Dissecting autism and schizophrenia through neuroimaging genomics.
Dissecting autism and schizophrenia through neuroimaging genomics.
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通过神经影像学基因组学解剖自闭症和精神分裂症。
DOI：
10.1093/brain/awab096
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发表时间：
2021-08-17
期刊：
影响因子：
--
通讯作者：
Jacquemont S
中科院分区：
文献类型：
--
作者：
Moreau CA;Raznahan A;Bellec P;Chakravarty M;Thompson PM;Jacquemont S
自闭症谱系障碍和精神分裂症的神经成像基因组研究主要采用了自顶向下的方法，从行为诊断开始，向下移动到中间大脑表型和潜在的遗传因素。影像和基因组学的进展已经成功地应用于越来越大的病例对照研究。与诊断优先的方法不同，自下而上的策略从分子因素开始，从而能够研究与生物风险相关的机制，而无论诊断或临床表现如何。后一种策略是从自上而下的研究提出的问题中产生的：为什么突变和大脑表型在有精神疾病诊断的个体中过多地出现？它们是与疾病的核心症状有关，还是与共病有关？为什么突变和脑表型与几种精神病学诊断有关？它们是否影响了所有诊断的单一维度？在这篇综述中，我们旨在总结自闭症和精神分裂症的影像基因组发现以及与这些疾病相关的神经精神变异体。自上而下对自闭症和精神分裂症的研究确定了神经成像改变的模式，其影响大小较小，并具有极端的多基因结构。基因组变异和神经成像模式在诊断类别中共享，表明在分子和大脑网络水平上的多效性机制。尽管该领域正在获得吸引力；其结果的重复性越来越强，但仅靠自上而下的方法不太可能解开自闭症或精神分裂症的机制。与自上而下的方法形成鲜明对比的是，自下而上的研究表明，高危神经精神疾病突变对神经成像和行为特征的影响同样大。低特异性一直令人困惑，研究表明，广泛类别的基因组变异影响类似范围的行为和认知维度，这可能与精神疾病的高度多基因架构一致。在遗传学方法和诊断方法之间观察到的令人惊讶的不一致效应大小强调了分解阻碍特发性条件下的病例对照研究的异质性的必要性。我们建议对广泛的神经精神病学变异进行系统研究，以确定特发性疾病潜在的潜在维度。大脑中时间、空间和细胞类型组织的基因表达数据也有相当大的潜力来解析这些维度的表型的机制。虽然大型神经成像基因组数据集现在可以在未选定的人群中使用，但迫切需要关于具有一系列精神症状和高风险基因组变异的个人的数据。这样的努力和更标准化的方法将改进诊断和临床结果的机械信息预测建模。Moreau等人。综述孤独症和精神分裂症神经影像基因组研究的进展和障碍。他们建议结合诊断优先(“自上而下”)和基因优先(“自下而上”)策略，分析基因和生物过程对这些精神疾病所涉及的维度的贡献。
Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a ‘top-down’ approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions’ phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes. Moreau et al. review advances and roadblocks in neuroimaging genomic studies of autism and schizophrenia. They propose combining diagnosis-first (‘top-down’) and gene-first (‘bottom-up’) strategies to parse out the contribution of genes and biological processes to dimensions involved in these psychiatric conditions.
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