Association analysis of rare variants with sequencing data

描述(由申请人提供)： 遗传学中一个新兴的研究领域是利用下一代测序数据来检测复杂特征和稀有变异(RV)之间的关联。由于轮状病毒的微小等位基因频率(MAF)极低，许多现有的常见变种(CV)测试，如基因组范围关联研究(GWA)中最流行的单变量测试，可能不再适用。为了增强统计能力，现有关联测试的一个共同主题是聚合同一基因中多个房车的信息。对于测序数据，因为大多数RV可能不是因果的，在这种情况下，即使不是全部，现有的关联测试的性能都严重恶化。我们建议开发和评估一种适应性测试，该测试可以在各种情况下保持高功率，包括在存在相反的关联方向和许多非关联的房车的情况下。我们将把建议的适应性检验扩展到通径分析和多性状分析。所开发的方法将用于检测RV-心血管特征与来自Charge-S和ESP队列的测序数据的关联。我们将开发和分发实现所建议方法的软件。

DESCRIPTION (provided by applicant): An emerging research area in genetics is to detect associations between complex traits and rare variants (RVs) with next-generation sequencing data. Due to extremely low minor allele frequencies (MAFs) of RVs, many existing tests for common variants (CVs), such as the univariate test on each individual variant, most popular in genome-wide association studies (GWAS), may no longer be suitable. To boost statistical power, a common theme of existing association tests is to aggregate information across multiple RVs in a gene. With sequencing data, since the majority of RVs may not be causal, in which case most, if not all, existing association tests have severely deteriorating performance. We propose developing and evaluating an adaptive test that can maintain high power across various situations, including in the presence of opposite association directions and of many non-associated RVs. We will extend the proposed adaptive test to pathway analysis and multi-trait analysis. The developed methods will be applied to detect associations of RV-cardiovascular traits with the sequencing data from the CHARGE-S and ESP cohorts. We will develop and distribute software implementing the proposed methods.