PLEIO：一种使用 GWAS 汇总统计数据进行基因多效性位点映射和解释的方法。
PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.
机构信息
Department of Biomedical Sciences, BK21 Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Republic of Korea.
Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.
出版信息
Am J Hum Genet. 2021 Jan 7;108(1):36-48. doi: 10.1016/j.ajhg.2020.11.017. Epub 2020 Dec 21.
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.
鉴定和解释多效性位点对于理解疾病和复杂特征之间的共同病因至关重要。一种常见的多效性位点映射方法是跨多个特征对 GWAS 汇总统计数据进行荟萃分析。然而，这种策略没有考虑到特征的复杂遗传结构，例如遗传相关性和遗传力。此外，由于表型通常具有不同的特征和单位，因此解释具有挑战性。我们提出了 PLEIO（使用最优检验进行多效性位点探索和解释），这是一种基于汇总统计数据的框架，可在对多种疾病和复杂特征的联合分析中映射和解释多效性位点。我们的方法通过系统地在关联测试中考虑特征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，无论是具有不同单位的二元或定量特征，都可以无缝地组合在一起。此外，我们的框架还提供了解释和可视化工具，以帮助下游分析。使用我们的方法，我们结合了 18 个与心血管疾病相关的特征，并鉴定出 13 个多效性位点，这些位点显示出四种不同的关联模式。
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