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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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/.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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).

  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).

  3. 3.

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

  4. 4.

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

  5. 5.

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

  6. 6.

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

  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).

  8. 8.

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

  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).

  10. 10.

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

  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).

  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).

  14. 14.

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

  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).

  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).

  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).

  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).

  20. 20.

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

  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).

  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).

  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).

  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).

  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).

  26. 26.

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

  27. 27.

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

  28. 28.

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

  29. 29.

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

  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).

  31. 31.

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

  32. 32.

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

  33. 33.

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

  34. 34.

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

  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).

  38. 38.

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

  39. 39.

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

  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).

  41. 41.

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

  42. 42.

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

  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).

  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).

  46. 46.

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

  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).

  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).

  50. 50.

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

  51. 51.

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

  52. 52.

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

  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).

  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).

  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).

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.

Author information

Author notes

  1. These authors jointly supervised this work: Michel G. Nivard, Meike Bartels.

  2. A list of members and affiliations appears in the Supplementary Note.

Affiliations

  1. Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    • Bart M. L. Baselmans
    • , Hill F. Ip
    • , Jenny van Dongen
    • , Margot P. van de Weijer
    • , Gonneke Willemsen
    • , Jouke-Jan Hottenga
    • , Dorret I. Boomsma
    • , Eco J. C. de Geus
    • , Michel G. Nivard
    •  & Meike Bartels
  2. Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, the Netherlands

    • Bart M. L. Baselmans
    • , Jenny van Dongen
    • , Abdel Abdellaoui
    • , Gonneke Willemsen
    • , Jouke-Jan Hottenga
    • , Dorret I. Boomsma
    • , Eco J. C. de Geus
    • , Michel G. Nivard
    •  & Meike Bartels
  3. Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    • Rick Jansen
  4. Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands

    • Rick Jansen
    • , Gonneke Willemsen
    • , Jouke-Jan Hottenga
    • , Dorret I. Boomsma
    • , Eco J. C. de Geus
    •  & Meike Bartels
  5. Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

    • Abdel Abdellaoui
  6. Institute for Social and Economic Research, University of Essex, Colchester, UK

    • Yanchun Bao
    • , Melissa Smart
    •  & Meena Kumari

Authors

  1. Search for Bart M. L. Baselmans in:

  2. Search for Rick Jansen in:

  3. Search for Hill F. Ip in:

  4. Search for Jenny van Dongen in:

  5. Search for Abdel Abdellaoui in:

  6. Search for Margot P. van de Weijer in:

  7. Search for Yanchun Bao in:

  8. Search for Melissa Smart in:

  9. Search for Meena Kumari in:

  10. Search for Gonneke Willemsen in:

  11. Search for Jouke-Jan Hottenga in:

  12. Search for Dorret I. Boomsma in:

  13. Search for Eco J. C. de Geus in:

  14. Search for Michel G. Nivard in:

  15. Search for Meike Bartels in:

Consortia

  1. BIOS consortium

    1. Social Science Genetic Association Consortium

      Contributions

      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.

      Competing interests

      The authors declare no competing interests.

      Corresponding authors

      Correspondence to Michel G. Nivard or Meike Bartels.

      Integrated supplementary information

      1. 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

      2. 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

      3. 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)

      4. 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

      5. 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

      6. 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

      Supplementary information

      1. Supplementary Text and Figures

        Supplementary Figures 1–6 and Supplementary Note

      2. Reporting Summary

      3. Supplementary Tables

        Supplementary Tables 1–22

      About this article

      Publication history

      Received

      Accepted

      Published

      DOI

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