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

• 批准号: 10324586
• 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
• 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/10324586
• 关键词: Accounting; Address; Advisory Committees; Algorithmic Software; Bayesian Modeling; Biological; Cells; Clinical; Clinical Management; Clinical Treatment; 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; Medical Genetics; Mendelian disorder; Mentors; Mentorship; Methodology; Methods; Modeling; Neurodevelopmental Disorder; Pathogenicity; Pathologist; Patients; Pattern; Pediatric Hospitals; Phenotype; Philadelphia; Population; Population Heterogeneity; Prevalence; 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

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：这项建议的指导研究的目标是通过以下方式将遗传变异与疾病联系起来： 分析不能容忍变异的区域。识别对新变异不耐受的区域有助于 定位具有潜在功能重要性和生物相关性的区域。大型公共人口财团是 现在积累了足够大的数据集，以检测受到进化选择压力的区域， 越来越多的层次。然而，仍然缺乏适当的分析工具， 具体解决不同人群之间疾病异质性的重要问题。尽管事实上 临床外显子组测序越来越多地用于改善诊断评估，许多遗传性疾病 仍然没有特征，诊断率仍然相对较低。在目标1中，我将开发一种方法， 定位对与疾病相关的变异和差异同种型表达不耐受的区域。许多基因 显示组织依赖性转录物同种型，表明不同同种型的潜在功能意义。我会 使用贝叶斯技术，通过观察基因表达的模式， 限制了大量的常设人口，主要是利用数百个公共数据集 数以千计的样本。然后，我将利用现有的表达数据， 与感兴趣的疾病相关的细胞和组织类型。然后通过解释地区性的不宽容 对于变异，可以采用联合转录组变异不耐受方法来改善疾病 关联测试在目标2中，我将分析遗传不耐受的祖先和跨物种模式， 变化量大多数遗传学研究都集中在欧洲人群中，这忽略了遗传和 表型多样性，可用于改善靶向和整体诊断和临床 能力的我将测试祖先和跨物种模式的遗传不容忍的变化和协会 疾病。扩大到更多的人口将扩大已经很大的一套参数估计; 因此，我将开发新的统计方法和软件，以改善参数估计的优化， 贝叶斯分层模型我会分离出在选择压力上有差异的关键祖先种群 验证发现，同时利用这些新方法和人群疾病模式， 祖先特异性选择压力的新信号。我会寻找物种间的保守区域 必需的外显子区域，同时也分离出人类特异性遗传变异背景下的独特区域 和疾病，如神经发育障碍。在本研究的培训期间，我将重点关注 提高我对生物学机制和临床遗传学的理解，以更好地为统计学提供信息。 我开发的遗传学方法我的导师和咨询委员会由一个强大的多学科团队组成 遗传学家，病理学家，计算科学家和生物学家，他们将指导我并与我合作， 完善我的工作，以改善变异解读并推进精准医学。