Haplotype-based analysis methods for population genomics

基于单体型的群体基因组分析方法

基本信息

批准号：
9198031
负责人：
John Novembre
金额：
$ 31.6万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-01-01 至 2019-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9198031
关键词：
Address Admixture Advanced Development African African American Algorithms Alleles Area Blood Chromosomes Code Communities Complex Computational algorithm Computer software DNA 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 Heart Hereditary Disease Historical Demography Human Human Genetics Individual Laboratories Latino Linkage Disequilibrium Maps Measures Meta-Analysis Methodology Methods Minor Modeling Modernization Pathogenicity Pattern Performance Population Population Analysis Population Genetics Population Sizes Probability Process Property Publications Research Research Personnel Sampling Series Single Nucleotide Polymorphism Site Source Statistical Methods Structure Techniques Technology Testing Time Variant Work base computerized tools density expectation experience experimental study fitness genome-wide genomic data human population genetics improved infancy innovation insight novel novel strategies open source public health relevance response simulation software development theories tool trait user friendly software

项目摘要

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，利用基于单倍型模型的力量来分析群体基因组数据。此类工具的开发尤为重要，因为测序的进步现在已经使收集完整染色体的序列数据成为惯例。单倍型数据中存在的连锁不平衡的多基因座模式为群体遗传学中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具有挑战性，并且对于许多特定问题，尚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上开发基于单倍型的模型。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。关键原则是混合个体中的祖先转换点可用于推断最近的重组事件。我们的工作将产生一个软件包，用于根据全基因组单核苷酸多态性数据推断重组率，以及一个单独的模拟包，用于生成用于测试该方法的数据。一项关键的创新将是开发和测试该方法的一个版本，该版本可以处理多路（>2 源群体）混合物。其次，我们将使用基于单倍型的方法来提高基于“进化和重新测序”范式的复杂性状映射方法的能力。功效的提高将来自于使用来自合并测序实验的原始读取数据中嵌入的单倍型信息。我们将再次开发推理软件和模拟来测试推理方法。第三，我们要考察净化到什么程度选择塑造了人类群体的单倍型多样性。预期分离有害变异将显示单倍型多样性降低，就像适应性变异一样。这一特征在很大程度上尚未被探索，我们将开发理论、经验和基于模拟的方法来确定这一特性是否存在，以及如何使用它来推断人类群体遗传数据中纯化选择的强度。最后，我们将推导出单倍型条件抽样分布（CSD）的一种新形式。尽管 CSD 在群体遗传学中的应用仍处于起步阶段，但它已取得了丰硕的成果。我们将开发一种方法来实现更准确的 CSD。新的 CSD 还将为非平衡人口统计和/或人口结构模型中计算单倍型概率的扩展打开大门。在整个项目中，将重点关注更广泛的群体基因组学社区的软件开发，以及克服基于单倍型的模型常见的计算和算法挑战。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有助于基本工具包群体遗传学家用于从大型基因组数据集中提取信息，并将加强对许多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谱、遗传病病因学和历史人口学相关。最后，我们预计所开发的算法将直接有用，或对项目中详细介绍的密切相关问题进行细微调整后有用。例如，我们在合并序列中进行单倍型频率估计的算法与在血液 DNA 测序中识别致病菌株丰度的问题密切相关。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Admixture, Population Structure, and F-Statistics.

混合、总体结构和 F 统计。

DOI：
发表时间：
2016-04
期刊：
影响因子：
3.3
作者：
Peter; Benjamin M
通讯作者：
Benjamin M

Reconstructing genetic history of Siberian and Northeastern European populations.

重建西伯利亚和东北欧人群的遗传史。

DOI：
发表时间：
2017
期刊：
影响因子：
7
作者：
Wong, Emily H M;Khrunin, Andrey;Nichols, Larissa;Pushkarev, Dmitry;Khokhrin, Denis;Verbenko, Dmitry;Evgrafov, Oleg;Knowles, James;Novembre, John;Limborska, Svetlana;Valouev, Anton
通讯作者：
Valouev, Anton

Recent advances in the study of fine-scale population structure in humans.

人类精细种群结构研究的最新进展。

DOI：
发表时间：
2016-12
期刊：
影响因子：
4
作者：
Novembre, John;Peter, Benjamin M
通讯作者：
Peter, Benjamin M

Genetic history from the Middle Neolithic to present on the Mediterranean island of Sardinia.

从新石器时代中期到现在地中海撒丁岛的遗传历史。

DOI：
发表时间：
2020
期刊：
影响因子：
16.6
作者：
Marcus, Joseph H;Posth, Cosimo;Ringbauer, Harald;Lai, Luca;Skeates, Robin;Sidore, Carlo;Beckett, Jessica;Furtwängler, Anja;Olivieri, Anna;Chiang, Charleston W K;Al;Dey, Kushal;Joseph, Tyler A;Liu, Chi;Der Sarkissian, Clio
通讯作者：
Der Sarkissian, Clio

Tread Lightly Interpreting Polygenic Tests of Selection.

谨慎解释选择的多基因测试。