Rare Copy Number Variation in Schizophrenia and Implications for Treatment.
Rare Copy Number Variation in Schizophrenia and Implications for Treatment.
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精神分裂症的罕见拷贝数变异及其治疗意义。
DOI：
10.1093/schbul/sbad028
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发表时间：
2023
影响因子：
6.6
通讯作者：
Docherty,AnnaR
中科院分区：
文献类型：
--
作者：
Docherty,AnnaR
十多年来，联盟的努力一直在有益地融合遗传信息的来源-常见和罕见的遗传变异-以阐明精神分裂症的潜在生物学。由于基因组上的常见变异而产生的信号，具有数以百万计的单个变异的小影响，现在通过精细作图和罕见变异信号的整合来增强。1罕见的拷贝数变异体（CNVs），反映了大量的复制或缺失，这些复制或缺失是渗透性的和有害的，对精神分裂症和更广泛的神经多样性有重要作用。常见和罕见的变异，包括这些罕见的CNV，可能与无数的环境暴露相互作用，并可能导致缺失的基因调控尚未被表征。[4]尽管取得了这些进展，但对遗传变异的基本临床意义的理解一直进展缓慢。早期的全基因组关联研究（GWAS）和基于基因的分析将常见的遗传变异映射到精神分裂症5的广义和狭义定义，以及精神病的阳性，阴性和思维障碍症状领域的变异，并通过项目水平的症状数据提供信息。6这些研究发现，常见变异是诊断和监测水平数据中的一些变异的原因。基于GWAS的遗传相关矩阵和表型的遗传结构方程模型，7，8及其相关的热图和网络图，为精神病疾病学家制作了美丽的挂毯和马赛克。基于GWAS的风险度量（即多基因评分），因为它们基于数百万种常见变异，已经变得越来越信息化，因为它们在新的独立队列中提供了正态分布遗传风险与重要临床数据（例如，自杀行为，成瘾或其他极端结果）关系的统计学强大模型。但是小的效应量仍然是临床应用的限制因素--在病例对照设计的背景下，单独的共同变异只能“预测”低群体基础率表型，而不能在临床或一般人群中预测。罕见的CNV，更难以检测和测量跨队列，不能解决低基础率或严重临床结局的常见变异研究固有的统计功效问题。然而，有很好的基础，研究罕见的CNVs赋予高风险的功能和临床信息的概念化和治疗精神病症状。CNV定义的遗传亚群可能是唯一的信息。在一定程度上，罕见的CNV可以在精神分裂症个体的大队列中被鉴定，它们与症状和预后的比较关联可以被研究。在这个问题上，Farrell等人利用一个大型队列中罕见的渗透性CNVs的数据来帮助了解治疗抵抗的临床问题。[9]抗精神病药物未能治疗大部分精神分裂症患者，这些初步数据表明，在与精神分裂症相关的相对高突变率区域，15号染色体上的拷贝数变异10（2%-9%）。这份报告，建立在精神病罕见变异研究的一些先前的成就，提供了一些第一个证据的一个微妙的，但潜在的临床相关的联系之间的罕见变异和抗精神病药物的持续无反应。重要的是，在某种程度上，罕见的CNV和常见的和罕见的全基因组变异效应一起解释抗精神病药物治疗反应，有希望更好地了解精神分裂症的基因调控，并为具有特定CNV的个体提供临床护理。除了……
For over a decade, consortia efforts have been gainfully merging sources of genetic information—common and rare genetic variation—to elucidate the underlying biology of schizophrenia. Signal due to common variation on the genome, with millions of single variants of small effect measured in aggregate, is now enhanced by finemapping and the integration of rare variant signal. 1 Rare copy number variants (CNVs), reflecting large swathes of duplications or deletions that are penetrant and deleterious, contribute significantly to schizophrenia and to neurodiversity more broadly. 2, 3 Common and rare variations, including these rare CNVs, could interact with myriad environmental exposures, and likely contribute to missing gene regulation yet to be characterized. 4 Despite this progress, understanding the basic clinical implications of genetic variation has been slow going. Early genome-wide association studies (GWAS) and gene-based analyses mapped common genetic variation to broad and narrow definitions of schizophrenia 5 and to variation in positive, negative, and thought disorder symptom domains of psychosis, informed by item-level symptom data. 6 These studies observed that common variation accounted for some of the variability in diagnostic and symptom-level data. GWAS-based genetic correlation matrices and genetic structural equation models of the phenome, 7, 8 and their related heatmaps and network graphs, have produced beautiful tapestries and mosaics for the psychiatric nosologist. And GWAS-based risk metrics (ie, polygenic scores), because they are based on millions of common variants, have become increasingly informative insofar as they provide statistically powerful models of the relationships of normally distributed genetic risks with important clinical data (for example, suicidal behavior, addiction, or other extreme outcomes) in new, independent cohorts. But small effect sizes remain a limiting factor to clinical application—common variation alone can only “predict” low population base rate phenotypes within the context of case–control designs, and not in the clinic or the general population. Rare CNVs, more difficult to detect and measure across cohorts, do not solve the problem of statistical power inherent to common variant studies of low base rates or severe clinical outcomes. However, there is good basis for studying rare CNVs conferring high risk as functionally and clinically informative to the conceptualization and treatment of psychotic symptoms. CNV-defined genetic subgroups may be uniquely informative. To the extent that rare CNVs can be identified in large cohorts of individuals with schizophrenia, their comparative association with symptoms and prognosis may be studied. In this issue, Farrell et al. leverage data on rare, penetrant CNVs in a large cohort to help inform the clinical problem of treatment resistance. 9 Antipsychotics fail to treat a large proportion of people with schizophrenia, and these preliminary data implicate copy number variation on chromosome 15 in a region of relatively high penetrance associated with schizophrenia 10 (2%–9%). This report, building on a number of previous accomplishments in the study of rare variation in psychosis, provides some of the first evidence of a subtle yet potentially clinically relevant link between rare variation and persistent nonresponse to antipsychotic medications. Importantly, to the extent that rare CNV and common and rare genome-wide variant effects together explain antipsychotic medication response, there is the hope of better understanding gene regulation in schizophrenia, and of informing clinical care for individuals with specific CNVs. Apart from the …