Population-Genetic Studies for Association Mapping

关联作图的群体遗传学研究

• 批准号: 7901901
• 负责人: Noah Rosenberg
• 金额: $ 32.11万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-31 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7901901
• 关键词: Accounting; Alleles; Chromosome Mapping; Collection; Communities; Complex; Computer Simulation; Computer software; Data; Development; Disease; Disease Association; Disease susceptibility; Ensure; Event; Evolution; Family; Frequencies; Future; Genetic; Genetic Models; Genetic Population Study; Genetic Processes; Genetic Recombination; Genetic Variation; Genome; Genotype; Goals; Haplotypes; Human; Humanities; Individual; Joints; Knowledge; Left; Linkage Disequilibrium; Maps; Measures; Methods; Microsatellite Repeats; Modeling; Mutation; Outcome; Pattern; Phase; Phenotype; Play; Population; Population Genetics; Predisposition; Production; Property; Publications; Recording of previous events; Research; Research Design; Research Personnel; Resources; Role; Sampling; Signal Transduction; Single Nucleotide Polymorphism; Software Tools; Statistical Methods; Statistical Models; Stochastic Processes; Stratification; Structure; Techniques; Time; Variant; Work; Writing; base; design; disease phenotype; genetic analysis; genetic association; genome wide association study; genome-wide; genotyping technology; human population genetics; mathematical theory; method development; novel strategies; programs; simulation; software development; statistics; theories; transmission process

DESCRIPTION (provided by applicant): Association studies provide a powerful approach for locating alleles that contribute to disease susceptibility and phenotypic variation. Since population-genetic processes play a central role in generating patterns of statistical association between diseases and their causal alleles, an understanding of human population- genetic history and its consequences for association is important for the development of methods to map disease susceptibility alleles. We propose four specific aims that will augment and capitalize on theoretical and empirical population genetics knowledge of human populations to advance the prospects for identifying disease susceptibility loci by association mapping. This work will be performed through a combination of mathematical theory, computer simulation, and analysis of human population-genetic data. First, we will extend methods for analysis of the production by population structure of spurious associations between genotypes and disease to accommodate clinal or spatially distributed populations. The new approaches will make it possible to reduce the occurrence of the false positive associations that arise from population structure or stratification in a broader set of scenarios than is currently possible. Second, we will develop population-genetic models of human evolution that use approximate Bayesian computation to account for patterns of haplotype variation among diverse worldwide populations. Third, we will develop a framework for statistical analysis of replication studies of genetic association that takes into account the fact that all humans are related by descent from shared ancestors. Fourth, we will compare properties of genetic association statistics computed from genotypes and from estimated haplotypes and will identify scenarios in which haplotype statistics provide more accurate association information than methods that do not require haplotype estimation. This work will enable more accurate estimation of the linkage disequilibrium important in association study design and analysis. The long-term goal of the project is to make optimal use of knowledge of human variation and evolutionary history for the design and analysis of association mapping studies. Our efforts will make use of genome-wide microsatellite and single-nucleotide polymorphism data that we have gathered in a worldwide collection of populations. As part of the project, we will be developing new statistical methods and implementing them in software tools that we will make publicly available.

描述（由申请人提供）：关联研究提供了一种有力的等位基因的方法，该等位基因有助于疾病敏感性和表型变异。由于种群遗传过程在产生疾病与其因果等位基因之间的统计关联模式中起着核心作用，因此对人口遗传史及其对关联的后果的理解对于开发映射疾病易感性等位基因的方法很重要。我们提出了四个具体目标，这些目标将增强并利用人群的理论和经验人群遗传学知识，以促进通过关联映射来识别疾病易感性基因座的前景。这项工作将通过数学理论，计算机模拟和人口遗传数据的分析的结合来进行。首先，我们将扩展通过基因型与疾病之间虚假关联的种群结构来分析生产的方法，以适应clinal或空间分布的种群。新方法将使减少比目前更广泛的场景中人口结构或分层产生的假积极关联的发生是可能的。其次，我们将开发人类进化的人群基因模型，这些模型使用近似贝叶斯计算来解释各种全球种群之间单倍型变化的模式。第三，我们将开发一个遗传关联复制研究的统计分析框架，该框架考虑到所有人类都通过共同祖先的血统相关的事实。第四，我们将比较从基因型和估计单倍型中计算出的遗传关联统计属性的特性，并将确定与不需要单倍型估计的方法相比，单倍型统计提供了更准确的关联信息。这项工作将使在关联研究设计和分析中重要的链接不平衡更准确地估计。该项目的长期目标是最佳利用人类变异和进化历史的知识，以设计和分析关联映射研究。我们的努力将利用我们在全球人群集合中收集的全基因组微卫星和单核苷酸多态性数据。作为项目的一部分，我们将开发新的统计方法，并在我们将公开可用的软件工具中实施它们。