Symposium 6th World Congress on Positive Psychology 2019

Multivariate Genome-Wide Analyses of the Well-being Spectrum (#419)

Bart Baselmans 1 , Margot van de Weijer 1 , Meike Bartels 1
  1. Vrije Universiteit Amsterdam, Amsterdam, NOORD HOLLAND, Netherlands

Progression in finding genetic variants influencing well-being has been hampered by the reliance on univariate traits, ignoring the added information in their interrelation.

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 GWAMA, and in a ~ 57% increase in the predictive power of polygenic risk scores. Supporting transcriptome -and methylome-wide analyses (TWAS/MWAS) 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.