European Neuropsychopharmacology最新文献

{"title":"NEW INSIGHTS INTO THE GENETIC ETIOLOGY AND THERAPEUTIC TARGETS OF SCHIZOPHRENIA","authors":"","doi":"10.1016/j.euroneuro.2024.08.039","DOIUrl":"10.1016/j.euroneuro.2024.08.039","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 13"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"POLYGENIC ARCHITECTURE AND DISEASE RISK PREDICTION","authors":"Na Cai (Speaker)","doi":"10.1016/j.euroneuro.2024.08.008","DOIUrl":"10.1016/j.euroneuro.2024.08.008","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 1"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PREDICTION OF ANTIDEPRESSANT SIDE EFFECTS IN THE GENETIC LINK TO ANXIETY AND DEPRESSION STUDY","authors":"Danyang Li ,&nbsp;Yuhao Lin ,&nbsp;Helena Davies ,&nbsp;Evangelos Vassos ,&nbsp;Raquel Iniesta ,&nbsp;Gerome Breen","doi":"10.1016/j.euroneuro.2024.08.065","DOIUrl":"10.1016/j.euroneuro.2024.08.065","url":null,"abstract":"<div><div>Antidepressants are the most common treatment for moderate or severe depression. Side effects are crucial indicators for antidepressants, but their expression varies widely among individuals.</div><div>In this study, we leveraged genetic and phenotypic data from self-reported questionnaires in the Genetic Link to Anxiety and Depression (GLAD) study to predict side effects and discontinuation (due to side effect) across three antidepressant classes (SSRI, SNRI, tricyclic antidepressants (TCA)) at the first and the last (most recent) year of prescription. About 260 predictors spanning genetic, clinical, comorbidity, demographic, and antidepressant information were included. XGBoost, random forest, cubist, elastic net, and support vector machine (with RBF and polynomial kernel) were trained, and their performance was compared.</div><div>The final dataset comprised 5358 individuals, with 4354 in the first and 3414 in the last year of prescription. The average prevalence of side effects and discontinuation was 74.1% and 28.7%, respectively. In the initial year, the best AUROC for predicting SSRI discontinuation and side effects were 0.65 and 0.60. In the last year of SSRI prescription, the highest AUROC reached 0.73 for discontinuation and 0.87 for side effects. Models for predicting discontinuation and side effects of SNRI and TCA showed comparable performance. The history of side effects and discontinuation of antidepressant use were the most influential predictors of the outcomes in the last year. When examining 30 common antidepressant side effect symptoms, most of them were differentially prevalent between antidepressant classes.</div><div>Our findings demonstrate the feasibility of predicting antidepressant side effects using a self-reported questionnaire, particularly for the last prescription. These results contribute valuable insights for the development of clinical decisions aimed at optimising treatment selection with enhanced tolerability.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 25"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"META-ANALYSIS OF RARE CNV GENOME-WIDE ASSOCIATION STUDIES ACROSS MAJOR PSYCHIATRIC DISORDERS IN EUR, AFR/AFAM, AND ASN/ASAM POPULATIONS","authors":"Omar Shanta ,&nbsp;Worrawat Engchuan ,&nbsp;Jeff MacDonald ,&nbsp;Marieke Klein ,&nbsp;Bhooma Thiruvahindrapuram ,&nbsp;Adam Maihofer ,&nbsp;Molly Sacks ,&nbsp;Mohammad Ahangari ,&nbsp;Sebastien Jacquemont ,&nbsp;Kimberley Kendall ,&nbsp;Ida Sonderby ,&nbsp;Guillaume Huguet ,&nbsp;Steven H. Scherer ,&nbsp;Jonathan Sebat ,&nbsp;The Bipolar Disorder, Schizophrenia, Post-Traumatic Stress Disorder, Attention-Deficit/Hyperactivity Disorder, Major Depressive Disorder, Autism Spectrum Disorder and Copy Number Variation Working groups of the Psychiatric Genomics Consortium","doi":"10.1016/j.euroneuro.2024.08.070","DOIUrl":"10.1016/j.euroneuro.2024.08.070","url":null,"abstract":"<div><div>Genome-wide association studies (GWAS) to date have been able to leverage large sample sizes to identify genomic loci that contribute to risk for various psychiatric disorders. However, GWAS of copy number variants (CNVs) have prioritized identifying risk loci within European populations due to the lack of power in diverse ancestry groups. In this study, we called CNVs in a diverse group of samples to create CNV datasets for 2 additional ancestry groups: African/African American (AFR/AFAM) and Asian/Asian American (ASN/ASAM). SNPweights was used to infer genome-wide genetic ancestry for each sample. We were then able to boost power at specific loci by using a meta-analysis to combine EUR, AFR/AFAM, and ASN/ASAM CNV analyses (N=571,803).