European Neuropsychopharmacology最新文献

{"title":"CURRENT CHALLENGES AND OPPORTUNITIES IN DATA-DRIVEN APPROACHES IN NEUROPSYCHIATRIC DISORDERS","authors":"","doi":"10.1016/j.euroneuro.2024.08.053","DOIUrl":"10.1016/j.euroneuro.2024.08.053","url":null,"abstract":"<div><div>As the technological landscape evolves, neuropsychiatric research increasingly relies on data-driven methodologies to uncover the mysteries of the human brain malfunction and genetic risk in neuropsychiatric disorders. This symposium brings together experts and stakeholders for a focused dialogue on the pressing challenges and innovative solutions within this rapidly progressing domain. Through a series of thought-provoking presentations, we will delve into the nuances of data integration, the intricacies of handling big data, and the critical aspects of ensuring research reproducibility and clinical applicability.</div><div>Dr. Chunyu Liu from the State University of New York (SUNY) Upstate Medical University will present a compelling discussion on the paramount importance of quality control in single-cell RNA-seq data. His presentation will underscore the necessity of stringent data evaluation and filtering protocols to bolster the precision of genomic analyses. The talk will provide participants with a solid foundation on the principles of data quality, which is pivotal for the reproducibility and credibility of research findings.</div><div>Dr. Zhongming Zhao from the University of Texas Health Science Center at Houston (UTHealth Houston) will present an in-depth exploration of cutting-edge machine-learning algorithms for the integration of omics, genetic, neuroimaging, and phenotypic data. His session will be centered on the elucidation of Alzheimer's Disease's molecular mechanisms and the identification of potential therapeutic targets. This presentation will demonstrate the transformative power of data analytics in shaping the future of neuropsychiatric therapeutics.</div><div>Dr. Peter Kochunov from the UTHealth Houston, will tackle the multifaceted challenges of data integration, scalability, and reproducibility within the scope of large-scale population imaging genetic studies. His expertise will offer a strategic guide for navigating the complexities of big data, ensuring the robustness of research outcomes, and bridging the gap between scientific discoveries and their clinical translation.</div><div>Dr. Xing-Ming Zhao from Fudan University will conclude the symposium with an insightful examination of the challenges and strategies related to data integration in psychiatric disorder research. His work will emphasize the significance of synthesizing diverse data streams, such as clinical assessments, neuroimaging, and genetic information, to achieve a comprehensive view of neuropsychiatric conditions. This session will highlight the potential of integrated data approaches to surmount the obstacles faced in the field.</div><div>Dr. Eric Gamazon from Vanderbilt University will serve as the discussant to summarize the talks and offer his own insight into harnessing large biobank data of diverse ancestries.</div><div>The symposium will weave these presentations into a coherent narrative, progressively building upon each topic to pro","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442129","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":"MULTI-ANCESTRY FINE-MAPPING REFINES BIPOLAR DISORDER RISK GENES","authors":"","doi":"10.1016/j.euroneuro.2024.08.035","DOIUrl":"10.1016/j.euroneuro.2024.08.035","url":null,"abstract":"<div><div>Genome wide association studies (GWAS) have identified hundreds of loci contributing to bipolar disorder (BD) risk. However, translating genome-wide significant (GWS) loci into causal genes and mechanisms for BD is challenging due to linkage disequilibrium (LD) between risk variants, and incomplete understanding of the non-coding regulatory mechanisms in the brain. Recently, the Psychiatric Genomics Consortium Bipolar Disorder Working Group has performed GWAS meta-analyses of BD in cohorts of European (N cases = 131,969), East Asian (N cases = 5,969), African American (N cases = 7,076) and Latino (N cases = 13,022) ancestries, as well as a multi-ancestry meta-analysis (Total N = 158,036 cases, N= 2,796,499 controls) by including datasets with different ascertainment strategies. These analyses led to the identification of 298 GWS risk loci for BD, further emphasizing the need to identify the true causal variants and elucidate their biological mechanisms at the cellular level.