New Bayesian algorithms for genome-wide association mapping

用于全基因组关联映射的新贝叶斯算法

• 批准号: 8370940
• 负责人: Yu Zhang
• 金额: $ 17.87万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-15 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8370940
• 关键词: Accounting; Algorithms; Architecture; Basic Science; Bayesian Method; Biological; Biology; Communities; Complex; Computer software; DNA Resequencing; Data; Data Analyses; Data Set; Databases; Dependence; Detection; Disease; Disease Association; Environment; Evaluation; Future; Galaxy; Genes; Genetic; Genome; Genomics; Goals; Grouping; Haplotypes; Hereditary Disease; Heterogeneity; Histones; Human; Human Genome; Individual; Inherited; Joints; Knowledge; Linkage Disequilibrium; Maps; Mediator of activation protein; Methods; Modeling; Mutation; Outcome; Pathway interactions; Penetrance; Performance; Phenotype; Point Mutation; Problem Solving; Regulatory Element; Research; Research Personnel; Resolution; Sampling; Site; Solutions; Source; Source Code; Statistical Models; Structure; System; Techniques; Technology; Testing; Validation; Variant; base; flexibility; gene environment interaction; genetic variant; genome wide association study; genome-wide; improved; innovation; interest; knowledge base; next generation; programs; research study; statistics; tool; trait; transcription factor; web site

DESCRIPTION (provided by applicant): Genome-wide association studies hold great promises to reveal the genetic architectures underlying human complex diseases. The disease variants are often non-Mendelian, demonstrating low penetrance and little effects to the disease individually, but interacting with each other and environments in unknown ways. With recent high-throughput sequencing technology, much more data are generated in the genome-scale, including not only genetic variants, but also regulatory elements at the individual-level. Regulatory factors are known to interact and act as mediators between sequence variation and phenotypic diversity. Multi-variant disease mapping therefore becomes more interesting and important for future genome-wide association studies. It is also hoped that, by collecting all variants in the human genome, we could identify the true causative variants, such that functional evaluation and validation experiments can be precisely developed at the identified sites to truly reveal their biological mechanisms to the disease. Identifying multi-variant association is extremely challenging. Current algorithms are still very limited. Particularly, high throughput sequencing data are now routinely generated in disease studies. These complete variants are highly dependent, for which existing methods have substantial computational difficulties and thus make it extremely difficult to pinpoint the true disease variants. It is also very challengingto detect disease associations from rare variants, which are however more abundant in the human genome, and could be the main contributor to human complex diseases. We propose to develop advanced algorithms to tackle the above problems. We will develop advanced algorithms to improve the power and the computational efficiency for whole genome multi-variant mapping. We also propose generalized methods to jointly test common and rare variants under a coherent full probabilistic model. Our approach automatically group variants for joint testing, account for dependence, incorporate biological priors, and identify causative variants. We further extend the methods via non-parametric Bayesian techniques to integrate various sources of public databases in disease mapping. My new algorithms will greatly enhance researchers' capability to analyze high-throughput genetic and genomic data. The software will be freely distributed to the community through the PI's website and the Galaxy system hosted at Penn State. PUBLIC HEALTH RELEVANCE: The goal of the project is to develop new powerful and efficient statistical tools to advance our capability in analyzing genome-wide data sets for human complex diseases, and to better integrate publicly available knowledge bases into disease association mapping. Tools developed in this project will be freely distributed to the research community to facilitate bio-discovery towards understanding the regulatory mechanisms underlying human inherited complex phenotypes.

描述（由申请人提供）：全基因组关联研究有望揭示人类复杂疾病的遗传结构。疾病变异通常是非孟德尔的，表现出低外显率和对疾病个体的影响很小，但以未知的方式相互作用和环境。随着最近的高通量测序技术的发展，在基因组尺度上产生了更多的数据，不仅包括遗传变异，还包括个体水平上的调控元件。已知调节因子在序列变异和表型多样性之间相互作用并作为中介。因此，多变异疾病作图对未来全基因组关联研究变得更加有趣和重要。我们也希望通过收集人类基因组中的所有变异，鉴定出真正的致病变异，从而在鉴定出的位点精确开展功能评估和验证实验，真正揭示其对疾病的生物学机制。识别多变量关联是极具挑战性的。目前的算法仍然非常有限。特别是，高通量测序数据现在通常在疾病研究中产生。这些完整的变异是高度依赖的，现有的方法有很大的计算困难，因此很难确定真正的疾病变异。从罕见变异中检测疾病关联也是非常具有挑战性的，然而这些变异在人类基因组中更为丰富，并且可能是人类复杂疾病的主要原因。我们建议开发先进的算法来解决上述问题。我们将开发先进的算法，以提高全基因组多变异定位的能力和计算效率。我们还提出了在一个连贯的全概率模型下联合检验共同变异和罕见变异的广义方法。我们的方法自动分组变量进行联合测试，考虑依赖性，结合生物先验，并识别致病变量。我们通过非参数贝叶斯技术进一步扩展了方法，以整合疾病制图中的各种公共数据库来源。我的新算法将大大提高研究人员分析高通量遗传和基因组数据的能力。该软件将通过PI的网站和宾夕法尼亚州立大学的银河系统免费分发给社区。