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

人类疾病线粒体基因组动力学贝叶斯建模的工具和数据

基本信息

批准号：
10720177
负责人：
Jenny Brynjarsdottir
金额：
$ 7.94万
依托单位：
CASE WESTERN RESERVE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10720177
关键词：
Address Age Aging Algorithms Alleles Bayesian Modeling Benign Biotechnology Blindness Cell Nucleus Cells ChIP-seq Characteristics Chromosomes Complex Computer software DNA DNA Sequence Alteration Data Data Analyses Data Set Data Sources Development Diabetes Mellitus Disease Ensure Generations Genes Genetic Genetic Anticipation Genome Genomics Goals Haplotypes Health Human Human Development Human Genome In Vitro Individual Language Liquid substance Malignant Neoplasms Medical Methodology Methods Mitochondria Mitochondrial DNA Modeling Modernization Mothers Mutation Nature Nuclear Nucleotides Organ Pathogenicity Phenotype Positioning Attribute Process Property Proteins Publishing Research Research Personnel Resolution Risk Risk Assessment Role Statistical Computing Statistical Models Stroke System Techniques Testing Time Tissues Validation Variant Work autism spectrum disorder base biomedical informatics clinically relevant computational platform computer infrastructure disease phenotype disease-causing mutation disorder risk exome experience experimental study flexibility genetic variant genomic data heteroplasmy human disease human genomics in vivo insight large datasets mitochondrial DNA mutation mitochondrial dysfunction mitochondrial genome next generation sequencing novel novel strategies offspring portability tool transcriptome tumor heterogeneity

项目摘要

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.

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〜3.2 构成人类基因组的十亿个核苷酸碱基位置。检测高遗传变异的能力吞吐量方式揭示了有助于人类表型变异的特定等位基因，包括与疾病风险有关。但是，绝大多数研究仅关注位于核中的基因组。在很大程度上被忽略的是细胞线粒体（mtDNA）中包含的DNA，该DNA 拥有编码负责产生大部分细胞能量的蛋白质的基因。重要的是，每个细胞的线粒体染色体拷贝数量大，可导致mtDNA变体共同具有野生型等位基因，一种称为异质的疾病。变体的异质水平可以改变在同一个人中，跨世代以及空间/时间上都有巨大的作用。使用新数据来源，第一次有可能开发高度准确的流程模型线粒体基因组动力学。由于这些过程具有层次结构，因此贝叶斯分层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获mtDNA动力学的框架； 2）将框架应用于来自各种临床的大型数据集相关设置； 3）全面的模型测试，实验验证和实施。线粒体DNA突变已与疾病表型有关，包括糖尿病，自闭症，脑病，中风，视力丧失，癌症等。随着其他相关数据可用的是，可以进一步阐明mtDNA变异对复杂表型的影响。这样的见解可能会导致遗传学和医疗应用的重要发现。这个项目将有助于通过提供可靠的计算基础架构来提高MTDNA的有效和准确建模突变动力学。所得的软件将在免费和便携式中实施和传播统计计算平台R。