Statistical Methods to Analyze Resequencing Data

描述（由申请人提供）：鉴定导致复杂疾病的遗传因素是后基因组时代的赠款挑战之一。最近使用全基因组关联研究（GWAS）设计取得了一系列令人兴奋的新发现。然而，从确认的关联信号到在给定基因座处收集因果变体构成了重大挑战。GWAS的一个理想的后续策略是在感兴趣的基因组区域进行全面的重测序分析。这将使科学家能够全面发现和研究所有序列变异，从而大大增加识别新的致病突变的机会。下一代测序技术的快速发展使这种策略越来越可行。需要开发新的统计方法，以分析从这些新的测序仪器产生的数据。在本提案中，我们专注于从Illumina基因组分析仪平台生成的重测序数据中识别单核苷酸多态性（SNP）。首先，我们将开发一种基于概率的模型，该模型允许我们同时进行多映射短测序读数的映射、鉴定测序错误以及调用SNP及其基因型。由于我们的方法将在贝叶斯框架下开发，因此可以将从GWAS获得的基因型等其他信息作为信息先验纳入，以改善我们的推断。其次，我们将开发基于概率的方法，该方法将来自多个个体的选定基因座处的联合收割机测序读段数据组合以改善SNP和基因型调用。目标是在样本池中借用力量，以解决低测序深度基因座处的模糊性。我们将在免费提供的软件工具中实施我们的统计方法，以促进对靶向重测序研究的分析。最后，我们计划将我们的方法应用于真实的靶向重测序研究产生的数据，该研究正在计划通过合作用于银屑病和2型糖尿病。 公共卫生相关性：下一代测序技术促进了大规模的重新测序研究，为我们提供了更好的机会来识别致病突变。在这项提案中，我们将开发新的统计方法，用于从所谓的超高通量测序数据中识别遗传变异。完成后，将免费提供软件工具和方法，以便更好地分析重新测序研究产生的数据。

DESCRIPTION (provided by applicant): Identification of genetic factors that contributing to complex diseases is one of the grant challenges in the post-genomic era. A series of exciting new findings were made recently using the genome wide association study (GWAS) design. However, moving from confirmed association signal to the collection of causal variants at a given locus poses significant challenges. A desirable follow-up strategy of GWAS is to conduct a comprehensively resequencing analysis at the genomic regions of interest. This will allow scientists to comprehensively discover and study all sequence variants, which greatly increase the chance of identifying new disease causing mutations. Rapid advances in the next generation sequencing technologies are making such a strategy increasingly feasible. Novel statistical methods need to be developed in order to analyze data generated from these new sequencing instruments. In this proposal, we focus on identifying single nucleotide polymorphisms (SNPs) from resequencing data generated from the Illumina Genome Analyzer platform. First, we will develop a probability-based model that allow us to simultaneously perform mapping of multi- mapped short sequencing reads, identifying sequencing errors, and calling SNPs and their genotypes. Since our method will be developed under the Bayesian framework, additional information such as the genotypes obtained from GWAS can be incorporated as informative priors to improve our inference. Second, we will develop a probability- based approach that combine sequencing read data at selected loci from multiple individuals to improve SNP and genotype calling. The goal is to borrow strength among a pool of samples to resolve ambiguity at loci with low sequencing depth. We will implement our statistical methods in freely available software tools to facilitate analysis of targeted resequencing studies. Finally, we plan to apply our methods on data generated from real targeted resequencing studies that is being planned for psoriasis and type 2 diabetes through collaboration. PUBLIC HEALTH RELEVANCE: Next generation sequencing technologies facilitate large scale resequencing studies which offer us better chances of identifying disease-causing mutations. In this proposal, we will develop novel statistical methods for the identification of genetic variants from the so called ultra-high-throughput sequencing data. When completed, software tools and methods will be made freely available to allow better analysis of data generated from resequencing studies.