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：
10673600
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/10673600
关键词：
Address Affinity Amino Acids Award Binding Proteins Bioinformatics Child 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 Combinations 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 body system candidate identification cohort computerized tools congenital heart disorder convolutional neural network data integration de novo mutation deep learning developmental disease disorder risk exome exome sequencing experimental study falls genetic analysis genetic architecture 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.

项目摘要我们旨在提高我们对结构性先天缺陷的遗传基础的理解。为此，我们建议开发和改善计算方法来解释稀有变体并执行综合性蛋白质编码和非编码变体的统计分析，以鉴定新的风险基因。汇总的结构出生缺陷在现场出生中很常见。虽然患者的存活率近几十年来，由于严重的先天缺陷，许多幸存的患者仍然有显着改善以后生活中的重大临床问题，包括生长，神经发育障碍，儿童癌和其他健康问题。更好地理解结构性先天缺陷的遗传基础将导致新的见解成为这些临床问题的原因，并将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资助的项目首先（GMKF）程序已经确定了新的风险基因，尤其是通过蛋白质编码的从头变体地区。但是，先天缺陷的遗传学很复杂。到目前为止，已知风险基因仅解释5％至30％常见的先天缺陷，例如先天性心脏病。大多数风险基因尚不清楚。贡献对于罕见的遗传变体或非编码变体的疾病风险而言，鲜为人知。调查这些有效的变体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改善稀有变体功能影响预测的工具。在这项研究中，我们提出了两个目标通过利用增长的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发展：特定目的1。开发和改进在遗传研究中优先考虑损害稀有错义和非编码变体的计算方法。具体的目标2。稀有编码和非编码变体的综合分析，以识别结构性出生的新风险基因缺陷。我们拟议的研究将通过将GMKF WGS数据集与其他外显子组或其他外显子组或同一先天缺陷的WGS数据，进而提高我们对多效效应和组织的理解出生缺陷中风险基因和变体的特异性。解释的新计算和统计工具罕见的变体将广泛地适用于先天缺陷和其他疾病的遗传研究。