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Shared genetic architectures of subjective well-being in East Asian and European ancestry populations

Abstract

Subjective well-being (SWB) has been explored in European ancestral populations; however, whether the SWB genetic architecture is shared across populations remains unclear. We conducted a cross-population genome-wide association study for SWB using samples from Korean (n = 110,919) and European (n = 563,176) ancestries. Five ancestry-specific loci and twelve cross-ancestry significant genomic loci were identified. One novel locus (rs12298541 near HMGA2) associated with SWB was also identified through the European meta-analysis. Significant cross-ancestry genetic correlation for SWB between samples was observed. Polygenic risk analysis in an independent Korean cohort (n = 22,455) demonstrated transferability between populations. Significant correlations between SWB and major depressive disorder, and significant enrichment of central nervous system-related polymorphisms heritability in both ancestry populations were found. Hence, large-scale cross-ancestry genome-wide association studies can advance our understanding of SWB genetic architecture and mental health.

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Fig. 1: Genetic association of SWB in samples of Korean and European ancestries.
Fig. 2: Effect sizes for SWB in populations of Korean and European ancestries.
Fig. 3: Polygenic risk prediction in an independent Korean cohort based on Korean GWAS and meta-analysis results for samples of Korean and European ancestries.
Fig. 4: Genetic correlations between SWB from the European meta-analysis and other traits from publicly available GWAS summary statistics of European ancestry.
Fig. 5: Results of the multiple-tissue analysis for SWB.

Data availability

Data from the UKB and 23andMe are available on application to each site (UKB, https://www.ukbiobank.ac.uk; 23andMe, https://research.23andme.com/dataset-access/). Summary statistics of Okbay and colleagues7 are publicly available at the Social Science Genetic Association Consortium (SSGAC, https://www.thessgac.org/). The full summary statistics of the KBA GWAS are available at the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads).

Code availability

Previously developed pipelines were used to produce the results for the current study. No custom code was developed. Please see the Supplementary Information for details on the software URLs and data used.

References

  1. Diener, E. Subjective well-being. The science of happiness and a proposal for a national index. Am. Psychol. 55, 34–43 (2000).

    CAS  PubMed  Article  Google Scholar 

  2. Steptoe, A., Deaton, A. & Stone, A. A. Psychological wellbeing, health and ageing. Lancet 385, 640–648 (2015).

    PubMed  Article  Google Scholar 

  3. Malone, C. & Wachholtz, A. The relationship of anxiety and depression to subjective well-being in a mainland chinese sample. J. Relig. Health 57, 266–278 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  4. Machado, L., de Oliveira, I. R., Peregrino, A. & Cantilino, A. Common mental disorders and subjective well-being: emotional training among medical students based on positive psychology. PLoS ONE 14, e0211926 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. Haworth, C. M., Carter, K., Eley, T. C. & Plomin, R. Understanding the genetic and environmental specificity and overlap between well-being and internalizing symptoms in adolescence. Dev Sci 20, e12376 (2017).

  6. Nes, R. B. & Røysamb, E. in Genetics of Psychological Well-Being: The Role of Heritability and Genetics in Positive Psychology (ed M. Pluess) 75–96 (Oxford Univ. Press, 2015).

  7. 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  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Baselmans, B. M. L. et al. Multivariate genome-wide analyses of the well-being spectrum. Nat. Genet. 51, 445–451 (2019).

    CAS  PubMed  Article  Google Scholar 

  10. Moon, S. et al. The korea biobank array: design and identification of coding variants associated with blood biochemical traits. Sci. Rep. 9, 1382 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  11. Spracklen, C. N. et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 582, 240–245 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Kim, Y., Han, B. G. & Ko, G. E. S. G. Cohort profile: The Korean Genome and Epidemiology Study (KoGES) consortium. Int J. Epidemiol. 46, e20 (2017).