</div><div>Rare copy number variants have been implicated in a cross-disorder European cohort (N=537,466) that includes major psychiatric disorders such as autism (ASD), schizophrenia (SCZ), major depressive disorder (MDD), bipolar disorder (BD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD). This analysis was able to identify novel loci with the statistical power that comes with being the largest CNV study to date. Naturally, the inclusion of diverse samples in this analysis can further lead to novel discoveries. Additional CNV-GWAS were performed for cross-disorder datasets in AFR/AFAM (N=17,474) and ASN/ASAM (N=16,863) populations. Meta-analysis of all 3 populations used an inverse-variance weighting to account for the disparity of sample size between populations. We compared EUR CNV-GWAS and burden results with those from the meta-analysis as these were the most well-powered tests. The effect was a substantial increase in significance levels at specific loci that reached testable CNV frequencies in the diverse groups. Comparing the EUR analysis with the trans-ancestry analysis allows us to quantify the contribution of the diverse groups and provide insight into the genomic loci associated with psychiatric disorders in AFR/AFAM and ASN/ASAM populations once similar sample sizes are reached. This study highlights the importance of expanding diversity during data collection so that the genotype-phenotype relationships can benefit people worldwide.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 27-28"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE ALLELIC ARCHITECTURE OF RARE VARIATION IN AUTISM AND OTHER NEURODEVELOPMENTAL CONDITIONS","authors":"Jack Fu ,&nbsp;F. Kyle Satterstrom ,&nbsp;Kirsty McWalter ,&nbsp;Harrison Brand ,&nbsp;Robert Kueffner ,&nbsp;David Cutler ,&nbsp;Kaitlin Samocha ,&nbsp;Elise Robinson ,&nbsp;Joseph Buxbaum ,&nbsp;Bernie Devlin ,&nbsp;Kathryn Roeder ,&nbsp;Paul Kruszka ,&nbsp;Stephan Sanders ,&nbsp;Mark Daly ,&nbsp;Michael Talkowski","doi":"10.1016/j.euroneuro.2024.08.046","DOIUrl":"10.1016/j.euroneuro.2024.08.046","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The fields of autism and neurodevelopmental disorder (NDD) genetics are rapidly advancing. Catalyzed by the power of large cohorts and integration of all classes of de novo and inherited protein-coding variation, dozens of genes have emerged to harbor variants that confer high relative risk for autism, and hundreds of genes have been associated with NDDs more broadly. Through examination of protein-truncating variants (PTVs), predicted damaging missense variation, and copy number variants (CNVs), our prior analyses have begun to map the allelic diversity of perturbations within 72 autism-associated genes and 373 genes associated with NDDs, finding intriguing evidence of genes with significantly higher mutation rates and differences in the distribution of clinical phenotypes in autism compared to NDD (Fu et al., 2022; Satterstrom et al., 2020). Despite this progress, cohort sizes remain insufficient for disentangling the shared and distinct genetic architectures of autism, NDDs, and other neuropsychiatric conditions, as well as associating genes with more subtle impacts on neurodevelopment.&lt;/div&gt;&lt;div&gt;To advance these boundaries, we present the largest to-date study of rare coding variants, consisting of 62,013 autistic individuals, including 38,088 probands and 9,567 unaffected siblings from complete trio and quartet families, respectively, and 23,925 additional autism cases without parental information contrasted against 26,931 controls. By aggregating across the Autism Sequencing Consortium (ASC), the Simons Simplex Collection (SSC), the Simons Foundation Powering Autism Research (SPARK), and individuals from a leading diagnostic laboratory (GeneDx), this dataset totals almost 200,000 individuals, nearly a three-fold increase over prior studies. When we stratified the clinically-referred GeneDx autistic probands by co-occurring DD/ID status, we found synonymous, missense, and PTV de novo mutation rates in autism probands without DD/ID from GeneDx that were nearly identical to individuals ascertained for a diagnosis of autism in the ASC, SSC, and SPARK research studies (0.296 vs 0.294, 0.767 vs 0.763, and 0.141 vs 0.145 respectively), while GeneDx autism probands with DD/ID exhibited mutation rates similar to those observed in previous research studies of DD.