Bayesian Hierarchical Methods for Localized Analysis of Genic Intolerance to Variation

用于基因变异不耐受局部分析的贝叶斯分层方法

基本信息

批准号：
10542431
负责人：
Tristan Jonathan Hayeck
金额：
$ 16.89万
依托单位：
CHILDREN'S HOSP OF PHILADELPHIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-05 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10542431
关键词：
Accounting Address Advisory Committees Algorithms Bayesian Modeling Biological Cells Child Clinical Clinical Management Clinical Treatment Collaborations Communities Complex Computer software Conserved Sequence Data Data Set Diagnosis Diagnostic Disease Environmental Exposure European Evaluation Exons Foundations General Population Genes Genetic Genetic Diseases Genetic Models Genetic Transcription Genetic Variation Genetic study Genome Genotype-Tissue Expression Project Goals Grouping Heterogeneity Human Immunology Investigation Joints Maps Medical Genetics Mendelian disorder Mentors Mentorship Methodology Methods Modeling Neurodevelopmental Disorder Parameter Estimation Pathogenicity Pathologist Patients Pattern Pediatric Hospitals Phenotype Philadelphia Population Population Heterogeneity Prevalence Proliferating Protein Isoforms RNA Sequences Research Resources Running Sample Size Sampling Scientist Signal Transduction Statistical Methods Structure Techniques Technology Testing Therapeutic Time Tissues Training Trans-Omics for Precision Medicine Transcript Variant Work analytical tool burden of illness cell type clinical diagnostics developmental disease differential expression disease heterogeneity disease phenotype disease-causing mutation disorder risk exome exome sequencing genome resource improved insight interest multidisciplinary novel population based precision medicine pressure scale up software development trait transcriptomics user friendly software whole genome

项目摘要

Project Summary: The goal of this proposed mentored research is to tie genetic variation to disease by analyzing regions that are intolerant to variation. Identifying regions that are intolerant to new variation can help localize regions of potential functional importance and biologic relevance. Large public population consortia are now accumulating datasets of sufficient size to detect regions subject to evolutionary selective pressures at an increasingly granular level. However, there remains a shortage of appropriate analytical tools that are built to specifically address important issues of disease heterogeneity across diverse populations. Despite the fact that clinical exome sequencing is increasingly used for improved diagnostic evaluation, many genetic disorders remain uncharacterized and diagnosis rates are still relatively low. In Aim 1, I will develop methodology that localizes regions intolerant to variation and differential isoform expression associated with disease. Many genes display tissue dependent transcript isoforms indicating potential functional implications of different isoforms. I will characterize selective pressure across all isoforms using Bayesian techniques by looking at patterns of genetic constraint across large standing populations, predominantly leveraging public data sets on the order of hundreds of thousands of samples. Then I will leverage existing expression data to isolate key isoforms across different cell and tissue types that are associated with diseases of interest. Then by accounting for regional intolerance to variation, a joint transcriptomic variation–intolerance approach can be employed to improve disease association testing. In Aim 2, I will analyze ancestry and cross species patterns of genetic intolerance to variation. The majority of genetic studies have focused on European populations, which ignores genetic and phenotypic diversity that can be leveraged to improve both targeted and overall diagnostic and clinical capabilities. I will test for ancestry and cross species patterns of genetic intolerance to variation and association with disease. Expanding to more populations will scale up the already large set of parameters being estimated; so, I will develop new statistical methods and software to improve optimization of parameter estimation for the Bayesian hierarchical models. I will isolate key ancestral populations with known differences in selective pressure to validate findings while then leveraging these new methods and population disease patterns further to isolate novel signals of ancestry-specific selective pressures. I will look for conserved regions across species to isolate essential exonic regions while also isolating unique regions in the context of human specific genetic variation and disease, such as neurodevelopmental disorders. During the training time for this proposed study I will focus on advancing my understanding of biologic mechanisms and clinical genetics to better inform the statistical genetics methods I develop. My mentorship and advisory committee consists of a strong multidisciplinary team of geneticists, pathologists, computational scientists, and biologists who will guide and collaborate with me to refine my work to improve variant interpretation and to advance precision medicine.

项目摘要：这项拟议的讨论研究的目的是将遗传变异与疾病联系起来分析不容忍变异的区域。识别不容忍新变化的区域可以帮助具有潜在功能重要性和生物学相关性的本地化区域。大型公共人口财团是现在积累了足够大小的数据集，以检测在一个受到进化选择压力的区域日益颗粒状的水平。但是，仍然缺乏适合的分析工具尽管事实是特别解决了潜水员种群中疾病异质性的重要问题。临床外显子组测序越来越多地用于改进诊断评估，许多遗传疾病保持未表征，诊断率仍然相对较低。在AIM 1中，我将开发方法论将与疾病相关的变异和差异同工型表达不耐受。许多基因显示组织依赖性转录本同工型，表明不同同工型的潜在功能意义。我会使用贝叶斯技术来表征所有同工型的选择性压力，以查看通用模式在大型群体中的约束，主要利用数百个顺序的公共数据集成千上万的样本。然后，我将利用现有的表达数据来隔离不同的键同工型与感兴趣的疾病有关的细胞和组织类型。然后考虑区域intlerance 为了变化，可以聘请联合转录组变异 - 含量方法来改善疾病关联测试。在AIM 2中，我将分析遗传肠道的祖先和跨物种模式变化。大多数遗传研究都集中在欧洲人群上，欧洲人口忽略了遗传和可以利用的表型多样性来改善目标和整体诊断和临床功能。我将测试遗传肠道对变异和关联的血统和跨物种模式疾病。扩展到更多的人群将扩展已经估计的已经很大的参数集；因此，我将开发新的统计方法和软件，以提高参数估计的优化贝叶斯分层模型。我将隔离关键祖先种群，在选择性压力方面有已知差异验证发现的同时利用这些新方法和种群疾病模式进一步分离祖先特定选择压力的新型信号。我将寻找跨规范的配置区域以隔离必不可少的外显子区域，同时在人类特异性遗传变异的背景下也隔离了独特的区域和疾病，例如神经发育障碍。在这项拟议的研究的培训期间，我将集中精力关于促进我对生物学机制和临床遗传学的理解，以更好地告知统计数据我开发的遗传学方法。我的精神制和咨询委员会由一个强大的多学科团队组成将指导和合作与我合作的遗传学家，病理学家，计算科学家和生物学家完善我的工作，以改善变体的解释并进步精确医学。