Variable selection in logistic regression for detecting SNP-SNP interactions: The rheumatoid arthritis example

European Journal of Human Genetics

Volumn 16, Issue 6, 2008, Pages 735-741

  Lin, Hui Yi ^a   Desmond, Renee ^a   Louis Bridges Jr , S ^b   Soong, Seng Jaw ^a  

^a University of Alabama at Birmingham   (United States)

^b The University of Alabama at Birmingham   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; FALSE POSITIVE RESULT; GENE INTERACTION; HUMAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; PRIORITY JOURNAL; RHEUMATOID ARTHRITIS; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE;

ARTHRITIS, RHEUMATOID; CASE-CONTROL STUDIES; HUMANS; LOGISTIC MODELS; POLYMORPHISM, SINGLE NUCLEOTIDE; SELECTION (GENETICS);

EID: 44449117973     PISSN: 10184813     EISSN: 14765438     Source Type: Journal    
DOI: 10.1038/sj.ejhg.5202010     Document Type: Article

References (33)

1. Van Emburgh BO, Hu JJ, Levine EA et al: Polymorphisms in drug metabolism genes, smoking, and p53 mutations in breast cancer. Mol Carcinog 2007; 47: 88-99.
2. Ge D, Zhu H, Huang Y et al: Multilocus analyses of renin - angiotensin-aldosterone system gene variants on blood pressure at rest and during behavioral stress in young normotensive subjects. Hypertension 2007; 49: 107-112.
3. Gu D, Su S, Ge D et al: Association study with 33 single-nucleotide polymorphisms in 11 candidate genes for hypertension in Chinese. Hypertension 2006; 47: 1147-1154.
4. Cook NR, Zee RY, Ridker PM: Tree and spline-based association analysis of gene -gene interaction models for ischemic stroke. Stat Med 2004; 23: 1439-1453.
5. Ritchie MD, Hahn LW, Roodi N et al: Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 2001; 69: 138-147.
6. Nelson MR, Kardia SL, Ferrell RE, Sing CF: A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res 2001; 11: 458-470.
7. Ripley B: Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press, 1996.
8. Bureau A, Dupuis J, Falls K et al: Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol 2005; 28: 171-182.
9. Hoffjan S, Ostrovnaja I, Nicolae D et al: Genetic variation in immunoregulatory pathways and atopic phenotypes in infancy. J Allergy Clin Immunol 2004; 113: 511-518.
10. Bae Y, Park C, Han J et al: Interaction between GNB3 C825T and ACE I/D polymorphisms in essential hypertension in Koreans. J Hum Hypertens 2007; 21: 159-166.
11. Miao X, Zhang X, Zhang L et al: Adenosine diphosphate ribosyl transferase and x-ray repair cross-complementing 1 polymorphisms in gastric cardia cancer. Gastroenterology 2006; 131: 420-427.
12. Zhang X, Miao X, Guo Y et al: Genetic polymorphisms in cell cycle regulatory genes MDM2 and TP53 are associated with susceptibility to lung cancer. Hum Mutat 2006; 27: 110-117.
13. Huber A, Keck CC, Hefler LA et al: Ten estrogen-related polymorphisms and endometriosis: A study of multiple gene -gene interactions. Obstet Gynecol 2005; 106: 1025-1031.
14. Marchini J, Donnelly P, Cardon LR: Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 2005; 37: 413-417.
15. Coffey CS, Hebert PR, Ritchie MD et al: An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene- gene interactions on risk of myocardial infarction: the importance of model validation. BMC bioinformatics 2004; 5 49.
16. Oestergaard MZ, Tyrer J, Cebrian A et al: Interactions between genes involved in the antioxidant defence system and breast cancer risk. Br J Cancer 2006; 95: 525-531.
17. Hoh J, Wille A, Zee R et al: Selecting SNPs in two-stage analysis of disease association data: A model-free approach. Ann Hum Genet 2000; 64: 413-417.
18. Zee RY, Hoh J, Cheng S et al: Multi-locus interactions predict risk for post-PTCA restenosis: An approach to the genetic analysis of common complex disease. Pharmacogenomics J 2002; 2: 197-201.
19. Park MY, Hastie T: Penalized logistic regression for detecting gene interactions. Biostatistics 2008; 9: 30-50.
20. Derksen S, Keselman HJ: Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. Br J Math Stat Psychol 1992; 45: 265-282.
21. Zellner D, Keller F, Zellner GE: Variable selection in logistic regression models. Communications in Statistics: Simulation and Computation 2004; 33: 787-805.
22. Kleinbaum DG: Logistic Regression: A Self-Learning Text. New York: Springer, 1994.
23. Shtatland ES, Cain E, Barton MB: The perils of stepwise logistic regression and how to escape them using information criteria and the output delivery system. SAS Users Group International 2001; 26 222.
24. Akaike H: A new look at the statistical model identification. IEEE Transactions on Automatic Control AC 1974; 19: 716-723.
25. Schwarz G: Estimating the dimension of a model. Ann Stat 1978; 6: 461-464.
26. Efron B, Tibshirani R: An Introduction to the Bootstrap. Boca Raton: CHAPMAN & HALL/CRC, 1993.
27. Sun GW, Shook TL, Kay GL: Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996; 49: 907-916.
28. Mantel N: Why step-down procedures in variable selection. Technometrics 1970; 12: 621-625.
29. Kallberg H, Padyukov L, Plenge RM et al: Gene- gene and gene-environment interactions involving HLA-DRB1, PTPN22, and smoking in two subsets of rheumatoid arthritis. Am J Hum Genet 2007; 80 867-875.
30. Bridges Jr SL, Hughes LB, Mikuls TR et al: Early rheumatoid arthritis in African-Americans: The CLEAR Registry. Clin Exp Rheumatol 2003; 21: S138-S145.
31. Burnham KP, Anderson DR: Model Selection and Multimodel Inference: A Practical Information-Theorectic Approach, 2nd edn. New York: Springer, 2002.
32. Heidema AG, Boer JM, Nagelkerke N, Mariman EC, van der AD, Feskens EJ: The challenge for genetic epidemiologists: How to analyze large numbers of SNPs in relation to complex diseases. BMC Genet 2006; 7: 23.
33. Skol AD, Scott LJ, Abecasis GR, Boehnke M: Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 2006; 38: 209-213.