Showing papers by "Josef Frank published in 2022"
TL;DR: In this article , a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals was conducted, and the authors reported common variant associations at 287 distinct genomic loci.
Abstract: Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies. A genome-wide association study including over 76,000 individuals with schizophrenia and over 243,000 control individuals identifies common variant associations at 287 genomic loci, and further fine-mapping analyses highlight the importance of genes involved in synaptic processes.
558 citations
TL;DR: Findings of this study based on common genetic variants indicate that TRS is heritable with a modest but significant single-nucleotide variation–based heritability.
Abstract: Key Points Question Can common genetic variants be used to differentiate between treatment-resistant schizophrenia (TRS) and other forms of this disorder? Findings Data from this genome-wide association study including 85 490 participants were used to estimate genome-wide single-nucleotide variation effect size differences between individuals with and without TRS, which were compatible with a polygenic model of treatment resistance. Results were used to generate a polygenic risk score, which was significantly associated with TRS status in independent incidence and prevalence samples. Meaning Findings of this study based on common genetic variants indicate that TRS is heritable with a modest but significant single-nucleotide variation–based heritability.
32 citations
TL;DR: Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction, which may help clinicians better predict which patients will respond to lithium treatment.
Abstract: Background Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. Aims To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. Method This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. Results The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. Conclusions Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
8 citations
TL;DR: In this article , the authors conducted a GWAS in 584 non-alcoholic chronic pancreatitis (NACP) patients and 6040 healthy controls and applied Bayesian colocalization analysis of identified genome-wide significant risk loci from both, our recently published alcoholic chronic Pancitis (ACP) and the novel NACP dataset, with pancreas eQTLs from the GTEx V8 European cohort to prioritize candidate causal genes and extracted credible sets of shared causal variants.
4 citations
TL;DR: In this paper , the authors examined the genetic relationship between mood disorders and biological rhythms and found that depression was negatively correlated with overall physical activity and positively with sedentary behaviour, while BIP-I showed associations in the opposite direction.
Abstract: Abstract Major Depression and Bipolar Disorder Type I (BIP-I) and Type II (BIP-II), are characterized by depressed, manic, and hypomanic episodes in which specific changes of physical activity, circadian rhythm, and sleep are observed. It is known that genetic factors contribute to variation in mood disorders and biological rhythms, but unclear to what extent there is an overlap between their underlying genetics. In the present study, data from genome-wide association studies were used to examine the genetic relationship between mood disorders and biological rhythms. We tested the genetic correlation of depression, BIP-I, and BIP-II with physical activity (overall physical activity, moderate activity, sedentary behaviour), circadian rhythm (relative amplitude), and sleep features (sleep duration, daytime sleepiness). Genetic correlations of depression, BIP-I, and BIP-II with biological rhythms were compared to discover commonalities and differences. A gene-based analysis tested for associations of single genes and common circadian genes with mood disorders. Depression was negatively correlated with overall physical activity and positively with sedentary behaviour, while BIP-I showed associations in the opposite direction. Depression and BIP-II had negative correlations with relative amplitude. All mood disorders were positively correlated with daytime sleepiness. Overall, we observed both genetic commonalities and differences across mood disorders in their relationships with biological rhythms: depression and BIP-I differed the most, while BIP-II was in an intermediate position. Gene-based analysis suggested potential targets for further investigation. The present results suggest shared genetic underpinnings for the clinically observed associations between mood disorders and biological rhythms. Research considering possible joint mechanisms may offer avenues for improving disease detection and treatment.
3 citations
TL;DR: In this paper , differentially methylated regions (DMRs) identified using the comb-p algorithm were found for treatment response or early improvement in major depressive disorder (MDD) patients.
Abstract: Abstract Although the currently available antidepressants are well established in the treatment of the major depressive disorder (MDD), there is strong variability in the response of individual patients. Reliable predictors to guide treatment decisions before or in an early stage of treatment are needed. DNA-methylation has been proven a useful biomarker in different clinical conditions, but its importance for mechanisms of antidepressant response has not yet been determined. 80 MDD patients were selected out of >500 participants from the Early Medication Change (EMC) cohort with available genetic material based on their antidepressant response after four weeks and stratified into clear responders and age- and sex-matched non-responders ( N = 40, each). Early improvement after two weeks was analyzed as a secondary outcome. DNA-methylation was determined using the Illumina EPIC BeadChip. Epigenome-wide association studies were performed and differentially methylated regions (DMRs) identified using the comb-p algorithm. Enrichment was tested for hallmark gene-sets and in genome-wide association studies of depression and antidepressant response. No epigenome-wide significant differentially methylated positions were found for treatment response or early improvement. Twenty DMRs were associated with response; the strongest in an enhancer region in SORBS2 , which has been related to cardiovascular diseases and type II diabetes. Another DMR was located in CYP2C18 , a gene previously linked to antidepressant response. Results pointed towards differential methylation in genes associated with cardiac function, neuroticism, and depression. Linking differential methylation to antidepressant treatment response is an emerging topic and represents a step towards personalized medicine, potentially facilitating the prediction of patients’ response before treatment.
