Baselmans, Bart ML and Jansen, Rick and Ip, Hill F and van Dongen, Jenny and Abdellaoui, Abdel and van de Weijer, Margot P and Bao, Yanchun and Smart, Melissa and Kumari, Meena and Willemsen, Gonneke and Hottenga, Jouke-Jan and Boomsma, Dorret I and de Geus, Eco JC and Nivard, Michel G and Bartels, Meike (2019) Multivariate genome-wide analyses of the well-being spectrum. Nature Genetics, 51 (3). pp. 445-451. DOI https://doi.org/10.1038/s41588-018-0320-8
Baselmans, Bart ML and Jansen, Rick and Ip, Hill F and van Dongen, Jenny and Abdellaoui, Abdel and van de Weijer, Margot P and Bao, Yanchun and Smart, Melissa and Kumari, Meena and Willemsen, Gonneke and Hottenga, Jouke-Jan and Boomsma, Dorret I and de Geus, Eco JC and Nivard, Michel G and Bartels, Meike (2019) Multivariate genome-wide analyses of the well-being spectrum. Nature Genetics, 51 (3). pp. 445-451. DOI https://doi.org/10.1038/s41588-018-0320-8
Baselmans, Bart ML and Jansen, Rick and Ip, Hill F and van Dongen, Jenny and Abdellaoui, Abdel and van de Weijer, Margot P and Bao, Yanchun and Smart, Melissa and Kumari, Meena and Willemsen, Gonneke and Hottenga, Jouke-Jan and Boomsma, Dorret I and de Geus, Eco JC and Nivard, Michel G and Bartels, Meike (2019) Multivariate genome-wide analyses of the well-being spectrum. Nature Genetics, 51 (3). pp. 445-451. DOI https://doi.org/10.1038/s41588-018-0320-8
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | BIOS consortium; Social Science Genetic Association Consortium; Brain; Interneurons; Humans; Multivariate Analysis; Computational Biology; Gene Expression; Multifactorial Inheritance; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Genome, Human; Genome-Wide Association Study; Transcriptome |
| Subjects: | Q Science > QH Natural history > QH426 Genetics |
| Divisions: | Faculty of Science and Health Faculty of Social Sciences Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of Faculty of Social Sciences > Institute for Social and Economic Research |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 29 Jan 2019 15:44 |
| Last Modified: | 30 Oct 2024 17:22 |
| URI: | http://repository.essex.ac.uk/id/eprint/23914 |
Available files
Filename: Baselmans et al Multivariate Genome-wide analyses of the Well-being spectrum_FINAL.pdf