDD. This work advances our understanding of MDD by highlighting unique and shared genetic components across different disease systems.

Biological heterogeneity presents a formidable challenge in understanding psychiatric illness, yet it remains underexplored at scale or in a pan-disease setting. We characterized biochemical and brain heterogeneity in 445,374 individuals, considering 9 psychiatric disorders and 30 non-psychiatric medical conditions. Psychiatric and non-psychiatric illnesses were characterized by greater biological heterogeneity than predicted by healthy variation. Notably, heterogeneity in psychiatric disorders was comparable to non-psychiatric illnesses and often diverged from diagnostic classification. Circulating glucose and glycated hemoglobin levels were the most heterogeneous traits in 65% of diagnoses, suggesting that glucose dysregulation is pervasive yet variable across diseases. Diagnosis-specific patterns also surfaced—e.g., in schizophrenia, developmental brain markers ranked among the most heterogeneous traits. These characterizations of biochemical and brain heterogeneity lay the groundwork for biological focal points to aid precision psychiatry research. To support this effort, we provide an interactive online resource for exploring and downloading illness-specific heterogeneity measures.

Children experiencing persistent/recurrent pain (PRP) report increased exposure to early-life adversity (ELA), often accompanied by more severe pain and worse mental health outcomes. Separate studies of PRP and ELA show similar alterations in brain morphology, yet their distinct and cumulative contributions remain poorly understood. Structural magnetic resonance imaging data at baseline and 2-year follow-up were accessed from the Adolescent Brain Cognitive Development cohort (N = 1671). Linear mixed models were used to determine the main effects of group (control, PRP only, ELA only, and PRP + ELA) and group-by-time interaction on measures of subcortical volume, cortical thickness, and surface area. In addition to determining brain morphological differences between the groups, support vector machine was applied to perform group classification based on brain measures to assess translational relevance of brain-based markers. The PRP + ELA group showed more pronounced reduction in left hippocampal volume and sensorimotor surface area and increase in left precuneus and right frontal pole surface area over time compared with all other groups. In addition, independently of time, the ELA only group showed overall smaller accumbens, thicker right prefrontal, and thinner left postcentral gyrus compared with the control and PRP only groups. Using all brain measures, support vector machine models could not identify group status better than 66.8% accuracy. Our findings provide evidence for unique brain signatures representing the cumulative effects of PRP and ELA in children during development. Identification of these brain markers can inform the choice of interventions in this population.

Observational studies have reported alteration of circulating metabolites across several psychiatric conditions, but these studies cannot resolve causal relationships. Emerging evidence suggests a genetic relationship exists between these traits requiring further investigation to identify clinically actionable biology. Here, we used the largest genome-wide association studies available to investigate genetic correlation and causal relationships between 10 psychiatric conditions and 249 circulating metabolites. This revealed 1,100 significantly correlated trait pairings, involving fatty acids, lipoproteins and other metabolites, with evidence for causal effects on the liability for major depressive disorder, post-traumatic stress disorder and anorexia nervosa. Notably, the most robust association was a putative causal effect of high-density lipoprotein properties on anorexia nervosa. We also observed significant relationships between metabolic traits and cortical thickness and surface area, as well as evidence of shared gene-level common variant associations amongst 23 metabolite-psychiatric pairings, converging in pathways with metabolic and neuronal function. These findings highlight specific metabolites as potential biomarkers and therapeutic targets in the clinical management of psychiatric disorders.

Observational epidemiology suggests a link between the dermatological disorder acne vulgaris and several psychiatric disorders. However, the biological mechanisms that underlie the relationship between acne and mental health are poorly characterised. Here, we employed a genetic approach using large-scale genome-wide association studies of acne and ten psychiatric disorders to both estimate causal effects and uncover potential shared genetic risk factors. Multiple psychiatric disorders displayed evidence of small-to-moderate genetic correlations with acne. However, only genetic liability to schizophrenia displayed some evidence of a causal effect on the risk of acne, though horizontal pleiotropy cannot be fully excluded. Using a Gaussian mixture-model based approach, we then identified a cluster of schizophrenia-associated variants with distinct effects on acne liability, refining the molecular mechanisms that may link schizophrenia and acne. Shared genetic risk architecture between schizophrenia and acne was also investigated using Bayesian approaches. This revealed a subset of genetic loci associated with both acne and schizophrenia via shared or different causal variants, implicating biological processes including glutamatergic signalling. Finally, we found that genetic risk for schizophrenia was also associated with increased acne severity using a population-based cohort. In summary, we revealed genetic support for a biological relationship between acne and schizophrenia that may at least partially drive the elevated rates of acne amongst people living with schizophrenia.

