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

描述(申请人提供)：从头DNA序列突变是不是从父母那里继承的突变，在许多人类疾病中发挥着重要作用，包括癌症、自闭症、精神分裂症和心脏疾病。然而，从头突变可能很难识别，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都必须通过昂贵且耗时的验证过程来验证。我们的目标是改进对从头突变的识别，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开始的突变，并将在软件中实现它，以便其他研究人员随时可以使用我们的方法。我们的第一个目标是在分析家系的短读测序数据时，确定明显的DNA序列变化是由于从头开始突变的概率。为了确定这种概率，我们将对其他可能的错误/噪声来源进行积分，包括测序错误、种群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，与前面讨论的相比，单细胞测序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谱系信息的共同方法来统一，以提高从头发现突变的准确性。最后，我们将在一个易于使用的软件包中实施这些方法，这将使科学家能够识别从头开始的突变，研究范围从突变率的变化到衰老的影响。这里开发的方法可以使数百项研究受益，如果不是数千项的话，这些研究将在未来十年寻找和描述从头突变的特征。这个包将是开源的，并可供社区免费使用。

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.