Search

Aims: Anxiety disorders are prevalent and anxiety symptoms (ANX) co‐occur with many psychiatric disorders. We aimed to identify genomic loci associated with ANX, characterize its genetic architecture, and genetic overlap with psychiatric disorders. Methods: We included a genome‐wide association study of ANX (meta‐analysis of UK Biobank and Million Veterans Program, n = 301,732), schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention‐deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), and validated the findings in the Norwegian Mother, Father, and Child Cohort (n = 95,841). We employed the bivariate causal mixture model and local analysis of covariant association to characterize the genetic architecture including overlap between the phenotypes. Conditional and conjunctional false discovery rate analyses were performed to boost the identification of loci associated with anxiety and shared with psychiatric disorders. Results: Anxiety was polygenic with 12.9k genetic variants and overlapped extensively with psychiatric disorders (4.1k–11.4k variants) with predominantly positive genetic correlations between anxiety and psychiatric disorders. We identified 119 novel loci for anxiety by conditioning on the psychiatric disorders, and loci shared between anxiety and MD n=47, BIP n=33, SCZ n=71, ADHD n=20, and ASD n=5. Genes annotated to anxiety loci exhibit enrichment for a broader range of biological pathways including cell adhesion and neurofibrillary tangle compared with genes annotated to the shared loci. Conclusions: Anxiety is highly polygenic phenotype with extensive genetic overlap with psychiatric disorders, and we identified novel loci for anxiety implicating new molecular pathways. The shared genetic architecture may underlie the extensive cross‐disorder comorbidity of anxiety, and the identified molecular underpinnings may lead to potential drug targets. [ABSTRACT FROM AUTHOR]

Introduction: There is a pressing need for non‐invasive, cost‐effective tools for early detection of Alzheimer's disease (AD). Methods: Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Cox proportional models were conducted to develop a multimodal hazard score (MHS) combining age, a polygenic hazard score (PHS), brain atrophy, and memory to predict conversion from mild cognitive impairment (MCI) to dementia. Power calculations estimated required clinical trial sample sizes after hypothetical enrichment using the MHS. Cox regression determined predicted age of onset for AD pathology from the PHS. Results: The MHS predicted conversion from MCI to dementia (hazard ratio for 80th versus 20th percentile: 27.03). Models suggest that application of the MHS could reduce clinical trial sample sizes by 67%. The PHS alone predicted age of onset of amyloid and tau. Discussion: The MHS may improve early detection of AD for use in memory clinics or for clinical trial enrichment. HIGHLIGHTS: A multimodal hazard score (MHS) combined age, genetics, brain atrophy, and memory.The MHS predicted time to conversion from mild cognitive impairment to dementia.MHS reduced hypothetical Alzheimer's disease (AD) clinical trial sample sizes by 67%.A polygenic hazard score predicted age of onset of AD neuropathology. [ABSTRACT FROM AUTHOR]

Opioid use disorder (OUD) and mental disorders are often comorbid, with increased morbidity and mortality. The causes underlying this relationship are poorly understood. Although these conditions are highly heritable, their shared genetic vulnerabilities remain unaccounted for. We applied the conditional/conjunctional false discovery rate (cond/conjFDR) approach to analyse summary statistics from independent genome wide association studies of OUD, schizophrenia (SCZ), bipolar disorder (BD) and major depression (MD) of European ancestry. Next, we characterized the identified shared loci using biological annotation resources. OUD data were obtained from the Million Veteran Program, Yale‐Penn and Study of Addiction: Genetics and Environment (SAGE) (15 756 cases, 99 039 controls). SCZ (53 386 cases, 77 258 controls), BD (41 917 cases, 371 549 controls) and MD (170 756 cases, 329 443 controls) data were provided by the Psychiatric Genomics Consortium. We discovered genetic enrichment for OUD conditional on associations with SCZ, BD, MD and vice versa, indicating polygenic overlap with identification of 14 novel OUD loci at condFDR < 0.05 and 7 unique loci shared between OUD and SCZ (n = 2), BD (n = 2) and MD (n = 7) at conjFDR < 0.05 with concordant effect directions, in line with estimated positive genetic correlations. Two loci were novel for OUD, one for BD and one for MD. Three OUD risk loci were shared with more than one psychiatric disorder, at DRD2 on chromosome 11 (BD and MD), at FURIN on chromosome 15 (SCZ, BD and MD) and at the major histocompatibility complex region (SCZ and MD). Our findings provide new insights into the shared genetic architecture between OUD and SCZ, BD and MD, indicating a complex genetic relationship, suggesting overlapping neurobiological pathways. [ABSTRACT FROM AUTHOR]

