Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Spousal correlations for nine psychiatric disorders are consistent across cultures and persistent over generations

Nature Human Behaviour volume 9, pages 2539–2547 (2025)Cite this article

Subjects

Abstract

Trait similarities between spouses are a key factor that shapes the landscape of complex human traits. The driving force behind the spousal correlations can increase the overall prevalence of disorders, influence occurrences of comorbidities and bias estimations of genetic architectures. However, there is a lack of large-scale studies examining cultural differences and generational trends in spousal correlations for psychiatric disorders. Focusing on three national registries, we performed a large-scale analysis on spousal correlations across nine psychiatric disorders. We obtained the trait correlations from five million spousal pairs in Taiwan and then compared them with estimates from the Danish national registry (571,534 pairs) and with published results from the Swedish national registry (707,263 pairs). Generational changes in Taiwan for people born after the 1930s were investigated as well. We found that a majority of psychiatric disorders have consistent spousal correlations across nations and over generations, indicating their importance in the population dynamics of psychiatric disorders.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spousal correlations across nine psychiatric disorders, with estimates from Taiwan versus those from Nordic countries (Denmark and Sweden).
Fig. 2: Generational trends of spousal correlations for psychiatric disorders.
Fig. 3: Generational trends of parent–offspring correlations.
Fig. 4: Distribution of the spousal correlations as a function of GWAS-based genetic covariance.

Similar content being viewed by others

Data availability

The individual-level data from the NHIRD used in this study are held by the Taiwan Ministry of Health and Welfare and under controlled access. Researchers interested in accessing the dataset can apply to the Ministry of Health and Welfare requesting access. The individual-level data from the Danish Civil Register can only be accessed through authorized Danish research environments due to Danish laws. Researchers can contact A.B.D. to inquire about Danish data access. All the summary data used in this study, including estimated spousal correlations, can be found in Supplementary Tables 1–13.

Code availability

The simulation code for this paper is available via GitHub at https://github.com/chunchiehfan/Simulations-for-Assortative-Mating/tree/v.1.0.alpha or via Zenodo at https://doi.org/10.5281/zenodo.14618454 (ref. 40).

References

  1. Vandenberg, S. G. Assortative mating, or who marries whom? Behav. Genet. 2, 127–157 (1972).

    Article  Google Scholar 

  2. Mare, R. D. Five decades of educational assortative mating. Am. Sociol. Rev. 56, 15–32 (1991).

    Article  Google Scholar 

  3. Sjaarda, J. & Kutalik, Z. Partner choice, confounding and trait convergence all contribute to phenotypic partner similarity. Nat. Hum. Behav. 7, 776–789 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Torvik, F. A. et al. Non-random mating patterns within and across education and mental and somatic health. Nat. Commun. 15, 10505 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Nielsen, J. Mental disorders in married couples (assortative mating). Br. J. Psychiatry 110, 683–697 (1964).

    Article  PubMed  CAS  Google Scholar 

  6. Merikangas, K. R. & Spiker, D. G. Assortative mating among in-patients with primary affective disorder. Psychol. Med. 12, 753–764 (1982).

    Article  PubMed  CAS  Google Scholar 

  7. Parnas, J. Mates of schizophrenic mothers: a study of assortative mating from the American-Danish High Risk Project. Br. J. Psychiatry 146, 490–497 (1985).

    Article  PubMed  CAS  Google Scholar 

  8. Galbaud du Fort, G., Kovess, V. & Boivin, J. F. Spouse similarity for psychological distress and well-being: a population study. Psychol. Med. 24, 431–447 (1994).

    Article  PubMed  CAS  Google Scholar 

  9. Qian, Z. Changes in assortative mating: the impact of age and education, 1970–1890. Demography 35, 279–292 (1998).

    Article  PubMed  CAS  Google Scholar 

  10. Domingue, B. W., Fletcher, J., Conley, D. & Boardman, J. D. Genetic and educational assortative mating among US adults. Proc. Natl Acad. Sci. USA 111, 7996–8000 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Xie, Y., Cheng, S. & Zhou, X. Assortative mating without assortative preference. Proc. Natl Acad. Sci. USA 112, 5974–5978 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Conley, D. et al. Assortative mating and differential fertility by phenotype and genotype across the 20th century. Proc. Natl Acad. Sci. USA 113, 6647–6652 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Peyrot, W. J., Robinson, M. R., Penninx, B. W. & Wray, N. R. Exploring boundaries for the genetic consequences of assortative mating for psychiatric traits. JAMA Psychiatry 73, 1189–1195 (2016).

