Laboratory of Molecular Neuro-Oncology, Howard Hughes Medical Institute, The Rockefeller University, New York, NY 10065, USA; email:
Annu Rev Neurosci. 2020 Jul 8;43:509-533. doi: 10.1146/annurev-neuro-100119-024851.
Autism is a common and complex neurologic disorder whose scientific underpinnings have begun to be established in the past decade. The essence of this breakthrough has been a focus on families, where genetic analyses are strongest, versus large-scale, case-control studies. Autism genetics has progressed in parallel with technology, from analyses of copy number variation to whole-exome sequencing (WES) and whole-genome sequencing (WGS). Gene mutations causing complete loss of function account for perhaps one-third of cases, largely detected through WES. This limitation has increased interest in understanding the regulatory variants of genes that contribute in more subtle ways to the disorder. Strategies combining biochemical analysis of gene regulation, WGS analysis of the noncoding genome, and machine learning have begun to succeed. The emerging picture is that careful control of the amounts of transcription, mRNA, and proteins made by key brain genes-stoichiometry-plays a critical role in defining the clinical features of autism.
自闭症是一种常见且复杂的神经发育障碍，其科学基础在过去十年中开始建立。这一突破的本质在于关注家庭，在家庭中进行基因分析最为有力，而不是进行大规模的病例对照研究。自闭症遗传学随着技术的发展而发展，从对拷贝数变异的分析到全外显子组测序（WES）和全基因组测序（WGS）。导致完全功能丧失的基因突变约占三分之一，主要通过 WES 检测到。这一局限性增加了人们对理解基因调控变体的兴趣，这些变体以更微妙的方式对疾病做出贡献。结合基因调控的生化分析、非编码基因组的 WGS 分析和机器学习的策略已经开始取得成功。新兴的图景表明，关键脑基因转录、mRNA 和蛋白质产生的数量的精细控制——化学计量学——在定义自闭症的临床特征方面起着关键作用。
