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      A Human-Specific Schizophrenia Risk Tandem Repeat Affects Alternative Splicing of a Human-Unique Isoform AS3MTd2d3 and Mushroom Dendritic Spine Density

      1 , 2 , 1 , 2 , 1 , 2 , 1 , 1 , 2 , 3 , 4 , 1
      Schizophrenia Bulletin
      Oxford University Press (OUP)

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          Abstract

          Recent advances in functional genomics have facilitated the identification of multiple genes and isoforms associated with the genetic risk of schizophrenia, yet the causal variations remain largely unclear. A previous study reported that the schizophrenia risk single-nucleotide polymorphism (SNP) rs7085104 at 10q24.32 was in high linkage disequilibrium (LD) with a human-specific variable number of tandem repeat (VNTR), and both were significantly associated with the brain mRNA expression of a human-unique AS3MTd2d3 isoform in Europeans and African Americans. In this study, we have shown the direct regulation of the AS3MTd2d3 mRNA expression by this VNTR through an in vitro minigene splicing assay, suggesting that it is likely a causative functional variation. Intriguingly, we have further confirmed that the VNTR and rs7085104 are significantly associated with AS3MTd2d3 mRNA expression in brains of Han Chinese donors, and rs7085104 is also associated with risk of schizophrenia in East Asians. Finally, the overexpression of AS3MTd2d3 in cultured primary hippocampal neurons results in significantly reduced densities of mushroom dendritic spines, implicating its potential functional impact. Considering the crucial roles of dendritic spines in neuroplasticity, these results reveal the potential regulatory impact of the schizophrenia risk VNTR on AS3MTd2d3 and provide insights into the underlying biological mechanisms.

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          Most cited references62

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            Biological Insights From 108 Schizophrenia-Associated Genetic Loci

            Summary Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here, we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain providing biological plausibility for the findings. Many findings have the potential to provide entirely novel insights into aetiology, but associations at DRD2 and multiple genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that play important roles in immunity, providing support for the hypothesized link between the immune system and schizophrenia.
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              Principal components analysis corrects for stratification in genome-wide association studies.

              Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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                Author and article information

                Contributors
                Journal
                Schizophrenia Bulletin
                Oxford University Press (OUP)
                0586-7614
                1745-1701
                January 01 2021
                January 23 2021
                July 14 2020
                January 01 2021
                January 23 2021
                July 14 2020
                : 47
                : 1
                : 219-227
                Affiliations
                [1 ]Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
                [2 ]Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
                [3 ]KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
                [4 ]CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
                Article
                10.1093/schbul/sbaa098
                0fd0c233-c8b1-4a1a-b4bf-79d941aaaeae
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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