Metrics and methods for cross-population fine mapping

跨人群精细制图的指标和方法

• 批准号: 8305856
• 负责人: Bogdan Pasaniuc
• 金额: $ 7.7万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-12 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8305856
• 关键词: Accounting; African; African American; Asians; Biological Assay; Budgets; Communities; Complex; Computer software; Data; Data Set; Disease; Etiology; European; Genetic; Genetic Variation; Genomics; Genotype; Hereditary Disease; Joints; Latino; Lead; Letters; Linkage Disequilibrium; Malignant Neoplasms; Maps; Methodology; Methods; Metric; Modeling; Native Americans; Noise; Pattern; Performance; Phenotype; Population; Probability; Publications; Research; Resistance; Statistical Methods; Techniques; Testing; Time; Variant; Work; disease phenotype; genetic variant; genome wide association study; improved; interest; malignant breast neoplasm; novel; programs; simulation; statistics

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.

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异方面非常成功。相比之下，由于在任何给定位点的高水平的连锁不平衡，到目前为止，只有少数的因果变异被确定。为了弥补这一差距，目前正在对欧洲人、亚洲人、非洲裔美国人或拉丁美洲人等多个人群进行一些涉及密集基因分型或测序的精细测绘研究。多种群精细图谱研究可以利用不同种群间的遗传变异，提高多种群联合分析中定位因果变异的准确性