Mechanisms and consequences of sequence context-dependency of human mutation rate

人类突变率序列上下文依赖性的机制和后果

基本信息

批准号：
10675033
负责人：
Ziyue Gao
金额：
$ 40.63万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2027-06-30
项目状态：
未结题

项目摘要

PROJECT SUMMARY/ABSTRACT All genetic variation—including that underlying heritable disease, cancer, and human evolution—originate from mutation. Recent large-scale genome sequencing efforts and innovative statistical analysis have uncovered substantial variation in mutation rate along the human genome, revealing strong impacts of the mutation type and flanking sequence. However, the molecular mechanisms of this context-dependency of mutation rate are poorly understood. In addition, mutation rate variation across genomic sites may bias inferences of selection signals from genomic data, which in turn hinders the identification and functional study of genes that drive disease. The goal of our research program is to develop computational methods to draw insights into the molecular mechanisms as well as functional and evolutionary consequences of context-dependent mutation rate variation. We will first focus on the hypermutability of CpG sites and take a multifaceted approach to investigate the lesion formation and repair at methylated cytosines in different regions of the genome, by utilizing existing genomic and epigenomic data from human populations and other species. Successful completion of this research will contribute to a mechanistic and quantitative understanding of the mutational processes at methylated cytosines. Next, we will improve computational methods for inferring selection on human genes by leveraging the inherent mutation rate variation across genomic sites. We propose to combine population genetics models and machine learning techniques to integrate the allele frequency, site-specific mutation rate, and functional information of variants. The application of these newly developed methods to the ever-growing genomic data will identify genes crucial to human health and reproduction, and improve estimates of the burden of deleterious variants introduced by new mutations in each generation. Finally, we will expand our research scope to somatic mutations and leverage the context-dependency of mutation rates to better understand cancer driver genes. We will evaluate the relative mutability of cancer driver genes under different mutational processes, and investigate how tissue-specific relative mutability and selective effect interact during somatic evolution of tumor. By taking an evolutionary perspective of tumorigenesis, this research promises to shed new light on the tissue-specificity of cancer driver genes. Together, the proposed research will develop novel computational approaches that translate the rich genomic and epigenomic data available into insights into mutational mechanisms, as well as selective forces acting on genes and genetic variants in human populations and somatic cells.

项目概要/摘要所有遗传变异——包括潜在的遗传性疾病、癌症和人类进化——都起源于最近的大规模基因组测序工作和创新的统计分析发现了突变。人类基因组突变率存在巨大差异，揭示了突变类型的强烈影响然而，这种突变率的上下文依赖性的分子机制是此外，基因组位点之间的突变率变化可能会导致选择的推论产生偏差。来自基因组数据的信号，这反过来又阻碍了驱动基因的识别和功能研究我们研究计划的目标是开发计算方法来深入了解疾病。背景依赖性突变的分子机制以及功能和进化后果我们将首先关注 CpG 位点的超可变性，并采取多方面的方法来解决。研究基因组不同区域甲基化胞嘧啶的损伤形成和修复，现有的利用人类和其他物种的基因组和表观基因组数据的方法已取得成功。这项研究的完成将有助于对突变的机制和定量理解接下来，我们将改进推断选择的计算方法。我们建议通过利用基因组位点之间固有的突变率变化来结合人类基因。群体遗传学模型和机器学习技术整合等位基因频率、位点特异性突变率和变体的功能信息的应用。不断增长的基因组数据将识别对人类健康和生殖至关重要的基因，并改善最后，我们将估计每一代新突变引入的有害变异的负担。将我们的研究范围扩展到体细胞突变，并利用突变率的上下文依赖性来我们将更好地了解癌症驱动基因。不同的突变过程，并研究组织特异性相对突变性和选择性效应如何从肿瘤发生的进化角度来看，这在肿瘤的体细胞进化过程中相互作用。这项研究有望为癌症驱动基因的组织特异性提供新的线索。研究将开发新的计算方法来翻译丰富的基因组和表观基因组数据可深入了解突变机制以及作用于基因和遗传的选择力人类群体和体细胞的变异。