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Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

A Corrigendum to this article was published on 29 November 2016

A Corrigendum to this article was published on 27 July 2016

This article has been updated

Abstract

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.

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Figure 1: Manhattan plots of GWAS results.
Figure 2: Genetic correlations.
Figure 3: Quasi-replication and lookup of lead SNPs.
Figure 4: Results from selected biological analyses.

Change history

  • 27 June 2016

    In the version of this article initially published, the following co-authors and their affiliations were incorrectly omitted from the author list: Gudmar Thorleifsson, Sven Bergmann, Gyda Bjornsdottir, David C. Liewald, John M. Starr, Kari Stefansson and Unnur Thorsteinsdottir. In addition, the middle initial for co-author Andreas J. Forstner was also omitted. The errors have been corrected in the HTML and PDF versions of the article.

  • 29 August 2016

    In the version of this article initially published, one of the affiliations listed for author Maciej Trzaskowski, to the Department of Public Health, Faculty of Medicine, University of Split, Split, Croatia, was included in error. The correct affiliation for this author is the Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

We are grateful to P.M. Visscher for advice, support, and feedback. We thank S. Cunningham and N. Galla for research assistance. This research was carried out under the auspices of the Social Science Genetic Association Consortium (SSGAC). The SSGAC seeks to facilitate studies that investigate the influence of genes on human behavior, well-being, and social–scientific outcomes using large GWAS meta-analyses. The SSGAC also provides opportunities for replication and promotes the collection of accurately measured, harmonized phenotypes across cohorts. The SSGAC operates as a working group within the CHARGE Consortium. This research has also been conducted using the UK Biobank Resource. The study was supported by funding from the US National Science Foundation (EAGER: 'Workshop for the Formation of a Social Science Genetic Association Consortium'), a supplementary grant from the National Institute of Health Office of Behavioral and Social Science Research, the Ragnar Söderberg Foundation (E9/11), the Swedish Research Council (421-2013-1061), the Jan Wallander and Tom Hedelius Foundation, an ERC Consolidator Grant (647648 EdGe), the Pershing Square Fund of the Foundations of Human Behavior, and the NIA/NIH through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810, and T32-AG000186-23 to NBER and R01-AG042568-02 to the University of Southern California. A full list of acknowledgments is provided in the Supplementary Note.

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Contributions

M.B., D.J.B., D.C., J.-E.D.N., P.D.K., and R.F.K. designed and oversaw the study. A.O. and B.M.L.B. were responsible for quality control and meta-analyses. Bioinformatics analyses were carried out by J.P.B., T.E., M.A.F., J.R.G., J.J.L. S.F.W.M., M.G.N., and H.-J.W. Other follow-up analyses were conducted by M.A.F., J.P.B., P.T., A.O., B.M.L.B., and R.K.L. Especially major contributions to writing and editing were made by M.B., D.J.B., J.P.B., D.C.C., J.-E.D.N., P.D.K., A.J.O., and P.T. All authors contributed to and critically reviewed the manuscript.

Corresponding authors

Correspondence to Philipp D Koellinger, Daniel J Benjamin or Meike Bartels.

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The authors declare no competing financial interests.

Additional information

University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

Integrated supplementary information

Supplementary Figure 1 Illustrating the tradeoff between maximizing sample size and having more precise and uniform phenotype measures across cohorts.

This figure plots RMSE versus n2 for various assumed values of R2 for β1 and of rg,LD, assuming that n1 = 200,000, and var(β2) = 7.24 × 10–7. A dotted line below the blue line indicates that including an additional independent cohort of size n2 with an imperfect phenotype measure with a cohort of size n1 tends to increase the accuracy and precision of the estimate of β1. See the Supplementary Note for additional details.

Supplementary Figure 2 Local Manhattan plots of the association between SNPs on chromosome 8 and neuroticism in UKB.

Results are shown without controlling for the inversion-tagging principal component (PC) (a) and conditional on the PC (b). (ce) Results without controls for the PC for individuals with inversion genotype 0 (c), inversion genotype 1 (d), and inversion genotype 2 (e). The gray background area indicates the inversion region. Note that the sample sizes differ in ce.

Supplementary Figure 3 Proxy-phenotype analyses: test of SNPs associated with subjective well-being at P < 1 × 10–4 for association with depressive symptoms and neuroticism.

In both analyses, the second-stage sample is restricted to non-overlapping cohorts. For interpretational ease, for each SNP, we choose the reference allele for subjective well-being to be the effect-increasing allele. See Supplementary Tables 16 and 17 for detailed results and the Supplementary Note for additional details.

Supplementary Figure 4 Quantile–quantile plots for primary subjective well-being analysis, post hoc subjective well-being analysis using 1000 Genomes Project SNPs, life satisfaction analysis, and positive affect analysis.

(a) Primary subjective well-being analysis. (b) Post hoc subjective well-being analysis using 1000 Genomes Project SNPs. (c) Life satisfaction analysis. (d) Positive affect analysis. The estimated LD Score intercepts used to adjust the standard errors were 1.012, 1.008, 1.007, and 1.011, respectively. The gray-shaded areas in the quantile−quantile plots represent the 95% confidence intervals under the null hypothesis.

Supplementary Figure 5 Manhattan plots for life satisfaction analysis, positive affect analysis, and post hoc subjective well-being analysis using 1000 Genomes Project SNPs.