Background: Mood and anxiety disorders account for a large share of the global burden of disability. Some studies suggest that early signs may emerge already in childhood. However, there is a lack of well‐powered, prospective studies investigating how and when childhood mental traits and trajectories relate to adolescent mood and anxiety disorders. Methods: We here examine cross‐sectional and longitudinal association between maternally reported temperamental traits, emotional and behavioral problems in childhood (0.5–8 years) and clinical diagnosis of mood or anxiety ("emotional") disorders in adolescence (10–18 years), using the prospective Norwegian Mother, Father and Child Cohort Study (MoBa) of 110,367 children. Results: Logistic regression analyses showed consistent and increasing associations between childhood negative emotionality, behavioral and emotional problems and adolescent diagnosis of emotional disorders, present from 6 months of age (negative emotionality). Latent profile analysis incorporating latent growth models identified five developmental profiles of emotional and behavioral problems. A profile of early increasing behavioral and emotional problems with combined symptoms at 8 years (1.3% of sample) was the profile most strongly associated with emotional disorders in adolescence (OR vs. reference: 5.00, 95% CI: 3.70–6.30). Conclusions: We found a consistent and increasing association between negative emotionality, behavioral and emotional problems in early to middle childhood and mood and anxiety disorders in adolescence. A developmental profile coherent with early and increasing disruptive mood dysregulation across childhood was the profile strongest associated with adolescent emotional disorders. Our results highlight the importance of early emotional dysregulation and childhood as a formative period in the development of adolescent mood and anxiety disorders, supporting potential for prevention and early intervention initiatives. [ABSTRACT FROM AUTHOR]

Background and aim: Schizophrenia (SCZ) and bipolar disorder (BD) have a high comorbidity of alcohol use disorder (AUD), and both comorbid AUD and excessive alcohol consumption (AC) have been linked to greater illness severity. We aimed to identify genomic loci jointly associated with SCZ, BD, AUD and AC to gain further insights into their shared genetic architecture. Design: We analysed summary data (P values and Z scores) from genome-wide association studies (GWAS) using conjunctional false discovery rate (conjFDR) analysis, which increases power to discover shared genomic loci. We functionally characterized the identified loci using publicly available biological resources. Setting: AUD and AC data provided by the Million Veteran Program, derived from the United States Department of Veterans Affairs Healthcare System. SCZ and BD data provided by the Psychiatric Genomics Consortium, based on cohorts from countries in Europe, North America and Australia. Participants: AUD (34 658 cases, 167 346 controls), AC (n = 200 680), SCZ (31 013 cases and 38 918 controls), BD (20 352 cases and 31 358 controls). All participants were of European ancestry. Measurements: Genomic loci shared between alcohol traits, SCZ and BD at conjFDR <0.05. Findings: Conditional Q-Q plots showed single-nucleotide polymorphism (SNP) enrichment for both alcohol traits across different levels of significance with SCZ and BD, and vice versa. Using conjFDR analysis we leveraged this genetic enrichment and identified several loci shared between SCZ and AUD (n = 28) and AC (n = 24), BD and AUD (n = 2) and AC (n = 8) at conjFDR <0.05. Among these loci, 24 are novel for AUD, 15 are novel for AC, three are novel for SCZ and one is novel for BD. There was a mixture of same and opposite effect directions among the shared loci. Conclusions: Alcohol use disorder and alcohol consumption share genomic loci with the psychiatric disorders schizophrenia and bipolar disorder with a mixed pattern of effect directions, indicating a complex genetic relationship between the phenotypes. [ABSTRACT FROM AUTHOR]

