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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N-GWAMA and MA-GWAMA software is available at: https://github.com/baselmans/multivariate_GWAMA/
Summary Statistics excluding results from 23AndMe can be downloaded from https://surfdrive.surf.nl/files/index.php/s/Ow1qCDpFT421ZOO. The data transfer agreement with 23AndMe stipulates that we can publish effect sizes associated with 10,000 SNPs. These summary statistics can be downloaded from https://surfdrive.surf.nl/files/index.php/s/Ow1qCDpFT421ZOO. For 23AndMe dataset access, see https://research.23andme.com/dataset-access/. The Understanding Society data are distributed by the UK Data Service. The genome-wide scan data were analyzed and deposited by the Wellcome Trust Sanger Institute. Information on how to access the data can be found on the Understanding Society website at https://www.understandingsociety.ac.uk/. Genotype-trait data access for UKHLS is available by application to Metadac through http://www.metadac.ac.uk/.
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We thank all participants in the cohort studies. This work was supported by the Netherlands Organization for Scientific Research (NWO: MagW/ZonMW grants 904‐61‐090, 985‐10‐002,904‐61‐193,480‐04‐004, 400‐05‐717, NWO‐bilateral agreement 463‐06‐001, NWO‐VENI 451‐04‐034, Addiction‐31160008, Middelgroot‐911‐09‐032, Spinozapremie 56‐464‐14192), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, 184.021.007), the VU University’s Institute for Health and Care Research (EMGO+) and Neuroscience Campus Amsterdam (NCA), the European Science Council (ERC Advanced, 230374), the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA) and the National Institutes of Health (NIH, R01D0042157‐01A). Part of the genotyping was funded by the Genetic Association Information Network (GAIN) of the Foundation for the US National Institutes of Health (NIMH, MH081802) and by the Grand Opportunity grants 1RC2MH089951‐01 and 1RC2 MH089995‐01 from the NIMH. Part of the analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org/), which is financially supported by the Netherlands Scientific Organization (NWO 480‐05‐003), the Dutch Brain Foundation, and the department of Behavioural and Movement Sciences of the VU University Amsterdam. M.B. is/was financially supported by a senior fellowship of the (EMGO+) Institute for Health and Care and a VU University Research Chair position. This work is supported by an ERC consolidator grant (WELL-BEING 771057 PI Bartels). M.G.N. is supported by a ZonMw grant: ‘Genetics as a research tool: A natural experiment to elucidate the causal effects of social mobility on health’ (pnr: 531003014), ZonMw project: ‘Can sex- and gender-specific gene expression and epigenetics explain sex-differences in disease prevalence and etiology?’ (pnr:849200011) and grant R01AG054628 02S. Understanding Society is an initiative funded by the Economic and Social Research Council (ES/H029745/1) and various Government Departments, with scientific leadership by the Institute for Social and Economic Research, University of Essex, and survey delivery by NatCen Social Research and Kantar Public. The BIOS and SSGAC consortia are acknowledged as banner-coauthors for the key role their previous work played. A detailed description of their role and membership appears in the Supplementary Note.
The authors declare no competing interests.
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Integrated supplementary information
Plot of nine simulation scenarios in which the rg between the four traits varied between .1 and .9 (X-axis). The red line represents the mean Pearson’s correlation (of the four traits) between the Beta’s of the univariate GWAMA and the true effects. Blue represents the correlation of the beta’s obtained from the N-GWAMA with the true effects. Green represents the correlation of the beta’s obtained from the MA-GWAMA and the true effects
Flowchart of the study design showing the trait-specific studies that were combined in the four univariate GWAMA’s: Life Satisfaction, Positive Affect, Neuroticism, and Depressive Symptoms
The result of polygenic risk prediction based on univariate discovery GWAMA, N-weighted discovery GWAMA, or model averaging discovery GWAMA. The unit on the y-axis is the R-squared in percentage, obtained from a regression of the trait on the PRS, age, sex and 10 principle components. (a) life satisfaction, (b) positive affect, (c) neuroticism, (d) depressive symptoms. The x-axis represents the chromosomal position, and the y-axis represents the significance on a –log10 scale.” Sample size of the included traits are displayed in Supplementary Table 3. The top dashed line represents the significant threshold (p < 5 X 10−8)
Supplementary Figure 4 Polygenic risk prediction based on univariate discovery GWAMA, N-weighted discovery GWAMA, or model averaging discovery GWAMA.
The result of polygenic risk prediction based on univariate discovery GWAMA, N-weighted discovery GWAMA, or model averaging discovery GWAMA. The unit on the y-axis is the R-squared in percentage, obtained from a regression of the trait on the PRS, age, sex and 10 principle components. (a) displays the polygenic prediction results from the Netherlands Twin Register, (b) displays the polygenic results from Understanding Society and (c) displays the combined N-weighted polygenic score results. LS is life satisfaction, PA is positive affect, NEU is neuroticism, and DS is depressive symptoms. Sample size used for the different polygenic scores are displayed in Supplementary Table 12. Error bars represent the 95% confident intervals
(a) provides a local Manhattan plot for the MHC region with interposed on top the LD with a strong eQTL for the C4 gene linked to neuronal pruning in adolescence and schizophrenia by Sekar et al.27 (b) is a scatter plot for the –log10(p) against the R2 with the C4 eQTL using Pearson’s correlation. (c) provides a local Manhattan plot for the MHC region with interposed on top the LD with SNP rs13194504, the strongest MHC signal found for schizophrenia. (d) is a scatter plot of the –log10(p) against the R2 with rs13194504 using Pearson’s correlation. Round symbols represent SNPs, square symbols represent gene transcripts and triangle symbols represent CpG sites. The sample size used for the local association can be found in Supplementary Table 3
The bar plot is reflecting the FDR adjusted p-value for tissue specific histone modified regions of the genome, as estimated using partitioned LD-score regression. Blue bars represent brain regions, black bars represent non-brain regions. The sample size used for the cell specific histone modified region enrichment can be found in Supplementary Table 3. A Z-test was used to test for significant enrichment
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Baselmans, B.M.L., Jansen, R., Ip, H.F. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat Genet 51, 445–451 (2019). https://doi.org/10.1038/s41588-018-0320-8
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