Carcinogenesis Advance Access originally published online on September 6, 2006
Carcinogenesis 2007 28(2):414-422; doi:10.1093/carcin/bgl159
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Multi-factor dimensionality reduction applied to a large prospective investigation on gene–gene and gene–environment interactions
1 ISI Foundation, Torino Italy
2 Department of Genetics, Biology and Biochemistry University of Turin, Turin, Italy
3 Department of Environmental and Occupational Medicine, Aarhus Universitet Aarhus, Denmark
4 Department of Oncology, Strangeways Research Laboratory University of Cambridge, Cambridge, UK
5 Genetics Research Institute, Milan Italy
6 International Agency for Research on Cancer, Lyon France
7 Tuscany Cancer Institute, Cancer Risk Factor Branch, CSPO-Scientific Institute of Tuscany Florence, Italy
8 Istituto di Ricerche Farmacologiche Mario Negri, Milan Italy
9 Department of Clinical Epidemiology, Aalborg Hospital, Aarhus University Hospital Aalborg, Denmark
10 Institute of Cancer Epidemiology, Danish Cancer Society Copenhagen, Denmark
11 INSERM U521, Institut Gustave Roussy Villejuif, France
12 Division of Clinical Epidemiology, Deutsches Krebsforschungszentrum Heidelberg, Germany
13 German Institute of Human Nutrition, Potsdam-Rehbücke Germany
14 Department of Hygiene and Epidemiology, Medical School University of Athens, Greece
15 Molecular and Nutritional Epidemiology Unit, CSPO-Scientific Institute of Tuscany Florence, Italy
16 Department of Epidemiology, National Cancer Istitute Milan, Italy
17 Cancer Registry, Azienda Ospedaliera ‘Civile MP Arezzo’ Ragusa, Italy
18 Dipartimento di Medicina Clinica e Sperimentale, Università Federico II Naples, Italy
19 Centre for Nutrition and Health, National Institute for Public Health and the Environment Bilthoven, The Netherlands
20 Department of Cancer Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands
21 Institute of Community Medicine, University of Tromso Norway
22 Department of Epidemiology, Catalan Institute of Oncology Consejería de Sanidad y Servicios Sociales, Barcelona, Spain
23 Andalusian School of Public Health, Granada Spain
24 Department of Public Health of Guipuzkoa, San Sebastian Spain
25 Public Health Institute, Navarra Spain
26 Consejería de Sanidad y Consumo, Murcia Spain
27 Public Health and Health Planning Directorate, Asturias Spain
28 Malmö Diet and Cancer Study, Lund University Malmö, Sweden
29 Department of Public Health and Clinical Medicine, University of Umeå Sweden
30 MRC Dunn Human Nutrition Unit, Cambridge UK
31 Cancer Research UK Epidemiology Unit, University of Oxford UK
32 Imperial College London, London UK
33 University of Torino Italy
*To whom correspondence should be addressed. Section of Epidemiology, ISI Foundation (Institute for Scientific Interchange), Viale Settimio Severo, 65, 10133 Torino, Italy. Tel: +390116705601; Fax: +390112365601; Email: matullo{at}isiosf.isi.it
It is becoming increasingly evident that single-locus effects cannot explain complex multifactorial human diseases like cancer. We applied the multi-factor dimensionality reduction (MDR) method to a large cohort study on gene–environment and gene–gene interactions. The study (case-control nested in the EPIC cohort) was established to investigate molecular changes and genetic susceptibility in relation to air pollution and environmental tobacco smoke (ETS) in non-smokers. We have analyzed 757 controls and 409 cases with bladder cancer (n = 124), lung cancer (n = 116) and myeloid leukemia (n = 169). Thirty-six gene variants (DNA repair and metabolic genes) and three environmental exposure variables (measures of air pollution and ETS at home and at work) were analyzed. Interactions were assessed by prediction error percentage and cross-validation consistency (CVC) frequency. For lung cancer, the best model was given by a significant gene–environment association between the base excision repair (BER) XRCC1-Arg399Gln polymorphism, the double-strand break repair (DSBR) BRCA2-Asn372His polymorphism and the exposure variable ‘distance from heavy traffic road’, an indirect and robust indicator of air pollution (mean prediction error of 26%, P < 0.001, mean CVC of 6.60, P = 0.02). For bladder cancer, we found a significant 4-loci association between the BER APE1-Asp148Glu polymorphism, the DSBR RAD52-3'-untranslated region (3'-UTR) polymorphism and the metabolic gene polymorphisms COMT-Val158Met and MTHFR-677C > T (mean prediction error of 22%, P < 0.001, mean CVC consistency of 7.40, P < 0.037). For leukemia, a 3-loci model including RAD52-2259C > T, MnSOD-Ala9Val and CYP1A1-Ile462Val had a minimum prediction error of 31% (P < 0.001) and a maximum CVC of 4.40 (P = 0.086). The MDR method seems promising, because it provides a limited number of statistically stable interactions; however, the biological interpretation remains to be understood.
Abbreviations: MDR, multi-factor dimensionality reduction; CVC, cross-validation consistency; DSBR, double-strand break repair; ETS, environmental tobacco smoke; FPRP, false positive report probability
Received May 9, 2006; revised August 25, 2006; accepted August 25, 2006.
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