CAREER: Computational Infrastructure for Full-Sequence Association Studies with Pooled Individuals

职业：与汇集个体进行全序列关联研究的计算基础设施

• 批准号: 0845677
• 负责人: Itshack Pe'er
• 金额: $ 40万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845677&HistoricalAwards=false
• 关键词: CAREER; Computational; Infrastructure; Full; Sequence

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Intellectual Merit.High throughput sequencing that allows human genetics to access rare variation "Next Generation" sequencing is transforming human genetics: several disruptive technologies are coming of age and now enable resequencing throughput of megabases per dollar. Specifically, thousands of individuals can now be sequenced for targeted regions of the genome, in pools of individuals. The complete spectrum of common and rare alleles thus revealed is a key resource for understanding origins, genomics, and heritable traits of our species. Naïve tests of association of a heritable trait to a common variant are inappropriate for analysis of rare gene variants, since the contribution of each such rare variant to the trait is often statistically undetectable. The hope for finding an associated gene therefore lies in accumulating association signal across multiple functional variants. The problem of multiple-variant association is complicated by background correlations between nearby variants.This proposal tackles two challenges:1.Initial task: Recovery of individual identity of mutation carriers from pooled sequencing data2.Main task: Using individual-level mutation data for scoring of association to multiple variants in a locusProposed solution: Bayesian scoring, decomposable by individual and by variant.This proposal involves design of overlapping pools for recovering mutation carrier identity. Each individual will be sequenced in a unique combination of pools. Mutations observed in such a set of pools will be inferred to be carried by the corresponding individual, addressing the initial task. This proposal tackles the main task by Bayesian scoring for genomic intervals containing functional variants. Comparative genomics is used to guide a prior distribution for whether a sequenced variant is likely to be functional. The association score is further decomposed to contributions of each sample and each site, with Markovian dependency between such contributions along the genome. A dynamic-program is proposed for optimizing the causal locus boundaries.Broad Impact.The outcomes of the project would facilitate new paradigms in genetic research, alongside the recently launched high throughput experimental technologies. Specifically, projected impacts include:- software tools and tailored interfaces to be disseminated to the reseaerch community.- Education for undergraduates by project courses implementing proposed research tasks and for K-12 students by curriculum development and delivery to high-school diversity students- allowing a generation to have widespread access to their individual DNA.

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。知识价值。“下一代”测序正在改变人类遗传学：一些颠覆性的技术正在成熟，现在可以实现每美元数百万个碱基的重测序吞吐量。具体来说，成千上万的个体现在可以在个体池中对基因组的目标区域进行测序。由此揭示的常见和罕见等位基因的完整谱是了解人类起源、基因组学和可遗传性状的关键资源。Naïve对一种遗传性状与一种常见变异的关联的测试不适用于分析罕见的基因变异，因为每一种这种罕见变异对该性状的贡献往往在统计上无法检测到。因此，寻找相关基因的希望在于在多个功能变异中积累相关信号。多变量关联问题由于邻近变量之间的背景相关性而变得复杂。该提案解决了两个挑战：1。初始任务：从汇集的测序数据中恢复突变携带者的个体身份2。主要任务：使用个体水平的突变数据对一个位点中的多个变异进行关联评分。建议的解决方案：贝叶斯评分，按个体和变异分解。这一建议涉及到重叠池的设计，以恢复突变载体身份。每个个体将在一个独特的组合池中进行测序。在这样一组池中观察到的突变将被推断为由相应的个体携带，解决初始任务。该方案解决了包含功能变异的基因组区间的贝叶斯评分的主要任务。比较基因组学用于指导一个序列变异是否可能具有功能的先验分布。关联得分进一步分解为每个样本和每个位点的贡献，这些贡献在基因组中具有马尔可夫依赖性。提出了一种优化因果轨迹边界的动态规划。广泛的影响。该项目的成果将促进基因研究的新范式，以及最近推出的高通量实验技术。具体而言，预计的影响包括：-向研究界传播的软件工具和量身定制的界面。-通过项目课程实施拟议的研究任务对本科生进行教育，通过课程开发和向高中多元化学生提供课程对K-12学生进行教育，使一代人能够广泛接触到他们的个人DNA。