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Meta-Analysis
. 2019 Mar;51(3):445-451.
doi: 10.1038/s41588-018-0320-8. Epub 2019 Jan 14.

Multivariate genome-wide analyses of the well-being spectrum

Bart M L Baselmans  1   2 , Rick Jansen  3   4 , Hill F Ip  1 , Jenny van Dongen  1   2 , Abdel Abdellaoui  2   5 , Margot P van de Weijer  1 , Yanchun Bao  6 , Melissa Smart  6 , Meena Kumari  6 , Gonneke Willemsen  1   2   4 , Jouke-Jan Hottenga  1   2   4 ; BIOS consortium; Social Science Genetic Association Consortium; Dorret I Boomsma  1   2   4 , Eco J C de Geus  1   2   4 , Michel G Nivard #  7   8 , Meike Bartels #  9   10   11
Affiliations
Meta-Analysis

Multivariate genome-wide analyses of the well-being spectrum

Bart M L Baselmans et al. Nat Genet. 2019 Mar.

Abstract

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.

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References

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