Exome Pool-Seq and systems biology approach to identify and characterize genes and networks in neurodevelopmental disorders

神经发育障碍(NDD)在临床和遗传上具有极高的异质性，迄今已发现1000多个潜在基因。下一代测序(NGS)技术的高通量测序极大地促进了新疾病基因的识别和确认。尽管取得了这些进展，尽管成本不断下降，但目前基于Trio的外显子组或基因组测序的黄金标准仍然昂贵得令人望而却步，对大型队列来说也很耗时。为了在更大的范围内进一步确定已知的和新的NDDS的遗传学原因，我们开发了Exome Pool-Seq，一种结合了DNA样本的池和基于捕获的标准外显子组测序的方法。这种方法在保持较高的检测率的情况下，大大降低了测序成本。我们现在计划在计算上改进这一方法，并筛选更多患有NDD的&gt；500人，以系统地识别导致NDD的已知和新的变异。Exome Pool-Seq的成功应用是两个申请者核心技能：计算数据分析和临床/遗传专家知识协同作用的结果。我们希望将这种相互作用扩展到从计算和功能上表征与NDDS相关的基因和途径。这项工作将基于我们建立的SysID数据库的进一步发展和改进，该数据库包含专家精选的NDD相关基因列表以及相关的表型和分子信息。此外，我们还将以黑腹果蝇为模型，对新发现的候选基因进行功能表征。这些综合努力将增加对特定候选基因的了解，也将增加在非脱氧核糖核酸中放松管制的常见过程。总之，我们的研究将为NDDS的临床和分子格局提供新的见解，并使研究人员和临床医生能够对NDDS中的基因-表型关系的有效性进行评分。

Neurodevelopmental disorders (NDD) are clinically and genetically extremely heterogeneous with more than 1000 underlying genes identified, so far. High throughput sequencing by Next-Generation sequencing (NGS) technologies has greatly advanced the identification and confirmation of novel disease genes. Despite these advances and despite falling costs, the current gold standard of trio-based exome or genome sequencing remains prohibitively expensive and time-consuming for large cohorts. To identify further known and novel genetics causes of NDDs on a larger scale we developed Exome Pool-Seq, an approach combining pooling of DNA samples and standard capture-based exome sequencing. This method greatly reduces sequencing costs albeit maintaining a high detection rate. We now plan to improve the method computationally and screen a larger cohort of >500 individuals with NDDs to systematically identify known and new NDD causing variants. The successful application of Exome Pool-Seq results from the synergy between the two applicants’ core skills: computational data analysis and clinical/genetic expert knowledge. We want to extend this interaction to computationally and functionally characterize genes and pathways involved in NDDs. This work will be based on the further development and improvement of our established SysID database, which contains an expert-curated list of NDD associated genes and related phenotypic and molecular information. Additionally, we will functionally characterize newly identified candidate genes by e.g. Drosophila melanogaster as a model. These integrated efforts will increase the knowledge on specific candidate genes but also common processes deregulated in NDDs. In summary, our study will provide new insights into the clinical and molecular landscape of NDDs and enable researchers and clinicians to score the validity of genotype-phenotype relationships in NDDs.