Logic Regression and Its Extensions

Advances in Genetics

Volumn 72, Issue C, 2010, Pages 25-45

  Schwender, Holger ^a   Ruczinski, Ingo ^a  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOLOGICAL MODEL; BOOK; GENERAL ASPECTS OF DISEASE; GENETIC ASSOCIATION; GENETICS; HUMAN; MONTE CARLO METHOD; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL;

ALGORITHMS; DISEASE; GENETIC ASSOCIATION STUDIES; HUMANS; LOGISTIC MODELS; MODELS, GENETIC; MONTE CARLO METHOD; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 77958450848     PISSN: 00652660     EISSN: None     Source Type: Book Series    
DOI: 10.1016/B978-0-12-380862-2.00002-3     Document Type: Book

References (49)

1. An P., Feitosa M., Ketkar S., Adelman A., Lin S., Borecki I., Province M. Epistatic interactions of CDKN2B-TCF7L2 for risk of type 2 diabetes and of CDKN2B-JAZF1 for triglyceride/high-density lipoprotein ratio longitudinal change: Evidence from the Framingham Heart Study. BMC Proc. 2009, 3(Suppl. 7):S71.
2. 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., et al. DNA repair polymorphisms modify bladder cancer risk: A multi-factor analytic strategy. Hum. Hered. 2008, 65:105-118.
3. Breiman L. Bagging predictors. Mach. Learn. 1996, 26:123-140.
4. Breiman L. Random forests. Mach. Learn. 2001, 45:5-32.
5. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, Wadsworth, Belmont.
6. Carty C.L., Heagerty P., Heckbert S.R., Jarvik G.P., Lange L.A., Cushman M., Tracy R.P., Reiner A.P. Interaction between fibrinogen and IL-6 genetic variants and associations with cardiovascular disease risk in the cardiovascular health study. Ann. Hum. Genet. 2010, 74:1-10.
7. Clark T.G., De Iorio M., Griffiths R.C., Farrall M. Finding associations in dense genetic maps: A genetic algorithm approach. Hum. Hered. 2005, 60:97-108.
8. Clark T.G., De Iorio M., Griffiths R.C. Bayesian logistic regression using a perfect phylogeny. Biostatistics 2007, 8:32-52.
9. Clark T.G., De Iorio M., Griffiths R.C. An evolutionary algorithm to find associations in dense genetic maps. IEEE Trans. Evol. Comp. 2008, 12:297-306.
10. Cordell H.J., Barratt B.J., Clayton D.G. 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. 2004, 26:167-185.
11. Culverhouse R., Suarez B.K., Lin J., Reich T. A perspective on epistasis: Limits of models displaying no main effect. Am. J. Hum. Genet. 2002, 70:461-471.
12. Eliot M., Azzoni L., Firnhaber C., Stevens W., Glencross D.K., Sanne I., Montaner L.J., Foulkes A.S. Tree-based methods for discovery of association between flow cytometry data and clinical endpoints. Adv. Bioinformatics 2009, 2009:235320.
13. Enquobahrie D.A., Smith N.L., Bis J.C., Carty C.L., Rice K.M., Lumley T., Hindorff L.A., Lemaitre R.N., Williams M.A., Siscovick D.S., et al. Cholesterol ester transfer protein, interleukin-8, peroxisome proliferator activator receptor alpha, and toll-like receptor 4 genetic variations and risk of incident nonfatal myocardial infarction and ischemic stroke. Am. J. Cardiol. 2008, 101:1683-1688.
14. Etzioni R., Kooperberg C., Pepe M., Smith R., Gann P.H. Combining biomarkers to detect disease with application to prostate cancer. Biostatistics 2003, 4:523-538.
15. Etzioni R., Falcon S., Gann P.H., Kooperberg C.L., Penson D.F., Stampfer M.J. Prostate-specific antigen and free prostate-specific antigen in the early detection of prostate cancer: Do combination tests improve detection?. Cancer Epidemiol. Biomarkers Prev. 2004, 13:1640-1645.
16. Feng Q., Balasubramanian A., Hawes S.E., Toure P., Sow P.S., Dem A., Dembele B., Critchlow C.W., Xi L., Lu H., et al. Detection of hypermethylated genes in women with and without cervical neoplasia. J. Natl. Cancer Inst. 2005, 97:273-282.
17. Fogel G.B., Corne D.W. An introduction to evolutionary computation for biologists. Evolutionary Computation in Bioinformatics 2003, 19-38. Morgan Kaufmann, San Francisco. G.B. Fogel, D.W. Corne (Eds.).
18. Fritsch A., Ickstadt K. Comparing logic regression based methods for identifying SNP interactions. Bioinformatics in Research and Development 2007, 90-103. Springer, Berlin. S. Hochreiter, R. Wagner (Eds.).
19. Garte S. Metabolic susceptibility genes as cancer risk factors: Time for a reassessment?. Cancer Epidemiol. Biomarkers Prev. 2001, 10:1233-1237.
20. Green P.J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 1995, 82:711-732.
21. Harth V., Schaefer M., Abel J., Maintz L., Neuhaus T., Besuden M., Primke R., Wilkesmann A., Thier R., Vetter H., et al. Head and neck squamous-cell cancer and its association with polymorphic enzymes of xenobiotic metabolism and repair. J. Toxicol. Environ. Health A 2008, 71:887-897.
22. Hosmer D.W., Lemeshow S. Applied Logistic Regression 1989, Springer, New York.
