Methods for inference of complex demography and selection from genomic data

复杂人口统计推断和基因组数据选择的方法

• 批准号: 8639647
• 负责人: Yun S Song
• 金额: $ 30.86万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8639647
• 关键词: Accounting; Admixture; Complex; Computing Methodologies; DNA; DNA Sequence; Data; Demography; Development; Diffusion; Disease; Evolution; Gene Frequency; Genetic; Genome; Genomics; Human; Human Genome; Intervention; Joints; Maps; Methods; Modeling; Population; Population Genetics; Process; Research; Research Infrastructure; Research Personnel; Shapes; Simulate; Statistical Methods; Technology; Time; Variant; base; genetic analysis; interest; public health relevance; statistics; theories

DESCRIPTION (provided by applicant): Recent advances in sequencing technology has fundamentally transformed population genetics. Using whole-genome DNA sequence data, population geneticists now hope to estimate jointly many parameters of interest in complex models of evolution involving multiple populations. However, many of the statistical methods available for population genetic analyses are not scalable to the whole genome level. The main objective of the research proposed here is to develop a suite of mathematical, statistical, and computational methods that will allow researchers to more fully take advantage of the availability of genomic data. Likelihood-based approaches that take linkage information into account utilize more information in the data than do methods based on summary statistics and should therefore be more statistically efficient. However, they tend to require intensive computation, thus limiting their applicability. Aim 1 will develop a new statistical approach to ful-likelihood inference that can be applied at the genomic scale using many more sequences than previously possible. The distribution of segments of shared genetic similarity, i.e., segments of identity-by-descent or identity-by-state, contain important information about past demography and selection. Aims 2 and 3,will derive new theoretical results concerning such information and apply them to develop new statistical methods to tackle challenging problems such as the estimation of admixture proportions and admixture times, and inference of admixed DNA tracts. Recently, there has been much interest in using allele frequency spectra to estimate parameters in complex demography models. Aim 4 will develop efficient methods based on coalescent theory to compute the expected joint allele frequency spectra for more populations than could be previously considered. The use of the Wright-Fisher diffusion is ubiquitous in population genetics as a model for the forwards-in-time dynamics of the frequency of an allele in a large population. There are several population genetic applications in which it is natural to study the associated diffusion bridge. Aim 5 will investigate methods for simulating diffusion bridges in the presence of selection and obtain analytic results on the distribution of important functionals of the bridge path.

描述(由申请人提供)：测序技术的最新进展从根本上改变了群体遗传学。利用全基因组DNA序列数据，种群遗传学家现在希望联合估计涉及多个种群的复杂进化模型中的许多感兴趣的参数。然而，许多可用于种群遗传分析的统计方法不能扩展到整个基因组水平。这里提出的研究的主要目标是开发一套数学、统计和计算方法，使研究人员能够更充分地利用基因组数据的可用性。考虑到联系信息的基于可能性的方法比基于汇总统计的方法在数据中利用更多的信息，因此在统计上应该更有效率。然而，它们往往需要密集的计算，从而限制了它们的适用性。目标1将开发一种新的统计方法来进行全似然推断，该方法可以应用于基因组规模，使用比以前可能的更多的序列。共有遗传相似性的片段的分布，即按血统或按州身份的片段，包含有关过去人口统计和选择的重要信息。目标2和目标3将得出与这些信息有关的新的理论结果，并将其用于开发新的统计方法，以解决具有挑战性的问题，如估计混合比例和混合时间，以及推断混合DNA区段。近年来，利用等位基因频谱来估计复杂人口学模型中的参数引起了人们的极大兴趣。目的4将开发基于合并理论的有效方法，以计算比先前所考虑的更多群体的预期联合等位基因频谱。在群体遗传学中，莱特-费舍尔扩散作为等位基因频率在大群体中的向前-时间动态的模型普遍存在。在一些群体遗传学应用中，研究相关的扩散桥是很自然的。目标5将研究模拟扩散桥的方法 存在选择，并获得关于桥路径重要泛函分布的解析结果。