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.

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Fig. 1: Genetic correlations and error correlations (cross-trait intercepts) between the included GWAMA data sets.
Fig. 2: Manhattan plots of N-weighted and model-averaging GWAMA.
Fig. 3: Local differential gene expression between subcortical structures and enrichment of individual cell-type enrichment.

Code availability

N-GWAMA and MA-GWAMA software is available at: https://github.com/baselmans/multivariate_GWAMA/

Data availability

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/.

References

  1. 1.

    Visscher, P. M. et al. 10 Years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  Article  Google Scholar 

  2. 2.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  Google Scholar 

  3. 3.

    Bulik-Sullivan, B. K. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  Article  Google Scholar 

  4. 4.

    Anttila, V. et al. Analysis of shared heritability in common disorders of the brain. Science 360, 6395 (2018).

    Google Scholar 

  5. 5.

    O’Reilly, P. F. et al. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS One 7, e34861 (2012).

    Article  Google Scholar 

  6. 6.

    Ferreira, M. A. R. & Purcell, S. M. A. multivariate test of association. Bioinformatics 25, 132–133 (2009).

    CAS  Article  Google Scholar 

  7. 7.

    Aschard, H. et al. Maximizing the power of principal-component analysis of correlated phenotypes in genome-wide association studies. Am. J. Hum. Genet. 94, 662–676 (2014).

    CAS  Article  Google Scholar 

  8. 8.

    Stephens, M. Unified framework for association analysis with multiple related phenotypes. PLoS One 8, e65245 (2013).

    CAS  Article  Google Scholar 

  9. 9.

    Zhu, X. et al. Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension. Am. J. Hum. Genet. 96, 21–36 (2015).

    CAS  Article  Google Scholar 

  10. 10.

    Turley, P. et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat. Genet. 50, 229–237 (2018).

    CAS  Article  Google Scholar 

  11. 11.

    van der Sluis, S., Posthuma, D. & Dolan, C. V. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genet. 9, e1003235 (2013).

    Article  Google Scholar 

  12. 12.

    Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. Preprint at https://doi.org/10.1101/166298 (2017).

  13. 13.

    Bartels, M. & Boomsma, D. I. Born to be happy? The etiology of subjective well-being. Behav. Genet. 39, 605–615 (2009).

    Article  Google Scholar 

  14. 14.

    Rietveld, C. A. et al. Molecular genetics and subjective well-being. Proc. Natl Acad. Sci. USA 110, 9692–9697 (2013).

    CAS  Article  Google Scholar 

  15. 15.

    Okbay, A. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat. Genet. 48, 624–633 (2016).

    CAS  Article  Google Scholar 

  16. 16.

    Baselmans, B. M. L. & Bartels, M. A genetic perspective on the relationship between eudaimonic -and hedonic well-being. Sci. Rep. 8, 1–10 (2018).

    CAS  Article  Google Scholar 

  17. 17.

    Brice, J., Buck, N. & Prentice-Lane, E. (eds.). British Household Panel Survey User Manual (University of Essex, Colchester, 1993).

  18. 18.

    Lo, M.-T. et al. Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders. Nat. Genet. 49, 152–156 (2017).

    CAS  Article  Google Scholar 

  19. 19.

    Hyde, C. L. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat. Genet. 48, 1031–1036 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    Hek, K. et al. A genome-wide association study of depressive symptoms. Biol. Psychiatry 73, 667–678 (2013).

    CAS  Article  Google Scholar 

  21. 21.

    Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668 (2018).

    CAS  Article  Google Scholar 

  22. 22.

    Wood, A. R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    CAS  Article  Google Scholar 

  23. 23.

    Willemsen, G. et al. The Adult Netherlands Twin Register: twenty-five years of survey and biological data collection. Twin. Res. Hum. Genet. 16, 271–281 (2013).

    Article  Google Scholar 

  24. 24.

    Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).

    CAS  Article  Google Scholar 

  25. 25.

    Qi, T. et al. Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood. Nat. Commun. 9, 2282 (2018).

    Article  Google Scholar 

  26. 26.

    Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS  Article  Google Scholar 

  27. 27.

    Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).

    CAS  Article  Google Scholar 

  28. 28.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using GWAS summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  Article  Google Scholar 

  29. 29.

    Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    CAS  Article  Google Scholar 

  30. 30.

    O’Mara, S. M., Commins, S., Anderson, M. & Gigg, J. The subiculum: a review of form, physiology and function. Prog. Neurobiol. 64, 129–155 (2001).

    Article  Google Scholar 

  31. 31.

    Herman, J. P. & Mueller, N. K. Role of the ventral subiculum in stress integration. Behav. Brain Res. 174, 215–224 (2006).

    CAS  Article  Google Scholar 

  32. 32.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539–542 (2016).

