Statistical Methods for Next-Gen Sequencing in Disease Association Studies

疾病关联研究中下一代测序的统计方法

• 批准号: 7943996
• 负责人: Eden R. Martin
• 金额: $ 50万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7943996
• 关键词: Acceleration; Address; Algorithms; Area; Classification Scheme; Computer software; Data; Data Set; Databases; Development; Disease; Disease Association; Evaluation; Genome; Genotype; Goals; Grant; Individual; Infusion procedures; Molecular; Nucleotides; One-Step dentin bonding system; Performance; Positioning Attribute; Probability; Reading; Sampling; Simulate; Site; Solutions; Statistical Methods; Technology; Testing; Time; Uncertainty; Variant; base; case control; design; genome sequencing; genome wide association study; meetings; method development; new technology; next generation; novel strategies; programs; theories

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.

疾病关联研究中下一代测序的统计方法 通过这个项目，我们建议开发用于基因型调用的统计方法和软件， 下一代序列数据中的关联测试。该领域是由分子进步驱动的，允许 负担得起的，大规模平行的测序。下一代统计方法的快速发展 疾病研究中的序列数据对于与进步的分子技术保持同步是必要的。下一个- 生成测序基于随机的短阅读技术；因此，任何核苷酸的覆盖范围是 高度可变且出现错误。 “ SNP-”需要区分随机误差和真正可变位点 呼吁“超出此一步的一步是识别个人在网站上的实际基因型。这是一个高度的 统计问题，我们尚未以统计上严格的方式看到这个问题。 我们提出的解决方案，以及使我们的方法新颖的方法假设我们有一个样本 个人，每个人都有下一代序列数据。我们预计测序最终可能会取代 GWAS SNP阵列用于疾病结合研究。虽然对于全基因组来说可能已经几年了 测序，对足够的人进行排序进行小型协会研究已经变得实用 具有目标捕获数组。我们可以利用来自下一代个人样本的信息 序列数据更准确地估计个人的基因型和位置特异性错误率。我们的 方法是在似然框架中表达基因型概率和错误率。然后我们可以使用 标准统计理论可以帮助我们称基因型。这种方法应该比打电话更好 单个个体的基因型按照当前的任意过滤器进行。 该统计框架的一个明显优势是，基因型调用中的不确定性可以是 直接纳入我们的疾病结合测试（例如病例对照和罕见变体分析）。在这个 通过错误或系统的丢失，我们将增加关联测试的功率并减少偏见。 将下一代序列数据纳入关联测试提供了完整的分析管道 从序列到关联。