Statistical Methods for Analyzing Birth Defects Cohorts

分析出生缺陷队列的统计方法

• 批准号: 10372041
• 负责人: HONGYU ZHAO
• 金额: $ 16.75万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10372041
• 关键词: Address; Affect; Biological; Biotechnology; Child; Communities; Computer software; Congenital Abnormality; Data; Data Aggregation; Data Set; Defect; Disease; Disease model; Economic Burden; Etiology; Family; Funding; Future; Genes; Genetic; Genetic study; Genome; Genomics; Goals; Guidelines; Heterogeneity; Heterozygote; Immune System Diseases; Individual; Inherited; Joints; Malignant Childhood Neoplasm; Mental disorders; Metabolic; Methods; Mutation; Newborn Infant; Parents; Pathway interactions; Pediatric Research; Performance; Phenotype; Play; Research; Research Personnel; Role; Sample Size; Signal Transduction; Societies; Statistical Methods; Structural Congenital Anomalies; Technology; United States National Institutes of Health; Variant; Work; cohort; congenital heart disorder; data resource; de novo mutation; disorder risk; exome sequencing; family burden; gene discovery; genomic data; health economics; improved; next generation sequencing; novel; phenotypic data; pleiotropism; programs; risk prediction; software development; success; tool

Project Summary Birth defects cause significant health and economic burdens to families and societies globally. In recent years, advances in biotechnologies, such as next-generation sequencing, have helped to identify many disease- causing genes for birth defects and childhood cancers. Although the identified genes only explain a small proportion of the cases, these advancements demonstrate the promise of identifying more birth defect-causing genes from the analysis of sequencing data through powerful statistical methods. The NIH Common Fund established the Gabriella Miller Kids First Pediatric Research Program (Kids First) to “develop a pediatric research data resource populated by genome sequence and phenotype data that will be of high value for the communities of investigators who study the genetics of childhood cancers and/or structural birth defects.” The ultimate goal of this project is to develop, implement, and apply novel statistical methods to improve the power of identifying genes causing birth defects across a number of conditions using data from the Kids First Data Resource Center and to make the developed tools available to the scientific community. This will be accomplished through three specific aims. First, we will develop a statistical framework that can simultaneously consider different disease models – including both de novo mutations and rare inherited variants – to more effectively identify disease-causing genes from whole exome sequencing data. Second, we will develop statistical methods to quantify the degree of shared de novo mutation contributions to different birth defects and also methods that can leverage this shared genetics to identify disease-causing genes. Third, after evaluating the performance of our developed methods, we will implement these methods and apply them to the birth defect cohorts currently available at the Kids First Data Resource Center as well as other data sets that will be added in the future. We will also disseminate the software to the scientific community. In accomplishing our aims, we will contribute new statistical tools to analyze birth defects cohorts as well as make new biological discoveries of genes and pathways for different birth defects.

项目摘要 出生缺陷在全球范围内为家庭和社会带来了重大的健康和经济燃烧。最近几年， 生物技术的进步，例如下一代测序，有助于确定许多疾病 - 导致出生缺陷和童年癌的基因。尽管所鉴定的基因仅解释了一个小的 案件的比例，这些进步证明了识别更多出生缺陷的希望 通过强大的统计方法对测序数据分析的基因。 NIH普通基金 建立了Gabriella Miller Kids第一儿科研究计划（儿童第一），以“开发儿科 基因组序列和表型数据所填充的研究数据资源将对该数据具有很高的价值 研究儿童癌症遗传学和/或结构性先天缺陷的研究者社区。 该项目的最终目标是开发，实施和应用新颖的统计方法来提高权力 使用来自儿童的数据，识别导致许多情况下导致先天缺陷的基因 资源中心并使开发的工具可用于科学界。这将是 通过三个特定目标完成。首先，我们将开发一个可以轻松的统计框架 考虑不同的疾病模型，包括从头突变和罕见的遗传变体 - 有效地从整个外显子组测序数据中鉴定出引起疾病的基因。第二，我们将发展 量化共享的从头突变贡献的统计方法对不同的先天缺陷 以及可以利用这种共同遗传学来鉴定引起疾病的基因的方法。第三，之后 评估我们开发方法的性能，我们将实施这些方法并将其应用于 目前在儿童第一数据资源中心以及其他数据集的出生缺陷队列 将来会添加。我们还将将软件传播给科学界。完成 我们的目的，我们将贡献新的统计工具来分析先天缺陷的同类群体并使新的生物学 不同先天缺陷的基因和途径的发现。