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。基因型插补方法是这一成功的关键贡献者。这些统计方法利用公开可用的参考组中的密集基因型来估计GWA研究中数百万个未测量的遗传标记的基因型。因此，它们使研究人员能够测试更多的疾病相关标记物，而不仅仅是那些已经通过实验测量的标记物，从而提高了检测风险变异的能力。随着新一代测序技术的出现，这将有助于检测罕见的遗传变异与疾病的关联，插补的重要性只会增加。然而，优化估算方法应用的若干挑战仍未得到解决。虽然插补的准确性取决于使用适当的参考个体，但关于如何最佳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插补算法的性能。随着遗传学研究开始将注意力集中在罕见变异上，将其作为不可解释的遗传性疾病风险的潜在重要来源，因此有必要改进此类多态性的基因型归因特性。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、插补算法和插补准确度指标，在非裔美国人和西班牙裔/拉丁裔人群中评价插补准确度和统计功效。该项目将通过优化这些人群中的插补来促进非裔美国人和西班牙裔/拉丁裔人群中疾病易感性位点的识别。第二，新的基于模型的统计技术的插补将被设计考虑到独特的马赛克结构的基因组混合的个人。这项工作建立在流行的fastPHASE软件的基础上，以进一步加强混合人群中的插补。第三，将设计和测试估算罕见变异（包括拷贝数变异）的方法。这项分析将使罕见变异在GWA测试中的使用成为可能，从而改善揭示其对疾病风险影响的前景。第四，将开发算法，用于最佳选择重新排序的个体，并用作插补的模板个体。这项工作将加强即将进行的GWA研究的设计，将纳入样本子集的重新测序数据。这些项目将通过模拟，理论和计算分析相结合来完成。此外，算法将应用于来自巴尔的摩的非裔美国人、来自德克萨斯州斯塔尔县的墨西哥裔美国人和1000个基因组项目的数据集。该项目产生的统计资源将以公开软件的形式传播，将为促进目前绘制疾病基因图谱的工作提供基本工具，特别是在非裔美国人和西班牙裔/拉丁裔人口中。 公共卫生相关性：许多疾病基因已经通过“关联研究”确定，该研究在人类基因组中寻找在携带疾病的个体中比在对照个体中更频繁发生的遗传变异。我们将通过确定最佳的统计策略，将遗传关联研究的数据与现有数据库的数据相结合，来改善识别疾病基因的前景。我们的项目将提供有关最佳研究特征和统计方法的指导方针，以在未充分研究的信息丰富的人群中找到疾病基因，如非洲裔美国人和墨西哥裔美国人。