&lt;/div&gt;&lt;div&gt;Further analyses of these data solidified previous observations of significant enrichment of de novo PTVs among autism probands of 3x compared to siblings among the genes most intolerant to PTVs in the human genome (i.e., lowest decile of LOEUF from gnomAD). We have also incorporated Alpha Missense (AM) pathogenicity estimates to complement our prior MPC scores for predicting damaging missense variation and identifying de novo missense variants acting with effect sizes comparable to de novo PTVs in constrained genes, with analysis of regional missense constraint within genes ongoing. We further leveraged the TADA Bayesian statistical method to jointly model these data in ","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 16-17"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"THE IDENTICAL DEPRESSION PHENOTYPING CONSORTIUM: DECONSTRUCTION AND PREDICTION OF MDD AND TREATMENT RESPONSE","authors":"Gerome Breen (Chair) ,&nbsp;Brittany Mitchell (Co-chair) ,&nbsp;Alexander Hatoum (Discussant)","doi":"10.1016/j.euroneuro.2024.08.063","DOIUrl":"10.1016/j.euroneuro.2024.08.063","url":null,"abstract":"<div><div>The Identical Depression Phenotyping Consortium consists of studies in the UK (Genetic Links to Anxiety and Depression or GLAD and UK Biobank), the Australian Genetics of Depression study, and the Biobanks Netherlands Internet Collaboration (BIONIC). The three studies are using the same method of phenotyping depression with detailed demographics, clinical record linkage, and data on over 130,000 cases of Major Depressive Disorder. We propose a symposium focused on advancing predictive models in MDD and its treatment, emphasizing the integration of polygenic scores, family history, and clinical data.</div><div>Wang will present on Joint Multi-Family History and Multi-Polygenic Score Prediction of Major Depressive Disorder. Machine learning integrating these factors in GLAD (9,927 MDD cases, 4,452 controls) revealed significant prediction accuracies for MDD, the number of recurrent MDD episodes. These findings were replicated in UK Biobank (40,667 MDD cases, 70,755 controls). Next, Li will present on incorporating genetic and clinical predictors for antidepressant side effects in &gt; 5K cases from the GLAD study. By employing machine learning models, they achieved significant success in predicting side effects and discontinuation rates, particularly when integrating data from prior prescriptions. Huider will present on genetic analyses of MDD on behalf of the BIONIC consortium presents a large-scale genetic analyses of MDD and its symptoms to explore depression heterogeneity within the Netherlands, utilizing uniform in-depth phenotyping in &gt; 30K cases. This ambitious project highlights the importance of large, homogeneous datasets in deciphering the complex genetics of depression. Finally, Mitchell will present on Using polygenic risk scores to characterise treatment resistant MDD in to explore the association of TRD with biological predictors such a polygenic score (PGS) and CYP2C19 and CYP2D16 metaboliser profiles, measured personality traits, and environmental predictors such as social support and exposure to stressful life events. Lastly, they tested for any gene-environment interactions across predictors. Their research identifies genetic factors that correlate with long-term treatment outcomes, providing a basis for personalized medicine in treating depression.</div><div>This symposium aims to showcase cutting-edge research that integrates genetic, familial, and clinical data to predict and manage major depressive disorder more effectively. Discussant Hatoum will consider the implications of integration of genetic prediction with machine learning approaches and the possibilities for clinical utility.