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Multi ancestry genome wide association meta analysis of urinary aMT6s levels
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  • Published: 22 April 2026

Multi ancestry genome wide association meta analysis of urinary aMT6s levels

  • Magdalena Żebrowska1,6,
  • Ziwei Zhang3,
  • Gwo-Tsann Chuang13,14,
  • Daniel S. Evans9,
  • Jesse Valliere6,
  • Matthew Maher6,
  • Jie Hu3,6,7,
  • Rebecca Richmond20,21,22,
  • Constance Turman3,
  • Jaime E. Hart2,12,
  • Jacqueline Lane6,18,
  • Loic Le Marchand23,
  • Lynne Wilkens23,
  • Matthias Wielscher1,8,
  • Christopher Haiman24,
  • Iona Cheng16,17,
  • A. Heather Eliassen2,3,25,
  • Katie L. Stone9,10,
  • Gregory J. Tranah9,
  • Yi-Cheng Chang11,14,15,
  • Lorelei Ann Mucci3,19,
  • Eva S. Schernhammer1,2,3 &
  • …
  • Richa Saxena4,5,6,7 

Scientific Reports (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biomarkers
  • Diseases
  • Endocrinology
  • Genetics

Abstract

Melatonin regulates circadian rhythms, metabolism, and immunity. Its primary metabolite, 6-sulfatoxymelatonin (aMT6s), is a biomarker linked to cancer risk and metabolic disorders. However, genetic determinants of aMT6s remain poorly understood, with only one prior GWAS limited to an East Asian cohort. We conducted the first multi-ancestry genome-wide association meta-analysis of urinary aMT6s, integrating 11,744 participants from five cohorts: East Asians (Taiwan Biobank), European women (Nurses’ Health Studies), European men (MrOS), and multiethnic participants (MEC). aMT6s was measured from overnight or first-morning urine samples. Association analyses were conducted using both ancestry-aware meta-regression (MR-MEGA) and fixed-effects meta-analysis (METAL). Polygenic risk scores (PRS) were constructed with PRS-CSx and evaluated in phenome-wide analyses in the Mass General Brigham Biobank and UK Biobank. No genome-wide significant loci were identified, and previously reported East Asian signals were not replicated. At suggestive significance, 23 loci emerged, with eight supported by both MR-MEGA and METAL. Several loci showed ancestry-specific heterogeneity, suggesting that genetic associations with urinary aMT6s may vary by population context, although limited power and cohort heterogeneity may also contribute. PRS analyses identified associations with sleep duration and metabolic traits, including type 2 diabetes, but these findings require cautious interpretation. Overall, our results suggest that urinary aMT6s is influenced by a polygenic and potentially population-dependent genetic architecture. This study provides a multi-ancestry framework for investigating melatonin-related biomarkers and highlights the importance of careful interpretation across diverse populations.

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Data availability

GWAS summary statistics generated in this study have been deposited in the NHGRI-EBI GWAS Catalog under GCP ID: GCP001636 (https://www.ebi.ac.uk/gwas/deposition/bodyofwork/GCP001636 ). The corresponding accession numbers are: GCST90833020: METAL meta-analysis (https://www.ebi.ac.uk/gwas/studies/GCST90833020), GCST90833021: MRMEGA meta-analysis (https://www.ebi.ac.uk/gwas/studies/GCST90833021), GCST90833022: TWB GWAS (https://www.ebi.ac.uk/gwas/studies/GCST90833022), GCST90833023: NHS GWAS (https://www.ebi.ac.uk/gwas/studies/GCST90833023), GCST90833024: MrOS GWAS (https://www.ebi.ac.uk/gwas/studies/GCST90833024), GCST90833025: MEC GWAS (https://www.ebi.ac.uk/gwas/studies/GCST90833025), GCST90833026: METAL meta-analysis among European ancestry participants (https://www.ebi.ac.uk/gwas/studies/GCST90833026).

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Acknowledgements

The authors would like to thank Dr Sarah Coseo Markt for her generous support, guidance and data sharing, which was critical for the completion of this study.

