Analysis of de novo mutation from sequencing of related individuals and cells

通过相关个体和细胞的测序分析从头突变

• 批准号: 9234033
• 负责人: DONALD F. CONRAD
• 金额: $ 50万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-08 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9234033
• 关键词: Address; Age; Aging; Alleles; Alpha Cell; Animal Model; Area; Autistic Disorder; Benchmarking; Biological; Cancer Biology; Cancerous; Cells; Child; Childhood; Chromosome Segregation; Communities; Complex; Computer software; DNA Resequencing; DNA Sequence; Data; Data Set; Detection; Development; Developmental Delay Disorders; Disease; Dropout; Drosophila genus; Environmental Exposure; Error Sources; Experimental Models; Family; Genealogy; Genes; Genetic; Genetic Research; Genome; Genomics; Genotype; Germ Cells; Germ-Line Mutation; Goals; Heart; Hereditary Disease; High-Throughput Nucleotide Sequencing; Human; Human Characteristics; Human Genetics; Individual; Investments; Joints; Malignant Neoplasms; Medical Genetics; Methodology; Methods; Microbe; Modeling; Mosaicism; Mus; Mutation; Mutation Detection; Noise; Parents; Phenotype; Phylogeny; Play; Population; Population Heterogeneity; Probability; Process; Property; Research; Research Personnel; Risk Factors; Role; Sampling; Schizophrenia; Scientist; Site; Somatic Mutation; Statistical Methods; Surveys; Technology; Testing; Time; Tissue Model; Tissue Sample; Tissues; Toxicogenetics; Trees; Unmarried person; Validation; Variant; age effect; base; clinical practice; cost; genetic pedigree; genome-wide; human disease; human tissue; improved; insertion/deletion mutation; insight; markov model; novel; open source; personalized genomic medicine; practical application; programs; public health relevance; rare variant; sex; single cell sequencing; tool; tumor; whole genome

DESCRIPTION (provided by applicant): De novo DNA sequence mutations are mutations not inherited from a parent and play an important role in many human disorders, including cancer, autism, schizophrenia, and heart conditions. However, de novo mutations can be difficult to identify because sequencing errors are more common than mutations. Current approaches used to analyze DNA sequence data are inadequate to identify de novo mutations successfully at a genome scale because each potential de novo mutation must be validated by a costly and time-consuming validation process. Our goal is to improve the identification of de novo mutations in order to understand their role in genetic disorders. We will develop a novel statistical approach to identify de novo mutations, and we will implement it in software to make our method readily available to other researchers. Our first objective is to determine the probability that an apparent DNA sequence change is due to a de novo mutation, when analyzing short-read sequencing data from families. To determine this probability, we will integrate over other possible sources of error/noise including sequencing error, population diversity, and chromosome segregation. Secondly, we will expand on this model to detect somatic de novo mutations between multiple tissues from the same individual (e.g. matched tumor-normal datasets). Thirdly, we will develop new models to handle sequencing data from single-cell sequencing, which generates different probabilities of error compared to those discussed previously. These three aims are unified by a common approach of using genealogical information relating people, tissues, or individual cells, to improve the accuracy of de novo mutation discovery. Finally, we will implement these methods in an easy-to-use software package that will make the identification of de novo mutations possible for scientists working on subjects ranging from variation in mutation rates to the effects of aging. The methods developed here can benefit the hundreds, if not thousands, of studies that will search for and characterize de novo mutations in the coming decade. This package will be open source and free for the community to use.

描述（由申请人提供）：从头DNA序列突变是不是从父母遗传的突变，并在许多人类疾病中发挥重要作用，包括癌症，自闭症，精神分裂症和心脏病。然而，从头突变可能难以识别，因为测序错误比突变更常见。目前用于分析DNA序列数据的方法不足以在基因组规模上成功识别从头突变，因为每个潜在的从头突变必须通过昂贵且耗时的验证过程进行验证。我们的目标是提高从头突变的识别，以了解它们在遗传疾病中的作用。我们将开发一种新的统计方法来识别从头突变，我们将在软件中实现它，使我们的方法随时可供其他研究人员使用。我们的第一个目标是在分析来自家族的短读段测序数据时，确定明显的DNA序列变化是由于从头突变引起的概率。为了确定该概率，我们将整合其他可能的误差/噪声来源，包括测序误差、群体多样性和染色体分离。其次，我们将扩展该模型以检测来自同一个体的多个组织之间的体细胞从头突变（例如匹配的肿瘤-正常数据集）。第三，我们将开发新的模型来处理来自单细胞测序的测序数据，与之前讨论的那些相比，这会产生不同的错误概率。这三个目标通过使用与人、组织或单个细胞相关的系谱信息的共同方法来统一，以提高从头突变发现的准确性。最后，我们将在一个易于使用的软件包中实现这些方法，该软件包将使研究从突变率变化到衰老影响的科学家能够识别从头突变。这里开发的方法可以使数百项（如果不是数千项）研究受益，这些研究将在未来十年内搜索和表征从头突变。这个软件包将是开源的，免费供社区使用。