Haplotype-based analysis methods for population genomics

基于单体型的群体基因组分析方法

• 批准号: 8788051
• 负责人: John Novembre
• 金额: $ 30.81万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8788051
• 关键词: Address; Admixture; Advanced Development; African; African American; Algorithms; Area; Blood; Chromosome Mapping; Chromosomes; Code; Communities; Complex; Computer software; DNA; DNA Resequencing; Data; Data Set; Demography; Development; Disease; Drosophila melanogaster; Etiology; European; Evaluation; Event; Evolution; Frequencies; Gene Frequency; Genealogy; Genetic; Genetic Recombination; Genetic Variation; Genome; Genome Scan; Genomics; Genotype; Goals; Grant; Haplotypes; Health; Heart; Hereditary Disease; Historical Demography; Human; Human Genetics; Individual; Laboratories; Latino; Lead; Linkage Disequilibrium; Maps; Measures; Meta-Analysis; Methods; Minor; Modeling; Pattern; Performance; Population; Population Analysis; Population Genetics; Population Sizes; Probability; Process; Property; Publications; Reading; Relative (related person); Research; Research Personnel; Sampling; Series; Shapes; Single Nucleotide Polymorphism; Site; Source; Statistical Methods; Structure; Techniques; Technology; Testing; Time; Variant; Work; base; computerized tools; density; empowered; expectation; experience; fitness; genome-wide; human population genetics; improved; infancy; innovation; insight; novel; novel strategies; open source; research study; response; simulation; simulation software; software development; theories; tool; trait; user friendly software

DESCRIPTION (provided by applicant): This project will develop a series of computational tools that exploit the power of haplotype-based models for the analysis of population genomics data. The development of such tools is particularly important as advances in sequencing have now made it routine for sequence data to be gathered across full chromosomes. The multi-locus patterns of linkage disequilibrium that are present in haplotype data are informative about a range of important processes in population genetics. Leveraging the information in haplotypes is methodologically challenging, and for many specific problems the appropriate analysis tools do not yet exist. In response, our research will develop haplotype-based models in four major directions. First, we will develop haplotype-based models to infer recombination rates using genetic data from admixed individuals. The key principle is that ancestry switch points in admixed individuals can be used to infer recent recombination events. Our work will produce a software package for inference of recombination rates based on genome-wide single-nucleotide polymorphism data, and a separate simulation package for generating data with which to test the method. A key innovation will be developing and testing a version of this approach that can handle multi-way (>2 source population) admixtures. Second, we will use haplotype-informed approaches to improve the power of complex trait mapping approaches based on the "evolve and resequence" paradigm. The improvement in power will come from using haplotype information embedded in the raw read data from pooled sequencing experiments. Again we will develop both inference software and simulations to test the inference methods. Third, we will investigate to what extent purifying selection has shaped haplotype diversity in human populations. The expectation is that segregating deleterious variants will show reduced haplotype diversity, much as adaptive variants do. This signature has largely been unexplored and we will develop theoretical, empirical, and simulation-based approaches to establish whether this property exists and how it can be used to infer the strength of purifying selection in human population genetic data. Finally, we will derive a novel form of the conditional sampling distribution (CSD) for a haplotype. The application of CSDs in population genetics has been very fruitful, even though the approach is in its infancy. We will develop an approach that leads to a more accurate CSD. The new CSD will also open the door to extensions for computing haplotype probabilities in models with non-equilibrium demography and/or population structure. Throughout the project there will be an emphasis on software development for the broader population genomics community, and on overcoming computational and algorithmic challenges that arise commonly with haplotype-based models. The contributions are essential for pushing forward population genetics into the genomic era. Project Relevance This project will contribute to the basic toolkit population geneticists use to extract information from large genomic datasets and will enhance research on a number of applied areas with practical relevance. In particular we will develop tools that empower researchers to measure recombination, map complex traits, and understand the fitness consequences of human genetic variation. These areas are relevant to disease trait mapping, genetic disease etiology, and historical demography. Finally, we expect the algorithms developed will be useful either directly or with minor adjustment to closely related problems beyond those detailed in the project. As an example, our algorithms for haplotype frequency estimation in pooled sequences are closely related to problems for identifying the abundance of pathogenic strains in sequencing of blood DNA.

描述(由申请人提供)： 该项目将开发一系列计算工具，利用基于单倍型的模型的力量来分析种群基因组数据。这类工具的开发尤为重要，因为测序技术的进步现在已经使跨整个染色体收集序列数据成为常规。在单倍型数据中存在的连锁不平衡的多位点模式是关于群体遗传学中的一系列重要过程的信息。利用单倍型中的信息在方法上具有挑战性，对于许多具体问题，还不存在适当的分析工具。作为回应，我们的研究将在四个主要方向发展基于单倍型的模型。首先，我们将开发基于单倍型的模型，利用混合个体的遗传数据来推断重组率。关键原理是混杂个体中的祖先转换点可以用来推断最近的重组事件。我们的工作将产生一个软件包，用于根据全基因组的单核苷酸多态数据推断重组率，以及一个单独的模拟包，用于生成用于测试该方法的数据。一个关键的创新将是开发和测试这种方法的一个版本，它可以处理多路(&gt；2源种群)混合。其次，我们将使用单倍型信息的方法来提高基于“进化和重测序”范例的复杂特征作图方法的能力。能力的提高将来自于使用嵌入在从混合测序实验中读取的原始数据中的单倍型信息。再次，我们将开发推理软件和模拟来测试推理方法。第三，我们将调查净化到什么程度 选择塑造了人类群体的单倍型多样性。人们的期望是，分离有害变异将显示出单倍型多样性降低，就像适应性变异所做的那样。这一特征在很大程度上还没有被探索，我们将开发理论、经验和模拟为基础的方法，以确定这一属性是否存在，以及如何使用它来推断人类种群基因数据中净化选择的强度。最后，我们将导出单倍型的条件抽样分布(CSD)的一种新形式。CSD在种群遗传学中的应用已经取得了非常丰硕的成果，尽管这种方法还处于初级阶段。我们将开发一种方法，以实现更准确的CSD。新的CSD还将为在具有非平衡人口统计和/或人口结构的模型中计算单倍型概率的扩展打开大门。在整个项目中，将强调为更广泛的人口基因组学社区开发软件，并克服基于单倍型的模型通常出现的计算和算法挑战。这些贡献对于推动群体遗传学进入基因组时代至关重要。项目相关性该项目将为基本工具包做出贡献 人口遗传学家用来从大型基因组数据集中提取信息，并将加强对一些具有实际意义的应用领域的研究。特别是，我们将开发工具，使研究人员能够测量重组，绘制复杂的特征图，并了解人类基因变异的适应性后果。这些领域与疾病特征图谱、遗传病病因学和历史人口学相关。最后，我们预计开发的算法将直接有用，或对项目中详细说明的密切相关问题进行微小调整。例如，我们在混合序列中单倍型频率估计的算法与血液DNA测序中识别致病菌株丰度的问题密切相关。