Advanced strategies for genotype imputation

基因型插补的高级策略

• 批准号: 8513386
• 负责人: Noah Rosenberg
• 金额: $ 36.68万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-13 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8513386
• 关键词: Address; Admixture; African American; Algorithms; Alleles; American; Baltimore; Categories; Cell Line; Collaborations; Collection; Communities; Computer Analysis; Computer software; Copy Number Polymorphism; County; DNA Resequencing; Data; Data Analyses; Data Set; Databases; Development; Disease; Disease Association; Disease susceptibility; European; Genes; Genetic; Genetic Markers; Genetic Polymorphism; Genome; Genomics; Genotype; Guidelines; Haplotypes; Hispanics; Human; Human Genome; Individual; Maps; Measures; Methods; Mexican Americans; Modeling; Paper; Participant; Pattern; Performance; Phase; Population; Population Genetics; Population Heterogeneity; Property; Publishing; Research; Research Personnel; Resources; Sample Size; Sampling; Series; Signal Transduction; Simulate; Single Nucleotide Polymorphism; Source; Statistical Methods; Statistical Models; Structure; Techniques; Technology; Testing; Texas; Uncertainty; Variant; Work; base; density; design; disorder risk; experience; genetic association; genetic variant; genome sequencing; genome wide association study; genome-wide; genotyping technology; improved; innovation; next generation sequencing; novel; public health relevance; risk variant; simulation; study characteristics; success; theories; tool

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.

描述（由申请人提供）：最近全基因组关联（GWA）的研究已经确定了许多导致疾病易感性的等位基因。基因型插补方法一直是这一成功的关键因素。在GWA研究中，这些统计方法利用公开参考面板中的致密基因型来估计数百万未衡量的遗传标记的基因型。因此，它们使研究人员能够测试除了经过实验测量的疾病关联的更多标记，从而提高了检测风险变异的能力。随着最近下一代测序技术的出现，将促进稀有遗传变异的疾病关联测试，插补的重要性只有可能增加。但是，优化插补方法应用的一些挑战仍然没有解决。尽管插补精度取决于适当的参考个体的使用，但对于如何最佳选择用作模板的个体，尤其是在混合混杂的人群中，例如非裔美国人和西班牙裔/拉丁裔人群中，存在有限的数据。此外，已经主要针对常见的遗传变异评估了归纳算法的性能。随着遗传学研究开始专注于罕见变异，这是无法解释的可遗传疾病风险的潜在重要来源，因此必须提高基因型的特性，以归因于此类多态性。提出了四个解决这些问题的项目。首先，将使用多个现有参考数据集，插补算法和插补精度措施来评估非洲裔美国人和西班牙裔/拉丁裔人口中的归合精度和统计能力。该项目将通过在这些人群中优化非裔美国人和西班牙裔/拉丁裔人群来识别疾病敏感性基因座。其次，通过考虑混合个体的基因组的独特镶嵌结构来设计基于模型的新型统计技术。这项工作以流行的快速软件为基础，以进一步增强混合群体中的插补。第三，将设计和测试归纳稀有变体（包括拷贝数变体）的方法。该分析将使在GWA测试中使用稀有变体，从而改善了发现其对疾病风险影响的前景。第四，将开发算法，以最佳选择个体来重新方便并用作模板个体进行插补。这项工作将增强即将进行的GWA研究的设计，该研究将结合样品子集的重新陈述数据。这些项目将通过模拟，理论和计算分析的结合来完成。此外，将使用来自巴尔的摩，来自德克萨斯州斯塔尔县的墨西哥裔美国人和1000个基因组项目的非裔美国人的数据集应用算法。该项目产生的统计资源将在公开可用的软件中传播，将为促进绘制疾病基因的持续努力提供必不可少的工具，尤其是在非裔美国人和西班牙裔/拉丁裔人群中。 公共卫生相关性：许多疾病基因已通过“关联研究”鉴定出来，这些疾病基因在人类基因组中寻找遗传变异，而遗传变异的发生在携带疾病的个体中比对照组更频繁地发生。我们将通过确定将遗传关联研究的数据与现有数据库的数据结合的最佳统计策略来改善鉴定疾病基因的前景。我们的项目将提供有关最佳研究特征和统计方法的准则，以在未经研究的人群（例如非裔美国人和墨西哥裔美国人）中找到疾病基因。