SNPs are plotted on the x axis according to their position on each chromosome against association with the phenotype on the y axis (shown as –log10 (P value)). The solid line indicates the threshold for genome-wide significance (P = 5 × 10–8), and the dashed line indicates the threshold for suggestive hits (P = 1 × 10–5). Each independent genome-wide significant association (lead SNP) is marked by ×.

Supplementary Figure 6 LocusZoom plots for reference SNPs rs13185787 and r6579956.

The plots are based on the summary statistics from our post hoc GWAS of subjective well-being using 1000 Genomes Project SNPs and on build hg19 (1000 Genomes Project November 2014 EUR). rs13185787 and r6579956 are the two genome-wide signals from our post hoc GWAS. rs13185787 and r6579956 are genome-wide signals from our baseline GWAS of subjective well-being using HapMap 2 SNPs that are in LD with rs13185787 (r2 = 0.95) and rs6579956 (r2 = 0.39), respectively.

Supplementary Figure 7 Quantile–quantile plots for depressive symptoms and neuroticism GWAS.

(a) Depressive symptoms. (b) Neuroticism. The estimated LD Score intercept used to adjust the standard errors was 1.008 for depressive symptoms. No adjustment was applied to the neuroticism results because the estimated intercept was below 1 (0.999). The gray-shaded areas in the quantile−quantile plots represent the 95% confidence intervals under the null hypothesis.

Supplementary Figure 8 LD Score regression plots based on the summary statistics from the GWAS of subjective well-being, depressive symptoms, and neuroticism.

Each point represents an LD Score quantile. The x and y coordinates of the point are the mean LD Score and the mean χ2 statistic of SNPs in that quantile, respectively. The fact that the intercepts are close to 1 and that the χ2 statistics increase linearly with the LD Scores for all three phenotypes suggests that the bulk of the inflation in the χ2 statistics for the three phenotypes is due to true polygenic signal and not to population stratification.

Supplementary Figure 9 Power of the sign test.

The estimated power of passing the GWAS/within-family sign test for subsets of approximately independent SNPs meeting various P-value thresholds. The x axis corresponds to the P-value threshold that defines the subset of SNPs used in the sign test. The y axis reports the expected power of the sign test given the posterior distribution of effect sizes corrected for winner’s curse. The non-monotonicity of this function is due to a tradeoff: as the P-value threshold increases, admitting a greater number of SNPs increases power, while admitting less significant SNPs decreases power.

Supplementary Figure 10 Histograms of the principal components tagging the inversions on chromosomes 8 and 17.

Superimposed over the histograms are the three-class normal mixtures fitted to the data with the purpose of assigning individuals to one of the three inversion genotypes for each inversion.

Supplementary Figure 11 Squared correlations between each SNP on chromosome 8 and the principal component tagging the inversion on chromosome 8.

(a) Squared correlations for all SNPs on chromosome 8. (b) Squared correlations for the SNPs from 6–14 Mb on chromosome 8. The red vertical lines are the borders chosen for the inversion in the UKB data. Note that SNPs around the borders of the inversion are missing because they do not pass quality control, likely as a result of the presence of the inversion.

Supplementary Figure 12 Local Manhattan plot for chromosome 17 (above horizontal line) and squared correlations between each SNP on chromosome 17 and the principal component tagging the inversion on chromosome 17 (below horizontal line).

Note the strong correspondence between the location of the genome-wide signals and the location of the inversion.

Supplementary Figure 13 Associations between inversion-tagging SNPs on chromosome 8 and neuroticism and subjective well-being.

(a) Associations with neuroticism are from the GPC (n = 63,661). (b) Associations with subjective well-being are from a meta-analysis of the cohorts that did not contribute to the neuroticism GWAS (n = 199,153). Using UKB data, we identified 126 SNPs whose pairwise R2 with the principal component tagging the inversion was above 0.75. Plotted are association statistics for the subsets of tagging SNPs that were available in the relevant lookup samples (111 SNPs for neuroticism and 53 SNPs for subjective well-being).

Supplementary Figure 14 Posterior credibility of GWAS results.

(a,c,e) The posterior probabilities that SNPs rs3756290 (a), rs2075677 (c), and rs4958581 (e) are non-null given the estimated effect of each SNP on subjective well-being. We use a mixture–Gaussian prior with point mass at 0, where the variance of the non-null SNPs is estimated by maximum likelihood using the GWAS data. The x axis corresponds to the fraction of SNPs that are assumed to be non-null by the prior, and the y axis corresponds to the probability that the corresponding SNP is non-null, given the prior and the estimated effect size. (b,d,f) The posterior mean effect sizes and 95% confidence intervals for SNPs rs3756290 (b), rs2075677 (d), and rs4958581 (f). The x axis corresponds to the fraction of SNPs that are assumed to be non-null by the prior, and the y axis corresponds to the posterior mean effect size (blue) and 95% confidence interval (red).

Supplementary Figure 15 Polygenic score prediction in HRS and NTR.

Predictive power of the polygenic score constructed from the subjective well-being GWAS results in two independent holdout cohorts (HRS and NTR). Predictive power is tested for subjective well-being, positive affect, life satisfaction, depressive symptoms, the Big Five personality traits (which include neuroticism), and height.

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Okbay, A., Baselmans, B., De Neve, JE. et al. Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet 48, 624–633 (2016). https://doi.org/10.1038/ng.3552

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