美国普林斯顿大学Olga G. Troyanskaya研究小组近日宣布，利用全基因组深度学习分析技术可以鉴定非编码突变对罹患自闭症风险的作用。相关研究成果于2019年5月27日发表在国际知名学术期刊《Nature Genetics》杂志。

该课题组的研究基于深度学习框架来预测基因突变的特定调控作用和对机体的不利影响，解决了如何探讨非编码突变在疾病发生中的作用这一难题。将该框架模式应用于1,790例自闭症谱系障碍（ASD）单纯性家族进行研究，结果表明与未受影响的同胞成员相比，携带非编码突变自闭症先证者由于同时具有能够在转录水平和转录后水平扰乱调控的从头突变，从而体现出显著增强的功能效应。进一步分析表明非编码基因突变参与了突触传递和神经元发育，与此同时，结合前期研究结果, 研究人员展示出自闭症患者一个包含编码区和非编码区突变的收敛的遗传图谱。

这项研究证明了在遗传病先证者中鉴定出的携带有优先化突变基因的序列具有等位基因特异性调控活性，并且强调了非编码突变与自闭症先证者智商水平的关联。他们的这项预测基因组学框架不仅阐明了非编码突变在自闭症中的作用，还可以用来确立对后续研究意义重大的基因突变的优先顺序，从而广泛适用于复杂人类疾病的研究。

附：英文原文

Title: Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk

Author: Jian Zhou, Christopher Y. Park, Chandra L. Theesfeld, Aaron K. Wong, Yuan Yuan, Claudia Scheckel, John J. Fak, Julien Funk, Kevin Yao, Yoko Tajima, Alan Packer, Robert B. Darnell, Olga G. Troyanskaya

Issue&Volume: Volume 51 Issue 6, June 2019

Abstract: We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,790 autism spectrum disorder (ASD) simplex families reveals a role in disease for noncoding mutationsASD probands harbor both transcriptional- and post-transcriptional-regulation-disrupting de novo mutations of significantly higher functional impact than those in unaffected siblings. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development and, taken together with previous studies, reveals a convergent genetic landscape of coding and noncoding mutations in ASD. We demonstrate that sequences carrying prioritized mutations identified in probands possess allele-specific regulatory activity, and we highlight a link between noncoding mutations and heterogeneity in the IQ of ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD and prioritizes mutations with high impact for further study, and is broadly applicable to complex human diseases.

DOI: 10.1038/s41588-019-0420-0

Source:https://www.nature.com/articles/s41588-019-0420-0