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: 7853195
• 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 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7853195
• 关键词: Acceleration; Address; Algorithms; Area; Classification; 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。该领域由分子进步驱动，允许 经济实惠的大规模并行测序。下一代统计方法的快速发展 疾病研究中的序列数据对于跟上先进的分子技术是必要的。下一个- 世代测序基于随机、短读长技术；因此任何核苷酸的覆盖度是 变化很大并且容易出错。 “SNP-”需要将随机误差与真正可变的位点区分开来。 呼叫”。除此之外的一步是在现场识别个体的实际基因型。这是一个高度 统计问题，我们还没有看到这个问题以统计上严格的方式得到解决。 我们提出的解决方案以及使我们的方法新颖的原因是假设我们有一个样本 个体，每个人都有下一代序列数据。我们预计测序可能最终取代 用于疾病关联研究的 GWAS SNP 阵列。虽然这对于全基因组来说可能还需要几年的时间 测序，对足够多的人进行单独测序以进行小型关联研究已经变得可行 与目标捕获阵列。我们可以利用来自下一代个体样本的信息 序列数据以更准确地估计个体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 标准统计理论帮助我们称呼基因型。这种方法应该比调用执行得更好 正如目前所做的那样，基于任意过滤器一次对单个个体进行基因型分析。 该统计框架的一个显着优点是基因型调用的不确定性可以是 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）。在这个 我们将以此方式增强关联测试的功效并减少由于错误或系统性缺失而导致的偏差。 将下一代序列数据纳入关联测试提供了完整的分析流程 从顺序到关联。