Metrics and methods for cross-population fine mapping

描述（由申请人提供）：全基因组关联研究在识别与复杂疾病和表型相关的数百种变体方面非常成功。相比之下，由于在任何给定的位点的连锁不平衡的高水平，只有少数的因果变异已被确定至今。为了弥补这一差距，目前正在欧洲人、亚洲人、非洲裔美国人或拉丁美洲人等多个人群中进行涉及密集基因分型或测序的几项精细定位研究。多个群体的精细定位研究可以利用群体间的不同遗传变异，以提高多个群体联合分析中定位因果变异的准确性 与一次只分析一个群体的研究相比。令人惊讶的是，尽管多种族精细定位研究的潜力很大，但目前的多人群精细定位研究在基因座特异性特设框架内采用标准统计技术。在本申请中，我们将介绍新的指标和自动化框架，用于量化精细映射方法的性能，以及利用多种族遗传变异来提高精细映射定位精度的新统计方法。 公共卫生相关性：已知对包括乳腺癌和各种其他疾病在内的广泛癌症的抗性包括大量的遗传成分。全基因组关联研究在鉴定与包括乳腺癌在内的各种疾病相关的基因座方面非常成功。相比之下，包括大多数癌症在内的大量表型的潜在遗传因果变异尚未确定。在本申请中，我们将开发用于多种族精细映射研究的新方法和指标，并将其应用于真实的精细映射乳腺癌数据集。

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. PUBLIC HEALTH RELEVANCE: Resistance to a wide range of cancers, including breast cancer and various other diseases, is known to include a substantial genetically heritable component. Genome wide association studies have been very successful in identifying loci associated to various diseases including breast cancer. In contrast, the underlying genetic causal variants have yet to be identified for large number of phenotypes including most cancers. In this application, we will develop novel methods and metrics for multi-ethnic fine mapping studies and apply them to real fine mapping breast cancer data sets.