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: 10544032
• 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 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10544032
• 关键词: Acceleration; Address; Aloral; Animal Model; Bioinformatics; Biological Models; Candidate Disease Gene; Cardiac; Cardiovascular system; Cell model; Cells; Child; Childhood; Classification; 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; 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.

项目摘要 先天性心脏病（CHD）遗传基础的鉴定得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，如NHLBI资助的 儿科心血管基因组学联盟（PCGC）已经对近100种心血管疾病的外显子组或基因组进行了测序。 3000例CHD患者和确定的极有可能导致CHD的变异。使用的方法， 确定了CHD患者人群中富集的罕见变异和破坏性影响预测算法， 支持的致病性，已确定了一系列潜在致病性变体。为进一步支持 致病性，这些变异体存在于与人类CHD有先前关联的基因中，或者已经被 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的 由于基因变异，在许多情况下，发现了一种以上的潜在遗传变异，使得后续分析变得困难。 在拟议的探索性资助中，我们将研究使用机器学习来使用从 本发明涉及CHD的小鼠和诱导多能干细胞（iPSC）模型的转录组学分析。而不是 建立一个共同的分析管道，包括所有可能的候选基因的所有心脏病，我们将使用基因 在显示在患者中观察到的表型的CHD模型系统中差异调节， 变体。为了实现这一点，患者的诊断将被用作输入，以识别RNA-seq数据集， 小鼠/iPSC模型，其具有来自基因表达综合数据库（GEO）的类似诊断。的基因 在这些数据集中的差异表达将在优先化管道中具有额外的权重。与此同时， 我们将研究来自发育中的人类胚胎的单细胞RNAseq数据集中的基因表达， 心中这将使我们能够评估基因在相关细胞类型中的表达，这些细胞类型有助于正常心脏 发展将介绍在多个CHD重叠亚型患者中观察到的基因 作为优先变量。这种分析管道将不排除任何遗传变异作为考虑因素。 候选人，但将使用CHD模型系统和单细胞转录组数据中的表达分析来对 变体。该流水线的结果将是每个患者中的变体的排名列表，这些变体是基于患者的基因序列排序的。 来自上述数据集的信息和变体优先级的当前标准，例如次要 等位基因频率和预测的损伤效应。作为一个直接的结果，我们希望发现新的候选人， 基因的CHD和确定基因的CHD病例的子集中具有较高的负担。创建、培训和 机器学习算法的测试将为CHD患者的变体优先级提供平台， 该模型具有扩展到其他先天性畸形的潜力。