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.

Original languageEnglish
Pages (from-to)445-451
Number of pages7
JournalNature Genetics
Volume51
Issue number3
DOIs
Publication statusE-pub ahead of print - Mar 2019

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Genome-Wide Association Study
Genome
Meta-Analysis
Interneurons
Computational Biology
Transcriptome
Hippocampus
Depression
Gene Expression
Brain
Genes
Neuroticism

Cite this

BIOS Consortium. / Multivariate genome-wide analyses of the well-being spectrum. In: Nature Genetics. 2019 ; Vol. 51, No. 3. pp. 445-451.
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title = "Multivariate genome-wide analyses of the well-being spectrum",
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.",
author = "Baselmans, {Bart M L} and Rick Jansen and Ip, {Hill F} and {van Dongen}, Jenny and Abdel Abdellaoui and {van de Weijer}, {Margot P} and Yanchun Bao and Melissa Smart and Meena Kumari and Gonneke Willemsen and Jouke-Jan Hottenga and Boomsma, {Dorret I} and {de Geus}, {Eco J C} and Nivard, {Michel G} and Meike Bartels and {BIOS Consortium}",
year = "2019",
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doi = "10.1038/s41588-018-0320-8",
language = "English",
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journal = "Nature Genetics",
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Multivariate genome-wide analyses of the well-being spectrum. / BIOS Consortium.

In: Nature Genetics, Vol. 51, No. 3, 03.2019, p. 445-451.

Research output: Contribution to JournalArticleAcademicpeer-review

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T1 - Multivariate genome-wide analyses of the well-being spectrum

AU - Baselmans, Bart M L

AU - Jansen, Rick

AU - Ip, Hill F

AU - van Dongen, Jenny

AU - Abdellaoui, Abdel

AU - van de Weijer, Margot P

AU - Bao, Yanchun

AU - Smart, Melissa

AU - Kumari, Meena

AU - Willemsen, Gonneke

AU - Hottenga, Jouke-Jan

AU - Boomsma, Dorret I

AU - de Geus, Eco J C

AU - Nivard, Michel G

AU - Bartels, Meike

AU - BIOS Consortium

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N2 - 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.

AB - 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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