Background: Chronic pain is associated with heightened prevalence of trauma exposure. Separate studies of chronic pain and trauma report overlapping brain alterations in older adults (age over 60). Despite this close relationship, the interplay between chronic pain and trauma on brain morphology remains poorly understood. Methods: Tabulated T1-weighted structural magnetic resonance imaging data were accessed from the UK biobank (N = 21,996). Linear mixed models were used to determine the main effects of group (control versus chronic pain), severity of trauma exposure and their interaction on measures of surface area, cortical thickness and subcortical volume. To examine the unique and cumulative impacts of trauma exposure at different developmental timing, group of participants exposed to childhood maltreatment only, trauma in adulthood only and both were derived. Results: Independently of the severity of trauma exposure, chronic pain was significantly associated with smaller surface area and thicker frontal, temporal, parietal, somatosensory, occipital and cingulate cortices. Independently of chronic pain, the severity of trauma exposure in adulthood and both childhood and adulthood, but not childhood alone, were associated with smaller bilateral nucleus accumbens, putamen, thalamus and left hippocampal volumes, as well as left inferior parietal gyrus surface area. No chronic pain-by-trauma severity interactions were significantly associated with variations in any regional measures. Conclusions: Specific effects of chronic pain and trauma severity were evident among older adults exposed to trauma at different developmental timing. However, there was no evidence for unique brain signatures representing the combined effects of chronic pain and trauma in older adults.

Major Depressive Disorder (MDD) is characterised by substantial comorbidity with medical conditions. To achieve better outcomes for patients with MDD, an improved understanding of the mechanisms underlying pervasive comorbidities is required. To this end, we map patterns of pleiotropy by defining four clusters of physical diseases (cardiovascular, metabolic, gastrointestinal, and immune) to analyse their genetic relationship with MDD using genomic structural equation modelling. Three disease clusters exhibited independent associations with MDD and accounted for 47% of MDD h ² _SNP , with the gastrointestinal disease cluster having the strongest association (β = 0.63, SE = 0.05, P = 3.04 × 10 ^-30 ). Additionally, we identified independent loci associated with the shared genetic liability between each disease cluster and MDD, revealing different pleiotropic components. Characterisation of these loci revealed previously unidentified associations with MDD and physical disease traits, along with unique biological pathways, drug groups, cell types and genes related to each disease-MDD cluster. Our findings reveal genetic connections implicating the gut-brain axis as a key mechanism underlying the comorbidity of physical diseases in MDD. This work advances our understanding of MDD by highlighting unique and shared genetic components across different disease systems.

Background Compared to typically developing children, those experiencing persistent/recurrent pain (PRP) often report increased exposure to early life adversity (ELA). Separate studies of children experiencing PRP or exposed to ELA show similar alterations in brain morphology. Despite the close relationship between ELA and PRP experiences, their combined effect on the brain remains poorly understood. Methods Structural magnetic resonance imaging data at baseline and 2-year follow-up was accessed from the Adolescent Brain Cognitive Development cohort (N = 1,671). Linear mixed models were used to determine the main effects of group (control, PRP only, ELA only and PRP+ELA) and group-by-time interaction on measures of subcortical volume, cortical thickness and surface area. Support vector machine was applied to perform group classification based on brain measures. Results The PRP+ELA group showed more pronounced reduction in left hippocampal volume and sensorimotor surface area and increase in left precuneus and right frontal pole surface area over time compared to all other groups. In addition, independently of time, the ELA only group showed overall smaller accumbens, thicker right prefrontal and thinner left postcentral gyrus compared to the control and PRP only groups Using all brain measures, support vector machine models could not identify group status better than 66.8% accuracy. Conclusions Our findings provide evidence for unique brain signatures representing the combined effects of PRP and ELA in children during development. Understanding effects of PRP and ELA on the brain of children can inform on treatment options to reduce pain symptoms and improve psychological outcomes.