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Open Access Study protocol

Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: study design and methods for pooling results of genetic epidemiological studies

Sara Raimondi12*, Sara Gandini1, Maria Concetta Fargnoli3, Vincenzo Bagnardi14, Patrick Maisonneuve1, Claudia Specchia5, Rajiv Kumar6, Eduardo Nagore7, Jiali Han1089, Johan Hansson11, Peter A Kanetsky12, Paola Ghiorzo13, Nelleke A Gruis14, Terry Dwyer15, Leigh Blizzard16, Ricardo Fernandez-de-Misa17, Wojciech Branicki18, Tadeusz Debniak19, Niels Morling20, Maria Teresa Landi21, Giuseppe Palmieri22, Gloria Ribas23, Alexander Stratigos24, Lynn Cornelius25, Tomonori Motokawa26, Sumiko Anno27, Per Helsing28, Terence H Wong29, Philippe Autier30, José C García-Borrón31, Julian Little32, Julia Newton-Bishop33, Francesco Sera34, Fan Liu35, Manfred Kayser35, Tamar Nijsten36, GEM Study Group and on behalf of the M-SKIP Study Group

Author Affiliations

1 Division of Epidemiology and Biostatistics, European Institute of Oncology, Via Ramusio 1, Milan, 20141, Italy

2 Department of Occupational Health, University of Milan, Milan, Italy

3 Department of Dermatology, University of L’Aquila, L’Aquila, Italy

4 Department of Statistics, University of Milan Bicocca, Milan, Italy

5 Department of Biomedical Sciences and Biotechnologies, University of Brescia, Brescia, Italy

6 Division of Molecular Genetic Epidemiology, German Cancer Research Center, Heidelberg, Germany

7 Department of Dermatology, Instituto Valenciano de Oncologia, Valencia, Spain

8 Department of Dermatology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

9 Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

10 Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA

11 Department of Oncology and Pathology, Cancer Center, Karolinska Institutet, Stockholm, Sweden

12 Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA

13 Department of Internal Medicine and Medical Specialties, University of Genoa, Genoa, Italy

14 Department of Dermatology, Leiden University Medical Center, Leiden, The Netherlands

15 Murdoch Childrens Research Institute, Royal Children’s Hospital, Victoria, Australia

16 Menzies Research Institute Tasmania, University of Tasmania, Hobart, Australia

17 Servicio de Dermatologia, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain

18 Institute of Forensic Research, Krakow, Polandfv

19 Department of Genetic and Pathology, Pomeranian Medical University, Polabska, Poland

20 Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark

21 Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA

22 Unit of Cancer Genetics, Istituto di Chimica Biomolecolare, CNR, Sassari, Italy

23 Dptd. Oncologia medica y hematologia, Fundacion Investigation Hospital Clinico Universitario de Valencia- INCLIVA, Valencia, Spain

24 Department of Dermatology, University of Athens, Andreas Sygros Hospital, Athens, Greece

25 Division of Dermatology, Washington University, St. Louis, MO, USA

26 Dermatological R&D Skin Research Department, POLA Chemical Industries, Yokohama, Japan

27 Shibaura Institute of Technology, Tokyo, Japan

28 Department of Dermatology, Oslo University Hospital, Oslo, Norway

29 Department of Dermatology, University of Edinburgh, Edinburgh, UK

30 International Prevention Research Institute, Lyon, France

31 Department of Biochemistry and Molecular Biology, University of Murcia, Murcia, Spain

32 Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Canada

33 Section of Epidemiology and Biostatistics, Leeds Institute of Molecular Medicine, University of Leeds, Leeds, UK

34 UCL Institute of Child Health, London, UK

35 Department of Forensic Molecular Biology, Erasmus MC University Medical Center, Rotterdam, The Netherlands

36 Department of Dermatology, Erasmus MC University Medical Center, Rotterdam, The Netherlands

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BMC Medical Research Methodology 2012, 12:116  doi:10.1186/1471-2288-12-116

Published: 3 August 2012



For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia.

Design and methods

Based on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling.


Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields.

Genetic epidemiology; Melanoma; Meta-analysis; Pooled-analysis; Skin cancer; Study design