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Hum Mol Genet. 2018 Dec 1;27(23):4145-4156. doi: 10.1093/hmg/ddy282.

Combining common genetic variants and non-genetic risk factors to predict risk of cutaneous melanoma.

Author information

1
Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
2
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
3
Department of Mathematics and Statistics, Laval University, Quebec, Canada.
4
Department of Dermatology, University of L'Aquila, L'Aquila, Italy.
5
Department of Dermatology, Maurizio Bufalini Hospital, Cesena, Italy.
6
Department of Internal Medicine and Medical Specialties, University of Genoa and Genetics of Rare Cancers, Ospedale Policlinico San Martino, Genoa, Italy.
7
Institute of Dermatology, Catholic University, Rome, Italy.
8
Dermatology Department, Melanoma Unit, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, Barcelona, Spain and Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Valencia, Spain.
9
Department of Immunology and Molecular Oncology, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy.
10
Cancer Genomics Program, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy.
11
Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
12
Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece.
13
Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
14
Section of Epidemiology and Biostatistics, Leeds Institute of Cancer and Pathology, University of Leeds, UK.
15
Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
16
Sydney School of Public Health, and Melanoma Institute Australia, The University of Sydney, Sydney, Australia.
17
1st Department of Dermatology-Venereology, National and Kapodistrian University of Athens School of Medicine, Andreas Sygros Hospital, Athens, Greece.
18
Department of Dermatology, Instituto Valenciano de Oncología, València, Spain.

Abstract

Melanoma heritability is among the highest for cancer and single nucleotide polymorphisms (SNPs) contribute to it. To date, only SNPs that reached statistical significance in genome-wide association studies or few candidate SNPs have been included in melanoma risk prediction models. We compared four approaches for building polygenic risk scores (PRS) using 12 874 melanoma cases and 23 203 controls from Melanoma Meta-Analysis Consortium as a training set, and newly genotyped 3102 cases and 2301 controls from the MelaNostrum consortium for validation. We estimated adjusted odds ratios (ORs) for melanoma risk using traditional melanoma risk factors and the PRS with the largest area under the receiver operator characteristics curve (AUC). We estimated absolute risks combining the PRS and other risk factors, with age- and sex-specific melanoma incidence and competing mortality rates from Italy as an example. The best PRS, including 204 SNPs (AUC = 64.4%; 95% confidence interval (CI) = 63-65.8%), developed using winner's curse estimate corrections, had a per-quintile OR = 1.35 (95% CI = 1.30-1.41), corresponding to a 3.33-fold increase comparing the 5th to the 1st PRS quintile. The AUC improvement by adding the PRS was up to 7%, depending on adjusted factors and country. The 20-year absolute risk estimates based on the PRS, nevus count and pigmentation characteristics for a 60-year-old Italian man ranged from 0.5 to 11.8% (relative risk  = 26.34), indicating good separation.

PMID:
30060076
PMCID:
PMC6240742
[Available on 2019-12-01]
DOI:
10.1093/hmg/ddy282
[Indexed for MEDLINE]

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