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

现代生物技术已经彻底改变了人类基因组学，使研究人员能够查询整个~3.2 构成人类基因组的10亿个核苷酸碱基。检测基因变异的能力- 通量方式揭示了有助于人类表型变异的特定等位基因，包括那些 与疾病风险有关。然而，绝大多数的研究都只集中在这一部分， 基因组位于细胞核中。最后被忽略的是细胞线粒体中的DNA， 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， 每个细胞的线粒体染色体拷贝数大而可变，可能导致线粒体DNA变异共同， 与野生型等位基因共存，这种情况被称为异质性。变异体的异质性水平可以改变 在同一个人的空间/时间上，也是如此。用新数据 来源，这是第一次有可能开发高度精确的模型的过程中， 线粒体基因组动力学由于这些过程具有分层方面，贝叶斯分层 建模是开发这种模型的理想框架。 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 2）将该框架应用于来自各种临床的大型数据集， 3）全面的模型测试、实验验证和实施。 线粒体DNA突变与疾病表型有关，包括糖尿病，自闭症， 脑肌病、中风、视力丧失、癌症和许多其他疾病。随着相关数据的增加， 现有的资料，进一步阐明线粒体DNA变异对复杂表型的影响是可能的。这样的见解 很可能会在遗传学和医学应用方面带来重大发现。该项目将促进 通过提供可靠的计算基础设施，为mtDNA的有效和准确建模提供了新的进展 突变动力学由此产生的软件将以免费和便携的 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.