Abstract
In single-nucleotide polymorphism (SNP) association studies interactions are often of main interest. Logic regression is a regression methodology that can identify complex Boolean interactions of binary variables. It has been applied successfully to SNP data but only identifies a single best model, while usually there is a number of models that are almost as good. Extensions of logic regression that consider several plausible models are Monte Carlo logic regression (MCLR) and a full Bayesian version of logic regression (FBLR) proposed in this paper. FBLR allows the incorporation of biological knowledge such as known pathways. We compare the performance in identifying SNP interactions associated with the case-control status of the three logic regression based methods and stepwise logistic regression in a simulation study and in a study of breast cancer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Golka, K., Prior, V., Blaszkewicz, M., Bolt, H.M.: The Enhanced Bladder Cancer Susceptibility of NAT2 Slow Acetylators Towards Aromatic Amines: a Review Considering Ethnic Differences. Tox. Lett. 128, 229–241 (2002)
Kooperberg, C., Ruczinski, I., LeBlanc, M.L., Hsu, L.: Sequence Analysis Using Logic Regression. Genet. Epidemiol. 21(Suppl. 1), 626–631 (2001)
Kooperberg, C., Ruczinski, I.: Identifying Interacting SNPs Using Monte Carlo Logic Regression. Genet. Epidemiol. 28, 157–170 (2005)
Ruczinski, I., Kooperberg, C., LeBlanc, M.: Logic Regression. J. Comp. Graph. Stat. 12, 475–511 (2003)
Otten, R.H., Ginneken, L.P.: The Annealing Algorithm. Kluwer Academic Publishers, Boston (1989)
Green, P.J.: Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination. Biometrika 82, 711–732 (1995)
Holmes, C.C., Denison, D.G.T.: Classification with Bayesian MARS. Mach. Learn. 50, 159–173 (2003)
Clark, T.G., de Iorio, M., Griffiths, R.C.: Bayesian Logistic Regression Using a Perfect Phylogeny. Biostatistics 8, 32–52 (2007)
Erichsen, H.C., Chanock, S.J.: SNPs in Cancer Research and Treatment. Brit. J. Cancer 90, 747–751 (2004)
Kooperberg, C., Bose, S., Stone, C.J.: Polychotomous Regression. J. Amer. Stat. Assoc. 92, 117–127 (1997)
R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2006), http://www.R-project.org
Justenhoven, C., Hamann, U., Pesch, B., Harth, V., Rabstein, S., Baisch, C., Vollmert, C., Illig, T., Ko, Y., Brüning, T., Brauch, H.: for the GENICA network: ERCC2 Genotypes and a Corresponding Haplotype Are Linked with Breast Cancer Risk in a German Population. Cancer Epid. Biomark. Prevent. 13, 2059–2064 (2004)
Ickstadt, K., Müller, T., Schwender, H.: Analyzing SNPs: Are There Needles in the Haystack? Chance 19(3), 21–26 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Fritsch, A., Ickstadt, K. (2007). Comparing Logic Regression Based Methods for Identifying SNP Interactions. In: Hochreiter, S., Wagner, R. (eds) Bioinformatics Research and Development. BIRD 2007. Lecture Notes in Computer Science(), vol 4414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71233-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-71233-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71232-9
Online ISBN: 978-3-540-71233-6
eBook Packages: Computer ScienceComputer Science (R0)