PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics
PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics
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DOI：
10.1016/j.ajhg.2020.11.017
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发表时间：
2021-01-07
影响因子：
9.8
通讯作者：
Han, Buhm
中科院分区：
文献类型：
--
作者：
Lee, Cue Hyunkyu;Shi, Huwenbo;Han, Buhm
识别和解释多效位点对于理解疾病和复杂性状之间的共同病因至关重要。绘制多效基因座的一种常用方法是对多个性状的GWAS汇总统计数据进行meta分析。然而，这种策略并不能解释性状复杂的遗传结构，如遗传相关性和遗传力。此外，这种解释具有挑战性，因为表型通常具有不同的特征和单位。我们提出了PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test)，这是一个基于汇总统计的框架，用于在多种疾病和复杂性状的联合分析中绘制和解释多效位点。我们的方法通过系统地计算关联测试中性状的遗传相关性和遗传力来最大化功效。任何一组相关的表型，二元或数量性状具有不同的单位，可以无缝地组合。此外，我们的框架提供解释和可视化工具来帮助下游分析。利用我们的方法，我们结合了18个与心血管疾病相关的性状，鉴定出13个多效位点，它们表现出4种不同的关联模式。
Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.