Multi-factor dimensionality reduction applied to a large prospective investigation on gene-gene and gene-environment interactions.

2.50
Hdl Handle:
http://hdl.handle.net/10146/25452
Title:
Multi-factor dimensionality reduction applied to a large prospective investigation on gene-gene and gene-environment interactions.
Authors:
Manuguerra, M.; Matullo, G.; Veglia, F.; Autrup, H.; Dunning, A.M.; Garte, S.; Gormally, E.; Malaveille, C.; Guarrera, S.; Polidoro, S.; Saletta, F.; Peluso, M.; Airoldi, L.; Overvad, K.; Raaschou-Nielsen, O.; Clavel-Chapelon, F.; Linseisen, J.; Boeing, H.; Trichopoulos, D.; Kalandidi, A.; Palli, D.; Krogh, V.; Tumino, R.; Panico, S.; Bueno-De-Mesquita, H.B.; Peeters, P.H.; Lund, E.; Pera, G.; Martinez, C.; Amiano, P.; Barricarte, A.; Tormo, M.J.; Quiros, J.R.; Berglund, G.; Janzon, L.; Jarvholm, B.; Day, N.E.; Allen, N.E.; Saracci, R.; Kaaks, R.; Ferrari, P.; Riboli, E.; Vineis, P.
Abstract:
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.
Citation:
Carcinogenesis 2007, 28 (2):414-422
Journal:
Carcinogenesis
Issue Date:
Feb-2007
URI:
http://hdl.handle.net/10146/25452
DOI:
10.1093/carcin/bgl159
PubMed ID:
16956909
Additional Links:
http://carcin.oxfordjournals.org/cgi/content/full/28/2/414
Type:
Article
Language:
en
ISSN:
0143-3334
Sponsors:
The authors wish to thank to Angeline Andrew and Margaret Karagas for their useful comments. This paper was made possible by European Community grants to PV (5th Framework Programme, GENAIR investigation, grant QLK4-CT-1999-00927; 6th Framework Programme, ECNIS investigation, grant 513943) and a grant of the Compagnia di San Paolo to the ISI Foundation. All authors are independent from funders. Also, the work described in the paper was carried out with the financial support of: Europe Against Cancer Program of the European Commission (SANCO); Deutsche Krebshilfe; Deutsches Krebsforschungszentrum; German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health; Spanish Regional Governments of Andalucia, Asturia, Basque Country, Murcia and Navarra; ISCIII, Red de Centros RCESP, C03/09, Spain; Cancer Research UK; Medical Research Council, United Kingdom; Stroke Association, United Kingdom; British Heart Foundation; Department of Health, United Kingdom; Food Standards Agency, United Kingdom; Wellcome Trust, United Kingdom; Greek Ministry of Health; Greek Ministry of Education; Italian Association for Research on Cancer (AIRC); Italian National Research Council; Dutch Ministry of Public Health, Welfare and Sports; World Cancer Research Fund; Swedish Cancer Society; Swedish Scientific Council; Regional Government of Skåne, Sweden; Norwegian Cancer Society; Research Council of Norway.
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Full metadata record

DC FieldValue Language
dc.contributor.authorManuguerra, M.-
dc.contributor.authorMatullo, G.-
dc.contributor.authorVeglia, F.-
dc.contributor.authorAutrup, H.-
dc.contributor.authorDunning, A.M.-
dc.contributor.authorGarte, S.-
dc.contributor.authorGormally, E.-
dc.contributor.authorMalaveille, C.-
dc.contributor.authorGuarrera, S.-
dc.contributor.authorPolidoro, S.-
dc.contributor.authorSaletta, F.-
dc.contributor.authorPeluso, M.-
dc.contributor.authorAiroldi, L.-
dc.contributor.authorOvervad, K.-
dc.contributor.authorRaaschou-Nielsen, O.-
dc.contributor.authorClavel-Chapelon, F.-
dc.contributor.authorLinseisen, J.-
dc.contributor.authorBoeing, H.-
dc.contributor.authorTrichopoulos, D.-
dc.contributor.authorKalandidi, A.-
dc.contributor.authorPalli, D.-
dc.contributor.authorKrogh, V.-
dc.contributor.authorTumino, R.-
dc.contributor.authorPanico, S.-
dc.contributor.authorBueno-De-Mesquita, H.B.-
dc.contributor.authorPeeters, P.H.-
dc.contributor.authorLund, E.-
dc.contributor.authorPera, G.-
dc.contributor.authorMartinez, C.-
dc.contributor.authorAmiano, P.-
dc.contributor.authorBarricarte, A.-
dc.contributor.authorTormo, M.J.-
dc.contributor.authorQuiros, J.R.-
dc.contributor.authorBerglund, G.-
dc.contributor.authorJanzon, L.-
dc.contributor.authorJarvholm, B.-
dc.contributor.authorDay, N.E.-
dc.contributor.authorAllen, N.E.-
dc.contributor.authorSaracci, R.-
dc.contributor.authorKaaks, R.-
dc.contributor.authorFerrari, P.-
dc.contributor.authorRiboli, E.-
dc.contributor.authorVineis, P.-
dc.date.accessioned2008-05-12T08:37:40Z-
dc.date.available2008-05-12T08:37:40Z-
dc.date.issued2007-02-
dc.identifier.citationCarcinogenesis 2007, 28 (2):414-422en
dc.identifier.issn0143-3334-
dc.identifier.pmid16956909-
dc.identifier.doi10.1093/carcin/bgl159-
dc.identifier.urihttp://hdl.handle.net/10146/25452-
dc.description.abstractIt 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.en
dc.description.sponsorshipThe authors wish to thank to Angeline Andrew and Margaret Karagas for their useful comments. This paper was made possible by European Community grants to PV (5th Framework Programme, GENAIR investigation, grant QLK4-CT-1999-00927; 6th Framework Programme, ECNIS investigation, grant 513943) and a grant of the Compagnia di San Paolo to the ISI Foundation. All authors are independent from funders. Also, the work described in the paper was carried out with the financial support of: Europe Against Cancer Program of the European Commission (SANCO); Deutsche Krebshilfe; Deutsches Krebsforschungszentrum; German Federal Ministry of Education and Research; Danish Cancer Society; Health Research Fund (FIS) of the Spanish Ministry of Health; Spanish Regional Governments of Andalucia, Asturia, Basque Country, Murcia and Navarra; ISCIII, Red de Centros RCESP, C03/09, Spain; Cancer Research UK; Medical Research Council, United Kingdom; Stroke Association, United Kingdom; British Heart Foundation; Department of Health, United Kingdom; Food Standards Agency, United Kingdom; Wellcome Trust, United Kingdom; Greek Ministry of Health; Greek Ministry of Education; Italian Association for Research on Cancer (AIRC); Italian National Research Council; Dutch Ministry of Public Health, Welfare and Sports; World Cancer Research Fund; Swedish Cancer Society; Swedish Scientific Council; Regional Government of Skåne, Sweden; Norwegian Cancer Society; Research Council of Norway.en
dc.language.isoenen
dc.relation.urlhttp://carcin.oxfordjournals.org/cgi/content/full/28/2/414en
dc.subject.meshDrug Resistance, Multiple-
dc.subject.meshHumans-
dc.subject.meshNeoplasms-
dc.subject.meshPolymorphism, Single Nucleotide-
dc.subject.meshProbability-
dc.subject.meshProspective Studies-
dc.titleMulti-factor dimensionality reduction applied to a large prospective investigation on gene-gene and gene-environment interactions.en
dc.typeArticleen
dc.identifier.journalCarcinogenesisen

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