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 24"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ETHICAL AND POLICY ISSUES IN A DIVERSE WORLD","authors":"Todd Lencz (Chair) ,&nbsp;Julia Sealock (Co-chair) ,&nbsp;Anna Docherty (Discussant)","doi":"10.1016/j.euroneuro.2024.08.058","DOIUrl":"10.1016/j.euroneuro.2024.08.058","url":null,"abstract":"<div><div>This will be the Symposium presented by the Ethics, Positions, and Public Policy Committee.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 22"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EXPLORING THE IMPACT OF INCLUSION/EXCLUSION CRITERIA ON THE GENETIC ARCHITECTURE OF MAJOR DEPRESSIVE DISORDER IN DANISH BIOBANKS","authors":"Mischa Lundberg ,&nbsp;Maria Didriksen ,&nbsp;Michael Schwinn ,&nbsp;Sarah Guagliardo ,&nbsp;Joel Mefford ,&nbsp;Na Cai ,&nbsp;Christian Erikstrup ,&nbsp;Ole B. Pedersen ,&nbsp;Erik Sørensen ,&nbsp;Hreinn Stefansson ,&nbsp;Kenneth Kendler ,&nbsp;Jonathan Flint ,&nbsp;Thomas Werge ,&nbsp;Sisse R. Ostrowski ,&nbsp;Andrew Schork","doi":"10.1016/j.euroneuro.2024.08.078","DOIUrl":"10.1016/j.euroneuro.2024.08.078","url":null,"abstract":"<div><div>Large samples are needed for genome-wide association studies (GWAS) of Major Depressive Disorder (MDD), and, indeed, recent studies aggregate more than a million individuals. Achieving these numbers means relaxing inclusion criteria for cases, often to a single self-report item, which may impact our ability to identify core disease mechanisms. Prior work suggests “shallow” criteria offer a different picture of the genetic architecture of MDD than more careful clinical criteria. Danish health registers, with long follow-ups, provide life-course medical history for those diagnosed with MDD and an ability to ask how representative samples are of the underlying population. Here, we study 50,000 MDD cases and 300,000 controls from three Danish biobanks with linkage to National register data: the iPSYCH2015 case-cohort study, the Danish Blood Donors Study, and the Copenhagen Hospital Biobank. We conduct a systemic analysis of the impact of ascertainment and inclusion/exclusion criteria on the inferred genetic architecture of MDD, seeking to clarify analogues for deeply phenotyped MDD. Among broadest possible MDD (BP-MDD; at least one diagnosis of ICD10: F32-33), we assessed the prevalence of potentially exclusionary diagnoses that reflect enrollment requirements for deeply phenotyped clinical studies (e.g., lifetime exclusions for schizophrenia, bipolar, or intellectual disability; post exclusions for onset of MDD subsequent to dementia or terminal illness diagnosis; event-based exclusions of MDD occurring within one year of an AUD, DUD or MCI diagnosis; and age-based exclusions for onset before 18 or after 50). We compare the genetic architecture of BP-MDD to clinically plausible MDD defined by the above criteria with or without age restrictions (CPA-MDD / CP-MDD) using GWAS and SNP-based variance components analysis. We then use polygenic score (PGS) profiles to test for differences in underlying genetic liability among cases with and without each potential exclusion criterion. Next, we compare the replication sensitivity of 250 index SNPs of previously identified MDD loci to inclusion/exclusion criteria. We observed an impact of exclusion criteria on GWAS power, replication sensitivity, and PGS profiles of individuals diagnosed with MDD. For example, we observed the PGS for MDD trended higher in CP-MDD while the PGS for SCZ and PTSD were significantly lower. Consistent with this, for some variants, effect sizes appear to increase when replicated in the smaller, more strictly defined CP-MDD, while other variants saw effect sizes diminish when moving from shallower BP-MDD to stricter CP-MDD. Our study highlights the impact that considering life-course exclusion criteria in defining MDD cases from biobanks has on the underlying genetic architecture. We believe this motivates important discussions regarding the next steps for MDD GWAS that could help improve the portability of results across cohorts and enable.