    PubMed  Article  Google Scholar 

  13. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. Brown, B. C. et al. Transethnic genetic–correlation estimates from summary statistics. Am. J. Hum. Genet 99, 76–88 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 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  PubMed  PubMed Central  Article  Google Scholar 

  16. Ward, L. D. & Kellis, M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucl. Acids Res. 40, D930–D934 (2012).

    CAS  PubMed  Article  Google Scholar 

  17. Li, X. et al. Common variants on 6q16.2, 12q24.31 and 16p13.3 are associated with major depressive disorder. Neuropsychopharmacology 43, 2146–2153 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Ward, J. et al. The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function. Mol. Psychiatry 25, 3091–3099 (2020).

    PubMed  Article  Google Scholar 

  19. Converge Consortium. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588–591 (2015).

    PubMed Central  Article  CAS  Google Scholar 

  20. Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).

    CAS  PubMed  Article  Google Scholar 

  21. Zhao, B. et al. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat. Genet. 51, 1637–1644 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Lindblad-Toh, K. et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature 478, 476–482 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Brainstorm, C. et al. Analysis of shared heritability in common disorders of the brain. Science 360, eaap8757 (2018).

    Article  CAS  Google Scholar 

  25. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Hsu, S. D. H. et al. Accurate genomic prediction of human height. Genetics 214, 231–497 (2020).

  27. Okada, Y., Eyre, S., Suzuki, A., Kochi, Y. & Yamamoto, K. Genetics of rheumatoid arthritis: 2018 status. Ann. Rheum. Dis. 78, 446–453 (2019).

    CAS  PubMed  Article  Google Scholar 

  28. Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Koyama, S. et al. Population-specific and trans-ancestry genome-wide analyses identify distinct and shared genetic risk loci for coronary artery disease. Nat. Genet. 52, 1169–1177 (2020).

    CAS  PubMed  Article  Google Scholar 

  30. Giri, A. et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat. Genet. 51, 51–62 (2019).

    CAS  PubMed  Article  Google Scholar 

  31. Lam, M. et al. Comparative genetic architectures of schizophrenia in East Asian and European populations. Nat. Genet. 51, 1670–1678 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Nagel, M. et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat. Genet. 50, 920–927 (2018).

    CAS  PubMed  Article  Google Scholar 

  34. Wray, N. R. et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat. Genet. 50, 668–681 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Mullins, N. et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat. Genet. 53, 817–829 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Pearson, C. A. et al. Foxp1 regulates neural stem cell self-renewal and bias toward deep layer cortical fates. Cell Rep. 30, 1964–1981 e1963 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. White, C. C. et al. Identification of genes associated with dissociation of cognitive performance and neuropathological burden: multistep analysis of genetic, epigenetic, and transcriptional data. PLoS Med. 14, e1002287 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. Maruani, A. et al. 11q24.2-25 Micro-rearrangements in autism spectrum disorders: relation to brain structures. Am. J. Med. Genet. A 167A, 3019–3030 (2015).

    PubMed  Article  CAS  Google Scholar 

  39. Xi, Y. et al. HMGA2 promotes adipogenesis by activating C/EBPβ-mediated expression of PPARgamma. Biochem. Biophys. Res. Commun. 472, 617–623 (2016).

    CAS  PubMed  Article  Google Scholar 

  40. Nishino, J., Kim, I., Chada, K. & Morrison, S. J. Hmga2 promotes neural stem cell self-renewal in young but not old mice by reducing p16Ink4a and p19Arf Expression. Cell 135, 227–239 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Gunasekaran, T. I. Identification of genetic loci associated with pathophysiological changes in Alzheimer’s disease progression using neuroimaging genetics. PhD dissertation, Chosun Univ. (2020).

  42. Ramanathan, S. et al. A case of autism with an interstitial deletion on 4q leading to hemizygosity for genes encoding for glutamine and glycine neurotransmitter receptor sub-units (AMPA 2, GLRA3, GLRB) and neuropeptide receptors NPY1R, NPY5R. BMC Med. Genet. 5, 10 (2004).

