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Journal Article

Multivariate genome-wide analyses of the well-being spectrum

Authors

Publication date

14 Feb 2019

Summary

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.

Published in

Nature Genetics

DOI

https://doi.org/10.1038/s41588-018-0320-8

ISSN

16

Subjects

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

Notes

University of Essex, Albert Sloman Library *University of Essex registered users - Campus access*


Related publications

  1. Multivariate genome-wide and integrated transcriptome and epigenome-wide analyses of the well-being spectrum

    Bart M. L. Baselmans, Rick Jansen, Hill Fung Ip, et al.

    1. Psychology
    2. Medicine
    3. Science And Technology
    4. Well Being
    5. Health
    6. Biology
    7. Genetics

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