</div><div>Here, we implemented SuSiEx, a statistical fine-mapping method leveraging differences in the LD architecture among different genetic ancestries, to prioritize likely causal SNPs, within these 298 GWS risk loci for BD. Then, we mapped these SNPs to their relevant gene(s), and investigated their likely functional consequences by aggregating multiple lines of evidence: (i) integration of variant annotation and brain cell-type epigenomic data (PLAC-seq data), (ii) implementation of Summary data-based Mendelian Randomization (SMR) to functionally interpret the likely causal SNPs in the context of brain bulk tissue quantitative trait loci (QTLs) (expression, splicing and methylation QTLs), and (iii) refining the cell-type specific context of likely causal SNPs via SMR, by leveraging a novel (unpublished) resource of brain single nuclei eQTLs.</div><div>Our comprehensive fine-mapping analysis prioritized 113 likely causal SNPs, from 298 GWS loci for BD using LD estimates from all 4 represented populations in the multi-ancestry GWAS. By integrating expression, splicing or methylation QTLs, preliminary results based on a previous BD GWAS indicated that the following genes, among others, are strongly implicated in BD: FURIN, FADS1, DCC, MED24, TTC12, SP4, POU6F2, TRANK1, and DDRD2. Additionally, our preliminary results showed that fine-mapped SNPs for BD can mediate their likely causal effect in specific brain cell-types, specifically inhibitory and excitatory neurons. Taken together, the abovementioned genes represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Finally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, thus highlighting the potential clinical utility of fine-mapping.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442131","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 POLYGENETIC ARCHITECTURE OF AUTISM","authors":"","doi":"10.1016/j.euroneuro.2024.08.045","DOIUrl":"10.1016/j.euroneuro.2024.08.045","url":null,"abstract":"<div><div>Autism is highly heritable and has been associated with multiple classes of genetic variation. Common genetic variation contributes substantially to autism. Previously, with 18,381 autistic individuals and 27,969 non-autistic individuals, five genome-wide significant loci were identified. Now with 38,717 autistic individuals and 232,725 non-autistic individuals, we report an updated genome-wide association study (GWAS) of autism with 12 genome-wide significant loci. We observe a moderate genetic correlation (0.675, SE=0.0434) between Europe-based (Nautistic=22,643; Nnon-autistic=204,389) and United States-based (Nautistic =16,074; Nnon-autistic=28,346) autism cohorts, which contributes to the decline of the estimated single nucleotide polymorphism (SNP) heritability (from 0.118 (SE=0.010) to 0.068 (SE=0.003)). The genetic correlation between autism with intellectual disability (ID) (Nautistic=6,590; Nnon-autistic= 43,071; h2=0.062; SE=0.012) and autism without ID (Nautistic=23,173; Nnon-autistic= 204,679; h2=0.089; SE=0.005) is 0.658 (SE=0.086). In the United States family-based cohorts, the genetic correlation between autism with ID (Nfamily=3,993; h2=0.159; SE=0.033) and autism without ID (Nfamily=4,357; h2=0.171; SE=0.031) is 0.812 (SE=0.157). Autism without ID was positively genetically correlated with educational attainment (0.163; P=4.84 × 10-11) and intelligence (0.233; P=1.95 × 10-11). Autism with ID genetically correlated with neither educational attainment (0.036; P=0.409) nor intelligence (-0.072; P=0.235). As ID alone is negatively genetically correlated with intelligence, the lack of correlation between autism with ID and intelligence strongly suggests that autism with ID is genetically different from ID alone. This difference has implications for both research and clinical nosology. Rare and de novo variants contribute substantially to autism in some individuals. Through rare variant analyses, 72 genes have been associated with autism at a genome-wide significant level to date. While de novo protein truncating variants (PTVs) and copy number deletions have been associated with autism, we report preliminary findings that the burden of inherited PTVs and copy number deletions among autistic individuals was elevated compared to their non-autistic siblings (P=4.00 × 10-5). Integration of multiple genetic factors will help us better understand the etiology of autism.