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

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.

项目概要我们的目标是提高对结构性出生缺陷遗传基础的理解。为了实现这一目标，我们建议开发和改进解释罕见变异的计算方法并进行综合对蛋白质编码和非编码变体进行统计分析，以识别新的风险基因。聚集性结构性出生缺陷在活产中很常见。虽然患者的生存率近几十年来，严重的出生缺陷得到了显着改善，许多幸存的患者仍然患有晚年的重大临床问题，包括生长、神经发育障碍、儿童癌症和其他健康问题。更好地了解结构性出生缺陷的遗传基础将带来新的见解探究这些临床问题的原因，并为医疗干预和治疗提供目标。最近的出生缺陷的大规模基因组测序研究，包括由 Gabriella Miller Kids 资助的项目第一个 (GMKF) 计划已识别出新的风险基因，特别是通过蛋白质编码中的从头变异地区。然而，出生缺陷的遗传学很复杂。到目前为止，已知的风险基因只能解释 5% 到 30% 常见的出生缺陷，如先天性心脏病。大多数风险基因是未知的。贡献罕见遗传变异或非编码变异所带来的疾病风险却鲜为人知。为了调查这些有效地识别变异类型并识别新的风险基因，我们需要更大的样本量和更好的计算能力改进罕见变异功能影响预测的工具。在这项研究中，我们提出两个目标通过利用跨队列不断增长的 GMKF 全基因组测序 (WGS) 数据集来解决这些问题以及机器学习和其他基因组数据集的最新进展：具体目标 1. 开发和改进在遗传研究中优先考虑破坏性罕见错义和非编码变异的计算方法。具体的目标 2. 罕见编码和非编码变异的综合分析，以确定新的结构出生风险基因缺陷。我们提出的研究将通过将 GMKF WGS 数据集与其他外显子组或相同出生缺陷的全基因组测序数据，进而提高我们对多效性效应和组织的理解出生缺陷风险基因和变异的特异性。用于解释的新计算和统计工具罕见变异将广泛适用于出生缺陷和其他疾病的基因研究。