A penalized robust semiparametric approach for gene–environment interactions

Statistics in Medicine

Volumn 34, Issue 30, 2015, Pages 4016-4030

  Wu, Cen ^a,b   Shi, Xingjie ^c   Cui, Yuehua ^d   Ma, Shuangge ^a,e  

^a YALE UNIVERSITY   (United States)

^b KANSAS STATE UNIVERSITY   (United States)

^c NANJING UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^d MICHIGAN STATE UNIVERSITY   (United States)

^e VA Cooperative Studies Program Coordinating Center   (United States)

Author keywords

gene environment interactions; partially linear varying coefficient model; penalized selection; robustness

Indexed keywords

ARTICLE; CANCER GROWTH; CANCER PROGNOSIS; ENVIRONMENTAL FACTOR; GENE EXPRESSION; GENETIC MARKER; GENOTYPE; GENOTYPE ENVIRONMENT INTERACTION; HAPLOTYPE; HEREDITY; HUMAN; HUMAN CELL; LUNG CANCER; MATHEMATICAL COMPUTING; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; SMOKING; ALGORITHM; BIOLOGICAL MODEL; BIOSTATISTICS; COMPUTER SIMULATION; FEMALE; GENETIC DATABASE; GENETICS; LUNG NEOPLASMS; MALE; STATISTICAL MODEL;

TUMOR MARKER;

ALGORITHMS; BIOMARKERS, TUMOR; BIOSTATISTICS; COMPUTER SIMULATION; DATABASES, GENETIC; FEMALE; GENE EXPRESSION; GENE-ENVIRONMENT INTERACTION; HUMANS; LINEAR MODELS; LUNG NEOPLASMS; MALE; MODELS, GENETIC; MODELS, STATISTICAL; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84973110389     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6609     Document Type: Article

References (41)

