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  • Andrew, A. S., Karagas, M. R., Nelson, H. H., Guarrera, S., Polidoro, S., Gamberini, S., Sacerdote, C., Moore, J. H., Kelsey, K. T., Demidenko, E., Vineis, P. & Matullo, G. (2008) DNA repair polymorphisms modify bladder cancer risk: A multi-factor analytic strategy. Hum Hered 65, 105118.
  • Baksh, M. F., Balding, D. J., Vyse, T. J. & Whittaker, J. C. (2006) A likelihood ratio approach to family-based association studies with covariates. Ann Hum Genet 70, 131139.
  • Baksh, M. F., Balding, D. J., Vyse, T. J. & Whittaker, J. C. (2007) Family-based association analysis with ordered categorical phenotypes, covariates and interactions. Genet Epidemiol 31, 18.
  • Breiman, L. (1996) Bagging predictors. Mach Learn 26, 123140.
  • Breiman, L. (2001) Random forests. Mach Learn 45, 532.
  • Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. (1984) Classification and regression trees. Belmont , CA : Wadsworth.
  • Buehlmann, P. & Yu, B. (2002) Analyzing bagging. Ann Statist 30, 927961.
  • Bureau, A., Dupuis, J., Falls, K., Lunetta, K. L., Hayward, B., Keith, T. P. & Eerdewegh, P. V. (2005) Identifying SNPs predictive of phenotype using Random Forests. Genet Epidemiol 28, 171182.
  • Chen, X., Liu, C. T., Zhang, M. & Zhang, H. (2007) A forest-based approach to identifying gene and gene–gene interactions. Proc Natl Acad Sci USA 104, 1919919203.
  • Clark, T. G., De Iorio, M. & Griffiths, R. C. (2007) Bayesian logistic regression using a perfect phylogeny. Biostatistics 8, 3252.
  • Clark, T. G., De Iorio, M. & Griffiths, R. C. (2008) An evolutionary algorithm to find associations in dense genetic maps. IEEE Trans Evol Comp 12, 297306.
  • Clark, T. G., De Iorio, M., Griffiths, R. C. & Farrall, M. (2005) Finding associations in dense genetic maps: A genetic algorithm approach. Hum Hered 60, 97108.
  • Cordell, H. J., Barratt, B. J. & Clayton, D. G. (2004) Case/pseudocontrol analysis in genetic association studies: A unified framework for detection of genotype and haplotype associations, gene–gene and gene-environment interactions, and parent-of-origin effects. Genet Epidemiol 26, 167185.
  • Cordell, H. J. & Clayton, D. G. (2002) A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: Application to HLA in type 1 diabetes. Am J Hum Genet 70, 124141.
  • Culverhouse, R., Klein, T. & Shannon, W. (2004) Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 27, 141152.
  • Culverhouse, R., Suarez, B. K., Lin, J. & Reich, T. (2002) A perspective on epistasis: Limits of models displaying no main effect. Am J Hum Genet 70, 461471.
  • Edwards, T. L., Turner, S. D., Torstenson, E. S., Dudek, S. M., Martin, E. R. & Ritchie, M. D. (2010) A general framework for formal tests of interaction after exhaustive search methods with applications to MDR and MDR-PDT. PLoS One 5, e9363.
  • Etzioni, R., Falcon, S., Gann, P. H., Kooperberg, C. L., Penson, D. F. & Stampfer, M. J. (2004) Prostate-specific antigen and free prostate-specific antigen in the early detection of prostate cancer: Do combination tests improve detection Cancer Epidemiol Biomarkers Prev 13, 16401645.
  • Feng, Q., Balasubramanian, A., Hawes, S. E., Toure, P., Sow, P. S., Dem, A., Dembele, B., Critchlow, C. W., Xi, L., Lu, H., McIntosh, M. W., Young, A. M. & Kiviat, N. B. (2005) Detection of hypermethylated genes in women with and without cervical neoplasia. J Natl Cancer Inst 97, 273282.
  • Garte, S. (2001) Metabolic susceptibility genes as cancer risk factors: Time for a reassessment Cancer Epidemiol Biomarkers Prev 10, 12331237.
  • Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B. M. D., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A. J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J. Y. H. & Zhang, J. (2004) Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol 5, R80.
  • Geschwind, D. H., Sowinski, J., Lord, C., Iversen, P., Shestack, J., Jones, P., Ducat, L., Spence, S. J. & AGRE Steering Committee (2001) The autism genetic resource exchange: A resource for the study of autism and related neuropsychiatric conditions. Am J Hum Genet 69, 463466.
