High-resolution map of human germline mutation patterns and inference of mutagenic mechanisms

人类种系突变模式的高分辨率图谱和诱变机制的推断

• 批准号: 9083570
• 负责人: Jun Li
• 金额: $ 30.1万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9083570
• 关键词: Address; Affect; African American; Aging; Algorithms; Alleles; American; Atlases; Biochemical Process; Biological Factors; Biological Process; Biology; Child; Chromatin; Communities; Complex; DNA; DNA Sequence; Data; Data Analyses; Data Set; Data Sources; Demographic Factors; Dependence; Disease; Environmental Risk Factor; European; Evolution; Family; Foundations; Frequencies; Future; Gene Conversion; Generations; Genes; Genetic; Genetic Polymorphism; Genetic Recombination; Genetic Variation; Genome; Genomic Segment; Genomics; Germ-Line Mutation; Guanine + Cytosine Composition; Hereditary Disease; High-Throughput Nucleotide Sequencing; Human; Human Genetics; Human Genome; Incidence; Individual; Inherited; Internet; Latino; Machine Learning; Maintenance; Malignant Neoplasms; Maps; Measures; Mendelian disorder; Methods; Mismatch Repair; Modeling; Molecular; Mutagenesis; Mutation; Natural Selections; Nucleotides; Parents; Pathogenicity; Pattern; Play; Population; Positioning Attribute; Probability; Process; Recording of previous events; Research; Resolution; Resource Sharing; Resources; Rest; Role; Sampling; Selection Bias; Site; Somatic Mutation; Source; Techniques; Technology; Time; Tissues; Variant; Weight; actionable mutation; base; data sharing; density; driving force; epigenomics; genetic evolution; genome sequencing; genome-wide; human disease; improved; next generation sequencing; prototype; public health relevance; rare variant; repository; transmission process; trend; whole genome

 DESCRIPTION (provided by applicant): Mutations are the ultimate source of genetic variation and one of the driving forces of evolution. Both the absolute mutation rate and the relative rate among mutation subtypes fluctuate along the genome, affected by adjacent nucleotide motifs and local features such as GC content and replication timing. Characterizing regional variation of mutation patterns is critical for understanding genome evolution and to identify variants causing genetic diseases. However, mutation rate and molecular spectrum are difficult to measure at high resolution, genomewide, and in an unbiased fashion. Estimates based on common variants and between- species substitutions are confounded by natural selection, population demographic history, and biased gene conversion (BGC). Methods relying on incidence rates of monogenic diseases or finding de novo variants by trio sequencing can inform global trends, but do not provide sufficient data to assess fine-scale local parameters. This study will overcome these limitations by using the extremely rare variants (ERVs) as a new data source to characterize patterns of recent germline variation in humans. ERVs, defined in this study as singletons in 30,000 samples, are becoming available via large-scale whole-genome sequencing (WGS) of population samples. Unlike common variants or substitutions, ERVs arose very recently and are largely unaffected by selection, BGC, etc. We will analyze 200-300 million singleton variants observed in 30,000 subjects at 20-30X coverage. The regional distribution of ERV subtypes will establish a quantitative atlas of the rate and spectrum of human germline mutations mostly unaltered by selection. We will share this resource with the research community and apply it to determine the impact of local genomic features and epigenomic attributes. We will use the systematic departures between ERVs and variants of higher frequencies (polymorphisms and substitutions) to infer local effects of selection, and this may uncover hitherto unknown functional regions of the genome. By comparing mutation signatures in ERVs with those in somatic variations observed in diverse cancers we will attribute distinct mutational signatures to known biochemical processes and thus infer the major contributors to new germline mutations in the human genome. This subtype-specific atlas will also be used to predict the probability of observing every possible single-base mutation in the genome, thus facilitating the interpretation of candidate causal variants of human diseases. We will assess mutation pattern differences among European Americans, African Americans and Latinos, and seek to discover genetic modifiers of germline mutation rate by finding functionally damaging mutations that show increased ERV counts in the surrounding genomic region, potentially identifying both known and previous unknown "mutator" genes that play a role in transmission fidelity in humans. This research will provide an essential resource to study the genesis and maintenance of germline mutations in humans. Understanding such a fundamental process will be the basis for a deeper understanding of human evolution and diseases.

 描述（申请人提供）：突变是遗传变异的最终来源，也是进化的驱动力之一。突变亚型之间的绝对突变率和相对率都沿着基因组波动，受到相邻核苷酸基序和局部特征（如）的影响。 GC 含量和复制时间表征突变模式的区域变异对于理解基因组进化和识别导致遗传疾病的变异至关重要。然而，突变率和分子谱很难在全基因组范围内进行高分辨率测量。基于常见变异和物种间替代的估计受到自然选择、人口统计历史和有偏差的基因转换（BGC）的影响，依赖于单基因疾病发病率或通过三重测序发现新变异的方法可以为全球提供信息。趋势，但没有提供足够的数据来评估精细的局部参数。本研究将通过使用极其罕见的变异（ERV）作为新的数据源来描述人类近期种系变异的模式，从而克服这些限制。这项研究涉及 30,000 个样本中的单例，可通过大规模的方法获得，与群体样本的全基因组测序 (WGS) 不同，ERV 是最近才出现的，并且基本上不受选择、BGC 等的影响。我们将分析 200 个样本。在 20-30 倍覆盖范围内，在 30,000 名受试者中观察到 -3 亿个单一变异。ERV 亚型的区域分布将建立一个比率的定量图谱。我们将与研究界共享这一资源，并将其应用于确定局部基因组特征和表观基因组属性的影响，我们将利用 ERV 和更高频率变异（多态性）之间的系统差异。和替换）来推断选择的局部效应，这可能会揭示迄今为止未知的基因组功能区域。通过将 ERV 中的突变特征与在不同癌症中观察到的体细胞变异特征进行比较，我们将把不同的突变特征归因于。该亚型特异性图谱还将用于预测观察到基因组中每个可能的单碱基突变的概率，从而有助于解释候选因果关系。我们将评估欧洲裔美国人、非洲裔美国人和拉丁裔美国人之间的突变模式差异，并通过发现周围基因组区域中 ERV 计数增加的功能性破坏性突变，寻求发现种系突变率的遗传修饰因子，从而有可能识别已知和以前未知的突变这项研究将为研究人类种系突变的发生和维持提供重要的资源，了解这一基本过程将成为更深入地了解人类进化和进化的基础。疾病。