Introduction: Patients with predementia Alzheimer's disease (AD) and at‐risk subjects are targets for promising disease‐modifying treatments, and improved polygenic risk scores (PRSs) could improve early‐stage case selection. Methods: Phenotype‐informed PRSs were developed by selecting AD‐associated variants conditional on relevant inflammatory or cardiovascular traits. The primary outcome was longitudinal changes in measures of AD pathology, namely development of pathological amyloid deposition, medial temporal lobe atrophy, and cognitive decline in a prospective cohort study including 394 adults without AD dementia. Results: High‐risk groups defined by phenotype‐informed AD PRSs had significantly steeper volume decline in medial temporal cortices, and the high‐risk group defined by the cardiovascular‐informed AD PRS had significantly increased hazard ratio of pathological amyloid deposition, compared to low‐risk groups. Discussion: AD PRSs informed by inflammatory disorders or cardiovascular risk factors and diseases are associated with development of AD pathology markers and may improve identification of subjects at risk for progression of AD. [ABSTRACT FROM AUTHOR]

Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population‐based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large‐scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract‐based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction. [ABSTRACT FROM AUTHOR]

Background: Multiple system atrophy (MSA) is a rare neurodegenerative disease characterized by intracellular accumulations of α‐synuclein and nerve cell loss in striatonigral and olivopontocerebellar structures. Epidemiological and clinical studies have reported potential involvement of autoimmune mechanisms in MSA pathogenesis. However, genetic etiology of this interaction remains unknown. We aimed to investigate genetic overlap between MSA and 7 autoimmune diseases and to identify shared genetic loci. Methods: Genome‐wide association study summary statistics of MSA and 7 autoimmune diseases were combined in cross‐trait conjunctional false discovery rate analysis to explore overlapping genetic background. Expression of selected candidate genes was compared in transgenic MSA mice and wild‐type mice. Genetic variability of candidate genes was further investigated using independent whole‐exome genotyping data from large cohorts of MSA and autoimmune disease patients and healthy controls. Results: We observed substantial polygenic overlap between MSA and inflammatory bowel disease and identified 3 shared genetic loci with leading variants upstream of the DENND1B and RSP04 genes, and in intron of the C7 gene. Further, the C7 gene showed significantly dysregulated expression in the degenerating midbrain of transgenic MSA mice compared with wild‐type mice and had elevated burden of protein‐coding variants in independent MSA and inflammatory bowel disease cohorts. Conclusion: Our study provides evidence of shared genetic etiology between MSA and inflammatory bowel disease with an important role of the C7 gene in both phenotypes, with the implication of immune and gut dysfunction in MSA pathophysiology. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Symptoms related to mood and anxiety disorders (emotional disorders) often present in childhood and adolescence. Some of the genetic liability for mental disorders, and emotional and behavioral difficulties seems to be shared. Yet, it is unclear how genetic liability for emotional disorders and related traits influence trajectories of childhood behavioral and emotional difficulties, and if specific developmental patterns are associated with higher genetic liability for these disorders.This study uses data from a genotyped sample of children (n = 54,839) from the Norwegian Mother, Father, and Child Cohort Study (MoBa). We use latent growth models (1.5–5 years) and latent profile analyses (1.5–8 years) to quantify childhood trajectories and profiles of emotional and behavioral difficulties and diagnoses. We examine associations between these trajectories and profiles with polygenic scores for bipolar disorder (PGSBD), anxiety (PGSANX), depression (PGSDEP), and neuroticism (PGSNEUR).Associations between PGSDEP, PGSANX, and PGSNEUR, and emotional and behavioral difficulties in childhood were more persistent than age‐specific across early childhood (1.5–5 years). Higher PGSANX and PGSDEP were associated with steeper increases in behavioral difficulties across early childhood. Latent profile analyses identified five profiles with different associations with emotional disorder diagnosis. All PGS were associated with the probability of classification into profiles characterized by some form of difficulties (vs. a normative reference profile), but only PGSBD was uniquely associated with a single developmental profile.Genetic risk for mood disorders and related traits contribute to both a higher baseline level of, and a more rapid increase in, emotional and behavioral difficulties across early and middle childhood, with some indications for disorder‐specific profiles. Our findings may inform research on developmental pathways to emotional disorders and the improvement of initiatives for early identification and targeted intervention. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusion We aimed to investigate the incidence of juvenile idiopathic arthritis (JIA) in the three geographic regions of Norway and whether potential regional incidence differences are explained by environmental or genetic factors across regions.We conducted a register‐based cohort study including all Norwegian children born from 2004 to 2019, with follow‐up throughout 2020. The JIA diagnosis, defined by at least two International Classification of Diseases, Tenth Revision codes for JIA, was validated against medical records. The incidence rate (IR) and hazard ratio (HR) for JIA were estimated for all Norway and for the North, Mid, and South regions. In a subsample from the Norwegian Mother, Father, and Child Cohort Study (MoBa), the genetic risk for JIA was assessed in the three regions.After median 9.1 (range 0.3–16.0) years of follow‐up, we identified 1,184 patients with JIA and 910,058 controls. The IR for JIA/100,000 person‐years was 14.4 in all of Norway, 25.9 in the North region, 17.9 in the Mid region, and 12.5 in the South region. The HR (95% confidence interval [CI]) of JIA in the North region was 2.07 (1.77–2.43) and in the Mid region HR 1.43 (95% CI 1.23–1.67) compared with the South region. Adjustments for perinatal factors, socioeconomic status, and early antibiotic exposure did not change our estimates substantially. In MoBa (238 patients with JIA, 57,392 controls), the association between JIA and region of birth was no longer significant when adjusting for genetic factors.We found a higher incidence of JIA with increasing latitude without evidence for available environmental factors explaining the observed gradient. In contrast, genetic factors modified the association, but further studies are warranted. [ABSTRACT FROM AUTHOR]

