Population-Genetic Studies for Association Mapping

关联作图的群体遗传学研究

• 批准号: 8055339
• 负责人: Noah Rosenberg
• 金额: $ 2.13万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8055339
• 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）：关联研究为定位导致疾病易感性和表型变异的等位基因提供了强有力的方法。由于群体遗传过程在产生疾病与其致病等位基因之间的统计关联模式中起着核心作用，因此了解人类群体遗传史及其关联后果对于开发疾病易感性等位基因图谱的方法非常重要。我们提出了四个具体的目标，这将增加和利用理论和经验的人口群体遗传学知识的人群，以推进通过关联映射确定疾病易感基因座的前景。这项工作将通过数学理论、计算机模拟和人类群体遗传数据分析相结合来进行。首先，我们将扩展的方法，分析生产的基因型和疾病之间的虚假关联的人口结构，以适应临床或空间分布的人口。新的方法将有可能在比目前可能的更广泛的一组情景中减少因人口结构或分层而产生的假阳性关联的发生。第二，我们将开发人类进化的群体遗传模型，该模型使用近似贝叶斯计算来解释世界各地不同群体之间的单倍型变异模式。第三，我们将开发一个框架，用于遗传关联的复制研究的统计分析，考虑到所有人类都是由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的单倍型计算的遗传关联统计的属性，并确定单倍型统计比不需要单倍型估计的方法提供更准确的关联信息的情况。这项工作将使更准确地估计连锁不平衡的关联研究的设计和分析的重要。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来设计和分析关联映射研究。我们的努力将利用我们在世界范围内收集的全基因组微卫星和单核苷酸多态性数据。作为该项目的一部分，我们将开发新的统计方法，并在我们将公开提供的软件工具中实施这些方法。