Multivariate genome-wide analyses of the well-being spectrum

Publication type

Journal Article

Published in

Nature Genetics


Bart M. L. Baselmans, Rick Jansen, Hill F. Ip, Jenny van Dongen, Abdel Abdellaoui, Margot P. van de Weijer, Yanchun Bao, Melissa Smart, Meena Kumari, Gonneke Willemsen, Jouke-Jan Hottenga, Dorret I. Boomsma, Eco J. C de Geus, Michel G. Nivard and Meike Bartels

Publication date


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 (Nobs = 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.

Volume and page numbers

51, 445-451





Psychology, Medicine, Science And Technology, Well Being, Health, Biology and Genetics


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