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

Manuguerra, M.; Matullo, G.; Veglia, F.; Autrup, H.; Dunning, A. M.; Garte, S.; Gormally, E.; Malaveille, C.; Guarrera, S. and Polidoro, S., et al. (2007) In Carcinogenesis 28(2). p.414-422
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... (More)
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. (Less)
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Carcinogenesis
volume
28
issue
2
pages
414 - 422
publisher
Oxford University Press
external identifiers
  • wos:000244124800021
  • scopus:33847270241
ISSN
0143-3334
DOI
10.1093/carcin/bgl159
language
English
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yes
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4a69a42e-cdce-4462-a2ba-9027d0a7676d (old id 674727)
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2007-12-13 13:06:54
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2017-09-10 03:36:27
@article{4a69a42e-cdce-4462-a2ba-9027d0a7676d,
  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 &lt; 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 &gt; T (mean prediction error of 22%, P &lt; 0.001, mean CVC consistency of 7.40, P &lt; 0.037). For leukemia, a 3-loci model including RAD52-2259C &gt; T, MnSOD-Ala9Val and CYP1A1-Ile462Val had a minimum prediction error of 31% (P &lt; 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.},
  author       = {Manuguerra, M. and Matullo, G. and Veglia, F. and Autrup, H. and Dunning, A. M. and Garte, S. and Gormally, E. and Malaveille, C. and Guarrera, S. and Polidoro, S. and Saletta, F. and Peluso, M. and Airoldi, L. and Overvad, K. and Raaschou-Nielsen, O. and Clavel-Chapelon, F. and Linseisen, J. and Boeing, H. and Trichopoulos, D. and Kalandidi, A. and Palli, D. and Krogh, V. and Tumino, R. and Panico, S. and Bueno-De-Mesquita, H. B. and Peeters, P. H. and Lund, E. and Pera, G. and Martinez, C. and Amiano, P. and Barricarte, A. and Tormo, M. J. and Quiros, J. R. and Berglund, Göran and Janzon, L. and Jarvholm, B. and Day, N. E. and Allen, N. E. and Saracci, R. and Kaaks, R. and Ferrari, P. and Riboli, E. and Vineis, P.},
  issn         = {0143-3334},
  language     = {eng},
  number       = {2},
  pages        = {414--422},
  publisher    = {Oxford University Press},
  series       = {Carcinogenesis},
  title        = {Multi-factor dimensionality reduction applied to a large prospective investigation on gene-gene and gene-environment interactions},
  url          = {http://dx.doi.org/10.1093/carcin/bgl159},
  volume       = {28},
  year         = {2007},
}