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442213","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":"YOUTH AT RISK OF BIPOLAR DISORDER: TRACKING TRAJECTORIES, OUTCOMES AND BIOMARKERS USING NEUROIMAGING, GENOMICS AND EPIGENOMICS","authors":"","doi":"10.1016/j.euroneuro.2024.08.021","DOIUrl":"10.1016/j.euroneuro.2024.08.021","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Longitudinal prospective studies in high-risk populations are key for identifying pre-morbid risk factors for the development of psychopathology. The Australia-US collaborative Bipolar high-risk study comprises 3 groups of participants aged 12-30 years: ‘high-risk’ (with a sibling or parent with bipolar-I or -II), controls with no family history, and an unrelated group of BD-probands; with clinical, demographic and biological data. Familial risk is sometimes considered a surrogate for genetic risk (that is indexed via inherited DNA variants), but we know that this is a simplification of the ‘heritable’ component, which might comprise both direct and indirect genetic effects as well as the impact of family environment. We have used multiple analytic approaches to define and characterize features of disease risk, using neuroimaging, genomics and epigenomics. Analysis of magnetic resonance imaging (MRI) data from 217 unrelated Australian ‘Bipolar Kids and Sibs study’ participants (baseline n=217, follow-up n=152) finds accelerated cortical thinning over time (two scans, 2 years apart) in high-risk subjects (n=105) compared to controls (n=112), suggesting an early brain over-growth followed by normalisation towards the typical age of BD onset. Accelerated thickness and volume reductions over time were observed in ‘high-risk’ individuals across multiple cortical regions, relative to controls, including right lateral orbitofrontal thickness (β=.033, p &lt; .001) and inferior frontal volume (β=.021, p &lt; .001). We also find that bipolar polygenic risk (PsychArray) interacts with stress to increase suicide risk. We examined polygenic risk for both suicide attempt and risky behaviour on structural variance in cortical parcellations that have previously shown replicable associations with suicide attempts, finding that structural differences in the anterior cingulate, parahippocampal, and cuneus warrant further investigation as potential biomarkers for suicide attempts, particularly within the context of BD. Examination of epigenetic markers (450k/EPIC array) shows that genome-wide methylation patterns are broadly impacted by polygenic risk; highlighting an important interplay between genomically inherited risk and the potential biological encoding of environmental exposures. We are now collecting a 3rd MRI scan to capture nonlinear cortical developmental trajectories, and a 2nd blood sample to extend our baseline epigenetic work, derive serum measures and examine mRNA transcription patterns as potential biomarkers of emergent psychopathology. Brain regions associated with both genetic and clinical measures of psychopathology may serve as viable biomarkers, with clinical utility for the identification of individuals who are at greatest risk of developing psychopathology or suicidal intent. Future work will enable integration of these features into a prediction model of disease, to identify biological subgroups on the trajectory towards mental il","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441626","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":"LEVERAGING CONTEXT-SPECIFIC EPIGENOMIC REGULATORY NETWORKS (EPINETS) TO DISSECT THE GENETICS OF NEUROPSYCHIATRIC DISORDERS","authors":"","doi":"10.1016/j.euroneuro.2024.08.024","DOIUrl":"10.1016/j.euroneuro.2024.08.024","url":null,"abstract":"<div><div>Expression quantitative locus (eQTL) mapping provides deep insights into the function of disease-associated variants from Genome-wide association studies (GWAS). However, previous studies and our research reported misalignment between eQTLs and GWAS signals, likely due to bulk eQTLs mapped in non-pathology-relevant contexts and lack of cell-type resolution. Alternatively, unraveling the links between cis-regulatory elements (CREs) and genes in various cellular contexts offers an independent strategy to associate GWAS variants with their target genes beyond eQTL mapping. The state-of-the-art approaches, such as experimental assays (e.g., Promoter Capture Hi-C and macro-C) and computational models (e.g., ABC and EpiMap), provide linking resources based on different pieces of evidence, however, are each confined to limited brain cell types or cellular states.