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 31"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HETEROGENEITY AND COMORBIDITY: PERSPECTIVES FROM BIOBANKS, REGISTRIES AND EHRS","authors":"Na Cai (Chair) ,&nbsp;Andrew Schork (Co-chair) ,&nbsp;Jonathan Flint (Discussant)","doi":"10.1016/j.euroneuro.2024.08.074","DOIUrl":"10.1016/j.euroneuro.2024.08.074","url":null,"abstract":"<div><div>Large-scale, population-based biobanks, electronic health records (EHRs) and medical registries have been transformative for psychiatric genetics research through providing unprecedented sample sizes. Critically, many of these resources are also providing longitudinal data across an expanding timeline from which the life-course of individual patients can be surveyed. The view is a complex one - varying recurrence, intervals between episodes, treatment experiences, comorbidities, secondary outcomes, and disease trajectories. In short, we observe an immense amount of variability across dataset and individuals. This creates a new opportunity to systematically interrogate if using diverse and nuanced descriptions of individual cases can impact on the genetic findings of underlying architecture of psychiatric disorders. In this symposium, we bring together researchers providing different perspectives and research angles interrogating the complex data from the UKBiobank, iPSYCH, Danish Blood Donors/Copenhagen Hospital Biobank, the Danish Health Registries, Swedish Health Registries, and the BioVu EHR. First, Lu Yi will describe her work using co-sibling analysis in the Swedish Health Registries to describe multiple clinical features of major depression that impact on its genetic architecture. Second, Sarah Guagliardo will dive into the sex-specific heterogeneity in ADHD using Vanderbilt University's BioVu EHR. Third, Jolien Rietkerk will systematically describe variability in polygenic profiles associated with pairwise comorbidity of five major psychiatric disorders using data from the UKBiobank and iPSYCH. Finally, Mischa Lundberg will describe how variations in biobank sampling schema and inclusion/exclusion criteria can impact on the genetic profiles of major depression cases across biobanks, EHRs, and medical registries. Jonathan Flint will discuss this work in the context of ongoing and future directions in psychiatric genetics. This symposium will provide an overview of the complexities and opportunities presented by larger and larger data resources with deeper and broader assessments.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Pages 29-30"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DISSECTING THE GENETIC BASIS OF SCHIZOPHRENIA USING FUNCTIONAL GENOMICS AND PLURIPOTENT STEM CELL MODELS","authors":"Michael Ziller","doi":"10.1016/j.euroneuro.2024.08.012","DOIUrl":"10.1016/j.euroneuro.2024.08.012","url":null,"abstract":"<div><div>Schizophrenia (SCZ) is a highly heritable mental disorder with thousands of associated genetic variants located mostly in the noncoding space of the genome. Translating these associations into insights into the underlying pathomechanisms has been challenging because the causal variants, their mechanisms of action, their target genes and their joint impact remain largely unknown. In this talk, I will discuss our efforts to address these challenges from three complementary angles. To understand the function of individual genetic variants, I will present our work on massively parallel genetic variant screening and functional annotation in induced pluripotent stem cell (iPSC) derived neuronal cell types. In addition, I will share our recent results on leveraging large cohorts of iPSC derived neurons from deeply phenotyped patients as in vitro models of polygenicity. In this context, we sought to translate the effects of highly heterogenous polygenic risk across individuals into common molecular mechanisms underlying the etiology in SCZ, identifying several molecular, cellular and circuit level endophenotypes as points of convergence. Lastly, I will discuss our newly developed strategies to link these molecular and cellular alterations to patient level intermediate phenotypes such as aberrant EEG patterns and cognitive impairment.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":"87 ","pages":"Page 2"},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}