Advancing discovery for developmental disorders - expanded analysis of the DDD-Africa resource

发育障碍(DD)是一种严重的慢性残疾，通常会导致儿童早期死亡， 或幸存者的长期发病率。由于许多非洲国家的传染病负担很重 在许多国家，对诸如痴呆症等罕见疾病的积极研究往往被忽视。血吸虫病的遗传病因学 DD是复杂的，因为DD综合征以临床和遗传异质性为标志。破译 非洲发展障碍研究(DDD-Africa)是为了解决这一知识差距而发起的， 建立的资源具有发现许多东西的潜力，特别是通过催化新的合作 并通过数据科学方法的应用。因此，我们建议使用临床 来自DDD-非洲研究的信息、遗传数据和DNA资源，以调查以下内容 假设： 1.基于外显子组的CNV呼叫工作流程整合了不同的工具，将识别可能的疾病- 导致结构变异，并增加DD-ID患者的诊断率。 2.识别全基因组DNA甲基化模式将有助于诊断基因未解决的问题 非洲DD-ID患者。 3.表型感知，人工智能软件可以提高基因与表型的相关性 非洲DD-ID患者。 通过这项研究，我们将增加DD-ID在非洲患者中的诊断产量，并扩大 非洲罕见疾病的知识发现潜力。这项工作将进一步增加健康数据 非洲的科学能力，并创造一个能够为罕见疾病提供当地解决方案的生态系统 在非洲被称为DD。

Developmental disorders (DD) are severe and chronic disabilities that often leads to early childhood death, or long-term morbidity in the survivors. Due to the high burden of infectious disease in many African countries, active research into rare diseases such as DD has often been neglected. The genetic aetiology of DD is complex as DD syndromes are marked by both clinical- and genetic heterogeneity. The Deciphering Developmental Disorders in Africa study (DDD-Africa) was initiated to address this knowledge gap, and the resource that was built holds the potential for much discovery, especially by catalyzing new collaborations and through the application of data science approaches. We therefore propose to use the clinical information, genetic data, and DNA resources from the DDD-Africa study to investigate the following hypotheses: 1. An exome-based CNV calling workflow that incorporates different tools, will identify putative disease- causing structural variants, and increase the diagnostic yield for DD-ID patients. 2. Identifying genome-wide DNA methylations patterns will assist in diagnosing genetically unsolved African DD-ID patients. 3. Phenotype-aware, artificial intelligence software can improve genotype-phenotype correlation in African DD-ID patients. Through this study, we will increase the diagnostic yield of DD-ID in African patients and broaden the knowledge discovery potential for rare diseases in Africa. This work will furthermore increase health data science capacity in Africa and create an ecosystem that can provide local solutions for rare diseases such as DD in Africa.