    PubMed  PubMed Central  Article  Google Scholar 

  43. Lie, B. A. et al. Association analysis in type 1 diabetes of the PRSS16 gene encoding a thymus-specific serine protease. Hum. Immunol. 68, 592–598 (2007).

    CAS  PubMed  Article  Google Scholar 

  44. Nguyen, T. A. et al. SIDT2 transports extracellular dsRNA into the cytoplasm for innate immune recognition. Immunity 47, 498–509 e496 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. Dewulf, J. P. et al. SLC13A3 variants cause acute reversible leukoencephalopathy and α-ketoglutarate accumulation. Ann. Neurol. 85, 385–395 (2019).

    CAS  PubMed  Article  Google Scholar 

  46. Shorts-Cary, L. et al. Bone morphogenetic protein and retinoic acid-inducible neural specific protein-3 is expressed in gonadotrope cell pituitary adenomas and induces proliferation, migration, and invasion. Endocrinology 148, 967–975 (2007).

    CAS  PubMed  Article  Google Scholar 

  47. Bennett, A. H. et al. RNA helicase, DDX27 regulates skeletal muscle growth and regeneration by modulation of translational processes. PLoS Genet. 14, e1007226 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. Chen, J. et al. Identifying candidate genes for Type 2 Diabetes Mellitus and obesity through gene expression profiling in multiple tissues or cells. J. Diabetes Res. 2013, 970435 (2013).

    PubMed  PubMed Central  Google Scholar 

  49. Kim, S. et al. Heritability estimates of individual psychological distress symptoms from genetic variation. J. Affect Disord. 252, 413–420 (2019).

    PubMed  Article  Google Scholar 

  50. Barros, V. V., Kozasa, E. H., Formagini, T. D., Pereira, L. H. & Ronzani, T. M. Smokers show lower levels of psychological well-being and mindfulness than non-smokers. PLoS ONE 10, e0135377 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  51. Cotter, D., Mackay, D., Landau, S., Kerwin, R. & Everall, I. Reduced glial cell density and neuronal size in the anterior cingulate cortex in major depressive disorder. Arch. Gen. Psychiatry 58, 545–553 (2001).

    CAS  PubMed  Article  Google Scholar 

  52. Moustafa, A. A., Mandali, A., Balasubramani, P. P. & Srinivasa Chakravarthy, V. in Computational Neuroscience Models of the Basal Ganglia (eds Chakravarthy, V. S. & Moustafa, A. A.) 21–39 (Springer, 2018).

  53. Apps, M. A., Rushworth, M. F. & Chang, S. W. The anterior cingulate gyrus and social cognition: tracking the motivation of others. Neuron 90, 692–707 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Barnes, M., Abhyankar, P., Dimova, E. & Best, C. Associations between body dissatisfaction and self-reported anxiety and depression in otherwise healthy men: a systematic review and meta-analysis. PLoS ONE 15, e0229268 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Kuykendall, L., Lei, X., Zhu, Z. & Hu, X. Leisure choices and employee well-being: comparing need fulfillment and well-being during TV and other leisure activities. Appl Psychol. Health Well Being 12, 532–558 (2020).

    PubMed  Article  Google Scholar 

  56. Shiue, I. Duration of daily TV/screen watching with cardiovascular, respiratory, mental and psychiatric health: Scottish Health Survey, 2012–2013. Int. J. Cardiol. 186, 241–246 (2015).

    PubMed  Article  Google Scholar 

  57. Gargiulo, R. & Stokes, M. Subjective well-being as an indicator for clinical depression. Soc. Indic. Res. 92, 517–527 (2009).

    Article  Google Scholar 

  58. Burns, R. A., Anstey, K. J. & Windsor, T. D. Subjective well-being mediates the effects of resilience and mastery on depression and anxiety in a large community sample of young and middle-aged adults. Aust. N. Z. J. Psychiatry 45, 240–248 (2011).