1. Mukherjee B, Ahn J, Gruber SB, Chatterjee N. Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons. American Journal of Epidemiology 2012; 175(3):177–190.
2. Hunter DJ. Gene-environment interactions in human diseases. Nature Reviews Genetics 2005; 6(4):287–298.
3. Qiao M, Li J. Two-way Gaussian mixture models for high dimensional classification. PLoS ONE 2010; 39:259–271.
4. Belloni A, Chernozhukov V. L1 penalized quantile regression in high dimensional sparse models. Annals of Statistics 2011; 39:82–130.
5. Shi XJ, Liu J, Huang J, Zhou Y, Yang X, Ma S. A penalized robust method for identifying gene-environment interactions. Genetic Epidemiology 2014; 38(3):220–230.
6. Liu J, Huang J, Zhang Y, Lan Q, Rothman N, Zheng T, Ma S. Identification of gene-environment interactions in cancer studies using penalization. Genomics 2013; 102(4):189–194.
7. Yang C, Wan X, Yang Q, Xue H, Yu W. Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group Lasso. BMB Bioinformatics 2010; 11:S18. DOI: 10.1186/1471-2105-11-S1-S18.
8. Bien J, Taylor J, Tibshirani R. A Lasso for hierarchical interactions. Annals of Statistics 2013; 41:1111–1141.
9. Hastie H, Tibshirani R. Varying-coefficient models. Journal of the Royal Statistical Society: Series B 1993; 55:757–796.
10. Park B, Mammen E, Lee Y, Lee E. Varying coefficient regression models: a review and new developments. International Statistical Review 2015; 83(1):36–64.
11. Ma SJ, Yang LJ, Romero R, Cui YH. Varying coefficient model for gene-environment interaction: a non-linear look. Bioinformatics 2011; 27:2119–2126.
12. Wu C, Cui YH. A novel method for identifying nonlinear gene-environment interactions in case-control association studies. Human Genetics 2013; 132(12):1413–1425.
13. Sievers G, Abebe A. Rank estimation of regression coefficients using iterated reweighted least squares. Journal of Statistical Computation and Simulation 2004; 74:821–831.
14. Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2006; 68:49–67.
15. Wei F, Huang J, Li H. Variable selection and estimation in high–dimensional varying coefficient models. Statistica Sinica 2011; 21:1515–1540.
16. Huang J, Ma S, Xie H, Zhang C. A group bridge approach for variable selection. Biometrika 2009; 96:339–355.
17. Cordell H. Genome–wide association studies detecting gene-gene interactions that underlie human diseases. Nature Reviews Genetics 2009; 10:392–404.
18. McKinney BA, Reif DM, Ritchie MD, Moore JH. Machine Learning for Detecting Gene-Gene Interactions: A Review. Applied Bioinformatics 2006; 5(2):77–88.
19. Breheny P, Huang J. Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Statistics and Computing 2015; 25(2):173–187.
20. Tseng P. Convergence of a block coordinate desCent method for nondifferentiable minimization. Journal of Optimization Theory and Applications 2001; 109:475–494.
21. Chen J, Chen Z. Extended Bayesian information criteria for model selection with large model spaces. Biometrika 2008; 95(3):759–771.
22. Wang HS, Xia YC. Shrinkage estimation of the varying coefficient model. Journal of the American Statistical Association 2008; 104:747–757.
23. Cui Y, Kang G, Sun K, Romero R, Qian M, Fu W. Gene-Centric genomewide association study via entropy. Genetics 2008; 179:637–650.
24. Hudson RR. Generating samples under a Wright-Fisher neutral model. Bioinformatics 2002; 18:337–338.
25. Xie Y, Xiao G, Coombes KR, Behrens C, Solis LM, Raso G, Girard L, Erickson HS, Roth J, Heymach JV, Moran C, Danenberg K, Minna JD, Wistuba II. Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients. Clinical Cancer Research 2011; 17(17):5705–5714.
26. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D'Amico AV, Richie JP, Lander ES, Loda M, Kantoff PW, Golub TR, Sellers WR. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 2002; 1:203–209.
27. Stute W. Distributional convergence under random Censorship when covariables are present. Scandinavian Journal of Statistics 1996; 23:461–471.
28. Huang J, Ma S. Variable selection in the accelerated failutr time model via the bridge method. Lifetime Data Analysis 2010; 16:176–195.
29. Simon R, Subramanian J, Li M, Menezes S. Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data. Briefings in Bioinformatics 2011; 12:203–214.
30. Yamazaki H, Inoue K, Hashimoto M, Shimada T. Roles of CYP2A6 and CYP2B6 in nicotine C-oxidation by human liver microsomes. Archives of Toxicology 1999; 73:65–70.
31. Oyama T, Sugio K, Isse T, Matsumoto A, Nose N, Uramoto H, Nozoe T, Morita M, Kagawa N, Osaki T, Muto M, Yasumoto K, Kawamoto T. Expression of Cytochrome P450 in non-small cell lung cancer. Frontiers in Bioscience 2008; 13:5787–5793.
32. Tychopoulos M, Corcos L, Genne P, Beaune P, Waziers I. A virus-directed enzyme prodrug therapy (VDEPT) strategy for lung cancer using a CYP2B6/NADPH-cytochrome P450 reductase fusion protein. Cancer Gene Therapy 2005; 12:497–508.
33. Ran Y, Hu B, Chen L, Yu L. SunL, Yang Z. A functional antibody targeting lung cancer and it's antigen. Chinese Journal of Cancer Biotherapy 2007; 2:65–70.
34. Lee Y, Yoon K, Joo J, Lee D, Bae K, Han J, Lee J. Prognostic implications of genetic variants in advanced non-small cell lung cancer: a genome-wide association study. Carcinogenesis 2013; 34:307–313.
35. Shepherd FA, Rodrigues Pereira J, Ciuleanu T, Tan EH, Hirsh V, Thongprasert S, Campos D, Maoleekoonpiroj S, Smylie M, Martins R, van Kooten M, Dediu M, Findlay B, Tu D, Johnston D, Bezjak A, Clark G, Santabárbara P, Seymour L, National Cancer Institute of Canada Clinical Trials Group. Erlotinib in previously treated non-small-cell lung cancer. New England Journal of Medicine 2005; 21:123–132.
36. Tan E, Ramlau R, Pluzanska A, Kuo HP, Reck M, Milanowski J, Au JS, Felip E, Yang PC, Damyanov D, Orlov S, Akimov M, Delmar P, Essioux L, Hillenbach C, Klughammer B, McLoughlin P, Baselga J. A multicentre phase II gene expression profiling study of putative relationships between tumour biomarkers and clinical response with erlotinib in non-small-cell lung cancer. Annals of Oncology 2010; 21:217–222.
37. Wissmann C, Wild PJ, Kaiser S, Roepcke S, Stoehr R, Woenckhaus M, Kristiansen G, Hsieh JC, Hofstaedter F, Hartmann A, Knuechel R, Rosenthal A, Pilarsky C. Wif1, a component of the Wnt pathway, is down-regulated in prostate, breast, lung, and bladder cancer. The Journal of Pathology 2003; 201:204–212.
38. Mazieres J, He B, You L, Xu Z, Lee A, Mikami I, Reguart N, Rosell R, McCormick F, Jablons D. Wnt Inhibitory Factor-1 is silenced by promoter Hypermethylation in human lung cancer. Cancer Research 2004; 64:4717–4720.
39. Kohno T, Kunitoh H, Shimada Y, Shiraishi K, Ishii Y, Goto K, Ohe Y, Nishiwaki Y, Kuchiba A, Yamamoto S, Hirose H, Oka A, Yanagitani N, Saito R, Inoko H, Yokota J. Individuals susceptible to lung adenocarcinoma defined by combined HLA–DQA1 and TERT genotypes. Carcinogenesis 2010; 31:834–841.
40. Kohno T, Kunitoh H, Mimaki S, Shiraishi K, Kuchiba A, Yamamoto S, Yokota J. Contribution of the TP53, OGG1, CHRNA3, and HLA-DQA1 Genes to the Risk for Lung Squamous Cell Carcinoma. Journal of Thoracic Oncology 2011; 6:813–817.
41. 0,+ (SLC6A14) in carcinoma of the cervix. Gynecologic Oncology 2006; 100:8–13.