使用 MTAG 进行全基因组关联汇总统计的多性状分析。
Multi-trait analysis of genome-wide association summary statistics using MTAG.
机构信息
Broad Institute, Cambridge, MA, USA.
Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA, USA.
出版信息
Nat Genet. 2018 Feb;50(2):229-237. doi: 10.1038/s41588-017-0009-4. Epub 2018 Jan 1.
We introduce multi-trait analysis of GWAS (MTAG), a method for joint analysis of summary statistics from genome-wide association studies (GWAS) of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (N = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). As compared to the 32, 9, and 13 genome-wide significant loci identified in the single-trait GWAS (most of which are themselves novel), MTAG increases the number of associated loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase the variance explained by polygenic scores by approximately 25%, matching theoretical expectations.
我们介绍了 GWAS 的多性状分析（MTAG），这是一种联合分析不同性状的全基因组关联研究（GWAS）汇总统计数据的方法，这些性状可能来自重叠的样本。我们将 MTAG 应用于抑郁症状（N=354862）、神经质（N=168105）和主观幸福感（N=388538）的汇总统计数据。与单性状 GWAS 中确定的 32、9 和 13 个全基因组显著位点（其中大多数是新的）相比，MTAG 将相关位点分别增加到 64、37 和 49。此外，MTAG 的关联统计数据产生了更具信息量的生物信息学分析，并使多基因评分解释的方差增加了约 25%，符合理论预期。
Zotero 插件