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.

项目摘要 先天性心脏病(CHD)遗传学基础的确定得益于外显子组的研究进展 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)已经对近几个基因的外显子或基因组进行了测序 3000名CHD患者，并确定了导致CHD的高可能性变异。使用以下方法 识别在冠心病患者群体中丰富的罕见变异和破坏性影响预测算法 为了支持致病性，已经确定了一系列潜在的致病变异体。为进一步支持 致病性，这些变异是在先前与人类冠心病相关的基因中发现的，或者已经被发现 在动物模型中与心脏发育有关。虽然这种方法有助于识别小说 关于变异，在许多情况下发现了一个以上的潜在遗传变异，使后续分析变得困难。 在拟议的探索性拨款中，我们将研究使用机器学习来使用从 CHD小鼠和诱导多能干细胞(IPSC)模型的转录分析。而不是 通过包括所有可能的CHD候选基因来建立一个通用的分析管道，我们将使用基因 在显示患者观察到的表型以确定优先顺序的CHD模型系统中进行差异调节 变种。为了实现这一点，将使用患者的诊断作为输入来识别来自 来自基因表达总览(GEO)数据库的类似诊断的小鼠/IPSC模型。基因 在这些数据集中表达的差异将在优先排序管道中具有额外的权重。同时， 我们将研究发育中的人类胚胎的单细胞RNAseq数据集中的基因表达 红心。这将使我们能够评估基因在有助于正常心脏的相关细胞类型中的表达 发展。将介绍在具有重叠亚型的多个冠心病患者中观察到的基因 作为区分优先顺序的变体。这一分析渠道将不排除任何基因变异被视为 候选人，但将使用CHD模型系统中的表达分析和单细胞转录数据来对 变种。这条管道的结果将是每个患者的变体排序列表，这些变体是根据 来自上述数据集的信息和不同优先顺序的当前标准，例如次要 等位基因频率和预测的损害效应。作为一个直接的结果，我们希望发现新的候选人 并在部分CHD病例中识别负担较高的基因。创造、培训和 机器学习算法的测试将为CHD患者和 该模型有可能推广到其他先天畸形。