Whole-genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery not addressed by the traditional one variant, one phenotype association study. Here, we introduce a Bayesian model comparison approach called MRP (multiple rare variants and phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies, requiring only summary statistic data. We apply our method to exome sequencing data (n = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, and IQGAP2 and mean platelet volume. Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes- and lipid-related traits. Overall, we show that the MRP model comparison approach improves upon useful features from widely used meta-analysis approaches for rare-variant association analyses and prioritizes protective modifiers of disease risk.

应用于大量人群或具有广泛表型分析的生物库的全基因组测序研究提出了新的分析挑战。同时考虑一个基因座或一组基因的许多变异的需要以及研究具有共享遗传结构的许多相关表型的潜力提供了传统的一个变异、一个表型关联研究无法解决的发现机会。在这里，我们引入了一种称为 MRP（多种罕见变异和表型）的贝叶斯模型比较方法，用于稀有变异关联研究，该方法考虑一组遗传变异、表型和研究中遗传效应的相关性、规模和方向，仅需要总结统计数据。我们将我们的方法应用于英国生物银行 2,019 个性状的外显子组测序数据 (n = 184,698)，聚合基因中的信号。MRP 表现出恢复信号的能力，例如PCSK9和 LDL 胆固醇水平之间的关联。我们还发现 MRP 在外显子组数据中进行荟萃分析是有效的。非生物标志物发现包括MC1R与红发颜色和皮肤颜色、IL17RA与单核细胞计数以及IQGAP2与平均血小板体积之间的关联。最后，我们在多表型环境中应用 MRP；基于遗传相关性估计对 35 个生物标志物表型进行聚类后，我们发现对这些表型的联合分析会导致基因-性状关联的显着功效增益，例如在包含糖尿病和脂质相关性状的聚类之一中的TNFRSF13B 。总体而言，我们表明，MRP 模型比较方法改进了广泛使用的荟萃分析方法的有用特征，用于罕见变异关联分析，并优先考虑疾病风险的保护性修饰因素。