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

项目摘要/摘要 所有的基因变异--包括潜在的可遗传疾病、癌症和人类进化--都源于 突变。最近的大规模基因组测序工作和创新的统计分析发现 人类基因组中突变率的显著变化，揭示了突变类型的强烈影响 和侧翼序列。然而，这种突变率的上下文依赖性的分子机制是 人们对此知之甚少。此外，不同基因组位置的突变率差异可能会使选择的推断产生偏差。 来自基因组数据的信号，这反过来又阻碍了对驱动基因的识别和功能研究 疾病。我们研究计划的目标是开发计算方法，以便深入了解 上下文相关突变的分子机制及其功能和进化后果 速率变化。我们将首先关注CpG位点的高度易变性，并采取多方面的方法 研究基因组不同区域甲基化胞嘧啶的损伤形成和修复 利用现有的人类种群和其他物种的基因组和表观基因组数据。成功 这项研究的完成将有助于从机制和数量上理解突变 胞嘧啶甲基化的过程。接下来，我们将改进推理选择的计算方法 通过利用基因组位置之间固有的突变率差异来获得人类基因。我们建议联合 群体遗传学模型和机器学习技术整合等位基因频率、特定位置 突变率和突变体的功能信息。将这些新开发的方法应用于 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，并改进 估计每一代中由新突变引入的有害变异的负担。最后，我们会 将我们的研究范围扩大到体细胞突变，并利用突变率的上下文依赖性来 更好地了解癌症驱动基因。我们将评估癌症驱动基因在以下条件下的相对变异性 不同的突变过程，并研究组织特异性的相对突变性和选择性效应 在肿瘤的体细胞进化过程中相互作用。通过从肿瘤发生的进化角度来看，这一点 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。总而言之，建议的 研究将开发新的计算方法来转换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基因的选择性作用力 人类种群和体细胞中的变异。

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.