2 citations
TL;DR: In this article , the authors investigated the similarity of DNAm signatures in matched blood and postmortem brain samples and performed epigenome-wide association studies in five brain regions belonging to the neurocircuitry of addiction: anterior cingulate cortex (ACC), Brodmann Area 9, caudate nucleus, putamen, and ventral striatum.
Abstract: (1) Background: Epigenome-wide association studies (EWAS) in peripheral blood have repeatedly found associations between tobacco smoking and aberrant DNA methylation (DNAm), but little is known about DNAm signatures of smoking in the human brain, which may contribute to the pathophysiology of addictive behavior observed in chronic smokers. (2) Methods: We investigated the similarity of DNAm signatures in matched blood and postmortem brain samples (n = 10). In addition, we performed EWASs in five brain regions belonging to the neurocircuitry of addiction: anterior cingulate cortex (ACC), Brodmann Area 9, caudate nucleus, putamen, and ventral striatum (n = 38–72). (3) Results: cg15925993 within the LOC339975 gene was epigenome-wide significant in the ACC. Of 16 identified differentially methylated regions, two (PRSS50 and LINC00612/A2M-AS1) overlapped between multiple brain regions. Functional enrichment was detected for biological processes related to neuronal development, inflammatory signaling and immune cell migration. Additionally, our results indicate the association of the well-known AHRR CpG site cg05575921 with smoking in the brain. (4) Conclusion: The present study provides further evidence of the strong relationship between aberrant DNAm and smoking.
2 citations
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1 citations
TL;DR: In this paper , the authors investigated epigenome-wide DNA methylation (DNAm) signatures of CUD in human post-mortem brain tissue of Brodmann area 9 (BA9) and found that CUD is associated with epigenomewide differences in DNAm levels in BA9 particularly related to synaptic signaling and neuroplasticity.
Abstract: Background Cocaine use disorder (CUD) is characterized by a loss of control over cocaine intake and is associated with structural, functional, and molecular alterations in the human brain. At the molecular level, epigenetic alterations are hypothesized to contribute to the higher-level functional and structural brain changes observed in CUD. Most evidence of cocaine-associated epigenetic changes comes from animal studies while only a few studies have been performed using human tissue. Methods We investigated epigenome-wide DNA methylation (DNAm) signatures of CUD in human post-mortem brain tissue of Brodmann area 9 (BA9). A total of N = 42 BA9 brain samples were obtained from N = 21 individuals with CUD and N = 21 individuals without a CUD diagnosis. We performed an epigenome-wide association study (EWAS) and analyzed CUD-associated differentially methylated regions (DMRs). To assess the functional role of CUD-associated differential methylation, we performed Gene Ontology (GO) enrichment analyses and characterized co-methylation networks using a weighted correlation network analysis. We further investigated epigenetic age in CUD using epigenetic clocks for the assessment of biological age. Results While no cytosine-phosphate-guanine (CpG) site was associated with CUD at epigenome-wide significance in BA9, we detected a total of 20 CUD-associated DMRs. After annotation of DMRs to genes, we identified Neuropeptide FF Receptor 2 (NPFFR2) and Kalirin RhoGEF Kinase (KALRN) for which a previous role in the behavioral response to cocaine in rodents is known. Three of the four identified CUD-associated co-methylation modules were functionally related to neurotransmission and neuroplasticity. Protein-protein interaction (PPI) networks derived from module hub genes revealed several addiction-related genes as highly connected nodes such as Calcium Voltage-Gated Channel Subunit Alpha1 C (CACNA1C), Nuclear Receptor Subfamily 3 Group C Member 1 (NR3C1), and Jun Proto-Oncogene, AP-1 Transcription Factor Subunit (JUN). In BA9, we observed a trend toward epigenetic age acceleration (EAA) in individuals with CUD remaining stable even after adjustment for covariates. Conclusion Results from our study highlight that CUD is associated with epigenome-wide differences in DNAm levels in BA9 particularly related to synaptic signaling and neuroplasticity. This supports findings from previous studies that report on the strong impact of cocaine on neurocircuits in the human prefrontal cortex (PFC). Further studies are needed to follow up on the role of epigenetic alterations in CUD focusing on the integration of epigenetic signatures with transcriptomic and proteomic data.
1 citations
TL;DR: Emotional well-being of children and mothers is negatively affected by the COVID-19 pandemic, with differences in development over time, and future studies should examine which mechanisms contribute to stress-related associations and at which age they can be identified.
