Bayesian Hierarchical Methods for Localized Analysis of Genic Intolerance to Variation

项目概述：这项拟议的指导性研究的目标是通过以下方式将基因变异与疾病联系起来 分析不能容忍变化的区域。找出对新变化不能容忍的地区会有所帮助 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 现在积累了足够大的数据集，以检测在 越来越细粒度的级别。然而，仍然缺乏适当的分析工具来构建 具体解决不同人群之间疾病异质性的重要问题。尽管事实是 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 仍然没有特征性，诊断率仍然相对较低。在目标1中，我将开发以下方法 定位与疾病相关的变异和不同亚型表达不耐受的区域。很多基因 显示组织依赖的转录本亚型，表明不同亚型的潜在功能含义。这就做 通过观察遗传模式，使用贝叶斯技术来表征所有异构体的选择压力 对大量常住人口的限制，主要利用数百个数量级的公共数据集 数以千计的样本。然后，我将利用现有的表达式数据在不同的 与相关疾病相关的细胞和组织类型。然后通过考虑地区不容忍 对于变异，可以采用联合转录切割变异-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，以 变种。大多数的遗传学研究都集中在欧洲人群上，而忽略了遗传和 表型多样性，可用于改善靶向和整体诊断和临床 能力。我将测试祖先和跨物种对变异和关联的遗传不耐受模式 带着疾病。扩大到更多的人口将扩大已经很大的一组正在估计的参数； 因此，我将开发新的统计方法和软件来改进参数估计的优化 贝叶斯分层模型。我将分离出在选择压力上存在已知差异的关键祖先种群 在验证发现的同时，利用这些新方法和人口疾病模式进一步分离 祖先特有的选择性压力的新信号。我将寻找跨物种的保守区域来分离 基本外显子区域，同时在人类特定遗传变异的背景下分离独特区域 以及疾病，如神经发育障碍。在这项拟议研究的培训时间内，我将专注于 增进我对生物机制和临床遗传学的理解，更好地为统计学提供信息 遗传学方法是我发展起来的。我的指导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 改进我的工作，提高变体解释，推进精准医学。

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.