Statistical methods for population and family-based whole-genome sequence data

基于人群和家系的全基因组序列数据的统计方法

• 批准号: 8694183
• 负责人: Wei Chen
• 金额: $ 35.35万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-25 至 2019-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8694183
• 关键词: Accounting; Address; Affect; Algorithms; Base Sequence; Biology; Chromosome Mapping; Chromosomes; Collaborations; Communities; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Disease; Documentation; Evaluation; Family; Family member; Future; Genetic Programming; Genomics; Genotype; Goals; Haplotypes; Human Genome; Individual; Large-Scale Sequencing; Linkage Disequilibrium; Methodology; Methods; Modeling; Mutation; Nature; Nuclear; Parents; Phase; Population; Population Analysis; Population Control; Publishing; Relative (related person); Research; Research Design; Sequence Analysis; Site; Source Code; Speed; Statistical Methods; Stratification; Stretching; Technology; Testing; Uncertainty; Variant; Walking; base; cost; design; disease phenotype; falls; family structure; flexibility; genome sequencing; genome wide association study; improved; innovation; interest; markov model; next generation sequencing; novel; open source; population based; portability; programs; public health relevance; rare variant; simulation; software development; tool; trait; transmission process; vector

DESCRIPTION (provided by applicant): Emerging sequencing technologies have made whole-genome sequencing become available for researches to study various phenotypes/diseases of interest, particularly focusing on rare variants sites. Although the first batch of sequencing projects has mainly focused on the analysis of unrelated individuals, numerous sequencing studies including related individuals have been carried out or launched recently as the sequencing cost reduces rapidly. However, the methodologies for analyzing family-based sequence data are largely falling behind partially due to the complexity of family structures and computational barrier. In this study, our primary goals are to efficiently and accurately infer individual genotypes and haplotypes - the key component of any sequencing project - by combining information from both family and population levels, and to study how differential sequencing errors will affect downstream association analysis. To achieve these goals, we propose specific aims as follows: 1) We will propose a novel statistical framework for genotyping calling and haplotype inference of sequence data including relative individuals. The new method takes advantages of both short stretches shared between unrelated individuals and long stretches shared between family members in a computationally feasible manner while retaining a high degree of accuracy via the synergy between two classic approaches: hidden Markov model (HMM) for linkage disequilibrium information and Lander-Green algorithm for inheritance vectors; 2) We will develop an exact algorithm for HMM computation to speed up a class of widely use genetics programs, including the method developed in Aim 1, without any sacrifice of accuracy; 3) We will assess the impact of sequencing errors on family-based association methods for rare variants and use the intrinsic stochastic nature of the proposed methods in Aim 1 to reduce the false positives under a framework of multiple imputation; 4) We will test and recalibrate our developed methods in collaboration with ongoing sequencing projects and systematically investigate different study designs. Successful completion of these aims will yield state-of-the-art statistical methods and software, which will facilitate the fast growing sequencing projects including family members and guide the design and analysis of future studies.

描述（由申请人提供）： 新兴的测序技术使全基因组测序可供研究人员研究各种感兴趣的表型/疾病，特别是关注罕见的变异位点。虽然第一批测序项目主要集中在无关个体的分析上，但随着测序成本的迅速降低，包括相关个体在内的大量测序研究近期已经开展或启动。然而，由于家族结构的复杂性和计算障碍，分析基于家族的序列数据的方法在很大程度上落后。在这项研究中，我们的主要目标是通过结合来自家族和群体水平的信息，高效准确地推断个体基因型和单倍型（任何测序项目的关键组成部分），并研究差异测序错误将如何影响下游关联分析。为了实现这些目标，我们提出以下具体目标：1）我们将提出一种新颖的统计框架，用于包括相关个体的序列数据的基因分型调用和单倍型推断。新方法以计算上可行的方式利用了无关个体之间共享的短序列和家庭成员之间共享的长序列，同时通过两种经典方法之间的协同作用保持了高精度：用于连锁不平衡信息的隐马尔可夫模型（HMM）和用于遗传向量的Lander-Green算法； 2）我们将开发一种精确的 HMM 计算算法，以加速一类广泛使用的遗传学程序，包括目标 1 中开发的方法，而不会牺牲任何准确性； 3）我们将评估测序错误对罕见变异的基于家族的关联方法的影响，并利用目标1中提出的方法的内在随机性来减少多重插补​​框架下的误报； 4) 我们将与正在进行的测序项目合作测试和重新校准我们开发的方法，并系统地研究不同的研究设计。成功完成这些目标将产生最先进的统计方法和软件，这将促进包括家庭成员在内的快速发展的测序项目，并指导未来研究的设计和分析。