A Multi-omic approach towards improving candidate gene identification and variant prioritization in patients with congenital heart disease

改善先天性心脏病患者候选基因识别和变异优先顺序的多组学方法

• 批准号: 10360965
• 负责人: Vidu Garg
• 金额: $ 11.55万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10360965
• 关键词: Address; Aloral; Animal Model; Bioinformatics; Biological Models; Candidate Disease Gene; Cardiac; Cardiovascular system; Cell model; Cells; Childhood; Clinical; Cohort Studies; Congenital Abnormality; Data; Data Set; Databases; Deposition; Diagnosis; Disease; Disease model; Embryonic Heart; Etiology; Frequencies; Funding; Funding Opportunities; Gene Expression; Gene Frequency; Genes; Genetic; Genetic Predisposition to Disease; Genome; Genomics; Grant; Heart; Human; Machine Learning; Methods; Minor; Modeling; Molecular Abnormality; Mus; National Heart, Lung, and Blood Institute; Ontology; Pathogenicity; Patients; Phenotype; Population; Process; Research; Techniques; Technology; Testing; Tissues; Training; United States National Institutes of Health; Validation; Variant; Weight; analysis pipeline; base; cardiogenesis; cell type; cohort; congenital heart disorder; differential expression; disorder subtype; exome; exome sequencing; follow-up; genetic variant; genome sequencing; genomic data; human data; improved; in silico; individual patient; induced pluripotent stem cell; kindred; machine learning algorithm; mouse model; multiple omics; novel; patient population; prediction algorithm; rare variant; secondary analysis; segregation; single cell sequencing; single-cell RNA sequencing; stem cell model; transcriptome; transcriptome sequencing; transcriptomics; variant of unknown significance

Project Summary Identification of the genetic basis for congenital heart disease (CHD) has benefitted from advances in exome sequencing (ES) and genome sequencing (GS) pipelines. Large cohort studies, such as the NHLBI-funded Pediatric Cardiovascular Genomics Consortium (PCGC), have sequenced the exomes or genomes of nearly 3000 CHD patients and identified variants with a high likelihood of contributing to CHD. Using approaches that identified rare variants enriched in CHD patient populations and damaging effect prediction algorithms that supported pathogenicity, a list of potentially pathogenic variants has been identified. In further support of pathogenicity, these variants are found in genes which have prior association with human CHD or have been implicated in heart development in animal models. While this approach has aided in identification of novel variants, more than one potential genetic variant is identified in many cases rendering follow-up analyses difficult. In the proposed exploratory grant, we will investigate the use of machine learning to use data obtained from transcriptomic analysis of both mouse and induced pluripotent stem cell (iPSC) models of CHD. Rather than building a common analytical pipeline by including all possible candidate genes for all CHDs, we will use genes differentially regulated in CHD model systems that display phenotypes observed in the patient to prioritize variants. To achieve this, the patient’s diagnosis will be used as input to identify RNA-seq datasets from mouse/iPSC models with similar diagnoses from the Gene Expression Omnibus (GEO) database. The genes differentially expressed in these datasets will carry additional weight in the prioritization pipeline. Simultaneously, we will examine the expression of the genes in single-cell RNAseq datasets from developing human embryonic hearts. This will allow us to evaluate a gene’s expression in relevant cell-types that contribute to normal heart development. Genes that are observed in multiple patients with overlapping subtypes of CHD will be presented as prioritized variants. This analysis pipeline will not exclude any genetic variant from consideration as a candidate but will use expression analysis in CHD-model systems and single-cell transcriptomic data to rank the variants. The result of this pipeline will be a ranked list of variants in each patient that are ordered based on the information from the datasets mentioned above and current standards of variant prioritization such as minor allele frequency and predicted damaging effect. As a direct consequence, we expect to discover novel candidate genes for CHD and identify genes with a higher burden in a subset of CHD cases. The creation, training and testing of the machine learning algorithm will provide a platform for variant prioritization in patients with CHD and this model has the potential to be extended to other congenital malformations.

项目概要 先天性心脏病 (CHD) 遗传基础的鉴定受益于外显子组的进展 测序（ES）和基因组测序（GS）管道。大型队列研究，例如 NHLBI 资助的 儿科心血管基因组学联盟 (PCGC) 已对近 10 名儿童的外显子组或基因组进行了测序 对 3000 名 CHD 患者进行了研究，并鉴定出极有可能导致 CHD 的变异。使用的方法 确定了 CHD 患者群体中丰富的罕见变异和破坏性影响预测算法 支持致病性，已经确定了一系列潜在的致病变异。为了进一步支持 致病性，这些变异存在于先前与人类 CHD 相关或已被研究过的基因中。 与动物模型中的心脏发育有关。虽然这种方法有助于识别新颖的 变异，在许多情况下发现了不止一种潜在的遗传变异，使得后续分析变得困难。 在拟议的探索性资助中，我们将研究如何使用机器学习来使用从 CHD 小鼠模型和诱导多能干细胞 (iPSC) 模型的转录组分析。而不是 通过包含所有 CHD 的所有可能候选基因来构建通用分析管道，我们将使用基因 在 CHD 模型系统中进行差异调节，显示在患者中观察到的表型以进行优先级排序 变种。为了实现这一目标，患者的诊断将被用作识别 RNA-seq 数据集的输入 小鼠/iPSC 模型具有来自基因表达综合 (GEO) 数据库的类似诊断。基因 这些数据集中的差异表达将在优先级排序管道中带来额外的权重。同时地， 我们将检查发育中的人类胚胎的单细胞 RNAseq 数据集中基因的表达 心。这将使我们能够评估有助于正常心脏的相关细胞类型中的基因表达 发展。将展示在多个具有重叠先心病亚型的患者中观察到的基因 作为优先变体。该分析流程不会将任何遗传变异排除在考虑范围之外 候选者，但将使用 CHD 模型系统中的表达分析和单细胞转录组数据来对 变种。该管道的结果将是每个患者的变异的排名列表，这些变异是根据 来自上述数据集的信息以及当前的变体优先级标准，例如次要的 等位基因频率和预测的破坏作用。直接的结果是，我们期望发现新的候选者 CHD 的基因，并识别出一部分 CHD 病例中负担较高的基因。创作、培训和 机器学习算法的测试将为先心病患者的变异优先排序提供一个平台 该模型有可能扩展到其他先天性畸形。