Background: Mood and anxiety disorders account for a large share of the global burden of disability. Some studies suggest that early signs may emerge already in childhood. However, there is a lack of well-powered, prospective studies investigating how and when childhood mental traits and trajectories relate to adolescent mood and anxiety disorders., Methods: We here examine cross-sectional and longitudinal association between maternally reported temperamental traits, emotional and behavioral problems in childhood (0.5-8 years) and clinical diagnosis of mood or anxiety ("emotional") disorders in adolescence (10-18 years), using the prospective Norwegian Mother, Father and Child Cohort Study (MoBa) of 110,367 children., Results: Logistic regression analyses showed consistent and increasing associations between childhood negative emotionality, behavioral and emotional problems and adolescent diagnosis of emotional disorders, present from 6 months of age (negative emotionality). Latent profile analysis incorporating latent growth models identified five developmental profiles of emotional and behavioral problems. A profile of early increasing behavioral and emotional problems with combined symptoms at 8 years (1.3% of sample) was the profile most strongly associated with emotional disorders in adolescence (OR vs. reference: 5.00, 95% CI: 3.70-6.30)., Conclusions: We found a consistent and increasing association between negative emotionality, behavioral and emotional problems in early to middle childhood and mood and anxiety disorders in adolescence. A developmental profile coherent with early and increasing disruptive mood dysregulation across childhood was the profile strongest associated with adolescent emotional disorders. Our results highlight the importance of early emotional dysregulation and childhood as a formative period in the development of adolescent mood and anxiety disorders, supporting potential for prevention and early intervention initiatives., (© 2022 The Authors. Acta Psychiatrica Scandinavica published by John Wiley & Sons Ltd.)