High-throughput computational modeling to assess the role of 3D genome folding in human congenital anomalies

项目概要 每年有近百分之一的活产婴儿患有先天性心脏病 (CHD)，并且是导致先天性心脏病的主要原因 与缺陷相关的婴儿死亡率。大多数先天性心脏病 (CHD) 的遗传学研究都集中在体内的变异上。 蛋白质编码外显子组；然而，大多数疾病风险基因座落在非编码区域，并且推测 其中一些代表基因表达的重要调节因子，例如顺式作用增强子和绝缘子。 尽管进行了这些研究，但只有不到一半的先心病遗传性是通过全基因组解释的 蛋白质编码基因和假定的调控元件的关联或负荷测试。在这个提案中，我们 假设改变 3D 基因组折叠的遗传变异通过破坏 发展中主要独联体监管机制的联系。一种可能的染色质结构 受影响的是拓扑关联域（TAD），它指的是染色质组织的水平 其特征是该域内的接触频率相对于该域外的基因座更高。已经是 结果表明，虽然未受影响的对照显示 TAD 边界区域的 SV 明显减少， 基因组，被诊断患有发育迟缓（DD）和自闭症的个体在基因组中没有表现出偏见 SV 的位置。基于这些发现以及 DD 和 CHD 之间的高并发发病率，第一个目标 表征 TAD 边界和 CHD 相关其他非编码调控区域的结构变异 对照，并将进一步确定 TAD 是否以区域特定方式富集 SV 接近发育中的心脏中活跃的基因。在第二个目标中，我们使用补充注释 - 我们小组开发了一种不可知的深度学习方法，用于预测由于以下原因而导致的染色质接触变化 CHD 患者的基因变异。我们将使用 Hi-C 测序来确认模型预测的染色质 iPSC 衍生的内皮细胞和心肌细胞中的接触效应，并另外使用 RNA 测序来 确定假设的转录中断是否发生。最后，在第三个目标中，我们将创建 使用每个个体中发现的遗传变异的遗传、表观遗传和转录特征的模型 预测它们的表型状态。通过分析用于进行预测的特征的相对重要性， 我们将能够确定哪些类型的生物机制和途径最能预测 先天性心脏异常。总的来说，这些发现将阐明目前被低估的来源 人类发育中的监管破坏，识别新的疾病相关基因和潜在的治疗方法 目标，并将完善和验证染色质接触频率高通量预测的方法 推进 3D 基因组学领域的发展。

PROJECT SUMMARY Congenital heart defects (CHD) occur in nearly one percent of live births each year and are the leading cause of defect-associated infant mortality. The majority of genetic studies in CHD have focused on variation within the protein-coding exome; however, most disease-risk loci fall in noncoding regions, and it is presumed that some of these represent important regulators of gene expression such as cis-acting enhancers and insulators. In spite of these studies, less than half of the heritability of CHD has been explained via genome-wide associations or burden testing of protein-coding genes and putative regulatory elements. In this proposal, we hypothesize that genetic variants that alter 3D genome folding contribute to the etiology of CHD by disrupting the contacts of key cis-regulatory mechanisms in development. One type of chromatin structure that could be affected is the Topologically Associating Domain (TAD), which refers to a level of chromatin organization characterized by higher contact frequency within the domain relative to loci outside of that domain. It has been shown that while unaffected controls show a clear depletion of SVs at TAD boundary regions across the genome, individuals diagnosed with Developmental Delay (DD) and autism showed no bias in the genomic location of SVs. Based on these findings and the high rates of co-morbidity between DD and CHD, the first aim characterizes structural variation at TAD boundaries and other non-coding regulatory regions in CHD relative to controls, and will further determine whether TADs are enriched for SVs in a region-specific manner based on proximity to genes active in the developing heart. In the second aim, we use a complementary annotation- agnostic deep learning approach developed in our group to predict chromatin contact changes as a result of genetic variants in CHD patients. We will use Hi-C sequencing to confirm the model-predicted chromatin contact effects in iPSC-derived endothelial cells and cardiomyocytes, and additionally use RNA sequencing to determine whether the hypothesized transcriptional disruption occurred. Finally, in the third aim we will create a model that uses genetic, epigenetic, and transcriptional features of genetic variants found in each individual to predict their phenotypic status. By analyzing the relative importance of features used to make predictions, we will be able to determine what types of biological mechanisms and pathways are most predictive for congenital heart anomalies. Collectively, these findings will elucidate a currently underappreciated source of regulatory disruption in human development, identify new disease-relevant genes and potential therapeutic targets, and will refine and validate a method for the high-throughput prediction of chromatin contact frequency to advance the field of 3D genomics.