权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Random Field Methods for integrative genomic analysis and high-dimensional risk prediction of congenital heart defects

用于先天性心脏病综合基因组分析和高维风险预测的随机场方法

基本信息

批准号：
10905156
负责人：
Ming Li
金额：
$ 48.76万
依托单位：
TRUSTEES OF INDIANA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-08 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10905156
关键词：
Address Bioinformatics Biological Biostatistical Methods Birth Cardiac Cardiovascular system Catalogs Child Childhood Clinical Complex Conceptions Congenital Abnormality Congenital Heart Defects Consultations Counseling Data Data Analyses Data Set Data Sources Development Diagnostic Disease Disease susceptibility Embryonic Development Environmental Exposure Epidemiology Epigenetic Process Etiology Family Fathers Genes Genetic Genetic Heterogeneity Genetic Predisposition to Disease Genetic Transcription Genomic Segment Genomics Individual Infant Infant Mortality Life Style Machine Learning Maternal Exposure Mendelian randomization Mentored Research Scientist Development Award Methods Methylation Modeling Modification Mothers National Heart, Lung, and Blood Institute National Institute of Child Health and Human Development Oligogenic Traits Pathway interactions Pediatric Cardiac Genomics Consortium Pediatric Hospitals Penetrance Performance Phenotype Predisposition Pregnancy Prevention Quantitative Trait Loci Regulatory Pathway Research Research Personnel Resources Risk Role Sampling Sampling Studies Standardization Susceptibility Gene Techniques Trans-Omics for Precision Medicine attributable mortality biobank clinical practice detection method disease heterogeneity disorder risk experience fetal flexibility gene environment interaction genetic approach genetic disorder diagnosis genetic testing genetic variant genome sequencing genome wide association study genome-wide high dimensionality improved innovation interest lifestyle factors multidimensional data multiple data sources multiple omics next generation sequencing novel postnatal predictive modeling rare variant reproductive risk prediction risk prediction model success treatment strategy whole genome

项目摘要

Project Summary Congenital heart defects (CHDs) are the most prevalent type of birth defects and the leading cause of infant mortality attributable to birth defects. Most CHDs are thought to result from a complex interaction between maternal exposures and genetic susceptibilities. In the past decade, large-scale genome-wide association studies and whole-genome sequencing studies have been conducted to comprehensively investigate the role of a deep catalog of genetic variants in CHDs. Although substantial progress has been made in the identification of individual genetic variants associated with CHDs, the complex interactions during embryogenesis have not been well understood. There has been even less success to identify rare genetic variants with heterogenous effect and the regulatory pathways among genetic variants, epigenetic modifications, and transcriptional changes leading to the disease development. To date, the etiology of CHDs has remained largely unknown, and the disease risk prediction models generally have low clinical utility. The proposed research aims to address the heterogeneous, interactive, and multi-omic etiology of CHDs by leveraging existing resources from multiple federal entities relevant to CHD research. We will conduct integrative genomic analysis using samples from the National Birth Defect Prevention Studies (NBDPS), the Pediatric Cardiac Genomics Consortium (PCGC) and the Trans-Omics for Precision Medicine (TOPMed) CHD Biobank. We will address the complex etiology of CHDs by applying novel random field methods developed by our research team. The planned specific aims are: 1) Identify and replicate genomic regions associated with CHD risk in the presence of disease heterogeneity and genetic interactions, 2) Detect methylation and expression quantitative trait loci and discover biological pathways among multi-omic factors contributing to CHD risk, and 3) Build a high-dimensional risk prediction model of CHDs based on genetic and maternal lifestyle profiles. The proposed research will be led by an early-stage new investigator, who has assembled a research team with complementary expertise in statistical genetics, bioinformatics, causal inference, CHD epidemiology, reproductive genetics, clinical cardiovascular genetics, and pediatric clinical practice. The successful completion of this research will lead to the discovery of novel disease- susceptible genes and biological pathways resulting in CHDs. The establishment of an accurate strategy for genetic risk prediction will provide new directions for precise preconceptional consultation, standardize genetic diagnosis after birth, and inform subsequent pregnancies.

项目总结