Functional logistic regression approach to detecting gene by longitudinal environmental exposure interaction in a case-control study

Genetic Epidemiology

Volumn 38, Issue 7, 2014, Pages 638-651

  Wei, Peng ^a   Tang, Hongwei ^b   Li, Donghui ^b  

^a UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Functional data analysis; Gene environment interaction; GWAS; Longitudinal exposure; Measurement error

Indexed keywords

ADOLESCENT; ADULT; AGE DISTRIBUTION; ARTICLE; BODY MASS; CANCER RISK; CASE CONTROL STUDY; CONTROLLED STUDY; ENVIRONMENTAL EXPOSURE; FUNCTIONAL LOGISTIC REGRESSION ANALYSIS; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENOTYPE ENVIRONMENT INTERACTION; HEREDITY; HUMAN; LOGISTIC REGRESSION ANALYSIS; LONGITUDINAL STUDY; MEASUREMENT ERROR; OBSERVATIONAL STUDY; PANCREAS CANCER; PRINCIPAL COMPONENT ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM; WEIGHT GAIN; BIOLOGICAL MODEL; COMPUTER SIMULATION; GENETIC PREDISPOSITION; GENETICS; MIDDLE AGED; PANCREAS TUMOR; STATISTICAL MODEL;

CASE-CONTROL STUDIES; COMPUTER SIMULATION; ENVIRONMENTAL EXPOSURE; GENE-ENVIRONMENT INTERACTION; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LOGISTIC MODELS; MIDDLE AGED; MODELS, GENETIC; PANCREATIC NEOPLASMS; POLYMORPHISM, SINGLE NUCLEOTIDE; PRINCIPAL COMPONENT ANALYSIS;

EID: 84908283050     PISSN: 07410395     EISSN: 10982272     Source Type: Journal    
DOI: 10.1002/gepi.21852     Document Type: Article

References (60)

