Tools and Data for Bayesian Modeling of Mitochondrial Genome Dynamics in Human Disease

现代生物技术使人类基因组学发生了革命性的变化，使研究人员能够在~3.2 构成人类基因组的十亿个核苷酸碱基位置。检测高基因变异的能力-- 吞吐量方式揭示了导致人类表型变异的特定等位基因，包括 与疾病风险相关。然而，绝大多数的研究都只关注于 基因组位于细胞核内。在很大程度上被忽视的是细胞线粒体中的DNA(MtDNA)，它 含有编码蛋白质的基因，这些蛋白质负责产生细胞的大部分能量。重要的是 每个细胞大量且可变的线粒体染色体拷贝数可导致mtDNA变异. 存在野生型等位基因，这种情况被称为异质性。变种的异质性水平可能会发生变化 戏剧性地跨越几代人，以及在同一个人的空间/时间上。使用新数据 消息来源，这是第一次有可能开发出基础过程的高精度模型 线粒体基因组动力学。由于这些过程具有层次性，因此贝叶斯层次化 建模是开发此类模型的理想框架。 鉴于此，我们建议追求以下三个具体目标：1)开发灵活的贝叶斯建模 捕捉线粒体DNA动力学的框架；2)将该框架应用于来自各种临床- 相关设置；3)全面的模型测试、实验验证和实施。 线粒体DNA突变与糖尿病、自闭症、 脑肌病、中风、失明、癌症和许多其他疾病。随着更多相关数据变得 进一步阐明线粒体DNA变异对复杂表型的影响是可能的。这样的洞察力 很可能会在遗传学和医学应用方面带来重大发现。这个项目将促进 为线粒体DNA的高效和准确建模提供可靠的计算基础设施方面的进展 突变动力学。由此产生的软件将以免费和可移植的方式实施和传播 统计计算平台R。

Modern biotechnologies have revolutionized human genomics, allowing researchers to query across the ~3.2 billion nucleotide base positions that form the human genome. The ability to detect genetic variants in a high- throughput manner has revealed specific alleles that contribute to human phenotypic variation, including those associated with disease risk. However, the vast majority of studies have focus exclusively on the portion of the genome located in the nucleus. Largely ignored is the DNA contained in the cell's mitochondria (mtDNA), which harbors the genes encoding proteins that are responsible for generating most of the cell's energy. Importantly, the large and variable numbers of mitochondrial chromosome copies per cell can result in an mtDNA variant co- existing with a wild-type allele, a condition known as heteroplasmy. The variant's heteroplasmy level can shift dramatically across generations, as well as spatially/temporally within the same individual. With new data sources, it is possible for the first time to develop highly accurate models of the processes underlying mitochondrial genome dynamics. Since these processes have hierarchical aspects, Bayesian hierarchical modeling is an ideal framework within which to develop such models. Given this, we propose to pursue the following three Specific Aims: 1) Develop a flexible Bayesian modeling framework to capture mtDNA dynamics; 2) Apply the framework to large data sets from a variety of clinically- relevant settings; and 3) Comprehensive model testing, experimental validation, and implementation. Mitochondrial DNA mutations have been implicated in disease phenotypes including diabetes, autism, encephalomyopathies, stroke, vision loss, cancer, and many others. As additional relevant data become available, further elucidation of the impact of mtDNA variation on complex phenotypes is possible. Such insights are likely to lead to important discoveries in genetics as well as medical applications. This project will facilitate advances by providing reliable computational infrastructure for efficient and accurate modeling of mtDNA mutational dynamics. The resulting software will be implemented and disseminated in the free and portable statistical computing platform R.