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1
Modeling Psychiatric Disorder Biology with Stem Cells.用干细胞对精神障碍生物学建模。
Curr Psychiatry Rep. 2020 Apr 21;22(5):24. doi: 10.1007/s11920-020-01148-1.
2
A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia.全转录组关联研究提示精神分裂症存在特定的突触前和突触后异常。
Hum Mol Genet. 2020 Jan 1;29(1):159-167. doi: 10.1093/hmg/ddz253.
3
Synergistic effects of common schizophrenia risk variants.常见精神分裂症风险变异的协同效应。
Nat Genet. 2019 Oct;51(10):1475-1485. doi: 10.1038/s41588-019-0497-5. Epub 2019 Sep 23.
4
High-Resolution Regulatory Maps Connect Vascular Risk Variants to Disease-Related Pathways.高分辨率调控图谱将血管风险变异与疾病相关途径联系起来。
Circ Genom Precis Med. 2019 Mar;12(3):e002353. doi: 10.1161/CIRCGEN.118.002353.
5
Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders.人类大脑发育过程中的表达数量性状基因座及其在神经精神障碍中的富集。
Genome Biol. 2018 Nov 12;19(1):194. doi: 10.1186/s13059-018-1567-1.
6
Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis.人类大脑皮层转录组的发育和遗传调控揭示了精神分裂症的发病机制。
Nat Neurosci. 2018 Aug;21(8):1117-1125. doi: 10.1038/s41593-018-0197-y. Epub 2018 Jul 26.
7
Recent Advances in the Genetics of Schizophrenia.精神分裂症遗传学的最新进展
Mol Neuropsychiatry. 2018 Jun;4(1):35-51. doi: 10.1159/000488679. Epub 2018 May 30.
8
Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection.常见的精神分裂症等位基因在突变不耐受基因和受强烈背景选择的区域中富集。
Nat Genet. 2018 Mar;50(3):381-389. doi: 10.1038/s41588-018-0059-2. Epub 2018 Feb 26.
9
Comprehensive integrative analyses identify GLT8D1 and CSNK2B as schizophrenia risk genes.综合整合分析确定 GLT8D1 和 CSNK2B 为精神分裂症风险基因。
Nat Commun. 2018 Feb 26;9(1):838. doi: 10.1038/s41467-018-03247-3.
10
Gene expression elucidates functional impact of polygenic risk for schizophrenia.基因表达阐明了精神分裂症多基因风险的功能影响。
Nat Neurosci. 2016 Nov;19(11):1442-1453. doi: 10.1038/nn.4399. Epub 2016 Sep 26.

精神分裂症风险等位基因通常会影响许多基因的表达,而每个基因对风险的影响可能不同:中介分析。

Schizophrenia risk alleles often affect the expression of many genes and each gene may have a different effect on the risk: A mediation analysis.

机构信息

Department of Biomedical Engineering, Whiting School of Engineering and School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

出版信息

Am J Med Genet B Neuropsychiatr Genet. 2021 Jun;186(4):251-258. doi: 10.1002/ajmg.b.32841. Epub 2021 Mar 8.

DOI:10.1002/ajmg.b.32841
PMID:33683021
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10315436/
Abstract

Variants identified by genome-wide association studies (GWAS) are often expression quantitative trait loci (eQTLs), suggesting they are proxies or are themselves regulatory. Across many data sets, analyses show that variants often affect multiple genes. Lacking data on many tissue types, developmental time points, and homogeneous cell types, the extent of this one-to-many relationship is underestimated. This raises questions on whether a disease eQTL target gene explains the genetic association or is a bystander and puts into question the direction of expression effect of on the risk, since the many variants-regulated genes may have opposing effects, imperfectly balancing each other. We used two brain gene expression data sets (CommonMind and BrainSeq) for mediation analysis of schizophrenia-associated variants. We confirm that eQTL target genes often mediate risk but the direction in which expression affects risk is often different from that in which the risk allele changes expression. Of 38 mediator genes significant in both data sets 33 showed consistent mediation direction (Chi test p = 6 × 10 ). One might expect that the expression would correlate with the risk allele in the same direction it correlates with the disease. For 15 of these 33 (45%), however, the expression change associated with the risk allele was protective, suggesting the likely presence of other target genes with overriding effects. Our results identify specific risk mediating genes and suggest caution in interpreting the biological consequences of targeted modifications of gene expression, as not all eQTL targets may be relevant to disease while those that are, might have different from expected directions.

摘要

全基因组关联研究 (GWAS) 鉴定的变体通常是表达数量性状基因座 (eQTL),这表明它们是替代物或本身就是调节物。在许多数据集的分析中表明,变体通常会影响多个基因。由于缺乏许多组织类型、发育时间点和同质细胞类型的数据,这种一一对应的关系的程度被低估了。这就提出了一个问题,即疾病 eQTL 靶基因是否解释了遗传关联,或者它只是一个旁观者,并质疑风险的表达效应的方向,因为许多受变体调节的基因可能具有相反的效应,彼此之间的平衡并不完美。我们使用了两个大脑基因表达数据集 (CommonMind 和 BrainSeq) 进行与精神分裂症相关变体的中介分析。我们证实,eQTL 靶基因通常介导风险,但表达影响风险的方向通常与风险等位基因改变表达的方向不同。在两个数据集都显著的 38 个中介基因中,有 33 个显示出一致的中介方向 (Chi 检验 p=6×10 )。人们可能期望表达与风险等位基因的相关性与其与疾病的相关性方向相同。然而,对于这 33 个中的 15 个 (45%),与风险等位基因相关的表达变化是保护性的,这表明可能存在其他具有压倒性影响的靶基因。我们的研究结果确定了特定的风险介导基因,并建议在解释针对基因表达的靶向修饰的生物学后果时要谨慎,因为并非所有的 eQTL 靶基因都与疾病相关,而那些与疾病相关的靶基因,其方向可能与预期的不同。