Collaborative Research: Statistical Methods and Algorithms for Genomic Data

合作研究：基因组数据的统计方法和算法

• 批准号: 0714839
• 负责人: Bruce Lindsay
• 金额: $ 29万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0714839&HistoricalAwards=false
• 关键词: Collaborative; Research; Statistical; Methods; Algorithms

In this research project the investigators construct statistical methods and algorithms for SNP analysis that are designed to enhance the biological realism of the underlying models. This project has three primary aims. The first extends development of a new method for constructing hierarchical trees from sequence data using maximum likelihood and modal inference. The tree construction is based upon application of either one of these two inference methods to an ancestral mixture model, a model whose parameters describe the population structure at each fixed time point T in the past. If one estimates this structure over a fine grid of time points T, the relationship between the estimates over time can be graphically described as a hierarchical tree. The second project aim is to enhance the biological realism of the ancestral mixture model to include (a) multi-state characters, (b) advanced models of sequence evolution, and (c) recombination. The extensions are based on using diffusion kernels constructed from continuous time Markov Chains. Empirical Bayes methods are also proposed to be employed to improve overall estimation precision. The third aim is development of a new method for reconstructing haplotype sequences from genotype data without knowing the parental information. The methods and algorithms are based on the ancestral mixture models together with a multi-moment approach that simplifies computation. In addition, the investigators propose to extend the method to long sequences by sliding a window along longer genotype sequences, then using the information from the overlapping estimates to construct longer haplotype estimates.The current release of the National Center for Biotechnology Information's(NCBI) database dbSNP contains over 11.5 million human single nucleotide polymorphism (SNP) records, representing a 10-fold increase over the last 4 years. Analysis of such data has become a focus of much research within bioinformatics and computational biology because it is the SNPs that carry the information that distinguishes the individuals within a species. Encoded in this data is important information about the relationship between the characteristics of an individual and their genetic code. The investigators are developing methods and models for this data that are fundamentally different in approach from the standard techniques currently used in two areas where SNP data is used, coalescent and phylogenetic inference, in which one reconstructs genetic relationships similar to family trees based on the current ancestors only. The broader impacts of this project include the development of methods and models with potential wide ranging uses across broad scientific disciplines, the increased fusion of biological and mathematical innovation, and opportunities for broad interdisciplinary training of diverse students.

在这个研究项目中，研究人员构建了SNP分析的统计方法和算法，旨在增强基础模型的生物真实性。该项目有三个主要目标。第一个扩展开发的一种新方法，用于构建层次树的序列数据，使用最大似然和模态推理。树的构建是基于这两种推理方法中的任一种应用于祖先混合模型，该模型的参数描述了过去每个固定时间点T的种群结构。 如果在时间点T的精细网格上估计该结构，则随时间的估计之间的关系可以图形地描述为分层树。第二个项目的目标是增强祖先混合模型的生物现实性，以包括（a）多态特征，（B）序列进化的高级模型，和（c）重组。扩展是基于使用从连续时间马尔可夫链构造的扩散核。经验贝叶斯方法也被提出用来提高整体估计精度。第三个目标是开发一种新的方法，用于从基因型数据重建单倍型序列，而无需知道亲本信息。该方法和算法是基于祖先的混合物模型与多时刻的方法，简化了计算。此外，研究人员还建议将该方法扩展到长序列，方法是沿着较长的基因型序列滑动一个窗口，然后使用重叠估计的信息构建较长的单倍型估计。美国国家生物技术信息中心（NCBI）数据库dbSNP的最新版本包含超过1150万条人类单核苷酸多态性（SNP）记录，在过去四年里增长了十倍。对这些数据的分析已经成为生物信息学和计算生物学中许多研究的焦点，因为它是携带区分物种内个体的信息的SNP。这些数据中编码的是关于个体特征与其遗传密码之间关系的重要信息。研究人员正在为这些数据开发方法和模型，这些方法和模型与目前使用SNP数据的两个领域中使用的标准技术（结合和系统发育推断）有着根本的不同，其中一种方法是仅基于当前祖先重建类似于家谱的遗传关系。该项目的更广泛的影响包括开发在广泛的科学学科中具有潜在广泛用途的方法和模型，增加生物和数学创新的融合，以及为不同学生提供广泛的跨学科培训的机会。