Computational methods to elucidate the role of long non-coding RNA in Congenital Heart Disease

项目总结 先天性心脏病(CHD)是最常见的出生缺陷，但该疾病的遗传学研究很少 明白了。该病的基因组机制包括独特的稀有拷贝数变异(CNV)和蛋白质. 编码单核苷酸变体(SNV)。无其他先天异常的先心病或单纯性先心病占75% 在所有的先心病中。对孤立的CHD的基因组测序(GS)研究主要集中在蛋白质编码区， 仅在约10%-20%的受试者中识别疾病原因变异。这一巨大的知识差距表明，其他人 病因，如非编码基因组的变异，可能起到一定作用。非编码基因组是巨大的，构成 98%的基因组，包含多种特征类型，包括非编码RNA。有越来越多的 证明长非编码RNA(LncRNAs)在疾病中的作用的证据，包括心脏发育障碍。 因此，这项研究的长期目标是阐明lncRNA在心脏畸形中的作用。这个 拟议调查的首要目标是开发计算方法来预测 参与心脏发育的lncRNA，并预测影响这些分子的变异的致病影响 导致心脏发育不良。我们将使用来自Gabriella Miller Kids First(GMKF)队列的GS数据来关联 冠心病易感基因的变异。然后我们将使用单细胞RNA测序(scRNA-Seq)数据来识别lncRNAs 在人类心脏发生的关键阶段，在相关细胞类型中表达。我们的中心假设是变种 在未解决的CHD病例中，lncRNAs是一个可能的原因，通过使用scRNA-seq数据，我们可以确定优先顺序 未来功能验证的候选对象。我们提出以下具体目标来应对这一挑战。在AIM 1，我们将开发一个机器学习(ML)工具来注释我们CHD队列中的lncRNA变体。缺少这样一个人 解释CNV和SNV影响lncRNA的生物学含义的工具。我们的初步数据有效地 应用ML注释临床验证的CNV与孤立性CHD相关。我们将把我们的方法扩展到 考虑影响lncRNA的CNV和SNV，以及那些影响蛋白质编码基因的CNV和SNV。AIM 2将应用网络 分析scRNA-Seq数据以阐明lncRNA在心脏发育中的作用。我们会把lncRNA-蛋白质因果联系起来 通过使用基因调控网络(GRN)的推理与一般心脏发育的关系。GRN 将从单细胞转录组数据中构建，以帮助发现涉及心脏的lncRNAs 发展。这项工作是创新的，因为我们将是第一个构建心脏特异性lncRNA的ML工具 不同的注释，并阐明lncRNAs在CHD的发展中可能发挥的作用。完成这个项目 将实现NHLBI的使命，即创造用于理解机制的计算技术 正常心脏形成的调节和NICHD理解高血压的遗传基础的目标 心脏缺陷。此外，这项研究具有重要意义，因为它可能导致发现新的遗传病因 冠心病和新的治疗靶点的确定。

PROJECT SUMMARY Congenital Heart Disease (CHD) is the most common birth defect, yet the genetics of this disease are poorly understood. The genomic mechanisms of this disease include distinct rare copy number variants (CNVs) and protein- coding single nucleotide variants (SNVs). CHDs without other congenital anomalies, or isolated CHD, comprise 75% of all CHDs. Genome sequencing (GS) studies of isolated CHD have focused primarily on protein-coding regions, identifying disease-causal variants in only ~10-20% of subjects. This substantial knowledge gap suggests that other etiologies, such as variation in the non-coding genome, may play a role. The non-coding genome is vast, constituting 98% of the genome, and encompasses multiple feature types, including the non-coding RNAs. There is growing evidence for the role of long non-coding RNAs (lncRNAs) in disease, including developmental disorders of the heart. As such, the long-term goal of this study is to elucidate lncRNA’s role in contributing to cardiac malformations. The overarching objective of the proposed investigation is to develop computational methods to predict the function of lncRNAs involved in heart development and predict the pathogenic impact of variants impacting these molecules leading to heart maldevelopment. We will use GS data from the Gabriella Miller Kids First (GMKF) cohort to associate variation in lncRNAs to CHD. We will then use single-cell RNA-sequencing (scRNA-Seq) data to identify lncRNAs expressed in relevant cell types during crucial stages of human cardiogenesis. Our central hypothesis is that variants in lncRNAs are a probable cause in unsolved CHD cases and that by using scRNA-seq data, we can prioritize candidates for future functional validation. We propose the following specific aims to address this challenge. In Aim 1, we will develop a machine learning (ML) tool to annotate lncRNA variants in our CHD cohort. There is a lack of tools to interpret the biological implications of CNVs and SNVs impacting lncRNAs. Our preliminary data effectively annotated clinically validated CNVs associated with isolated CHD by applying ML. We will extend our methods to consider CNVs and SNVs impacting lncRNAs and those impacting protein-coding genes. Aim 2 will apply network analysis on scRNA-Seq data to elucidate lncRNA’s role in heart development. We will associate lncRNA-protein causal relationships with general heart development by using inference from the gene regulatory networks (GRN). GRN will be built from single-cell transcriptomics data to contribute to the discovery of lncRNAs involved in heart development. This work is innovative as we will be the first to construct an ML tool for cardiac-specific lncRNA variant annotation and clarify the role that lncRNAs may play in the development of CHD. Completing this project will achieve the NHLBI’s mission of creating computational techniques for understanding the mechanisms underlying the regulation of normal heart formation and NICHD’s objective of comprehending the genetic basis of heart defects. In addition, the research is significant since it may lead to the discovery of novel genetic etiologies in CHD and the identification of novel therapeutic targets.