Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project

Study design and methods for pooling results of genetic epidemiological studies

Sara Raimondi, Sara Gandini, Maria Concetta Fargnoli, Vincenzo Bagnardi, Patrick Maisonneuve, Claudia Specchia, Rajiv Kumar, Eduardo Nagore, Jiali Han, Johan Hansson, Peter A. Kanetsky, Paola Ghiorzo, Nelleke A. Gruis, Terry Dwyer, Leigh Blizzard, Ricardo Fernandez-De-Misa, Wojciech Branicki, Tadeusz Debniak, Niels Morling, Maria Teresa Landi & 16 others Giuseppe Palmieri, Gloria Ribas, Alexander Stratigos, Lynn Cornelius, Tomonori Motokawa, Sumiko Anno, Per Helsing, Terence H. Wong, Philippe Autier, José C. García-Borrón, Julian Little, Julia Newton-Bishop, Francesco Sera, Fan Liu, Manfred Kayser, Tamar Nijsten

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Background: 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. Discussion. 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.

Original languageEnglish (US)
Article number116
JournalBMC Medical Research Methodology
Volume12
DOIs
StatePublished - Aug 7 2012
Externally publishedYes

Fingerprint

Receptor, Melanocortin, Type 1
Skin Neoplasms
Epidemiologic Studies
Logistic Models
Meta-Analysis
Research Personnel
Guidelines
Costs and Cost Analysis
Genes
Neoplasms

Keywords

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

ASJC Scopus subject areas

  • Epidemiology
  • Health Informatics

Cite this

Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project : Study design and methods for pooling results of genetic epidemiological studies. / Raimondi, Sara; Gandini, Sara; Fargnoli, Maria Concetta; Bagnardi, Vincenzo; Maisonneuve, Patrick; Specchia, Claudia; Kumar, Rajiv; Nagore, Eduardo; Han, Jiali; Hansson, Johan; Kanetsky, Peter A.; Ghiorzo, Paola; Gruis, Nelleke A.; Dwyer, Terry; Blizzard, Leigh; Fernandez-De-Misa, Ricardo; Branicki, Wojciech; Debniak, Tadeusz; Morling, Niels; Landi, Maria Teresa; Palmieri, Giuseppe; Ribas, Gloria; Stratigos, Alexander; Cornelius, Lynn; Motokawa, Tomonori; Anno, Sumiko; Helsing, Per; Wong, Terence H.; Autier, Philippe; García-Borrón, José C.; Little, Julian; Newton-Bishop, Julia; Sera, Francesco; Liu, Fan; Kayser, Manfred; Nijsten, Tamar.

In: BMC Medical Research Methodology, Vol. 12, 116, 07.08.2012.

Research output: Contribution to journalArticle

Raimondi, S, Gandini, S, Fargnoli, MC, Bagnardi, V, Maisonneuve, P, Specchia, C, Kumar, R, Nagore, E, Han, J, Hansson, J, Kanetsky, PA, Ghiorzo, P, Gruis, NA, Dwyer, T, Blizzard, L, Fernandez-De-Misa, R, Branicki, W, Debniak, T, Morling, N, Landi, MT, Palmieri, G, Ribas, G, Stratigos, A, Cornelius, L, Motokawa, T, Anno, S, Helsing, P, Wong, TH, Autier, P, García-Borrón, JC, Little, J, Newton-Bishop, J, Sera, F, Liu, F, Kayser, M & Nijsten, T 2012, 'Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project: Study design and methods for pooling results of genetic epidemiological studies', BMC Medical Research Methodology, vol. 12, 116. https://doi.org/10.1186/1471-2288-12-116
Raimondi, Sara ; Gandini, Sara ; Fargnoli, Maria Concetta ; Bagnardi, Vincenzo ; Maisonneuve, Patrick ; Specchia, Claudia ; Kumar, Rajiv ; Nagore, Eduardo ; Han, Jiali ; Hansson, Johan ; Kanetsky, Peter A. ; Ghiorzo, Paola ; Gruis, Nelleke A. ; Dwyer, Terry ; Blizzard, Leigh ; Fernandez-De-Misa, Ricardo ; Branicki, Wojciech ; Debniak, Tadeusz ; Morling, Niels ; Landi, Maria Teresa ; Palmieri, Giuseppe ; Ribas, Gloria ; Stratigos, Alexander ; Cornelius, Lynn ; Motokawa, Tomonori ; Anno, Sumiko ; Helsing, Per ; Wong, Terence H. ; Autier, Philippe ; García-Borrón, José C. ; Little, Julian ; Newton-Bishop, Julia ; Sera, Francesco ; Liu, Fan ; Kayser, Manfred ; Nijsten, Tamar. / Melanocortin-1 receptor, skin cancer and phenotypic characteristics (M-SKIP) project : Study design and methods for pooling results of genetic epidemiological studies. In: BMC Medical Research Methodology. 2012 ; Vol. 12.
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AU - Raimondi, Sara

AU - Gandini, Sara

AU - Fargnoli, Maria Concetta

AU - Bagnardi, Vincenzo

AU - Maisonneuve, Patrick

AU - Specchia, Claudia

AU - Kumar, Rajiv

AU - Nagore, Eduardo

AU - Han, Jiali

AU - Hansson, Johan

AU - Kanetsky, Peter A.

AU - Ghiorzo, Paola

AU - Gruis, Nelleke A.

AU - Dwyer, Terry

AU - Blizzard, Leigh

AU - Fernandez-De-Misa, Ricardo

AU - Branicki, Wojciech

AU - Debniak, Tadeusz

AU - Morling, Niels

AU - Landi, Maria Teresa

AU - Palmieri, Giuseppe

AU - Ribas, Gloria

AU - Stratigos, Alexander

AU - Cornelius, Lynn

AU - Motokawa, Tomonori

AU - Anno, Sumiko

AU - Helsing, Per

AU - Wong, Terence H.

AU - Autier, Philippe

AU - García-Borrón, José C.

AU - Little, Julian

AU - Newton-Bishop, Julia

AU - Sera, Francesco

AU - Liu, Fan

AU - Kayser, Manfred

AU - Nijsten, Tamar

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N2 - Background: 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. Discussion. 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.

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