Mathematical Models and Statistical Methods for Large-Scale Population Genomics

大规模群体基因组学的数学模型和统计方法

• 批准号: 9328097
• 负责人: Yun S Song
• 金额: $ 29.87万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9328097
• 关键词: Accounting; Address; Admixture; Affect; Age; Algorithms; Alleles; Area; Classification; Communities; Complex; Computer software; DNA; DNA sequencing; Data; Data Sources; Demography; Development; Diffusion; Event; Evolution; Gene Frequency; Genetic; Genetic Models; Genetic Recombination; Genetic Variation; Genome; Genomic Segment; Genomics; Goals; Human Biology; Human Genetics; Individual; Joints; Learning; Link; Machine Learning; Mathematics; Methods; Modeling; Mutation; Phase; Physiologic pulse; Population; Population Genetics; Population Sizes; Recording of previous events; Research; Research Personnel; STEM research; Sampling; Series; Site; Statistical Methods; Technology; Time; Time Series Analysis; Trees; Uncertainty; Work; analytical method; base; computer based statistical methods; computerized tools; disorder risk; fitness; flexibility; genetic analysis; genetic selection; genome-wide; genomic data; genomic variation; human disease; interest; markov model; mathematical model; migration; novel; open source; pressure; public health relevance; tool; whole genome

 DESCRIPTION (provided by applicant): Technological advances in DNA sequencing have dramatically increased the availability of genomic variation data over the past few years. This development offers a powerful window into understanding the genetic basis of human biology and disease risk. To facilitate achieving this goal, it is crucial to develop efficient analytical methods that will allow researchers to more fuly utilize the information in genomic data and consider more complex models than previously possible. The central goal of this project is to tackle this important challenge, by carrying out te following Specific Aims: In Aim 1, we will develop efficient inference tools for whole-genome population genomic analysis by extending our ongoing work on coalescent hidden Markov models and apply them to large-scale data. The methods we develop will enable researchers to analyze large samples under general demographic models involving multiple populations with population splits, migration, and admixture, as well as variable effective population sizes and temporal samples (ancient DNA). Multi-locus full-likelihood computation is often prohibitive in most population genetic models with high complexity. To address this problem, we will develop in Aim 2 a novel likelihood-free inference framework for population genomic analysis by applying a highly active area of machine learning research called deep learning. We will apply the method to various parameter estimation and classification problems in population genomics, particularly joint inference of selection and demography. In addition to carrying out technical research, we will develop a useful software package that will allow researchers from the population genomics community to utilize deep learning in their own research. It is becoming increasingly more popular to utilize time-series genetic variation data at the whole-genome scale to infer allele frequency changes over a time course. This development creates new opportunities to identify genomic regions under selective pressure and to estimate their associated fitness parameters. In Aim 3, we will develop new statistical methods to take full advantage of this novel data source at both short and long evolutionary timescales. Specifically, we will develop and apply efficient statistical inference methods for analyzing time-series genomic variation data from experimental evolution and ancient DNA samples. Useful open-source software will be developed for each specific aim. The novel methods developed in this project will help to analyze and interpret genetic variation data at the whole-genome scale.

 描述(由申请人提供)： 在过去的几年里，DNA测序技术的进步极大地增加了基因组变异数据的可用性。这一进展为理解人类生物学和疾病风险的遗传基础提供了一个强大的窗口。为了促进这一目标的实现，至关重要的是开发有效的分析方法，使研究人员能够更充分地利用基因组数据中的信息，并考虑比以前可能的更复杂的模型。该项目的中心目标是通过实现以下具体目标来应对这一重要挑战：在目标1中，我们将通过扩展我们正在进行的合并隐马尔可夫模型的工作并将其应用于大规模数据，来开发用于全基因组群体基因组分析的高效推理工具。我们开发的方法将使研究人员能够在一般人口模型下分析大样本，包括人口分裂、迁移和混合的多个人口，以及可变的有效人口大小和时间样本(古代DNA)。在大多数复杂的群体遗传模型中，多位点全似然计算往往是不可能的。为了解决这个问题，我们将在目标2中开发一个新的无似然推理框架，用于种群基因组分析，方法是应用机器学习中一个非常活跃的领域，称为深度学习。我们将把该方法应用于种群基因组学中的各种参数估计和分类问题，特别是选择和人口统计学的联合推断。除了进行技术研究外，我们还将开发一个有用的软件包，让种群基因组学社区的研究人员在自己的研究中利用深度学习。利用全基因组尺度上的时间序列遗传变异数据来推断等位基因频率在一段时间内的变化正变得越来越流行。这一发展创造了新的机会来识别选择压力下的基因组区域并估计其相关的适合度参数。在目标3中，我们将开发新的统计方法，以在短时间和长时间尺度上充分利用这一新的数据源。具体地说，我们将开发和应用有效的统计推断方法来分析来自实验进化和古代DNA样本的时间序列基因组变异数据。将为每个具体目标开发有用的开放源码软件。该项目开发的新方法将有助于在全基因组尺度上分析和解释遗传变异数据。