Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands.
Nat Genet. 2019 Mar;51(3):445-451. doi: 10.1038/s41588-018-0320-8. Epub 2019 Jan 14.
We introduce two novel methods for multivariate genome-wide-association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (N = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMAs and in an ~57% increase in the predictive power of polygenic risk scores. Supporting transcriptome- and methylome-wide analyses (TWAS and MWAS, respectively) uncovered an additional 17 and 75 independent loci, respectively. Bioinformatic analyses, based on gene expression in brain tissues and cells, showed that genes differentially expressed in the subiculum and GABAergic interneurons are enriched in their effect on the well-being spectrum.
我们介绍了两种用于相关性状的多变量全基因组关联荟萃分析（GWAMA）的新方法，这些方法可纠正样本重叠问题。广泛的模拟场景支持我们的多变量方法相对于单变量 GWAMA 的附加价值。我们将新方法应用于生活满意度、积极情绪、神经质和抑郁症状，统称为幸福感谱（N=2370390），并发现了 304 个显著的独立信号。与四个单变量 GWAMA 相比，我们的多变量方法使独立信号的数量增加了 26%，多基因风险评分的预测能力增加了约 57%。支持转录组和甲基化组全关联分析（TWAS 和 MWAS）分别发现了另外 17 个和 75 个独立基因座。基于脑组织和细胞中基因表达的生物信息学分析表明，在海马下托和 GABA 能中间神经元中差异表达的基因在其对幸福感谱的影响上是富集的。
