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.

项目摘要/摘要 唐氏综合症（DS）的个体在整个生活中往往具有许多同时发生的条件 跨越阿尔茨海默氏病，自闭症和先天性心脏缺陷（CHD）。非常引人注目， DS是CHD的强大危险因素。与一般人群相比，DS儿童具有 2000倍增加了出现室内间隔缺陷（AVSD）的风险，这是一种冠心病。 在这里，我们建议系统地应用和评估个性化的贝叶斯推断 （IBI）通过框架框架到统一的临床和整个基因组测序（WGS）数据 第一个孩子（KF），包括识别CHD潜在的重要变体的程序 没有DS的患者，并了解为什么DS患者的风险增加 开发冠心。与基于人群的基因组广泛关联研究（GWAS）相比 IBI考虑了个体间的基因组异质性，并渗透了个性化的重要性 推进精密医学的变体； IBI也能够检测到罕见或低频 通过关注每个人的基因组，这可能会被相关的变体 GWAS分析在同一队列中。特定目标1：应用并验证IBI框架 集成KF并包括队列并进一步识别基础的重要基因组变体 DS患者的CHD。特定目标2：在卡瓦蒂加建造和部署一部标准化小说 IBI与KF共享的工作流程，并包括识别重要基因组的社区 小儿疾病的变体。如果成功，该项目将产生一部经过验证的小说 IBI的标准化和可共享的工作流程，以推断出大量疾病变体 个人特定方式，在推进个性化医学方面具有巨大潜力 影响DS个人和普通人群的条件。这个有影响力的出版 IBI在Cavativa上的工作流程也可能吸引新用户，并大大增加对 KF并包括数据。此外，我们的努力可能会导致对可能基因组的新见解 DS个人中冠心病患病率很高的原因，并进一步告知设计 这些疾病的个性化预防或治疗策略。