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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.

项目总结/摘要所有的遗传变异--包括潜在的遗传性疾病、癌症和人类进化--都起源于突变最近的大规模基因组测序工作和创新的统计分析揭示了人类基因组中突变率沿着的显著变化，揭示了突变类型的强烈影响和侧翼序列。然而，突变率的这种背景依赖性的分子机制是不太了解。此外，基因组位点之间的突变率变化可能会使选择的推断产生偏差基因组数据的信号，这反过来又阻碍了基因的识别和功能研究，疾病我们的研究计划的目标是开发计算方法，以了解背景依赖突变的分子机制以及功能和进化后果速率变化我们将首先关注CpG位点的超突变性，并采取多方面的方法，研究损伤的形成和修复甲基化胞嘧啶在不同区域的基因组，利用来自人类群体和其他物种的现有基因组和表观基因组数据。成功这项研究的完成将有助于对突变的机制和定量的理解。在甲基化胞嘧啶上的过程。接下来，我们将改进用于推断选择的计算方法，通过利用基因组位点之间的固有突变率变化来分析人类基因。我们建议将联合收割机群体遗传学模型和机器学习技术，以整合等位基因频率，位点特异性突变率和变体的功能信息。这些新开发的方法的应用，不断增长的基因组数据将确定对人类健康和生殖至关重要的基因，估计每一代中新突变引入的有害变体的负担。最后我们将将我们的研究范围扩大到体细胞突变，并利用突变率的背景依赖性，更好地了解癌症驱动基因。我们将评估癌症驱动基因的相对突变性，不同的突变过程，并探讨如何组织特异性相对突变性和选择性效应在肿瘤体细胞进化过程中相互作用。从肿瘤发生的进化角度来看，研究有望为癌症驱动基因的组织特异性提供新的线索。在一起，拟议的研究将开发新的计算方法，翻译丰富的基因组和表观基因组数据，可用于洞察突变机制，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。