Department of Psychology, University of Texas at Austin, Austin, TX, USA.
Psychiatric and Neurodevelopmental Genetics Unit (PNGU) and the Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
Psychol Med. 2021 Oct;51(13):2210-2216. doi: 10.1017/S0033291721000829. Epub 2021 Mar 17.
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.
精神障碍在遗传水平上有很大的重叠,基于家族的方法很早就指出了跨诊断的风险途径。在过去的十年中,精神疾病基因组学取得了快速进展,揭示了跨疾病风险在多个分析层面的生物学构成。已经确定了超过一百种影响多种疾病的遗传变异,随着样本量的继续增加,还会发现更多的遗传变异。跨疾病机制研究基于这些发现,将跨诊断的变异聚类为有意义的类别,包括这些变异在哪些组织或发育阶段表达。在最高层次上,已经开发了方法来估计两对特征之间的总体共享遗传信号(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,以确定总体的、基因组风险因素。这些因素可以随后与外部特征(例如功能成像表型)相关联,以开始理解这些跨诊断风险因素的构成。随着精神疾病基因组学研究的不断扩展,我们可以通过包括更精细的表型(即症状水平的数据)并明确考虑环境来获得更大的洞察力。这些努力的最终结果将有助于为我们当前的分类学提供自下而上的修订。