Population-Genetic Studies for Association Mapping

描述(由申请人提供)：关联性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一种强有力的方法。由于群体遗传过程在疾病及其因果等位基因之间的统计关联模式的产生中起着核心作用，因此了解人类群体遗传历史及其对关联的影响对于开发绘制疾病易感等位基因图谱的方法非常重要。我们提出了四个具体目标，它们将增强和利用人类群体的理论和经验群体遗传学知识，以推进通过关联图谱识别疾病易感基因座的前景。这项工作将通过数学理论、计算机模拟和对人类种群遗传数据的分析相结合来完成。首先，我们将扩展按种群结构分析基因型与疾病之间虚假关联的分析方法，以适应克隆或空间分布的种群。新的办法将有可能减少在比目前可能的情况更广泛的情况下，由于人口结构或分层而产生的假阳性联系的发生。其次，我们将开发人类进化的种群遗传模型，该模型使用近似贝叶斯计算来解释世界各地不同种群之间单倍型变异的模式。第三，我们将开发一个统计分析框架，用于对遗传关联的复制研究进行统计分析，其中考虑到所有人类都是通过共同祖先的后裔而联系在一起的事实。第四，我们将比较从基因类型和从估计的单倍型计算的遗传关联统计的性质，并将识别单倍型统计提供比不需要单倍型估计的方法更准确的关联信息的情况。这项工作将使对关联研究设计和分析中重要的连锁不平衡的更准确的估计成为可能。该项目的长期目标是最大限度地利用人类变异和进化史的知识来设计和分析关联图谱研究。我们的努力将利用我们在世界范围内收集的种群集合中收集的全基因组微卫星和单核苷酸多态数据。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。

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.