Association analysis of rare variants with sequencing data

遗传学领域的一项新兴和具有挑战性的研究是检测COM和fi之间的关联。 Plex特征和稀有变异(RV)与下一代测序和Exome芯片数据。由于前- RV的极低的次要等位基因频率(MAF)，许多现有的常见变异(CV)测试， 例如对每个个体变异的单变量测试，这在全基因组关联中最流行 研究(GWAS)，可能不再适用。为了增强力量和促进生物学解释， 我们建议合并多个数据源的信息，这些数据源可能是也可能不是 同样的类型。对于前者，它导致了高度适应性的Meta分析，适用于和强大的COM组件。 结合多民族队列；对于后者，我们将DNA基因分型和测序数据与基因相结合 用于轮状病毒关联分析的网络、基因表达数据和代谢组学数据。一种常见的 提出的方法的主题是明确地解释遗传和表型的异质性。为 例如，为了解释遗传异质性，我们提出了一种基于网络的自适应关联 对聚集在单个队列的网络中的多个因果基因的信息进行汇总的测试； 对于多个群体，特别是多种族群体，我们提出的荟萃分析检验具有很强的适应性。 到不同的和不同的跨队列的关联模式(例如，只有少数队列包含 因果房车)和房车之间。所开发的方法将被应用于检测轮状病毒- 心血管特征与来自ARIC研究的测序和其他基因组数据。我们将发展 并分发实现所提出的方法的软件。建议的研究符合 NHLBI对全基因组/外显子组测序和整合组学分析的持续兴趣 它的TOPMed计划和NIH的其他精密医学倡议就是明证。

An emerging and challenging research field in genetics is to detect associations between com- plex traits and rare variants (RVs) with next-generation sequencing and Exome Chip data. Due to ex- tremely 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 power and facilitate biological interpretation, we propose combining information across multiple sources of data, which may or may not be of the same type. For the former, it leads to highly adaptive meta analysis suitable and powerful for com- bining multi-ethnic cohorts; for the latter, we integrate DNA genotype and sequencing data with gene networks, gene expression data and metabolomic data for association analysis of RVs. A common theme of the proposed methods is to explicitly account for genetic and phenotypic heterogeneity. For example, to account for genetic heterogeneity, we propose an adaptive network-based association test to aggregate information across multiple causal genes clustered in a network for a single cohort; for multiple cohorts, especially multi-ethnic ones, our proposed meta-analysis test is highly adaptive to heterogeneous and varying association patterns across cohorts (e.g. only few cohorts contain causal RVs) and among RVs. The developed methods will be applied to detect associations of RV- cardiovascular traits with the sequencing and other omic data from the ARIC study. We will develop and distribute software implementing the proposed methods. The proposed research is in line with the NHLBI's continuing interest in whole genome/exome sequencing and integrative omics analysis as evidenced by its TOPMed Program and NIH's other Precision Medicine initiatives.