Funding

This study was supported by European Union, European Research Council (ERC) Advanced Grant CLOCKrisk (grant number 101053225), Department of Epidemiology, Medical University of Vienna to PI Eva Schernhammer. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. Richmond is supported by Cancer Research UK (grant number C18281/A29019), the Medical Research Council Integrative Epidemiology Unit (grant number: MC_UU_0032/1) and the NIHR Oxford Health Biomedical Research Centre (grant number: NIHR203316). This research was conducted using the UK Biobank resource under application number 48576. We thank all the participants and staff of the UK Biobank for enabling us to conduct this research. Taiwan Biobank : Grants from the Ministry of Science and Technology in Taiwan (MOST 105-2314-B-182-062, MOST 106-2314-B-182-043), the Translational Medical Research Program of Academia Sinica (ASTM-108-01-04), National Taiwan University Hospital, Yunlin Branch Intramural Grant (NTUHYL106.X003, NTUHYL.107S004, NTUHYL 111.X019, NTUHYL110.X017) and Chang Gung University, Taoyuan, Taiwan (NMRPD1F154, NMRPD1G0711, and BMRPD08). NHS and NHS2: This work was supported by National Institute of Health: P01CA87969, P01CA055075, P01DK070756, U01HG004728, UM1CA186107, U01CA176726, UM1CA176726, U01 CA167552, R01CA49449, R01CA50385, R01CA67262, R01CA131332, R01HL034594, R01HL088521, R01HL35464, R01HL116854, R01EY015473, R01EY022305, P30EY014104, R03DC013373 and R03CA165131. MrOS : The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, R01 AG066671, and UL1 TR002369. The National Heart, Lung, and Blood Institute (NHLBI) provides funding for the MrOS Sleep ancillary study “Outcomes of Sleep Disorders in Older Men” under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839. MEC : This research was supported by the Public Health Service (National Cancer Institute) grant RO1 CA 5428 and U01 CA164973.

Author information

Authors and Affiliations

  1. Department of Epidemiology, Center for Public Health, Medical University of Vienna, Kinderspitalgasse 15, Vienna, 1090, Austria

    Magdalena Żebrowska, Matthias Wielscher & Eva S. Schernhammer

  2. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Jaime E. Hart, A. Heather Eliassen & Eva S. Schernhammer

  3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Ziwei Zhang, Jie Hu, Constance Turman, A. Heather Eliassen, Lorelei Ann Mucci & Eva S. Schernhammer

  4. Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA

    Richa Saxena

  5. Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, 02115, USA

    Richa Saxena

  6. Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA

    Magdalena Żebrowska, Jesse Valliere, Matthew Maher, Jie Hu, Jacqueline Lane & Richa Saxena

  7. Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA

    Jie Hu & Richa Saxena

  8. Department of Dermatology, Medical University of Vienna, Vienna, Austria

    Matthias Wielscher

  9. Research Institute, California Pacific Medical Center, San Francisco, CA, USA

    Daniel S. Evans, Katie L. Stone & Gregory J. Tranah

  10. Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA

    Katie L. Stone

  11. Department of Endocrinology and Metabolism, National Taiwan University, Taipei, Taiwan

    Yi-Cheng Chang

  12. Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Jaime E. Hart

  13. Department of Pediatrics, College of Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan, ROC

    Gwo-Tsann Chuang

  14. Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei, 100, Taiwan, ROC

    Gwo-Tsann Chuang & Yi-Cheng Chang

  15. Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan

    Yi-Cheng Chang

  16. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Iona Cheng

  17. San Francisco Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA

    Iona Cheng

  18. Broad Institute, Cambridge, MA, 02142, USA

    Jacqueline Lane

  19. Discovery Science, American Cancer Society, Atlanta, Georgia

    Lorelei Ann Mucci

  20. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    Rebecca Richmond

  21. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

    Rebecca Richmond

  22. NIHR Oxford Health Biomedical Research Centre, University of Oxford, Oxford, UK

    Rebecca Richmond

  23. University of Hawaii Cancer Center, Honolulu, HI, USA

    Loic Le Marchand & Lynne Wilkens

  24. Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

    Christopher Haiman

  25. Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, 02215, USA

    A. Heather Eliassen

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  1. Magdalena Żebrowska
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Contributions

M.Z. and R.S. wrote the main manuscript text; M.Z performed NHS data analyses and meta analysis; Z.Z. analysed MEC data; G-T.C. and Y-C.C. analysed TBB data; D.S.E analysed MrOS data; J.V. performed MGBB pheWAS and UKB analyses; M.W. shared UKB pheWAS pipeline; K.L.S.,G.J.T. : conceptualization, supervision (MrOS); A.H.E.,J.E.H , C.T.: conceptualization, supervision (NHS); L.M.,I.C.,C.H.,L.W.; L.L.M,:conceptualization, supervision (MEC); Y-C.C: conceptualization, supervision (TBB); M.M., J.H.: conceptualization, technical support in data analysis, supervision (general); E.S.S., R.S., J.L., R.R.: conceptualization, supervision (general); All authors read and reviewed the manuscript.

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Correspondence to Magdalena Żebrowska.

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Żebrowska, M., Zhang, Z., Chuang, GT. et al. Multi ancestry genome wide association meta analysis of urinary aMT6s levels. Sci Rep (2026). https://doi.org/10.1038/s41598-026-49491-2

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  • Received: 26 September 2025

  • Accepted: 15 April 2026

  • Published: 22 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-49491-2

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Keywords

  • Melatonin
  • Sulphatoxymelatonin
  • GWAS
  • Meta-analysis
  • Multi-ancestry
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