Models and Methods for Population Genomics

群体基因组学模型和方法

• 批准号: 8296777
• 负责人: JOHN D STOREY
• 金额: $ 30万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-25 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8296777
• 关键词: Accounting; Algorithms; Bioinformatics; Biological; Characteristics; Complex; Computer software; Data; Data Analyses; Data Set; Disease; Equilibrium; Foundations; General Population; Genetic; Genetic Models; Genetic Variation; Genome; Genomics; Genotype; Goals; Human; Human Genome Diversity Project; Individual; Linear Models; Measures; Medical Research; Methodology; Methods; Modeling; Pattern; Population; Population Genetics; Principal Component Analysis; Programming Languages; Research; Resolution; Role; Simulate; Source Code; Statistical Methods; Structure; Testing; Time; Trust; Update; Variant; Writing; base; case control; cost; database of Genotypes and Phenotypes; genome sequencing; genome wide association study; genome-wide; human disease; human population genetics; innovation; interest; population genetic structure; programs; statistics; theories; trait; web site

DESCRIPTION (provided by applicant): Significant improvements in the cost and ability to obtain genome-wide genotypes, even entire genome sequencing, of individuals provides an opportunity to understand genetic variation among humans and its relationship to complex trait variation at an unprecedented level of resolution. During this time, it has been discovered that human population genetic structure is more of a continuous phenomenon rather than being manifested as a set of well-defined discrete subpopulations. However, classical population genetic theory and methods for population structure are largely based on the assumption of non-overlapping, discretely structured populations. For example, Wright's F-statistics and association testing have mostly been studied in the context of K distinct populations. A number of innovative algorithms for analyzing structured populations with dense genotyping have recently been proposed. However, there has yet to be a firm statistical foundation developed for this setting. We propose to tackle a number of open problems in such a way that a coherent theoretical framework and set of statistical methodologies will emerge. We will perform extensive data analyses based on these methods on existing data sets, including the Human Genome Diversity Project, the 1000 Genomes Project, and GWAS studies involving structured populations. PUBLIC HEALTH RELEVANCE: Understanding genome-wide patterns of genetic variation among individuals and how this relates to complex diseases is one of the primary goals of modern medical research. The proposed research will contribute to this goal by tackling a number of open problems in such a way that a coherent statistical framework and set of methodologies will emerge that can be applied to data sets of genome-wide genetic variation to produce a clearer picture of the genetic basis of human disease.

描述（由申请人提供）：获得个体全基因组基因型，甚至全基因组测序的成本和能力的显著提高，为以前所未有的分辨率水平了解人类遗传变异及其与复杂性状变异的关系提供了机会。在这段时间里，人们发现人类群体的遗传结构更多的是一种连续的现象，而不是表现为一组定义明确的离散亚群。然而，经典的种群遗传理论和种群结构的方法在很大程度上是基于非重叠，离散结构的群体的假设。例如，Wright的F-统计量和关联检验大多是在K个不同群体的背景下研究的。最近提出了一些用于分析具有密集基因分型的结构化群体的创新算法。然而，还没有为这一背景建立一个坚实的统计基础。我们建议解决一些开放的问题，这样一个连贯的理论框架和一套统计方法将出现。我们将基于这些方法对现有数据集进行广泛的数据分析，包括人类基因组多样性计划，千个基因组计划和涉及结构化人群的GWAS研究。 公共卫生关系：了解个体间遗传变异的全基因组模式及其与复杂疾病的关系是现代医学研究的主要目标之一。拟议的研究将有助于实现这一目标，解决一些悬而未决的问题，从而形成一个连贯的统计框架和一套方法，可应用于全基因组遗传变异的数据集，以更清楚地了解人类疾病的遗传基础。