    Article  PubMed  Google Scholar 

  14. Horwitz, T. B., Balbona, J. V., Paulich, K. N. & Keller, M. C. Evidence of correlations between human partners based on systematic reviews and meta-analyses of 22 traits and UK Biobank analysis of 133 traits. Nat. Hum. Behav. 7, 1568–1583 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Nordsletten, A. E. et al. Patterns of nonrandom mating within and across 11 major psychiatric disorders. JAMA Psychiatry 73, 354–361 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Border, R. et al. Cross-trait assortative mating is widespread and inflates genetic correlation estimates. Science 378, 754–761 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Yamamoto, K. et al. Genetic footprints of assortative mating in the Japanese population. Nat. Hum. Behav. 7, 65–73 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Trubetskoy, V. et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Demontis, D. et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat. Genet. 51, 63–75 (2019).

    Article  PubMed  CAS  Google Scholar 

  20. Grove, J. et al. Identification of common genetic risk variants for autism spectrum disorder. Nat. Genet. 51, 431–444 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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, 668–681 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Otowa, T. et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol. Psychiatry 21, 1391–1399 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS). Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol. Psychiatry 23, 1181–1188 (2018).

  25. Walters, R. K. et al. Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. Nat. Neurosci. 21, 1656–1669 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Watson, H. J. et al. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nat. Genet. 51, 1207–1214 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Border, R. et al. Assortative mating biases marker-based heritability estimators. Nat. Commun. https://doi.org/10.1038/s41467-022-28294-9 (2022).

  28. Torvik, F. A. et al. Modeling assortative mating and genetic similarities between partners, siblings, and in-laws. Nat. Commun. https://doi.org/10.1038/s41467-022-28774-y (2022).

  29. Feng, L.-Y. & Li, J.-H. New psychoactive substances in Taiwan: challenges and strategies. Curr. Opin. Psychiatry 33, 306–311 (2020).

    Article  PubMed  Google Scholar 

  30. Kendler, K. S., Abrahamsson, L., Ohlsson, H., Sundquist, J. & Sundquist, K. Obsessive-compulsive disorder and its cross-generational familial association with anxiety disorders in a national Swedish extended adoption study. JAMA Psychiatry 80, 314–322 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Wang, S. H. et al. Paternal age and 13 psychiatric disorders in the offspring: a population-based cohort study of 7 million children in Taiwan. Mol. Psychiatry 27, 5244–5254 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Athanasiadis, G. et al. A comprehensive map of genetic relationships among diagnostic categories based on 48.6 million relative pairs from the Danish genealogy. Proc. Natl Acad. Sci. USA 119, e2118688119 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Meijsen, J. et al. Quantifying the relative importance of genetics and environment on the comorbidity between mental and cardiometabolic disorders using 17 million Scandinavians. Nat. Commun. 15, 5064 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Revelle, W. psych: procedures for psychological, psychometric, and personality research. R version 2.5.3 (2025). https://CRAN.R-project.org/package=psych

  35. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022); https://www.R-project.org/

  36. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

    Article  Google Scholar 

  37. Wray, N. R. & Gottesman, I. I. Using summary data from the Danish national registers to estimate heritabilities for schizophrenia, bipolar disorder, and major depressive disorder. Front. Genet. https://doi.org/10.3389/fgene.2012.00118 (2012).

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Grotzinger, A. D. et al. Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat. Hum. Behav. 3, 513–525 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Fan, C. C. Simulations for assortative mating. Zenodo https://doi.org/10.5281/zenodo.14618454 (2025).

Download references

Acknowledgements

This work was supported by the National Health Research Institutes (grant nos NHRI-EX109-10931PI, NHRI-EX110-10931PI and NHRI-EX111-10931PI; S.-H.W.) and the National Science and Technology Council (grant no. NSTC114-2314-B-400-031-MY3; S.-H.W.). C.C.F. is supported by NIH grants R01 MH122688 and R01 MH128959. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, USA

    Chun Chieh Fan, Bohan Xu, Robert Loughnan & Wesley K. Thompson

  2. Department of Radiology, University of California, La Jolla, CA, USA

    Chun Chieh Fan

  3. Institute of Biological Psychiatry, Mental Health Services in the Capital Region of Denmark, Mental Health Centre Sct Hans, Roskilde, Denmark