    CAS  Article  Google Scholar 

  33. 33.

    Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

    CAS  Article  Google Scholar 

  34. 34.

    Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

    CAS  Article  Google Scholar 

  35. 35.

    Grotzinger, A. D. et al. Genomic SEM provides insights into the multivariate genetic architecture of complex traits. Preprint at https://doi.org/10.1101/305029 (2018).

  36. 36.

    Vlaming, R. de, Johannesson, M., Magnusson, P. K. E., Ikram, M. A. & Visscher, P. M. Equivalence of LD-score regression and individual-level-data methods. Preprint at https://doi.org/10.1101/211821 (2017).

  37. 37.

    Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).

    CAS  Article  Google Scholar 

  38. 38.

    Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    CAS  Article  Google Scholar 

  39. 39.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    Lee, D., Bigdeli, T. B., Riley, B. P., Fanous, A. H. & Bacanu, S. A. DIST: direct imputation of summary statistics for unmeasured SNPs. Bioinformatics 29, 2925–2927 (2013).

    CAS  Article  Google Scholar 

  41. 41.

    Akaike, H. A. Bayesian extension of the minimum AIC procedure of autoregressive model fitting. Biometrika 66, 237–242 (1979).

    Article  Google Scholar 

  42. 42.

    Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2009).

    Google Scholar 

  43. 43.

    Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c), R Package version 2.1-1 (R Foundation for Statistical Computing, 2017).

  44. 44.

    van Beijsterveldt, C. E. M. et al. The Young Netherlands Twin Register (YNTR): longitudinal twin and family studies in over 70,000 children. Twin. Res. Hum. Genet. 16, 252–267 (2013).

    Article  Google Scholar 

  45. 45.

    Diener, E. D., Emmons, R. A., Larsen, R. J. & Griffin, S. The satisfaction with life scale. J. Pers. Assess. 49, 71–75 (1985).

    CAS  Article  Google Scholar 

  46. 46.

    Lyubomirsky, S. & Lepper, H. S. A measure of subjective happiness: preliminary reliability and construct validation. Soc. Indic. Res. 46, 137–155 (1999).

    Article  Google Scholar 

  47. 47.

    Costa, P. T. & McCrae, R. R. The Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI) Professional Manual (Psychological Assessment Resources Inc., Odessa, 1992).

    Google Scholar 

  48. 48.

    Achenbach, T. M. & Rescorla, L. Manual for the ASEBA Adult Forms & Profiles (University of Vermont, Burlington, Vermont, USA, 2003).

  49. 49.

    Tennant, R. et al. The Warwick-Edinburgh mental well-being scale (WEMWBS): Development and UK validation. Health Qual. Life Outcomes 5, 1–13 (2007).

    Article  Google Scholar 

  50. 50.

    Altshuler, D. M. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  Google Scholar 

  51. 51.

    Jansen, R. et al. Conditional eQTL analysis reveals allelic heterogeneity of gene expression. Hum. Mol. Genet. 26, 1444–1451 (2017).

    CAS  Article  Google Scholar 

  52. 52.

    Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017).

    CAS  Article  Google Scholar 

  53. 53.

    Fehrmann, R. S. N. et al. Trans-eqtls reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the hla. PLoS Genet. 7, e1002197 (2011).

    CAS  Article  Google Scholar 

  54. 54.

    Bonder, M. J. et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat. Genet. 49, 131–138 (2017).

    CAS  Article  Google Scholar 

  55. 55.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

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.

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M.B., M.G.N., and B.M.L.B. oversaw the study. The theory underlying N-GWAMA and MA-GWAMA was developed by M.G.N., with contributions from B.M.L.B and M.B. Simulations were performed by B.M.LB. and M.G.N. The N-GWAMA and MA-GWAMA software was developed by B.M.L.B., H.F.I., and M.G.N. Data analyses were conducted by B.M.L.B., R.J., H.F.I., J.v.D., A.A., M.P.v.d.W., Y.B., and M.G.N. Data curation was done by R.J., Y.B., MS., M.K., G.W., J.-J.H., E.J.C.d.G., D.I.B., and M.B. The manuscript was written by B.M.L.B., M.G.N., and M.B., with helpful contributions from E.J.C.d.G. All authors provided input and revisions for the final manuscript.

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Correspondence to Michel G. Nivard or Meike Bartels.

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Integrated supplementary information

Supplementary Figure 1 Simulation scenarios performed for N-GWAMA and MA GWAMA.

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

Supplementary Figure 2 Flowchart of the study design.

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

Supplementary Figure 3 Manhattan plots of univariate GWAMAs.

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

Supplementary Figure 5 Local association in the MHC region.

(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

Supplementary Figure 6 220 cell-specific histone-modified-region enrichment.

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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Supplementary Figures 1–6 and Supplementary Note

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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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