Most previous genome-wide association studies (GWAS) of depression have used data from individuals of European descent. This limits the understanding of the underlying biology of depression and raises questions about the transferability of findings between populations.
The Integrative Psychiatric Research (iPSYCH) consortium has established a large Danish population-based Case-Cohort sample (iPSYCH2012) aimed at unravelling the genetic and environmental architecture of severe mental disorders. The iPSYCH2012 sample is nested within the entire Danish population born between 1981 and 2005, including 1 472 762 persons. This paper introduces the iPSYCH2012 sample and outlines key future research directions. Cases were identified as persons with schizophrenia (N=3540), autism (N=16 146), attention-deficit/hyperactivity disorder (N=18 726) and affective disorder (N=26 380), of which 1928 had bipolar affective disorder. Controls were randomly sampled individuals (N=30 000). Within the sample of 86 189 individuals, a total of 57 377 individuals had at least one major mental disorder. DNA was extracted from the neonatal dried blood spot samples obtained from the Danish Neonatal Screening Biobank and genotyped using the Illumina PsychChip. Genotyping was successful for 90% of the sample. The assessments of exome sequencing, methylation profiling, metabolome profiling, vitamin-D, inflammatory and neurotrophic factors are in progress. For each individual, the iPSYCH2012 sample also includes longitudinal information on health, prescribed medicine, social and socioeconomic information, and analogous information among relatives. To the best of our knowledge, the iPSYCH2012 sample is the largest and most comprehensive data source for the combined study of genetic and environmental aetiologies of severe mental disorders.
Abstract Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2×10 −6 ) while the inverse was not significant (19 regions, p=0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several significant correlations with: 1) SCZ subphenotypes: negative symptoms (SCZ, p=3.6×10 −6 ) and manic symptoms (BD, p=2×10 −5 ), 2) BD subphenotypes: psychotic features (SCZ p=1.2×10 −10 , BD p=5.3×10 −5 ) and age of onset (SCZ p=7.9×10 −4 ). Finally, we show that psychotic features in BD has significant SNP-heritability (h 2 snp =0.15, SE=0.06), and a significant genetic correlation with SCZ (r g =0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p=0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes ( DARS2 , ARFGEF2 , DCAKD and GATAD2A ) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.
Abstract Psychiatric family history or a high autism polygenic risk score (PRS) have been separately linked to autism spectrum disorder (ASD) risk. The study aimed to simultaneously consider psychiatric family history and individual autism genetic liability (PRS) in autism risk. We performed a case–control study of all Denmark singleton births, May 1981–December 2005, in Denmark at their first birthday and a known mother. Cases were diagnosed with ASD before 2013 and controls comprised a random sample of 30,000 births without ASD, excluding persons with non‐Denmark‐born parents, missing ASD PRS, non‐European ancestry. Adjusted odds ratios (aOR) were estimated for ASD by PRS decile and by psychiatric history in parents or full siblings (8 mutually‐exclusive categories) using logistic regression. Adjusted ASD PRS z ‐score least‐squares means were estimated by psychiatric family history category. ASD risk (11,339 ASD cases; 20,175 controls) from ASD PRS was not substantially altered after accounting for psychiatric family history (e.g., ASD PRS 10th decile aOR: 2.35 (95% CI 2.11–2.63) before vs 2.11 (95% CI 1.91–2.40) after adjustment) nor from psychiatric family history after accounting for ASD PRS (e.g., ASD family history aOR: 6.73 (95% CI 5.89–7.68) before vs 6.32 (95% CI 5.53–7.22) after adjustment). ASD risk from ASD PRS varied slightly by psychiatric family history. While ASD risk from psychiatric family history was not accounted for by ASD PRS and vice versa, risk overlap between the two factors will likely increase as measures of genetic risk improve. The two factors are best viewed as complementary measures of family‐based autism risk. Lay Summary Autism risk from a history of mental disorders in the immediate family was not explained by a measure of individual genetic risk (autism polygenic risk score) and vice versa. That is, genetic risk did not appear to overlap family history risk. As genetic measures for autism improve then the overlap in autism risk from family history versus genetic factors will likely increase, but further study may be needed to fully determine the components of risk and how they are inter‐related between these key family factors. Meanwhile, the two factors may be best viewed as complementary measures of autism family‐based risk.
Diagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy.To develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics.This study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022.At least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ.The predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance.A total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72.Results of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.
Screening for SCID was added to the Danish Neonatal Screening Program in February 2020. The screening uses a RealtimePCR kit and we here present the results and experiences with the validation of the kit and the first 10 months of screening.
Major depressive disorder is a clinically heterogeneous psychiatric disorder with a polygenic architecture. Genome-wide association studies have identified a number of risk-associated variants across the genome and have reported growing evidence of NETRIN1 pathway involvement. Stratifying disease risk by genetic variation within the NETRIN1 pathway may provide important routes for identification of disease mechanisms by focusing on a specific process, excluding heterogeneous risk-associated variation in other pathways. Here, we sought to investigate whether major depressive disorder polygenic risk scores derived from the NETRIN1 signaling pathway (NETRIN1-PRSs) and the whole genome, excluding NETRIN1 pathway genes (genomic-PRSs), were associated with white matter microstructure. We used two diffusion tensor imaging measures, fractional anisotropy (FA) and mean diffusivity (MD), in the most up-to-date UK Biobank neuroimaging data release (FA: n = 6401; MD: n = 6390). We found significantly lower FA in the superior longitudinal fasciculus (β = −.035, pcorrected = .029) and significantly higher MD in a global measure of thalamic radiations (β = .029, pcorrected = .021), as well as higher MD in the superior (β = .034, pcorrected = .039) and inferior (β = .029, pcorrected = .043) longitudinal fasciculus and in the anterior (β = .025, pcorrected = .046) and superior (β = .027, pcorrected = .043) thalamic radiation associated with NETRIN1-PRS. Genomic-PRS was also associated with lower FA and higher MD in several tracts. Our findings indicate that variation in the NETRIN1 signaling pathway may confer risk for major depressive disorder through effects on a number of white matter tracts.
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