    Saeid Rasekhi Dehkordi, Thomas Werge, Andrew J. Schork & Alfonso Buil Demur

  4. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Richard Border & Noah Zaitlen

  5. Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA

    Richard Border & Noah Zaitlen

  6. Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Richard Border

  7. Biostatistic Program, University of California, San Diego, La Jolla, CA, USA

    Lucy Shao

  8. Center for Human Development, University of California, San Diego, La Jolla, CA, USA

    Robert Loughnan

  9. Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, USA

    Wesley K. Thompson

  10. Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

    Le-Yin Hsu

  11. Graduate Program of Data Science, National Taiwan University and Academia Sinica, Taipei, Taiwan

    Le-Yin Hsu

  12. National Center for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan, Taiwan

    Mei-Chen Lin, Chi-Fung Cheng, Wei-Yi Kao, Chi-Shin Wu & Shi-Heng Wang

  13. Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan

    Rou-Yi Lai & Mei-Hsin Su

  14. Department of Psychiatry, Virginia Institute for Psychiatric Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA

    Mei-Hsin Su

  15. Department of Psychiatry, National Taiwan University Hospital, Yunlin Branch, Yunlin, Taiwan

    Chi-Shin Wu

  16. Neurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ, USA

    Andrew J. Schork

  17. Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark

    Andrew J. Schork

  18. Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA

    Noah Zaitlen

  19. Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan

    Shi-Heng Wang

  20. Center for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Taiwan

    Shi-Heng Wang

Authors
  1. Chun Chieh Fan
    View author publications

    Search author on:PubMed Google Scholar

  2. Saeid Rasekhi Dehkordi
    View author publications

    Search author on:PubMed Google Scholar

  3. Richard Border
    View author publications

    Search author on:PubMed Google Scholar

  4. Lucy Shao
    View author publications

    Search author on:PubMed Google Scholar

  5. Bohan Xu
    View author publications

    Search author on:PubMed Google Scholar

  6. Robert Loughnan
    View author publications

    Search author on:PubMed Google Scholar

  7. Wesley K. Thompson
    View author publications

    Search author on:PubMed Google Scholar

  8. Le-Yin Hsu
    View author publications

    Search author on:PubMed Google Scholar

  9. Mei-Chen Lin
    View author publications

    Search author on:PubMed Google Scholar

  10. Chi-Fung Cheng
    View author publications

    Search author on:PubMed Google Scholar

  11. Rou-Yi Lai
    View author publications

    Search author on:PubMed Google Scholar

  12. Mei-Hsin Su
    View author publications

    Search author on:PubMed Google Scholar

  13. Wei-Yi Kao
    View author publications

    Search author on:PubMed Google Scholar

  14. Thomas Werge
    View author publications

    Search author on:PubMed Google Scholar

  15. Chi-Shin Wu
    View author publications

    Search author on:PubMed Google Scholar

  16. Andrew J. Schork
    View author publications

    Search author on:PubMed Google Scholar

  17. Noah Zaitlen
    View author publications

    Search author on:PubMed Google Scholar

  18. Alfonso Buil Demur
    View author publications

    Search author on:PubMed Google Scholar

  19. Shi-Heng Wang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

C.C.F. conceptualized the study. C.C.F., A.B.D. and S.-H.W. designed the study, obtained the data and implemented the analytic framework. S.R.D., L.S., B.X., L.-Y.H., M.-C.L. and C.-F.C. conducted the analyses and extracted the summary statistics. C.C.F., A.B.D. and S.-H.W. summarized and interpreted the results. C.C.F. and S.-H.W. wrote the paper. R.B., R.L., W.K.T., R.-Y.L., M.-H.S., W.-Y.K., T.W., C.-S.W., A.J.S. and N.Z. contributed substantially to the writing of the paper. All authors provided substantial suggestions and comments to the paper.

Corresponding authors

Correspondence to Chun Chieh Fan, Alfonso Buil Demur or Shi-Heng Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks Loïc Yengo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–6 and descriptions of Supplementary Tables 1–13.

Reporting Summary (download PDF )

Supplementary Tables (download XLSX )

Supplementary Tables 1–13. Descriptions of each table are included in the Supplementary Information.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, C.C., Dehkordi, S.R., Border, R. et al. Spousal correlations for nine psychiatric disorders are consistent across cultures and persistent over generations. Nat Hum Behav 9, 2539–2547 (2025). https://doi.org/10.1038/s41562-025-02298-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41562-025-02298-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing