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Shared genetic architecture across psychiatric disorders
Psychological Medicine ( IF 5.9 ) Pub Date : 2021-03-17 , DOI: 10.1017/s0033291721000829
Andrew D Grotzinger 1, 2
Affiliation  

Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has progressed rapidly in the last decade, shedding light on the biological makeup of cross-disorder risk at multiple levels of analysis. Over a hundred genetic variants have been identified that affect multiple disorders, with many more to be uncovered as sample sizes continue to grow. Cross-disorder mechanistic studies build on these findings to cluster transdiagnostic variants into meaningful categories, including in what tissues or when in development these variants are expressed. At the upper-most level, methods have been developed to estimate the overall shared genetic signal across pairs of traits (i.e. single-nucleotide polymorphism-based genetic correlations) and subsequently model these relationships to identify overarching, genomic risk factors. These factors can subsequently be associated with external traits (e.g. functional imaging phenotypes) to begin to understand the makeup of these transdiagnostic risk factors. As psychiatric genomic efforts continue to expand, we can begin to gain even greater insight by including more fine-grained phenotypes (i.e. symptom-level data) and explicitly considering the environment. The culmination of these efforts will help to inform bottom-up revisions of our current nosology.

中文翻译:

跨精神疾病的共享遗传结构

精神疾病在基因水平上存在大量重叠,基于家庭的方法长期以来一直指向跨诊断风险途径。精神病学基因组学在过去十年中发展迅速,在多层次分析中揭示了交叉疾病风险的生物学构成。已经确定了一百多种影响多种疾病的遗传变异,随着样本量的不断增加,还有更多的遗传变异有待发现。跨疾病机制研究以这些发现为基础,将跨诊断变异分类为有意义的类别,包括在哪些组织中或何时表达这些变异。在最高级别,已经开发出方法来估计跨性状对的整体共享遗传信号(即 基于单核苷酸多态性的遗传相关性)并随后对这些关系进行建模以识别总体的基因组风险因素。这些因素随后可以与外部特征(例如功能成像表型)相关联,以开始了解这些跨诊断风险因素的构成。随着精神病基因组工作的不断扩大,我们可以通过包括更细粒度的表型(即症状级数据)并明确考虑环境来开始获得更深入的了解。这些努力的成果将有助于为我们当前的疾病分类自下而上的修订提供信息。功能成像表型)开始了解这些跨诊断风险因素的构成。随着精神病基因组工作的不断扩大,我们可以通过包括更细粒度的表型(即症状级数据)并明确考虑环境来开始获得更深入的了解。这些努力的成果将有助于为我们当前的疾病分类自下而上的修订提供信息。功能成像表型)开始了解这些跨诊断风险因素的构成。随着精神病基因组工作的不断扩大,我们可以通过包括更细粒度的表型(即症状级数据)并明确考虑环境来开始获得更深入的了解。这些努力的成果将有助于为我们当前的疾病分类自下而上的修订提供信息。
更新日期:2021-03-17
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