    PubMed  Article  Google Scholar 

  59. Zhang, X. C., Woud, M. L., Becker, E. S. & Margraf, J. Do health-related factors predict major depression? A longitudinal epidemiologic study. Clin. Psychol. Psychother. 25, 378–387 (2018).

    PubMed  Article  Google Scholar 

  60. Goldberg, D. P. et al. The validity of two versions of the GHQ in the WHO study of mental illness in general health care. Psychol. Med. 27, 191–197 (1997).

    CAS  PubMed  Article  Google Scholar 

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

    PubMed  PubMed Central  Article  Google Scholar 

  62. Choi, J. Y. et al. Recapitulation of previously reported associations for type 2 diabetes and metabolic traits in the 126K East Asians. Genomics Inf. 17, e48 (2019).

    Article  Google Scholar 

  63. Abraham, G., Qiu, Y. & Inouye, M. FlashPCA2: principal component analysis of Biobank-scale genotype datasets. Bioinformatics 33, 2776–2778 (2017).

    CAS  PubMed  Article  Google Scholar 

  64. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. Shin, D. M., Hwang, M. Y., Kim, B. J., Ryu, K. H. & Kim, Y. J. GEN2VCF: a converter for human genome imputation output format to VCF format. Genes Genom. 42, 1163–1168 (2020).

    CAS  Article  Google Scholar 

  66. Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. Loh, P. R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. Rietveld, C. A. et al. GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340, 1467–1471 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article  CAS  Google Scholar 

  73. Alfaro-Almagro, F. et al. Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage 166, 400–424 (2018).

    PubMed  Article  Google Scholar 

  74. Mazziotta, J. et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B 356, 1293–1322 (2001).

    CAS  Article  Google Scholar 

  75. Xia, M., Wang, J. & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    PubMed  Article  Google Scholar 

  77. Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  78. Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  79. Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 40, 304–314 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  80. Jeon, S. et al. Korean genome project: 1094 Korean personal genomes with clinical information. Sci. Adv. 6, eaaz7835 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

KBA genotype data were provided by the Collaborative Genome Program for Fostering New Post-Genome Industry (3000–3031b), and UKB data were obtained under application no. 33002. This study was supported by the National Research Foundation of Korea Grant funded by the Ministry of Science and Information and Communication Technologies, South Korea (grant no. NRF‐2018R1C1B6001708 and NRF-2021R1A2C4001779 to W.M. and NRF-2019R1A2C4070496 and NRF-2022R1A2C2009998 to H.H.W.) and by an intramural grant from the Korea National Institute of Health (2019-NG-053-01). This research was also supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant nos. HI19C1132 and HI19C1328000020 to H.H.W. and S.K., respectively). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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H.H.W., W.M. and Y.J.K. had full access to all data in the study and take responsibility for the integrity of the data and accuracy of the data analysis. S.K., K.K., H.H.W. and W.M. conceived and designed the study. S.K., M.Y.H. and H.H.W. performed the statistical analyses. S.K., K.K., M.Y.H., H.H.W., W.M. and Y.J.K. drafted the manuscript. H.H.W., W.M. and Y.J.K. supervised the entire study. All authors contributed to the interpretation of the data and have read and approved the final draft for submission. S.K., K.K. and M.Y.H. contributed equally to this work. H.H.W., W.M. and Y.J.K. jointly supervised this work.

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Correspondence to Young Jin Kim, Woojae Myung or Hong-Hee Won.

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Woong-Yang Park is employed by a commercial company, GENINUS. GENINUS has no particular conflict of interest with the current work. The remaining authors declare no competing interests.

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Kim, S., Kim, K., Hwang, M.Y. et al. Shared genetic architectures of subjective well-being in East Asian and European ancestry populations. Nat Hum Behav (2022). https://doi.org/10.1038/s41562-022-01343-5

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