</div><div>Addressing this challenge, our study proposes a machine-learning approach to predict CRE-gene associations by combining protein-protein interactions and transcription factor (TF) binding predictions based on ATAC-seq, an assay measuring genomic accessibility. This computational approach facilitates the discovery of CRE-gene connections across different contexts (combinations of cell types and various conditions) whenever ATAC-seq data are available, enriching our understanding of the cis-regulatory networks between TF-CRE-gene.</div><div>We have amassed over 130 cell-sorted and single-cell ATAC-seq datasets encompassing a variety of brain cell types—excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte progenitor cells (OPCs), astrocytes, microglia, immune cells, and brain vascular cells—under a range of conditions including chemical perturbations, genetic modifications, infections, and disease status. Utilizing this extensive data collection and our integrative pipeline, we have constructed an atlas of TF-CRE-gene linking, namely cEpiNets. We finally employ the atlas to evaluate the enrichment of GWAS signals in CRE modules under various cellular contexts and to prioritize target genes and key drivers across a spectrum of neuropsychiatric disorders.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441628","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":"LEVERAGING COMORBIDITY TO REFINE PTSD ASSOCIATIONS AND DISSECT GENETIC RISK","authors":"","doi":"10.1016/j.euroneuro.2024.08.014","DOIUrl":"10.1016/j.euroneuro.2024.08.014","url":null,"abstract":"<div><div>Leveraging comorbidity to refine PTSD associations and dissect genetic risk</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441631","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":"INNATE AND ADAPTIVE IMMUNITY IN PSYCHIATRY: INSIGHTS FROM GENETIC ASSOCIATION STUDIES AND PERIPHERAL BLOOD IMMUNOPHENOTYPING","authors":"","doi":"10.1016/j.euroneuro.2024.08.027","DOIUrl":"10.1016/j.euroneuro.2024.08.027","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441634","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":"WHAT IS THE IMPACT OF COMPOUND HETEROZYGOUS EVENTS INVOLVING DELETIONS AND SEQUENCE-LEVEL VARIANTS IN AUTISM?","authors":"","doi":"10.1016/j.euroneuro.2024.08.080","DOIUrl":"10.1016/j.euroneuro.2024.08.080","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The majority of human genes maintain normal biological function when they become haploid due to a genomic deletion. However, pathogenicity may still arise when the remaining allele is affected by additional functional variation. Here, we describe analytical strategies for examining a specific type of compound heterozygosity, namely the co-occurrence of a deletion and a sequence-level variant affecting the other allele, hereafter referred to as deletion compound heterozygosity (DelCH). We report preliminary results in using these strategies to assess DelCH in Autism Spectrum Disorder (ASD).&lt;/div&gt;&lt;div&gt;We analyzed whole-genome sequencing data from MSSNG, Simons Simplex Collection, and SPARK cohorts (collectively 11,636 autistic individuals and 22,962 family members).&lt;/div&gt;&lt;div&gt;We developed multiple analytical strategies to examine rare (event rate &lt; 1%) DelCH:&lt;ul&gt;&lt;li&gt;&lt;span&gt;1)&lt;/span&gt;&lt;span&gt;&lt;div&gt;The burden analysis uses conditional logistic regression for group-level comparisons of DelCH rates between a) probands and their deletion-transmitting parents, with inherited deletion as a random effect variable, or b) probands and their family members, with family ID as a random effect variable;&lt;/div&gt;&lt;/span&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;2)&lt;/span&gt;&lt;span&gt;&lt;div&gt;The transmission disequilibrium test (TDT) compares the rates with which deletion-non-transmitting parents transmit sequence-level variants within genes affected by deletions to their autistic offspring. Association is indicated by transmission of non-synonymous variants at a rate higher than predicted by chance. This approach was repeated in unaffected siblings as an additional control analysis.