Journal
JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY
Volume 79, Issue 1, Pages 36-+Publisher
MOSBY-ELSEVIER
DOI: 10.1016/j.jaad.2018.02.052
Keywords
genetic risk score; melanoma; postmenopausal; single-nucleotide polymorphism; women; Women's Health Initiative
Categories
Funding
- Dermatology Foundation Grant
- Stanford Medical Scholars Research Program
- National Heart, Lung, and Blood Institute, National Institutes of Health
- US Department of Health and Human Services [HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSN271201100004C]
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Background: Single-nucleotide polymorphisms (SNPs) associated with melanoma have been identified though genome-wide association studies. However, the combined impact of these SNPs on melanoma development remains unclear, particularly in postmenopausal women who carry a lower melanoma risk. Objective: We examine the contribution of a combined polygenic risk score on melanoma development in postmenopausal women. Methods: Genetic risk scores were calculated using 21 genome-wide association study-significant SNPs. Their combined effect on melanoma development was evaluated in 19,102 postmenopausal white women in the clinical trial and observational study arms of the Women's Health Initiative dataset. Results: Compared to the tertile of weighted genetic risk score with the lowest genetic risk, the women in the tertile with the highest genetic risk were 1.9 times more likely to develop melanoma (95% confidence interval 1.50-2.42). The incremental change in c-index from adding genetic risk scores to age were 0.075 (95% confidence interval 0.041-0.109) for incident melanoma. Limitations: Limitations include a lack of information on nevi count, Fitzpatrick skin type, family history of melanoma, and potential reporting and selection bias in the Women's Health Initiative cohort. Conclusion: Higher genetic risk is associated with increased melanoma prevalence and incidence in postmenopausal women, but current genetic information may have a limited role in risk prediction when phenotypic information is available.
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