Investigation and deployment of novel Bayesian inference algorithms in CAVATICA for identifying genomic variants underlying congenital heart defects in Down syndrome individuals

在 CAVATICA 中研究和部署新型贝叶斯推理算法，用于识别唐氏综合症个体先天性心脏缺陷的基因组变异

• 批准号: 10658217
• 负责人: Jinling Liu
• 金额: $ 2.45万
• 依托单位: MISSOURI UNIVERSITY OF SCIENCE & TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10658217
• 关键词: Address; Affect; Algorithms; Alzheimer' s Disease; Automobile Driving; Bayesian Analysis; Big Data; Catalogs; Child; Childhood; Clinical; Clinical Data; Cloud Computing; Communities; Congenital Heart Defects; Data; Data Set; Development; Disease; Down Syndrome; Frequencies; General Population; Genome; Genomics; Health; Heterogeneity; High Prevalence; Individual; Investigation; Learning; Longevity; Malignant Neoplasms; Medical; Patients; Phenotype; Population; Prevention strategy; Probability; Publications; Quality of life; Research Personnel; Risk; Risk Factors; Sample Size; Standardization; Validation; Variant; Work; atrioventricular septal defect; autism spectrum disorder; cardiovascular risk factor; causal model; cohort; computational platform; data harmonization; data hub; data portal; data resource; design; genetic variant; genome sequencing; genome wide association study; improved; individual patient; individualized prevention; insight; novel; personalized genomics; personalized medicine; personalized predictions; phenotypic data; population based; portability; precision medicine; programs; theories; tool; treatment strategy; tumor; variant detection; whole genome

Project Summary/Abstract Individuals with Down syndrome (DS) tend to have many cooccurring conditions across the life span, such as Alzheimer’s disease, autism and congenital heart defects (CHD). Very strikingly, DS is a strong risk factor for CHD. Compared to the general population, children with DS have a 2000-fold increased risk of developing atrioventricular septal defects (AVSD), a type of CHD. Here, we propose to systematically apply and evaluate an individualized Bayesian inference (IBI) framework to the harmonized clinical and whole genome sequencing (WGS) data by the Kids First (KF) and INCLUDE programs to identify significant variants underlying CHD in patients without and with DS, and to understand why there is an increased risk for DS patients to develop CHD. Compared to the population-based genome wide association studies (GWAS), IBI considers the inter-individual genomic heterogeneity and infers personalized significant variants for advancing precision medicine; IBI is also capable of detecting rare or low-frequency variants by focusing on each individual’s genome that may have been missed by the parallel GWAS analysis in the same cohort. Specific Aim 1: Apply and validate the IBI framework in an integrated KF and INCLUDE cohort and further identify significant genomic variants underlying CHD in DS patients. Specific Aim 2: Build and deploy in CAVATICA a standardized novel workflow of IBI to share with the KF and INCLUDE community for identifying significant genomic variants of pediatric conditions. If successful, this project will produce a novel, validated, standardized and shareable workflow of IBI for inferring significant variants of diseases in an individual-specific manner, which has a great potential in advancing personalized medicine for conditions that affect DS individuals and the general population. The publication of this impactful IBI workflow on CAVATIVA may also attract new users and significantly increase utilization of KF and INCLUDE data. Moreover, our efforts may lead to new insights on probable genomic causes that underlie the high prevalence of CHD in DS individuals, and further inform the design of personalized prevention or treatment strategies for these diseases.

项目概要/摘要 唐氏综合症 (DS) 患者一生中往往会同时出现多种病症 跨度，例如阿尔茨海默病、自闭症和先天性心脏病（CHD）。 DS 是冠心病的一个重要危险因素，与普通人群相比，DS 儿童的患病率更高。 发生房室间隔缺损 (AVSD)（一种 CHD）的风险增加 2000 倍。 在这里，我们建议系统地应用和评估个性化贝叶斯推理 (IBI) 框架到统一的临床和全基因组测序 (WGS) 数据 Kids First (KF) 和 INCLUDE 计划可识别 CHD 的重要变异 患有和没有 DS 的患者，并了解为什么 DS 患者的风险增加 与基于人群的全基因组关联研究（GWAS）相比， IBI 考虑个体间基因组异质性并推断个性化显着性 IBI 还能够检测罕见或低频 通过关注每个人的基因组来发现可能被平行分析遗漏的变异 在同一队列中进行 GWAS 分析具体目标 1：在一个队列中应用并验证 IBI 框架。 整合 KF 和 INCLUDE 队列并进一步识别潜在的重要基因组变异 DS 患者的 CHD 具体目标 2：在 CAVATICA 中构建和部署标准化小说。 IBI 工作流程与 KF 和 INCLUDE 社区共享，用于识别重要的基因组 如果成功，该项目将产生一种新颖的、经过验证的、 IBI 的标准化和可共享工作流程，用于推断疾病的显着变异 个体特异性方式，在推进个体化医疗方面具有巨大潜力 影响 DS 个人和一般人群的情况 这篇影响深远的文章的出版。 CAVATIVA 上的 IBI 工作流程还可能吸引新用户并显着提高利用率 此外，我们的努力可能会带来对可能的基因组的新见解。 DS 个体中 CHD 高患病率的原因，并进一步为设计提供信息 这些疾病的个性化预防或治疗策略。