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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：
9809586
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 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9809586
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 Dimensions 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 Nucleotides Pathogenicity Pathway interactions Pattern Point Mutation Population Population Control Proteins Public Health Research Design Resources Risk Science Scoring Method 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 computerized tools data resource exome genetic architecture genome sequencing genomic tools genomic variation improved innovation insertion/deletion mutation insight novel prenatal testing programs therapeutic development tool 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.

摘要