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Computational analysis of whole genome sequence data for discovering novel risk genes of structural birth defects

全基因组序列数据的计算分析，以发现结构性出生缺陷的新风险基因

基本信息

批准号：
10354418
负责人：
Yufeng Shen
金额：
$ 15.96万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10354418
关键词：
Address Affinity Amino Acids Award Binding Proteins Bioinformatics Clinical Code Collaborations Complex Computer Analysis Computing Methodologies Congenital Abnormality Congenital diaphragmatic hernia Copy Number Polymorphism Data Data Set Development Developmental Biology Disease Distal Esophageal Atresia First Births Funding Gene Expression Genes Genetic Genetic Diseases Genetic study Genome Genomics Growth Health Human Human Development Inherited Intervention Life Live Birth Machine Learning Malignant Childhood Neoplasm Malignant Neoplasms Medical Methods Molecular Neurodevelopmental Disorder Open Reading Frames Patients Population Positioning Attribute Post-Transcriptional Regulation Property Proteins Publications RNA-Binding Proteins Role Sample Size Specificity Statistical Data Interpretation Structural Congenital Anomalies Structure Survival Rate Testing Tissues Tracheoesophageal Fistula Untranslated RNA Variant autism spectrum disorder base body system candidate identification cohort computerized tools congenital heart disorder convolutional neural network de novo mutation deep learning developmental disease disorder risk exome exome sequencing experimental study falls genetic analysis genetic architecture genetic variant genome sequencing genome-wide genomic data graph neural network improved insight interest mutation screening novel performance tests pleiotropism predictive tools programs rare variant risk variant tool whole genome

项目摘要

Project Summary We aim to improve our understanding of the genetic basis of structural birth defects. To achieve that, we propose to develop and improve computational methods for interpretation of rare variants and perform integrative statistical analysis of both protein-coding and noncoding variants to identify new risk genes. Structural birth defects in aggregation are common in live births. Although the survival rate of patients with severe birth defects has been dramatically improved in recent decades, many survived patients still have significant clinical problems later in life, including growth, neurodevelopmental disorders, childhood cancer, and other health issues. Better understanding of the genetic basis of structural birth defects will lead to new insights into the cause of these clinical issues and will provide targets for medical intervention and treatment. Recent large-scale genomic sequencing studies of birth defects, including projects funded by the Gabriella Miller Kids First (GMKF) program, have identified new risk genes, especially through de novo variants in protein coding regions. However, the genetics of birth defects is complex. By far, known risk genes only explain 5 to 30% of common birth defects such as congenital heart disease. The majority of risk genes are unknown. The contribution to the disease risk from rare inherited variants or noncoding variants is much less known. To investigate these types of variants effectively and identify new risk genes, we need larger sample size and better computational tools that improve the prediction of functional impact of rare variants. In this study, we propose two aims to address these questions by leverage growing GMKF whole genome sequencing (WGS) data sets across cohorts and latest development in machine learning and other genomic data sets: Specific Aim 1. Develop and improve computational methods to prioritize damaging rare missense and noncoding variants in genetic studies. Specific Aim 2. Integrative analysis of rare coding and noncoding variants to identify new risk genes of structural birth defects. Our proposed study will identify new risk genes by combining GMKF WGS data sets with other exome or WGS data of the same birth defects, and in turn improve our understanding of the pleiotropic effects and tissue specificity of risk genes and variants in birth defects. The new computational and statistical tools for interpreting rare variants will be broadly applicable to genetic studies of birth defects and other conditions.

项目总结