Genome-wide Structured Association Testing & Regional Admixture Mapping

对数十万到数百万个SNP进行全基因组关联研究现在是可行的。 种群混合和其他随机交配的偏离可能会扰乱遗传关联研究 并产生假阳性。种群混合也可以掩盖真正的基因-表型关联和 产生假阴性。控制人口潜在混杂效应的统计方法 通过使用个人血统测量和相关技术的混合被称为结构化的 关联测试(SAT)。混合种群也有优势，可以用来检测基因组 通过区域混合作图(RAM)包含影响性状的基因的区域(即，找到连锁)。在……里面 原理，SAT和RAM允许定位包含影响性状的基因的基因组区域，即使在 无血缘关系的个体样本。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 个人血统和特定地区的混合估计(这是SAT和RAM的关键输入 程序)，并详细说明和评价#年评估这种估计的可靠性的新程序 真实数据；2)扩展我们为SAT和RAM开发的高度灵活的通用框架，以 适应个人血统估计中的测量误差；3)将一般框架与 一种不依赖于不可观测知识的理论条件方程(ACE)方法 过去的事件，不需要先验地指定祖先信息标记，更灵活，(我们 假设)对最近混杂的种群同样有效，而对种群更有效 具有时间上遥远和复杂的混合历史；4)提供了一种允许测试关联的方法 条件是与区域中的多态相关联，从而测试是否 多态似乎解释了用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，该方法有可能大幅提高功率和 混合种群全基因组关联研究的效率，同时保持总体错误率； 6)通过对几个实际数据集的应用，说明了所提出的方法。

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.