23. Huang J., Lin A., Narasimhan B., Quertermous T., Hsiung C.A., Ho L.T., Grove J.S., Olivier M., Ranade K., Risch N.J., Olshen R.A. Tree-structured supervised learning and the genetics of hypertension. Proc. Natl. Acad. Sci. USA 2004, 101:10529-10534.
24. Ickstadt K., Schaefer M., Fritsch A., Schwender H., Abel J., Bolt H.M., Bruening T., Ko Y.D., Vetter H., Harth V. Statistical methods for detecting genetic interactions: A head and neck squamous-cell cancer study. J. Toxicol. Environ. Health A 2008, 71:803-815.
25. Janes H., Pepe M., Kooperberg C., Newcomb P. Identifying target populations for screening or not screening using logic regression. Stat. Med. 2005, 24:1321-1338.
26. Justenhoven C., Hamann U., Schubert F., Zapatka M., Pierl C.B., Rabstein S., Selinski S., Mueller T., Ickstadt K., Gilbert M., et al. Breast cancer: A candidate gene approach across the estrogen metabolic pathway. Breast Cancer Res. Treat. 2008, 108:137-149.
27. Keles S., van der Laan M.J., Vulpe C. Regulatory motif finding by logic regression. Bioinformatics 2004, 20:2799-2811.
28. Kirkpatrick S., Gelatt C.D.J., Vecchi M.P. Optimization by simulated annealing. Science 1983, 220:671-680.
29. Kooperberg C., Ruczinski I. Identifying interacting SNPs using Monte Carlo logic regression. Genet. Epidemiol. 2005, 28:157-170.
30. Kooperberg C., Ruczinski I., LeBlanc M., Hsu L. Sequence analysis using logic regression. Genet. Epidemiol. 2001, 21:626-631.
31. Kooperberg C., Bis J.C., Marciante K.D., Heckbert S.R., Lumley T., Psaty B.M. Logic regression for analysis of the association between genetic variation in the renin-angiotensin system and myocardial infarction or stroke. Am. J. Epidemiol. 2007, 165:334-343.
32. Koza J.R. Genetic Programming-On the Programming of Computers by Means of Natural Selection 1993, MIT Press, Cambridge.
33. 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. Detection of SNP-SNP interactions in trios of parents with schizophrenic children. Genet. Epidemiol. 2010, 34(5):396-406.
34. Marchini J., Donnely P., Cardon R.C. Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat. Genet. 2005, 37:413-416.
35. Nicodemus K.K., Callicott J.H., Higier R.G., Luna A., Nixon D.C., Lipska B.K., Vakkalanka R., Giegling I., Rujescu D., St.Clair D., et al. Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: Biological validation with functional neuroimaging. Hum. Genet. 2010, 127:441-452.
36. Nunkesser R., Bernholt T., Schwender H., Ickstadt K., Wegener I. Detecting high-order interactions of single nucleotide polymorphisms using genetic programming. Bioinformatics 2007, 23:3280-3288.
37. Poole E.M., Hsu L., Xiao L., Kulmacz R.J., Carlson C.S., Rabinovitch P.S., Makar K.W., Potter J.D., Ulrich C.M. Genetic variation in prostaglandin e2 synthesis and signaling, prostaglandin dehydrogenase, and the risk of colorectal adenoma. Cancer Epidemiol. Biomarkers Prev. 2010, 19:547-557.
38. R Development Core Team R: A Language and Environment for Statistical Computing 2009, R Foundation for Statistical Computing, Vienna, Austria.
39. Ruczinski I. Logic Regression and Statistical Issues Related to the Protein Folding Problem. 2000, Ph.D. thesis. Department of Statistics, University of Washington, Seattle.
40. Ruczinski I., Kooperberg C., LeBlanc M. Logic regression. J. Comput. Graph. Stat. 2003, 12:475-511.
41. Ruczinski I., Kooperberg C., LeBlanc M. Exploring interactions in high-dimensional genomic data: An overview of logic regression, with applications. J. Mult. Anal. 2004, 90:178-195.
42. Schaid D.J. General score tests for associations of genetic markers with disease using cases and their parents. Genet. Epidemiol. 1996, 13:423-449.
43. Schaid D.J. Likelihoods and TDT for the case-parents design. Genet. Epidemiol. 1999, 16:250-260.
44. Schwender H., Ickstadt K. Identification of SNP interactions using logic regression. Biostatistics 2008, 9:187-198.
45. Segal M.R., Barbour J.D., Grant R.M. Relating HIV-1 sequence variation to replication capacity via trees and forests. Stat. Appl. Genet. Mol. Biol. 2004, 3:2.
46. 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., et al. Epigenetic-genetic interactions in the apc/wnt, ras/raf, and p53 pathways in colorectal carcinoma. Clin. Cancer Res. 2008, 14:2560-2569.
47. 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. Urinary biomarkers for sensitive and specific detection of acute kidney injury in humans. Clin. Transl. Sci. 2008, 3:200-208.
48. Witte J.S., Fijal B.A. Introduction: Analysis of sequence data and population structure. Genet. Epidemiol. 2001, 21:600-601.
49. Yaziji H., Battifora H., Barry T.S., Hwang H.C., Bacchi C.E., McIntosh M.W., Kussick S.J., Gown A.M. Evaluation of 12 antibodies for distinguishing epithelioid mesothelioma from adenocarcinoma: Identification of a three-antibody immunohistochemical panel with maximal sensitivity and specificity. Mod. Pathol. 2006, 19:514-523.