Analysis of Somatic Mutations in Longitudinal Whole-genome Sequencing Data

项目摘要 虽然最近的努力集中在更好地了解不同的生殖细胞突变率， 虽然DNA变异的类型不同，但对人类基因组中体细胞突变的动力学知之甚少。 体细胞突变是一种众所周知的癌症原因，并可能在其他疾病中发挥致病作用， 衰老体细胞突变的分析通常仅限于单细胞或低覆盖率的研究。 为了准确评估随着时间推移的体细胞突变率，需要纵向的高覆盖率。 数据集。对于犹他州CEPH（人类多态性研究中心）家系中的约450个个体， 在间隔约15年的两个时间点抽取血液。该提案描述了实验， 利用高覆盖率（720x）确定整个人类基因组的体细胞突变率 全基因组测序数据在这两个时间点在一个子集的CEPH个人。基因组 包括在这项研究中，将用于确定体细胞突变率，并提高我们的 了解个体之间的这种比率差异。在以往工作的基础上， 犹他州大学，我们将确定种系和体细胞突变率之间的关系。 此外，使用为这些个体收集的详细表型数据，将进行实验以 确定这一比率与癌症和心血管疾病等疾病结局的相关性。 从这项工作中，将产生一系列过滤算法，以全面分析 在同一个体的两个时间点之间发生的体细胞突变。这个数据集为我提供了 这是一个利用高质量数据研究体细胞突变的独特机会。 这个建议建立在我分析移动的元件介导的体细胞突变的经验之上 在癌症患者的纵向全基因组数据中，我最近的工作分析了生殖系短 CEPH家系中的串联重复突变。该项目的K99部分将重点确定 纵向，高覆盖率的全基因组测序数据中的体细胞突变率， 过滤算法来提高我们的检测能力。在此期间我将接受的额外培训 期间将奠定一个独立的研究计划，将确定的意义的基础， 人类基因组中的体细胞突变以及这些突变与疾病结果的关系。

PROJECT SUMMARY While recent efforts have focused on better understanding the germline mutation rate of different types of DNA variants, the dynamics of somatic mutations in the human genome are poorly understood. Somatic mutations are a well-known cause of cancer and may play a causative role in other diseases and aging. Analyses of somatic mutations have generally been limited to single-cell or low-coverage studies. To accurately assess the rate of somatic mutations over time would require a longitudinal, high coverage dataset. For ~450 individuals in the Utah CEPH (Centre d'Etude du Polymorphism Humain) pedigrees, blood was drawn at two timepoints approximately 15 years apart. This proposal describes experiments to determine the rate of somatic mutations throughout the human genome by utilizing high-coverage (720x) whole-genome sequencing data at both of these timepoints in a subset of CEPH individuals. The genomes included in this study will be used to determine the rate of somatic mutations and improve our understanding of differences in this rate between individuals. Building on previous work performed at the University of Utah, we will determine the relationship between the germline and somatic mutation rate. Further, using detailed phenotypic data collected for these individuals, experiments will be performed to determine how this rate is correlated with disease outcomes such as cancer and cardiovascular disease. From this work, a series of filtering algorithms will be produced to allow for comprehensive analysis of somatic mutations occurring between two timepoints in the same individual. This dataset provides me with a unique opportunity to study somatic mutations over time using high-quality data. This proposal builds upon my experience in analyzing mobile element-mediated somatic mutations in longitudinal whole-genome data of cancer patients, and my recent work analyzing germline short tandem repeat mutations in the CEPH pedigrees. The K99 portion of this project will focus on determining the rate of somatic mutations in longitudinal, high-coverage whole-genome sequencing data while creating filtering algorithms to improve our detection capabilities. The additional training I will receive during this period will lay the foundation of an independent research program that will determine the significance of somatic mutations in the human genome and how these relate to disease outcomes.