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，以利用基于单倍型的模型的力量来分析人口基因组学数据。这种工具的开发尤其重要，因为现在测序的进步使得序列数据的常规是在完整的染色体上收集。单倍型数据中存在的链接不平衡的多个链接模式对于人群遗传学的一系列重要过程提供了信息。利用单倍型中的信息在方法上是具有挑战性的，对于许多特定问题，适当的分析工具尚不存在。作为回应，我们的研究将在四个主要方向上开发基于单倍型的模型。 首先，我们将使用来自混合个体的遗传数据来开发基于单倍型的模型来推断重组率。关键原则是，可以使用混合个体中的祖先切换点来推断最近的重组事件。我们的工作将基于全基因组单核苷酸多态性数据来推断重组率的推理软件包，以及一个单独的仿真软件包，用于生成使用该方法的数据。一个关键的创新将开发和测试这种方法的一种可以处理多路（> 2个来源人群）混合物的版本。 其次，我们将使用单倍型信息的方法来改善基于“ Evolve and Resquence”范式的复杂性状映射方法的功能。功率的提高将来自使用汇总测序实验的原始读取数据中嵌入的单倍型信息。同样，我们将同时开发推理软件和仿真来测试推理方法。 第三，我们将调查在多大程度上净化 选择在人群中塑造了单倍型的多样性。期望的是，分离有害变体将显示出降低的单倍型多样性，就像自适应变体一样。这种签名在很大程度上没有探索，我们将开发理论，经验和基于模拟的方法来确定该特性是否存在，以及如何使用它来推断人口遗传数据中纯化选择的强度。 最后，我们将为单倍型提供一种新型的条件采样分布（CSD）。尽管该方法仍处于起步阶段，但CSD在人群遗传学中​​的应用仍然非常富有成果。我们将开发一种导致更准确的CSD的方法。新的CSD还将为具有非平衡人口统计学和/或种群结构的模型中计算单倍型概率的扩展门打开大门。 在整个项目中，将着重于更广泛的人群基因组学界的软件开发，以及克服基于单倍型的模型通常会引起的计算和算法挑战。这些贡献对于将前进的人群遗传学推向基因组时代至关重要。项目相关性该项目将有助于基本工具包 人群遗传学家用来从大型基因组数据集中提取信息，并将增强对具有实际相关性的许多应用领域的研究。特别是，我们将开发工具，使研究人员能够测量重组，绘制复杂性状并了解人类遗传变异的适应性后果。这些领域与疾病特征图，遗传疾病病因和历史人口相关。最后，我们预计开发的算法将是直接有用的，或者对项目中详细介绍的问题进行了少量调整，以调整与密切相关的问题。例如，我们在合并序列中进行单倍型频率估计的算法与确定血液DNA测序中的致病性菌株丰富的问题​​密切相关。