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Genome-wide Structured Association Testing & Regional Admixture Mapping

全基因组结构化关联测试

基本信息

批准号：
7925643
负责人：
Nianjun Liu
金额：
$ 27.29万
依托单位：
UNIVERSITY OF ALABAMA AT BIRMINGHAM
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-01 至 2012-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7925643
关键词：
Abbreviations Accounting Admixture Animals Blood Pressure Body Composition Calibration Characteristics Complex Computer Simulation Computing Methodologies Coupled Data Data Set Detection Development Equation Equilibrium Event Family Future Genes Genetic Markers Genetic Polymorphism Genetic Research Genomics Genotype Human Individual Knowledge Least-Squares Analysis Left Left Ventricular Hypertrophy Left Ventricular Mass Linkage Disequilibrium Literature Maps Markov Chains Masks Measurement Measures Methodology Methods Modeling Partner in relationship Performance Phenotype Population Procedures Property Proxy Quantitative Trait Loci Recording of previous events Research Research Personnel Sampling Short Tandem Repeat Signal Transduction Solutions Specific qualifier value Staging Statistical Methods Structure Techniques Terminology Testing TimeLine Validation Variant Vision Work Writing base case control conditioning flexibility genetic association genetic pedigree genome wide association study genome-wide human subject interest novel performance tests simulation tool trait

项目摘要

Genome-wide association studies with hundreds of thousands to millions of SNPs are now feasible. Population admixture and other departures from random mating can confound genetic association studies and produce false positives. Population admixture can also mask true genotype-phenotype associations and produce false negatives. Statistical methods for controlling for the potential confounding effects of population admixture via use of measures of individual ancestry and related techniques is referred to as structured association testing (SAT). Admixed populations also have advantages and can be used to detect genomic regions containing trait-influencing genes (i.e., to find linkage) via regional admixture mapping (RAM). In principle, SAT and RAM allow localization of genomic regions containing trait-influencing genes even in samples of unrelated individuals. We propose developing, evaluating, and applying SAT and RAM methods in the context of genome-wide association studies. We aim to: 1) Assess the measurement properties of individual ancestry and region-specific admixture estimates (which are key inputs into SAT and RAM procedures) and elaborate and evaluate new procedures for assessing the reliability of such estimates in real data; 2) Extend a highly flexible general framework we have developed for both SAT and RAM to accommodate measurement error in individual ancestry estimates; 3) Combine the general framework with an atheoretical conditioning equations (ACE) approach that does not depend on knowledge of unobservable past events, does not require ancestry informative markers to be specified a priori, is more flexible, and (we hypothesize) will be equally effective for recently admixed populations and more effective for populations with temporally remote and complex admixture histories; 4) Offer a method to allow testing for linkage conditional upon association with a polymorphism in a region and, thereby, testing whether that polymorphism appears to account for an observed linkage signal that was detected with RAM; 5) Develop and evaluate an entirely novel two-stage approach that has the potential to substantially increase power and efficiency in genome-wide association studies of admixed populations while preserving overall error rates; and 6) Illustrate the methods developed by application to several real datasets.

现在，用数十万到数百万个SNP进行全基因组关联研究是可行的。种群混合和其他偏离随机交配的现象会混淆遗传关联研究产生假阳性。群体混合也可以掩盖真正的基因型-表型关联，产生假阴性。控制人群潜在混杂效应的统计方法通过使用个体祖先的测量和相关技术的混合被称为结构化的关联测试（SAT）混合群体也具有优势，可用于检测基因组包含性状影响基因的区域（即，通过区域混合物映射（RAM）找到联系）。在原则上，SAT和RAM允许定位包含性状影响基因的基因组区域，即使在无关个体的样本。我们建议开发，评估和应用SAT和RAM方法在全基因组关联研究的背景下。我们的目标是：1）评估的测量特性个人血统和区域特定混合物估计（这是SAT和RAM的关键输入并制定和评价评估这种估计的可靠性的新程序，真实的数据; 2）扩展我们为SAT和RAM开发的高度灵活的通用框架，适应个体祖先估计中的测量误差; 3）联合收割机将一般框架与非理论条件方程（ACE）方法，不依赖于不可观测的知识过去的事件，不需要祖先信息标记被指定的先验，是更灵活的，和（我们假设）对于最近混合的种群同样有效，而对于种群更有效时间上遥远和复杂的混合物历史; 4）提供一种方法，允许测试链接条件是与一个地区的多态性相关联，从而测试是否多态性似乎解释了用RAM检测到的观察到的连锁信号; 5）发展并评估一种全新的两阶段方法，该方法有可能大幅增加功率，混合群体全基因组关联研究的效率，同时保持总体错误率; 以及6）通过应用于几个真实的数据集来说明所开发的方法。