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.

项目总结/摘要 患有唐氏综合症（DS）的个体在一生中往往有许多并发症 跨度，如阿尔茨海默氏病，自闭症和先天性心脏病（CHD）。非常引人注目的是， DS是CHD的一个强危险因素。与一般人群相比，患有DS的儿童 2000-患房室间隔缺损（AVSD）的风险增加一倍，AVSD是一种CHD。 在这里，我们建议系统地应用和评估个性化的贝叶斯推理 (IBI)协调临床和全基因组测序（WGS）数据的框架 Kids First（KF）和INCLUDE计划，以确定CHD潜在的重要变异， 没有和患有DS的患者，并了解为什么DS患者的风险增加 发展成CHD。与基于人群的全基因组关联研究（GWAS）相比， IBI考虑了个体间的基因组异质性，并推断个体化显著性 用于推进精准医学的变体; IBI还能够检测罕见或低频 通过关注每个个体的基因组， 同一队列中的GWAS分析。具体目标1：在一个 整合KF和INCLUDE队列，并进一步确定潜在的显著基因组变异 DS患者的CHD。具体目标2：在CAVATICA中构建和部署标准化的小说 IBI的工作流程与KF和INCLUDE社区分享，以确定重要的基因组 儿科条件的变化。如果成功，这个项目将产生一个新颖的，有效的， IBI的标准化和可共享的工作流程，用于推断疾病的重要变异， 个体特异性的方式，这在推进个性化医疗方面具有巨大的潜力， 影响DS个体和一般人群的条件。这本影响深远的 CAVATIVA上的IBI工作流程还可以吸引新用户， KF和INCLUDE数据此外，我们的努力可能会导致新的见解，可能的基因组 导致DS个体CHD高患病率的原因，并进一步告知设计 针对这些疾病的个性化预防或治疗策略。