Exploring the genetic architecture of structural birth defects

探索结构性出生缺陷的遗传结构

基本信息

批准号：
10004116
负责人：
MICHAEL E TALKOWSKI
金额：
$ 16.8万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10004116
关键词：
3-Dimensional Algorithms Biological Categories Child Code Collaborations Complex Computing Methodologies Copy Number Polymorphism Counseling DNA Insertion Elements Data Data Aggregation Data Set Databases Detection Development Disease Disease model Embryonic Development Etiology European Event Family Genes Genetic Genetic Predisposition to Disease Genetic Risk Genets Genome Heritability Human Development Human Genome Joints Maps Mediating Methods Modeling Mutation Neurodevelopmental Disorder Newborn Infant Pathogenicity Pathway interactions Pattern Point Mutation Population Population Control Proteins Public Health Research Design Resources Risk Science Scoring Method Single Nucleotide Polymorphism Statistical Data Interpretation Statistical Methods Statistical Models Structural Congenital Anomalies Structure Synaptic Transmission Testing Transcriptional Regulation Untranslated RNA Validation Variant autism spectrum disorder case finding chromatin modification chromothripsis cloud based cohort computational pipelines computerized tools data resource de novo mutation exome genetic architecture genome sequencing genomic tools genomic variation improved innovation insertion/deletion mutation insight large scale data novel prenatal testing programs therapeutic development tool variant detection whole genome

项目摘要

ABSTRACT Structural birth defects (SBDs) impact millions of children in the US and around the world every year. The Gabriella Miller Kids First (GMKF) program and other initiatives have committed significant resources toward understanding the genetic basis of these conditions, yet the genetic etiology and underlying pathogenic mechanisms remain unknown for most children that present with SBDs. This application will focus on two classes of genomic variation that likely contribute to this unexplained etiology in SBDs: structural variation (SV) and noncoding regulatory variation. Broadly defined as variants >50bp in size, SVs have been discovered to be far more prevalent and diverse in each human genome than previously appreciated. In this proposal, we will use our recently developed SV detection algorithms from whole-genome sequencing across GMKF SBD cohorts, and compare these findings to large population-scale datasets in the genome aggregation database (gnomAD-SV). In addition to variant detection and characterization, we will pursue disease association analyses in SBD cohorts. Given that most new (de novo) mutations that occur in children reside outside of the coding sequence of genes, it is imperative that models of disease association account for both coding and noncoding variation. Here, we will leverage recently developed analytic frameworks of ‘category-wide association study’ (CWAS) and de novo risk score approaches to perform association tests across all coding and noncoding single nucleotide variants, indels and structural variants in GMKF. Finally, we will integrate genome and exome findings from cases with neurodevelopmental disorders (NDD) to investigate commonalities in genes and pathways associated with SBDs and NDDs. Taken together, we expect these analyses to develop a comprehensive resource to interrogate the genetic landscape of SV and noncoding associations across SBDs in GMKF families and population controls.

抽象的结构性出生缺陷（SBD）每年都会影响美国和世界各地数百万儿童今年，加布里埃拉·米勒儿童优先 (GMKF) 计划和其他举措已做出承诺。用于了解这些疾病的遗传基础的重要资源，但对于大多数儿童来说，遗传病因和潜在的致病机制仍然未知该应用程序将重点关注可能存在的两类基因组变异。导致 SBD 中这种无法解释的病因的原因是：结构变异 (SV) 和非编码 SVs 广泛定义为大小 >50bp 的变异。每个人类基因组中的现象都比以前想象的要普遍和多样化。建议，我们将使用我们最近开发的全基因组 SV 检测算法对 GMKF SBD 队列进行测序，并将这些结果与大规模人群进行比较除了变异检测之外，基因组聚合数据库（gnomAD-SV）中的数据集。鉴于此，我们将在 SBD 队列中进行疾病关联分析。儿童中发生的大多数新的（从头）突变位于编码序列之外基因，疾病关联模型必须同时考虑编码和在这里，我们将利用最近开发的分析框架 “全类别关联研究”（CWAS）和从头风险评分方法来执行跨所有编码和非编码单核苷酸变体、插入缺失和插入缺失的关联测试最后，我们将整合病例的基因组和外显子组发现。与神经发育障碍 (NDD) 一起研究基因和通路的共性结合起来，我们期望这些分析能够与 SBD 和 NDD 相关。询问 SV 和非编码关联的遗传景观的综合资源跨越 GMKF 家庭和人口控制中的 SBD。