Metrics and methods for cross-population fine mapping

描述(申请人提供)：全基因组关联研究已经非常成功地识别了数百个与复杂疾病和表型相关的变异。相比之下，由于在任何给定的基因座上都存在高度的连锁不平衡，到目前为止只发现了少数几个因果变异。为了弥补这一差距，目前正在对欧洲人、亚洲人、非裔美国人或拉美人等多个群体进行几项涉及密集基因分型或测序的精细图谱研究。对多个群体的精细定位研究可以利用不同群体之间的不同遗传变异，以提高在多个群体的联合分析中定位因果变异的准确性 与一次只分析一个人群的研究相比。令人惊讶的是，尽管多种族精细作图研究的潜力很大，但目前的多群体精细作图研究在特定座位的特别框架内使用了标准的统计技术。在这项应用中，我们将介绍新的指标和自动化框架，以量化精细作图方法的性能，以及利用多种族遗传变异来提高精细作图的定位精度的新统计方法。

DESCRIPTION (provided by applicant): Genome-wide association studies have been very successful in identifying hundreds of variants associated to complex diseases and phenotypes. In contrast, due to high levels of linkage disequilibrium at any given locus, only a handful of causal variants have been identified so far. In an attempt to bridge this gap, several fine- mapping studies involving dense genotyping or sequencing are currently being performed in multiple populations such as Europeans, Asians, African Americans or Latinos. Fine mapping studies over multiple populations can leverage different genetic variation across populations to increase the accuracy for localizing the causal variant in a joint analysis of multiple populations as compared to studies in which only one population is analyzed at a time. Surprisingly, despite the large potential of multi ethnic fine mapping studies, current multi population fine mapping studies employ standard statistical techniques within locus specific ad- hoc frameworks. In this application we will introduce novel metrics and automated frameworks for quantifying the performance of fine mapping methods as well as novel statistical methods that leverage multi ethnic genetic variation to increase the localization accuracy for fine mapping.