Advanced strategies for genotype imputation

描述（由申请人提供）：最近的全基因组关联（GWA）研究已经确定了许多与疾病易感性有关的等位基因。基因型插入方法是这一成功的关键因素。这些统计方法利用公开参考面板中的密集基因型来估计GWA研究中数百万未测量的遗传标记的基因型。因此，它们使研究人员能够在实验测量之外测试更多的疾病关联标记，从而提高检测风险变异的能力。随着新一代测序技术的出现，这种技术将促进罕见遗传变异与疾病关联的检测，代入的重要性只会增加。然而，优化计算方法应用的几个挑战仍然没有得到解决。虽然归算的准确性取决于使用适当的参考个体，但关于如何最佳地选择用作模板的个体的数据有限，特别是在混合人群中，如非洲裔美国人和西班牙裔/拉丁裔人群。此外，主要针对常见的遗传变异评估了代入算法的性能。随着遗传学研究开始关注罕见变异作为无法解释的遗传性疾病风险的潜在重要来源，改善此类多态性的基因型归算特性至关重要。为解决这些问题，提出了四个项目。首先，将使用多个现有参考数据集、代入算法和代入精度度量，评估非裔美国人和西班牙裔/拉丁裔人群的代入精度和统计能力。该项目将通过优化在非洲裔美国人和西班牙裔/拉丁裔人群中的代入，促进疾病易感位点的鉴定。其次，考虑到混合个体独特的基因组镶嵌结构，将设计新的基于模型的统计方法。这项工作建立在流行的fastPHASE软件上，以进一步增强混合种群的输入。第三，将设计和测试包括拷贝数变异在内的稀有变异的归因方法。这一分析将使在GWA检测中使用罕见变异成为可能，从而改善揭示其对疾病风险影响的前景。第四，将开发算法，以最优地选择个体进行重测序，并将其用作模板个体进行imputation。这项工作将加强即将进行的GWA研究的设计，这些研究将纳入样本子集的重测序数据。这些项目将通过模拟、理论和计算分析的结合来完成。此外，算法将应用于来自巴尔的摩的非洲裔美国人、来自德克萨斯州斯塔尔县的墨西哥裔美国人以及1000基因组计划的数据集。该项目产生的统计资源将以可公开获得的软件分发，它将提供基本工具，促进正在进行的绘制疾病基因图的工作，特别是在非洲裔美国人和西班牙裔/拉丁裔人口中。

DESCRIPTION (provided by applicant): Recent genome-wide association (GWA) studies have identified many alleles contributing to disease susceptibility. Genotype imputation methods have been a key contributor to this success. These statistical approaches leverage dense genotypes in publicly available reference panels to estimate genotypes at millions of unmeasured genetic markers in a GWA study. Thus, they enable investigators to test many more markers for disease association beyond those that have been experimentally measured, thereby improving power to detect risk variants. With the recent advent of next-generation sequencing technologies that will facilitate the testing of rare genetic variants for disease association, the importance of imputation is only likely to increase. However, several challenges for optimizing the application of imputation methods remain unaddressed. While imputation accuracy depends on the use of appropriate reference individuals, limited data exist on how to optimally choose the individuals used as a template, particularly in admixed populations such as African Americans and Hispanic/Latino populations. Moreover, the performance of imputation algorithms has been evaluated primarily for common genetic variants. As genetic studies begin to focus on rare variation as a potentially important source for unexplained heritable disease risk, it is essential to improve the properties of genotype imputation for such polymorphisms. Four projects are proposed for addressing these issues. First, imputation accuracy and statistical power will be evaluated in African Americans and in a Hispanic/Latino population, using multiple existing reference datasets, imputation algorithms, and imputation accuracy measures. This project will facilitate the identification of disease-susceptibility loci in African Americans and Hispanic/Latino populations by optimizing imputation in these populations. Second, new model-based statistical techniques for imputation will be devised by considering the unique mosaic structure of genomes of admixed individuals. This work builds on the popular fastPHASE software to further enhance imputation in admixed populations. Third, methods of imputing rare variants, including copy-number variants, will be devised and tested. This analysis will enable the use of rare variants in GWA tests, thereby improving the prospects for uncovering their effects on disease risk. Fourth, algorithms will be developed for optimally selecting individuals for resequencing and use as template individuals for imputation. This work will enhance the design of forthcoming GWA studies that will incorporate resequencing data on subsets of the sample. The projects will be accomplished through a combination of simulation, theory, and computational analysis. Furthermore, algorithms will be applied using datasets on African Americans from Baltimore, Mexican Americans from Starr County, Texas, and the 1000 Genomes Project. Statistical resources generated from the project, which will be disseminated in publicly available software, will provide essential tools for facilitating the ongoing effort of mapping disease genes, particularly in African Americans and Hispanic/Latino populations. PUBLIC HEALTH RELEVANCE: Many disease genes have been identified by "association studies" that search the human genome for genetic variants that occur more frequently in individuals who carry a disease than in control individuals. We will improve the prospects for identifying disease genes by determining the best statistical strategies for combining data from genetic association studies with data from existing databases. Our project will provide guidelines about optimal study characteristics and statistical methods to find disease genes in understudied, informative populations such as African Americans and Mexican Americans.