Statistical Methods for Next-Gen Sequencing in Disease Association Studies

疾病相关性研究中下一代测序的统计方法 通过这个项目，我们建议开发统计方法和软件的基因型呼叫和 下一代序列数据中的关联测试。这个场是由分子进步驱动的，允许 负担得起的大规模并行测序。下一代统计方法的快速发展 为了跟上先进的分子技术，疾病研究中的序列数据是必要的。下一步- 世代测序是基于随机的短读技术；因此，任何核苷酸的覆盖率都是 高度多变且容易出错的。SNP需要区分随机误差和真正的可变点 更进一步的一步是确定个体在该站点的实际基因。这是一个高度 这是一个统计问题，我们还没有看到这个问题以一种严格的统计方式解决。 我们提出的解决方案，以及使我们的方法新颖的原因，假设我们有一个样本 个体，每个个体都有下一代序列数据。我们预计测序最终可能会取代 用于疾病关联研究的GWASSNP阵列。虽然这可能需要几年的时间才能实现全基因组 测序，为一项小型关联研究单独测序足够多的人已经变得可行 使用目标捕获阵列。我们可以利用来自下一代个人样本的信息 序列数据，以更准确地估计个体的基因型和特定位置的错误率。我们的 方法是在似然框架中表示基因型概率和错误率。然后我们就可以使用 标准的统计理论，帮助我们判断基因类型。此方法的性能应该比调用 就像目前所做的那样，根据一个任意的过滤器，一次只对一个人进行基因分型。 这种统计框架的一个明显优势是，基因型呼叫中的不确定性可以是 直接纳入我们的疾病关联测试(例如，病例对照和罕见变异分析)。在这 通过这种方式，我们将增加我们的联合测试的能力，并减少由于错误或系统缺失而产生的偏差。 将下一代序列数据合并到关联测试中提供了完整的分析管道 从序列到关联。

Statistical Methods for Next-Generation Sequencing in Disease Association Studies Through this project we propose to develop statistical approaches and software for genotype calling and association testing in next-generation sequence data. The field is driven by molecular advances that allow for affordable, massively parallel sequencing. The rapid development of statistical methods for next-generation sequence data in disease studies is necessary to keep pace with the advancing molecular technology. Next- generation sequencing is based on random, short-read technology; thus the coverage of any nucleotide is highly variable and subject to error. Distinguishing random error from truly variable sites is required for "SNP- calling". One step beyond this is identifying the individual's actual genotype at the site. This is a highly statistical problem and we have yet to see this problem addressed in a statistically rigorous manner. The solution that we propose, and what makes our approach novel, assumes that we have a sample of individuals, each with next-generation sequence data. We anticipate that sequencing may ultimately replace GWAS SNP arrays for disease-association studies. While this may be several years away for whole-genome sequencing, sequencing enough people individually for a small association study is already becoming practical with target capture arrays. We can leverage the information from a sample of individuals with next-generation sequence data to more accurately estimate an individual's genotype and the position-specific error rate. Our approach is to express the genotype probabilities and error rate in a likelihood framework. We can then use standard statistical theory to help us call genotypes. This approach should perform better than calling genotypes for a single individual at a time based on an arbitrary filter as is currently done. A distinct advantage of this statistical framework is that the uncertainty in the genotype calls can be incorporated directly into our disease-association tests (e.g., case-control and rare variant analysis). In this way we will increase power of our association tests and reduce bias due to error or systematic missingness. Incorporation of next-generation sequence data into the association tests provides a complete analysis pipeline from sequence to association.