Oral Presentation Australasian Melanoma Conference 2026

Additional risk loci and a comprehensive polygenic risk score for melanoma from the largest GWAS to date: Towards melanoma risk‑based screening (#82)

G J M Shanika R Jayasinghe 1 2 , Rohit Thakur 3 , Lee E Whelees 4 , Huanwei Wang 1 5 6 , Anne E Cust 7 , Sarah V Ward 8 , Rajiv Kumar 9 , Florence Demenais 10 , Christopher I Amos 11 , Georgina V Long 12 13 14 15 , Serigne Lo 12 , Alisa M Goldstein 16 , Nicholas K Hayward 17 , Diane T Smelser 18 , Jiali Han 19 , Andrew Bakshi 20 , David L Duffy 21 , Miguel Renteria 22 , Lars G Fritsche 23 24 25 , Paola Ghiorzo 26 , Eduardo Nagore 27 28 , Alexander Stratigos 29 , Susana Puig 30 , Chiara Menin 31 , Maria Concetta Fargnoli 32 , Cristina Pellegrini 32 , David Millan-Esteban 27 28 , Mai Xu 16 , Phuc H Hoang 16 , Kiarash Khosrotehrani 2 , Diptavo Dutta 16 , David C Whiteman 33 , Stuart MacGregor 5 34 , Mark M Iles 35 , Mathias Seviiri 5 6 34 , Jianxin Shi 16 , Rebecca I Hartman 36 , Kevin M Brown 16 , Maria Teresa Landi 16 , Matthew H Law 1 6 34
  1. Genetics and Skin Cancer Group, Population Health Programme, QIMR Berghofer, Brisbane, QLD, Australia
  2. Frazer Institute, University of Queensland, Brisbane, Australia
  3. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, National Institutes of Health, Bethesda, Maryland, USA
  4. Tennessee Valley Healthcare System VA Medical Centre , Nashville, TN, USA
  5. Statistical Genetics Group, Population Health Program, QIMR Berghofer, Brisbane, QLD, Australia
  6. School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
  7. The Daffodil Centre, The University of Sydney, and Cancer Council NSW, Sydney, NSW, Australia
  8. Medical School, The University of Western Australia, Perth, WA, Australia
  9. Division of Molecular Genetic Epidemiology, German Cancer Research Center, Heidelberg, Germany
  10. HealthFex Unit, Université Paris Cité, Inserm, Paris, France
  11. Department of Medicine, Baylor College of Medicine, Houston, TX, USA
  12. Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia
  13. Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
  14. Department of Medical Oncology, Mater Hospital, North Sydney, NSW, Australia
  15. Department of Medical Oncology, Royal North Shore Hospital, St Leonards, NSW, Australia
  16. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, National Institutes of Health, Bethesda, Maryland, USA
  17. Oncogenomics Group, Cancer Research Program, QIMR Berghofer, Brisbane, QLD, Australia
  18. Department of Genomic Health, Geisinger Clinic, Geisinger Health System, Danville, Pennsylvania, USA
  19. Department of Epidemiology, Richard M. Fairbanks School of Public Health, Melvin and Bren Simon Cancer Center, Indiana University, Indianapolis, IN, USA
  20. School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
  21. Genetic Epidimiology Group, Brain and Mental Health Programme, QIMR Berghofer, Brisbane, QLD, Australia
  22. Computational Neurogenomics Lab, Brain and Mental Health Programme, QIMR Berghofer, Brisbane, QLD, Australia
  23. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan , USA
  24. Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
  25. Center for Precision Health Data Science, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
  26. University of Genoa; IRCCS Ospedale Policlinico San Martino, Genoa, Italy
  27. Department of Dermatology, Fundación Instituto Valenciano de Oncología , Valencia, Spain
  28. School of Medicine, Universidad Católica de Valencia San Vicente Mártir, Valencia, Spain
  29. Department of Dermatology, University of Athens School of Medicine, Andreas Sygros Hospital, Athens, Greece
  30. Department of Dermatology, Hospital Clínic de Barcelona, Universitat de Barcelona, Barcelona, Spain
  31. Immunology and Molecular Oncology Unit, Veneto Institute of Oncology - IOV IRCCS, Padua, Italy
  32. Dermatology, Department of Biotechnological and Applied Clinical Sciences, University of L′Aquila, L′Aquila, Italy
  33. Cancer Control Group, Population Health Programme, QIMR Berghofer, Brisbane, QLD, Australia
  34. School of Biomedical Sciences, Faculty of Health, Medicine and Behavioural Sciences, University of Queensland, Brisbane, QLD, Australia
  35. Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
  36. Dermatology Section, VA Boston Healthcare System, Boston, Massachusetts, USA

Background: Cutaneous melanoma (CM) is the deadliest form of skin cancer. Genetic factors explain a substantial proportion of CM risk (twin h2 = 58%), underscoring the potential importance of polygenic risk scores (PRS) in capturing inherited susceptibility and improving identification of individuals at high risk. However, much of the genetic architecture underlying melanoma remains unexplained. Larger genome-wide association studies (GWASs) enable the discovery of new risk loci and improve PRS estimation, strengthening their potential use in precision prevention and early detection strategies to manage melanoma better.

Methods: We performed an expanded European-ancestry meta-analysis by combining 35 GWASs from 29 cohorts across Australia, the United States, Canada and Europe. In total, the analysis included 69,871 cases and 1,616,233 controls, more than doubling the previous sample sizes. We further conducted a multi-trait GWAS analysis (MTAG) incorporating correlated melanoma risk factors, including nevus count, pigmentation-related traits, telomere length and vitiligo, to expand the discovery of risk loci. We constructed a new PRS from this larger GWAS meta-analysis using SBayesRC and validated it in independent cohorts from the US (AllofUS, PLCO) and Australia (QSkin).

Results: The meta-analysis identified 116 independent CM risk loci, including 51 of the 54 previously known loci. MTAG adds 25 additional candidate loci, with concordant effects in replication (r = 0.70, 95% CI 0.43-0.86, P = 8.7×10-5). Among novel signals, we observe hits within or near several cancer-related genes NOTCH2, MDM4, TP63, PIK3CA, and CDKN1B, as well as the immune-related HLA-A region. The new PRS improves discrimination over the baseline model that includes age, sex, and PCs (principal components) of genetic ancestry (AUC increase of 0.06-0.09) across independent validation datasets. Overall predictive performance reaches an AUC of 0.70, indicating robustness across populations with different ultraviolet exposure profiles.

Conclusion: This expanded genetic analysis substantially improves both locus discovery and performance of melanoma PRS. The results support the potential use of PRS for melanoma risk stratification and targeted screening, particularly in early detection settings.