Association analysis of rare variants with sequencing data

描述（由申请人提供）： 遗传学的一个新兴研究领域是利用下一代测序数据检测复杂性状和罕见变异（RV）之间的关联。由于RV的次要等位基因频率（MAF）极低，许多现有的常见变异（CV）测试，例如在全基因组关联研究（GWAS）中最流行的对每个个体变异的单变量测试，可能不再适合。为了提高统计能力，现有关联测试的一个共同主题是聚合基因中多个RV的信息。对于测序数据，由于大多数RV可能不是因果关系，在这种情况下，大多数（如果不是全部的话）现有的关联测试具有严重恶化的性能。我们建议开发和评估一种自适应测试，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RV。我们将建议的自适应测试扩展到通径分析和多性状分析。所开发的方法将应用于检测RV-心血管性状与来自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提出的方法的软件。

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.