1. American Cancer Society. 2013. Cancer Facts & Figures 2013. Atlanta: American Cancer Society.
2. Amundadottir L, Kraft P, Stolzenberg-Solomon RZ, Fuchs CS, Petersen GM, Arslan AA, Bueno-de-Mesquita HB, Gross M, Helzlsouer K, Jacobs EJ and others. 2009. Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer. Nat Genet 41(9):986-990.
3. Aschard H, Lutz S, Maus B, Duell EJ, Fingerlin TE, Chatterjee N, Kraft P, Van Steen K. 2012. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet 131(10):1591-1613.
4. Berndt SI, Gustafsson S, Magi R, Ganna A, Wheeler E, Feitosa MF, Justice AE, Monda KL, Croteau-Chonka DC, Day FR and others. 2013. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet 45(5):501-512.
5. Bhadra D, Daniels MJ, Kim S, Ghosh M, Mukherjee B. 2012. A Bayesian semiparametric approach for incorporating longitudinal information on exposure history for inference in case-control studies. Biometrics 68(2):361-370.
6. Bookman EB, McAllister K, Gillanders E, Wanke K, Balshaw D, Rutter J, Reedy J, Shaughnessy D, Agurs-Collins T, Paltoo D and others. 2011. Gene-environment interplay in common complex diseases: forging an integrative model-recommendations from an NIH workshop. Genet Epidemiol 35(4):217-225.
7. Buzkova P, Lumley T, Rice K. 2011. Permutation and parametric bootstrap tests for gene-gene and gene-environment interactions. Ann Hum Genet 75(1):36-45.
8. Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu C. 2006. Measurement Error in Nonlinear Models: A Modern Perspective. New York: Chapman & Hall.
9. Casey VA, Dwyer JT, Berkey CS, Coleman KA, Gardner J, Valadian I. 1991. Long-term memory of body weight and past weight satisfaction: a longitudinal follow-up study. Am J Clin Nutr 53(6):1493-1498.
10. Cook MB, Gamborg M, Aarestrup J, Sorensen TI, Baker JL. 2013. Childhood height and birth weight in relation to future prostate cancer risk: a cohort study based on the copenhagen school health records register. Cancer Epidemiol Biomarkers Prev 22(12):2232-2240.
11. Cornelis MC, Tchetgen EJ, Liang L, Qi L, Chatterjee N, Hu FB, Kraft P. 2012. Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Am J Epidemiol 175(3):191-202.
12. Fitzmaurice GM, Laird NM, Ware JH. 2011. Applied Longitudinal Analysis. Hoboken, NJ: Wiley.
13. Garcia-Closas M, Rothman N, Figueroa JD, Prokunina-Olsson L, Han SS, Baris D, Jacobs EJ, Malats N, De Vivo I, Albanes D and others. 2013. Common genetic polymorphisms modify the effect of smoking on absolute risk of bladder cancer. Cancer Res 73(7):2211-2220.
14. Gauderman WJ, Zhang P, Morrison JL, Lewinger JP. 2013. Finding novel genes by testing G x E interactions in a genome-wide association study. Genet Epidemiol 37(6):603-13.
15. Goeman JJ, van de Geer S, van Houwelingen HC. 2006. Testing against a high dimensional alternative. J R Stat Soc B 68:477-493.
16. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. 2009. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106(23):9362-9367.
17. Hoffmann TJ, Kvale MN, Hesselson SE, Zhan Y, Aquino C, Cao Y, Cawley S, Chung E, Connell S, Eshragh J and others 2011. Next generation genome-wide association tool: design and coverage of a high-throughput European-optimized SNP array. Genomics 98(2):79-89.
18. Hsu L, Jiao S, Dai JY, Hutter C, Peters U, Kooperberg C. 2012. Powerful cocktail methods for detecting genome-wide gene-environment interaction. Genet Epidemiol 36(3):183-194.
19. Hutter CM, Mechanic LE, Chatterjee N, Kraft P, Gillanders EM. 2013. Gene-Environment Interactions in cancer epidemiology: A National Cancer Institute Think Tank Report. Genet Epidemiol 37(7):643-657.
20. Jiao S, Hsu L, Bezieau S, Brenner H, Chan AT, Chang-Claude J, Le Marchand L, Lemire M, Newcomb PA, Slattery ML and others. 2013. SBERIA: set-based gene-environment interaction test for rare and common variants in complex diseases. Genet Epidemiol 37(5):452-464.
21. Klein AP, Lindstrom S, Mendelsohn JB, Steplowski E, Arslan AA, Bueno-de-Mesquita HB, Fuchs CS, Gallinger S, Gross M, Helzlsouer K and others. 2013. An absolute risk model to identify individuals at elevated risk for pancreatic cancer in the general population. PLoS ONE 8(9):e72311.
22. Ko YA, Saha-Chaudhuri P, Park SK, Vokonas PS, Mukherjee B. 2013. Novel likelihood ratio tests for screening gene-gene and gene-environment interactions with unbalanced repeated-measures data. Genet Epidemiol 37(6):581-591.
23. Kooperberg C, Leblanc M. 2008. Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genet Epidemiol 32(3):255-263.
24. Leng X, Muller HG. 2006. Classification using functional data analysis for temporal gene expression data. Bioinformatics 22(1):68-76.
25. Li D, Morris JS, Liu J, Hassan MM, Day RS, Bondy ML, Abbruzzese JL. 2009. Body mass index and risk, age of onset, and survival in patients with pancreatic cancer. JAMA 301(24):2553-2562.
26. Li Y, Wang N, Carroll RJ. 2010. Generalized functional linear models with semiparametric single-Index interactions. J Am Stat Assoc 105(490):621-633.
27. Li S, Mukherjee B, Taylor JM, Rice KM, Wen X, Rice JD, Stringham HM, Boehnke M. 2014. The role of environmental heterogeneity in meta-analysis of gene-environment interactions with quantitative traits. Genet Epidemiol 38(5):416-429.
28. Luo L, Boerwinkle E, Xiong M. 2011. Association studies for next-generation sequencing. Genome Res 21(7):1099-1108.
29. Manolio TA. 2013. Bringing genome-wide association findings into clinical use. Nat Rev Genet 14(8):549-558.
