GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Wang, Lu  (6)
  • Wu, Yong  (6)
  • 1
    In: Psychiatry Research, Elsevier BV, Vol. 337 ( 2024-07), p. 115929-
    Type of Medium: Online Resource
    ISSN: 0165-1781
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 1500675-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 2
    In: Neuropsychopharmacology, Springer Science and Business Media LLC
    Type of Medium: Online Resource
    ISSN: 0893-133X , 1740-634X
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2008300-2
    SSG: 15,3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  BMC Medicine Vol. 20, No. 1 ( 2022-11-30)
    In: BMC Medicine, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2022-11-30)
    Abstract: Bipolar disorder (BD) is a highly heritable psychiatric illness exhibiting substantial correlation with intelligence. Methods To investigate the shared genetic signatures between BD and intelligence, we utilized the summary statistics from genome-wide association studies (GWAS) to conduct the bivariate causal mixture model (MiXeR) and conjunctional false discovery rate (conjFDR) analyses. Subsequent expression quantitative trait loci (eQTL) mapping in human brain and enrichment analyses were also performed. Results Analysis with MiXeR suggested that approximately 10.3K variants could influence intelligence, among which 7.6K variants were correlated with the risk of BD (Dice: 0.80), and 47% of these variants predicted BD risk and intelligence in consistent allelic directions. The conjFDR analysis identified 37 distinct genomic loci that were jointly associated with BD and intelligence with a conjFDR 〈 0.01, and 16 loci (43%) had the same directions of allelic effects in both phenotypes. Brain eQTL analyses found that genes affected by the “concordant loci” were distinct from those modulated by the “discordant loci”. Enrichment analyses suggested that genes related to the “concordant loci” were significantly enriched in pathways/phenotypes related with synapses and sleep quality, whereas genes associated with the “discordant loci” were enriched in pathways related to cell adhesion, calcium ion binding, and abnormal emotional phenotypes. Conclusions We confirmed the polygenic overlap with mixed directions of allelic effects between BD and intelligence and identified multiple genomic loci and risk genes. This study provides hints for the mesoscopic phenotypes of BD and relevant biological mechanisms, promoting the knowledge of the genetic and phenotypic heterogeneity of BD. The essential value of leveraging intelligence in BD investigations is also highlighted.
    Type of Medium: Online Resource
    ISSN: 1741-7015
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2131669-7
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 4
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2023
    In:  Cerebral Cortex Vol. 33, No. 11 ( 2023-05-24), p. 6990-7000
    In: Cerebral Cortex, Oxford University Press (OUP), Vol. 33, No. 11 ( 2023-05-24), p. 6990-7000
    Abstract: Patients with bipolar disorder (BD) and their first-degree relatives exhibit alterations in brain volume and cortical structure, whereas the underlying genetic mechanisms remain unclear. In this study, based on the published genome-wide association studies (GWAS), the extent of polygenic overlap between BD and 15 brain structural phenotypes was investigated using linkage disequilibrium score regression and MiXeR tool, and the shared genomic loci were discovered by conjunctional false discovery rate (conjFDR) and expression quantitative trait loci (eQTL) analyses. MiXeR estimated the overall measure of polygenic overlap between BD and brain structural phenotypes as 4–53% on a 0–100% scale (as quantified by the Dice coefficient). Subsequent conjFDR analyses identified 54 independent loci (71 risk single-nucleotide polymorphisms) jointly associated with BD and brain structural phenotypes with a conjFDR & lt; 0.05, among which 33 were novel that had not been reported in the previous BD GWAS. Follow-up eQTL analyses in respective brain regions both confirmed well-known risk genes (e.g. CACNA1C, NEK4, GNL3, MAPK3) and discovered novel risk genes (e.g. LIMK2 and CAMK2N2). This study indicates a substantial shared genetic basis between BD and brain structural phenotypes, and provides novel insights into the developmental origin of BD and related biological mechanisms.
    Type of Medium: Online Resource
    ISSN: 1047-3211 , 1460-2199
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1483485-6
    SSG: 12
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 5
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2023
    In:  Schizophrenia Bulletin Vol. 49, No. 4 ( 2023-07-04), p. 914-922
    In: Schizophrenia Bulletin, Oxford University Press (OUP), Vol. 49, No. 4 ( 2023-07-04), p. 914-922
    Abstract: Schizophrenia is a complex and heterogeneous disorder involving multiple regions and types of cells in the brain. Despite rapid progress made by genome-wide association studies (GWAS) of schizophrenia, the mechanisms of the illness underlying the GWAS significant loci remain less clear. Study Design We investigated schizophrenia risk genes using summary-data-based Mendelian randomization based on single-cell sequencing data, and explored the types of brain cells involved in schizophrenia through the expression weighted cell-type enrichment analysis. Results We identified 54 schizophrenia risk genes (two-thirds of these genes were not identified using sequencing data of bulk tissues) using single-cell RNA-sequencing data. Further cell type enrichment analysis showed that schizophrenia risk genes were highly expressed in excitatory neurons and caudal ganglionic eminence interneurons, suggesting putative roles of these cells in the pathogenesis of schizophrenia. We also found that these risk genes identified using single-cell sequencing results could form a large protein-protein interaction network with genes affected by disease-causing rare variants. Conclusions Through integrative analyses using expression data at single-cell levels, we identified 54 risk genes associated with schizophrenia. Notably, many of these genes were only identified using single-cell RNA-sequencing data, and their altered expression levels in particular types of cells, rather than in the bulk tissues, were related to the increased risk of schizophrenia. Our results provide novel insight into the biological mechanisms of schizophrenia, and future single-cell studies are necessary to further facilitate the understanding of the disorder.
    Type of Medium: Online Resource
    ISSN: 0586-7614 , 1745-1701
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2180196-4
    SSG: 15,3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 6
    Online Resource
    Online Resource
    Elsevier BV ; 2022
    In:  Psychiatry Research Vol. 317 ( 2022-11), p. 114843-
    In: Psychiatry Research, Elsevier BV, Vol. 317 ( 2022-11), p. 114843-
    Type of Medium: Online Resource
    ISSN: 0165-1781
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 1500675-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...