Abstract: Stress is an established risk factor for somatic and mental disorders. The COVID-19 pandemic and the related countermeasures severely affect the lives of families. Prenatal stress, dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, and genetic factors might impact the well-being of individuals. The present work is part of an ongoing birth cohort study and aims to investigate maternal perceived stress, early childhood HPA axis activity and polygenic risk scores (PRSs) as predictors of emotional well-being during the COVID-19 pandemic. All participants are part of the ongoing birth cohort study POSEIDON. Emotional well-being of children (n = 259) and mothers (n = 211) was assessed during the COVID-19 pandemic using the CRISIS questionnaire. Furthermore, associations between previously assessed maternal perceived stress (Perceived Stress Scale), children's salivary and morning urine cortisol measures at 45 months, PRSs for depression, schizophrenia, loneliness and current emotional well-being were investigated. A positive association between the child's and the mother's emotional well-being was found. A worse emotional well-being was observed in both children and mothers during the pandemic compared to before. Children's emotional well-being improved over the course of the pandemic, while mothers' well-being worsened. Maternal perceived stress, salivary and morning urine cortisol and PRSs were not significantly associated with the assessed emotional wellbeing. The present study confirms that emotional well-being of children and mothers is negatively affected by the COVID-19 pandemic, with differences in development over time. Future studies should examine which mechanisms contribute to stress-related associations and at which age they can be identified.
TL;DR: In this article , the authors focus on early childhood, and investigate urbanicity, behavior problems and the regulation of the hypothalamus-pituitary-adrenal (HPA) axis, a central circuit of the stress system.
TL;DR: In this article , the authors used the GWAS and Sequencing Consortium of Alcohol and Nicotine (GSCAN) Consortium were used to calculate polygenic risk scores (PRS) in a sample of ~2,200 smokers/never-smokers.
Abstract: Introduction Formal genetics studies show that smoking is influenced by genetic factors; exploring this on the molecular level can offer deeper insight into the etiology of smoking behaviours. Methods Summary statistics from the GWAS and Sequencing Consortium of Alcohol and Nicotine (GSCAN) Consortium were used to calculate polygenic risk scores (PRS) in a sample of ~2,200 smokers/never-smokers. The association of PRS for Smoking Initiation (i.e. Lifetime Smoking; SI-PRS) with smoking status, and PRS for Cigarettes per Day (CpD-PRS) and Smoking Cessation (SC-PRS) with Fagerstrom Test for Nicotine Dependence (FTND) score were examined, as were distinct/additive effects of parental smoking on smoking status. Results SI-PRS explained 6.65% of variance (Nagelkerke-R2) in smoking status (p=1.71x10-24). In smokers, CpD-PRS (R2=3.15%, p=1.82x10-8) and SC-PRS (R2=2.01%; p=7.18x10-6) were associated with FTND score. Parental smoking alone explained R2=3.06% (p=2.43x10-12) of smoking status, and 1.39% when added to the most informative SI-PRS model (total R2=8.04%). Conclusion These results show the potential utility of molecular genetic data for research investigating smoking prevention. The fact that PRS explains more variance than family history highlights progress from formal to molecular genetics; the overlap and increased predictive value when using both suggests the importance of combining these approaches. Implications: This study underlines the value of using PRS to predict smoking status/behaviour. It highlights the importance of molecular genetic methods in research investigating smoking prevention and points to the necessity of combining family history and molecular genetic data.
TL;DR: One human-specific single nucleotide polymorphism (rs6265) in the BDNF gene causes a substitution of valine (Val) to methionine (Met) at codon 66 in the pro domain of the protein (Val66Met) as discussed by the authors .
TL;DR: In this article , the authors investigated the association between overall genetic variability in the NPS/NPSR1 system and psychological and cortisol stress regulation in everyday life and found a significant GxE interaction for the area under the curve with respect to the ground.
TL;DR: In this article , the authors measured fast sleep spindles in 150 healthy adults and investigated its association with a genome-wide polygenic score for schizophrenia (SCZ-PGS).
Abstract: Cognitive impairment is a common feature in schizophrenia and the strongest prognostic factor for long-term outcome. Identifying a trait associated with the genetic background for cognitive outcome in schizophrenia may aid in a deeper understanding of clinical disease subtypes. Fast sleep spindles may represent such a biomarker as they are strongly genetically determined, associated with cognitive functioning and impaired in schizophrenia and unaffected relatives. We measured fast sleep spindle density in 150 healthy adults and investigated its association with a genome-wide polygenic score for schizophrenia (SCZ-PGS). The association between SCZ-PGS and fast spindle density was further characterized by stratifying it to the genetic background of intelligence. SCZ-PGS was positively associated with fast spindle density. This association mainly depended on pro-cognitive genetic variants. Our results strengthen the evidence for a genetic background of spindle abnormalities in schizophrenia. Spindle density might represent an easily accessible marker for a favourable cognitive outcome which should be further investigated in clinical samples.