Analysis of Somatic Mutations in Longitudinal Whole-genome Sequencing Data

项目总结 虽然最近的努力集中在更好地了解不同种系的胚系突变率 人类基因组中DNA变异的类型和体细胞突变的动态还知之甚少。 体细胞突变是众所周知的癌症病因，可能在其他疾病和 衰老。对体细胞突变的分析通常局限于单细胞或低覆盖率的研究。 要准确评估随时间推移的体细胞突变率，需要一个纵向的高覆盖率 数据集。对于犹他州CEPH(人类多态中心)家系中的约450人， 抽血是在大约15年的两个时间点上进行的。这份提案描述了实验以 利用高覆盖率(720倍)确定整个人类基因组的体细胞突变率 CEPH患者子集在这两个时间点的全基因组测序数据。基因组 这项研究将用于确定体细胞突变率并改善我们的 了解个体之间这一比率的差异。建立在以前在 我们将确定胚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关系。 通过这项工作，将产生一系列过滤算法，以允许全面分析 发生在同一个体的两个时间点之间的体细胞突变。此数据集为我提供了 这是使用高质量数据研究随时间推移的体细胞突变的独特机会。 这一建议是基于我在分析移动元件介导的体细胞突变方面的经验 在癌症患者的纵向全基因组数据中，以及我最近的工作分析种系短 CEPH家系中串联重复序列突变的研究该项目的K99部分将侧重于确定 在纵向、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，以提高我们的检测能力。在此期间，我将接受额外的培训 时期将为一个独立的研究计划奠定基础，该计划将确定 人类基因组中的体细胞突变以及这些突变与疾病结局的关系。

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.