  • Goodman, J. E., Mechanic, L. E., Luke, B. T., Ambs, S., Chanock, S. & Harris, C. C. (2006) Exploring SNP-SNP interactions and colon cancer risk using polymorphism interaction analysis. Int J Cancer 118, 17901797.
  • Greene, C. S., Himmelstein, D. S., Nelson, H. H., Kelsey, K. T., Williams, S. M., Andrew, A. S., Karagas, M. R. & Moore, J. H. (2010) Enabling personal genomics with an explicit test of epistasis. Pac Symp Biocomput, 327336.
  • Hahn, L. W., Ritchie, M. D. & Moore, J. H. (2003) Multifactor dimensionality reduction software for detecting gene–gene and gene–environment interactions. Bioinformatics 19, 376382.
  • Harth, V., Schaefer, M., Abel, J., Maintz, L., Neuhaus, T., Besuden, M., Primke, R., Wilkesmann, A., Thier, R., Vetter, H., Ko, Y. D., Bruening, T., Bolt, H. M. & Ickstadt, K. (2008) Head and neck squamous-cell cancer and its association with polymorphic enzymes of xenobiotic metabolism and repair. J Toxicol Environ Health A 71, 887897.
  • Heidema, G. A., Boer, J. M. A., Nagelkerke, N., Mariman, E. C. M., van de A, D. L. & Feskens, E. J.M. (2006) The challenge for genetic epidemiologists: How to analyze large numbers of SNPs in relation to complex diseases. BMC Genet 7, 23.
  • Justenhoven, C., Hamann, U., Schubert, F., Zapatka, M., Pierl, C. B., Rabstein, S., Selinski, S., Mueller, T., Ickstadt, K., Gilbert, M., Ko, Y. D., Baisch, C., Pesch, B., Harth, V., Bolt, H. M., Vollmert, C., Illig, T., Eils, R., Dippon, J. & Brauch, H. (2008) Breast cancer: A candidate gene approach across the estrogen metabolic pathway. Breast Cancer Res Treat 108, 137149.
  • Keles, S., van der Laan, M. J. & Vulpe, C. (2004) Regulatory motif finding by logic regression. Bioinformatics 20, 27992811.
  • Kooperberg, C. & Ruczinski, I. (2005) Identifying interacting SNPs using Monte Carlo logic regression. Genet Epidemiol 28, 157170.
  • Kooperberg, C., Ruczinski, I., LeBlanc, M. & Hsu, L. (2001) Sequence analysis using logic regression. Genet Epidemiol 21, 626631.
  • Kotti, S., Bickeboeller, H. & Clerget-Darpoux, F. (2007) Strategy for detecting susceptibility genes with weak or no marginal effect. Hum Hered 63, 8592.
  • Li, Q., Fallin, M. D., Louis, T. A., Lasseter, V. K., McGrath, J. A., Avramopoulos, D., Wolyniec, P. S., Valle, D., Liang, K. Y., Pulver, A. E. & Ruczinski, I. (2010a) Detection of SNP–SNP interactions in trios of parents with schizophrenic children. Genet Epidemiol 34, 396406.
  • Li, Q., Louis, T. A., Fallin, M. D. & Ruczinski, I. (2010b) Detection of SNP–SNP interactions in case-parent trios (in revision).
  • Lucek, P. R. & Ott, J. (1997) Neural network analysis of complex traits. Genet Epidemiol 14, 11011106.
  • Lunetta, K. L., Faraone, S. V., Biederman, J. & Laird, N. M. (2000) Family-based tests of association and linkage that use unaffected sibs, covariates, and interactions. Am J Hum Genet 66, 605614.
  • Lunetta, K. L., Hayward, L. B., Segal, J. & van Eerdewegh, P. (2004) Screening large-scale association study data: Exploiting interactions using random forests. BMC Genet 10, 32.
  • Marchini, J., Donnely, P. & Cardon, R. C. (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37, 413416.
  • Martin, E. R., Ritchie, M. D., Hahn, L., Kang, S. & Moore, J. H. (2006) A novel method to identify gene–gene effects in nuclear families: The MDR-PDT. Genet Epidemiol 30, 111123.
  • McKinney, B. A., Reif, D. M., Ritchie, M. D. & H., M. J. (2006) Machine learning for detecting gene–gene interactions: A review. Appl Bioinform 5, 7788.
  • Musani, S. K., Shriner, D., Liu, N., Feng, R., Coffey, C. S., Yi, N., Tiwari, H. K. & Allison, D. B. (2007) Detection of gene × gene interactions in genome-wide association studies of human population data. Hum Hered 63, 6784.