&lt;/div&gt;&lt;/span&gt;&lt;/li&gt;&lt;/ul&gt;&lt;/div&gt;&lt;div&gt;Each strategy has different strengths and weaknesses. The first burden analysis (1a) achieves perfect matching of deleted sequence and unambiguous phasing of variants but is restricted to proband-parent pairs. The second burden analysis (1b) benefits from a larger sample size but cannot distinguish between de novo and inherited variation. In addition, unambiguous phasing is possible only for SNVs within deletion boundaries. In contrast, while the TDT (2) can include SNVs outside deletion boundaries, thereby increasing statistical power, de novo events are not analyzed.&lt;/div&gt;&lt;div&gt;Our preliminary findings show variability in results as a function of the analytical strategy. Findings from the burden analysis suggest a modest enrichment of DelCH in ASD which was inversely proportional to the variant frequency thresholds applied.&lt;/div&gt;&lt;div&gt;Given that the mechanism consists of two rare events at the same locus, on the population level the role of DelCH in ASD etiology is likely modest, requiring large samples sizes for sufficient statistical power. In addition to this “lightning striking twice”, data preparation is demanding, as every subject has unique deletion regions in which sequence-level variants on the other allele are tallied. Variant selection metrics include allele frequency thres","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442143","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":"COMBINING MENDELIAN RANDOMISATION WITH DEPRESSION TRAJECTORIES TO IDENTIFY DEVELOPMENTALLY SPECIFIC PREDICTORS OF CHANGE IN DEPRESSIVE SYMPTOMS","authors":"","doi":"10.1016/j.euroneuro.2024.08.092","DOIUrl":"10.1016/j.euroneuro.2024.08.092","url":null,"abstract":"<div><div>Prevalence of depression is increasing, especially amongst adolescents and young adults, representing a key risk period where intervention is critical. When using Mendelian randomisation (MR) to identify causal risk factors for depression, estimates are limited to average lifetime effects, rather than being specific to developmental stages.</div><div><strong>Methods.</strong> We have combined trajectories of depressive symptoms with MR to identify developmentally specific risk factors. We used repeated measures of depressive symptoms (short Moods and Feelings Questionnaire) in the ALSPAC cohort, with 11 repeated assessments covering ages 9 to 27 years. First, we used a repeated measures multi-level model (MLM) to describe the average trajectory of depressive symptoms. Linear splines split by knot points were used to explain the non-linear pattern of growth. Second, we used latent class analysis to explore heterogeneity in depression trajectories. Third, we combined both trajectory models with genetic instruments for depression (positive control) and with modifiable risk factors for depression.</div><div>Our models included 44,611 repeated assessments of sMFQ from 6,422 unique individuals. Our best fitting MLM trajectory had three linear splines corresponding to puberty (9-14.5 years), adolescence (14.5-21 years) and early adulthood (21-27 years). Latent classes were stable low, decreasing, transient, increasing and stable high. Positive control genetic instrument for MDD predicted trajectories, most strongly membership into the increasing and stable high class. Genetic instruments for BMI and educational attainment were not associated with change in population average depressive symptoms at any of the different developmental stages nor with class membership. This could suggest no causal effects of these risk factors at these developmental stages, or low power.</div><div>We are continuing to develop our methods, test power and incorporate additional risk factors. We believe that combining outcome trajectories with MR analyses has wide ranging application to improve specificity of causal effects and recommendations for intervention development.</div></div>","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442152","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":"ANCESTRALLY DIVERSE SAMPLES IMPROVE FINE-MAPPING OF DEPRESSION-ASSOCIATED LOCI","authors":"","doi":"10.1016/j.euroneuro.2024.08.034","DOIUrl":"10.1016/j.euroneuro.2024.08.034","url":null,"abstract":"","PeriodicalId":12049,"journal":{"name":"European Neuropsychopharmacology","volume":null,"pages":null},"PeriodicalIF":6.1,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442130","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}