30. Mechanic LE, Chen HS, Amos CI, Chatterjee N, Cox NJ, Divi RL, Fan R, Harris EL, Jacobs K, Kraft P and others. 2012. Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. Genet Epidemiol 36:22-35.
31. Mukherjee B, Chatterjee N. 2008. Exploiting gene-environment independence for analysis of case-control studies: an empirical Bayes-type shrinkage estimator to trade-off between bias and efficiency. Biometrics 64(3):685-694.
32. Mukherjee B, Ahn J, Gruber SB, Chatterjee N. 2012a. Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons. Am J Epidemiol 175(3):177-190.
33. Mukherjee B, Ko YA, Vanderweele T, Roy A, Park SK, Chen J. 2012b. Principal interactions analysis for repeated measures data: application to gene-gene and gene-environment interactions. Statist Med 31(22):2531-2551.
34. Müller H-G. 2009. Functional Modeling of Longitudinal Data. In: Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G, editors. Longitudinal Data Analysis (Handbooks of Modern Statistical Methods). New York: Wiley, pp. 223-252.
35. Müller H-G, Stadtmüller, U. 2005. Generalized functional linear models. Ann Statist 33(2):774-805.
36. Murcray CE, Lewinger JP, Gauderman WJ. 2009. Gene-environment interaction in genome-wide association studies. Am J Epidemiol 169(2):219-226.
37. Murcray CE, Lewinger JP, Conti DV, Thomas DC, Gauderman WJ. 2011. Sample size requirements to detect gene-environment interactions in genome-wide association studies. Genet Epidemiol 35(3):201-210.
38. Pan W. 2009. Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genet Epidemiol 33(6):497-507.
39. Pan W. 2011. Relationship between genomic distance-based regression and kernel machine regression for multi-marker association testing. Genet Epidemiol 35:211-216.
40. Pan W, Basu S, Shen X. 2011. Adaptive tests for detecting gene-gene and gene-environment interactions. Hum Hered 72(2):98-109.
41. Pepe MS, Heagerty P, Whitaker R. 1999. Prediction using partly conditional time-varying coefficients regression models. Biometrics 55(3):944-950.
42. Petersen GM, Amundadottir L, Fuchs CS, Kraft P, Stolzenberg-Solomon RZ, Jacobs KB, Arslan AA, Bueno-de-Mesquita HB, Gallinger S, Gross M and others. 2010. A genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22.1, 1q32.1 and 5p15.33. Nat Genet 42(3):224-228.
43. Prentice RL, Pyke R. 1979. Logistic disease incidence models and case-control studies. Biometrika 66(3):403-411.
44. Ramsay JO, Silverman BW. 2005. Functional Data Analysis. New York: Springer-Verlag.
45. Sanchez BN, Hu H, Litman HJ, Tellez-Rojo MM. 2011. Statistical methods to study timing of vulnerability with sparsely sampled data on environmental toxicants. Environ Health Persp 119(3):409-415.
46. Sorensen H, Goldsmith J, Sangalli LM. 2013. An introduction with medical applications to functional data analysis. Stat Med 32(30):5222-5240.
47. Splansky GL, Corey D, Yang Q, Atwood LD, Cupples LA, Benjamin EJ, D'Agostino RB, Sr., Fox CS, Larson MG, Murabito JM and others. 2007. The Third Generation Cohort of the National Heart, Lung, and Blood Institute's Framingham Heart Study: design, recruitment, and initial examination. Am J Epidemiol 165(11):1328-1335.
48. Stevens J, Keil JE, Waid LR, Gazes PC. 1990. Accuracy of current, 4-year, and 28-year self-reported body weight in an elderly population. Am J Epidemiol 132(6):1156-1163.
49. Sutcliffe S, Colditz GA. 2013. Prostate cancer: is it time to expand the research focus to early-life exposures? Nat Rev Cancer 13(3):208-518.
50. Tang H, Dong X, Hassan M, Abbruzzese JL, Li D. 2011. Body mass index and obesity- and diabetes-associated genotypes and risk for pancreatic cancer. Cancer Epidemiol Biomarkers Prev 20(5):779-792.
51. Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen GM, Bracci PM and others. 2014a. Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene and pathway-based interaction analysis of GWAS data. Carcinogenesis 35:1039-1045.
52. Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen GM, Bracci PM and others. 2014b. Genes-environment interactions in obesity- and diabetes-associated pancreatic cancer: a GWAS data analysis. Cancer Epidemiol Biomarkers Prev 23(1):98-106.
53. Tchetgen Tchetgen EJ, Kraft P. 2011. On the robustness of tests of genetic associations incorporating gene-environment interaction when the environmental exposure is misspecified. Epidemiology 22(2):257-261.
54. Thomas D. 2010. Gene-environment-wide association studies: emerging approaches. Nat Rev Genet 11(4):259-272.
55. Visscher PM, Brown MA, McCarthy MI, Yang J. 2012. Five years of GWAS discovery. Am J Hum Genet 90(1):7-24.
56. Wei P, Tang H, Li D. 2012. Insights into pancreatic cancer etiology from pathway analysis of genome-wide association study data. PLoS ONE 7(10):e46887.
57. Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, Lin X. 2010. Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet 86(6):929-942.
58. Wu C, Kraft P, Zhai K, Chang J, Wang Z, Li Y, Hu Z, He Z, Jia W, Abnet CC and others. 2012. Genome-wide association analyses of esophageal squamous cell carcinoma in Chinese identify multiple susceptibility loci and gene-environment interactions. Nat Genet 44(10):1090-1097.
59. Yao F, Müller HG, Wang JL. 2005. Functional data analysis for sparse longitudinal data. J Am Stat Assoc 100:577-590.
60. Zuk O, Hechter E, Sunyaev SR, Lander ES. 2012. The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci USA 109(4):1193-1198.