  • Nicodemus, K. K. & Malley, J. D. (2009) Predictor correlation impacts machine learning algorithms: Implications for genomic studies. Bioinformatics 25, 18841890.
  • Nicodemus, K. K., Callicott, J. H., Higier, R. G., Luna, A., Nixon, D. C., Lipska, B. K., Vakkalanka, R., Giegling, I., Rujescu, D., Clair, D. S., Muglia, P., Shugart, Y. Y. & Weinberger, D. R. (2010) Evidence of statistical epistasis between disc1, cit and ndel1 impacting risk for schizophrenia: Biological validation with functional neuroimaging. Hum Genet 127, 441452.
  • North, B. V., Curtis, D., Cassell, P. G., Hitman, G. A. & Sham, P. C. (2003) Assessing optimal neural network architecture for identifying disease-associated multi-marker genotypes using a permutation test, and application to calpain 10 polymorphisms associated with diabetes. Ann Hum Genet 67, 348356.
  • Nunkesser, R., Bernholt, T., Schwender, H., Ickstadt, K. & Wegener, I. (2007) Detecting high-order interactions of single nucleotide polymorphisms using genetic programming. Bioinformatics 23, 32803288.
  • Ritchie, M. D., Hahn, L. W. & Moore, J. H. (2003) Power of multifactor dimensionality reduction for detecting gene–gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 24, 150157.
  • Ritchie, M. D., Hahn, L. W., Roodi, N., Bailey, L. R., Dupont, W. D., Parl, F. F. & Moore, J. H. (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69, 138147.
  • Ritchie, M. D., White, B. C., Parker, J. S., Hahn, L. W. & Moore, J. H. (2003b) Optimization of neural network architecture using genetic programming improves detection and modeling of gene–gene interactions in studies of human diseases. BMC Bioinformatics 4, 28.
  • Ruczinski, I., Kooperberg, C. & LeBlanc, M. (2003) Logic regression. J Comput Graph Stat 12, 475511.
  • Ruczinski, I., Kooperberg, C. & LeBlanc, M. (2004) Exploring interactions in high-dimensional genomic data: An overview of logic regression, with applications. J Mult Anal 90, 178195.
  • Schaid, D. J. (1996) General score tests for associations of genetic markers with disease using cases and their parents. Genet Epidemiol 13, 423449.
  • Schaid, D. J. (1999) Likelihoods and TDT for the case-parents design. Genet Epidemiol 16, 250260.
  • Schmid, M. & Hothorn, T. (2008) Flexible boosting of accelerated failure time models. BMC Bioinform 9, 269.
  • Schwender, H. & Ickstadt, K. (2008) Identification of SNP interactions using logic regression. Biostatistics 9, 187198.
  • Schwender, H., Ruczinski, I. & Ickstadt, K. (2010) Testing SNPs and sets of SNPs for importance in association studies. Biostatistics, doi:10.1093/biostatistics/kxq042.
  • Segal, M. R., Barbour, J. D. & Grant, R. M. (2004) Relating HIV-1 sequence variation to replication capacity via trees and forests. Stat Appl Genet Mol Biol 3, 2.
  • Suehiro, Y., Wong, C. W., Chirieac, L. R., Kondo, Y., Shen, L., Webb, C. R., Chan, Y. W., Chan, A. S.Y., Chan, T. L., Wu, T. T., Rashid, A., Hamanaka, Y., Hinoda, Y., Shannon, R. L., Wang, X., Morris, J., Issa, J. P. J., Yuen, S. T., Leung, S. Y. & Hamilton, S. R. (2008) Epigenetic-genetic interactions in the apc/wnt, ras/raf, and p53 pathways in colorectal carcinoma. Clin Cancer Res 14, 25602569.
  • Tomita, Y., Tomida, S., Hasegawa, Y., Suzuki, Y., Shirakawa, T., Kobayashi, T. & Honda, H. (2004) Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma. BMC Bioinformatics 5, 120.
  • Vaidya, V. S., Waikar, S. S., Ferguson, M. A., Collings, F. B., Sunderland, K., Gioules, C., Bradwin, G., Matsouaka, R., Betensky, R., Curhan, G. C. & Bonventre, J. V. (2008) Urinary biomarkers for sensitive and specific detection of acute kidney injury in humans. Clin Transl Sci 3, 200208.
  • Witte, J. S. & Fijal, B. A. (2001) Introduction: Analysis of sequence data and population structure. Genet Epidemiol 21, 600601.