Powerful Tukey’s one degree-of-freedom test for detecting gene-gene and gene-environment interactions

Cancer Informatics

Volumn 14, Issue , 2015, Pages 209-218

  Wang, Yaping ^a   Li, Donghui ^b   Wei, Peng ^a,c  

^a UNIVERSITY OF TEXAS   (United States)

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

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

Author keywords

G E interaction; G G interaction; GWAS; PCA; SNP; Tukey s 1 df test

Indexed keywords

INTERLEUKIN 12 RECEPTOR BETA2; INTERLEUKIN 12P40;

ARTICLE; CASE CONTROL STUDY; COMPUTER SIMULATION; CONTROLLED STUDY; CROHN DISEASE; GENE GENE INTERACTION; GENE INTERACTION; GENE LOCUS; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC MODEL; GENETIC SUSCEPTIBILITY; GENOTYPE; GENOTYPE ENVIRONMENT INTERACTION; HUMAN; IL12B GENE; IL12RB2 GENE; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MATHEMATICAL COMPUTING; PANCREAS ADENOCARCINOMA; PARSIMONY ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM; SMOKING; STATISTICAL ANALYSIS; STATISTICAL MODEL; TUKEY ONE DEGREE OF FREEDOM TEST;

EID: 84930655499     PISSN: None     EISSN: 11769351     Source Type: Journal    
DOI: 10.4137/CIN.S17305     Document Type: Article

References (47)

1. Hindorff LA, Sethupathy P, Junkins HA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009;106(23):9362-7.
2. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet. 2012;90(1):7-24.
3. Stein EA, Mellis S, Yancopoulos GD, et al. Effect of a monoclonal antibody to PCSK9 on LDL cholesterol. N Engl J Med. 2012;366(12):1108-18.
4. Michailidou K, Hall P, Gonzalez-Neira A, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013;45(4):353-61.
5. Chatterjee N, Wheeler B, Sampson J, Hartge P, Chanock SJ, Park JH. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat Genet. 2013;45(4):400-5.
6. Zuk O, Schaffner SF, Samocha K, et al. Searching for missing heritability: Designing rare variant association studies. Proc Natl Acad Sci U S A. 2014;111(4):E455-64.
7. Pan W, Kim J, Zhang Y, Shen X, Wei P. A powerful and adaptive association test for rare variants. Genetics. 2014;197(4):1081-95.
8. Zuk O, Hechter E, Sunyaev SR, Lander ES. The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci U S A. 2012;109(4):1193-8.
9. Campa D, Kaaks R, Le Marchand L, et al. Interactions between genetic variants and breast cancer risk factors in the breast and prostate cancer cohort consortium. J Natl Cancer Inst. 2011;103(16):1252-63.
10. Hutter CM, Mechanic LE, Chatterjee N, Kraft P, Gillanders EM. Gene-environment interactions in cancer epidemiology: a national cancer institute think tank report. Genet Epidemiol. 2013;37(7):643-57.
11. Thomas D. Gene-environment-wide association studies: Emerging approaches. Nat Rev Genet. 2010;11(4):259-72.
12. Kooperberg C, Leblanc M. Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genet Epidemiol. 2008;32(3):255-63.
13. Gauderman WJ, Zhang P, Morrison JL, Lewinger JP. Finding novel genes by testing G x E interactions in a genome-wide association study. Genet Epidemiol. 2013;37(6):603-13.
14. Hsu L, Jiao S, Dai JY, Hutter C, Peters U, Kooperberg C. Powerful cocktail methods for detecting genome-wide gene-environment interaction. Genet Epidemiol. 2012;36(3):183-94.
15. Murcray CE, Lewinger JP, Gauderman WJ. Gene-environment interaction in genome-wide association studies. Am J Epidemiol. 2009;169(2):219-26.
16. Murcray CE, Lewinger JP, Conti DV, Thomas DC, Gauderman WJ. Sample size requirements to detect gene-environment interactions in genome-wide association studies. Genet Epidemiol. 2011;35(3):201-10.
17. He J, Wang K, Edmondson AC, Rader DJ, Li C, Li M. Gene-based interaction analysis by incorporating external linkage disequilibrium information. Eur J Hum Genet. 2011;19(2):164-72.
18. Pan W, Basu S, Shen X. Adaptive tests for detecting gene-gene and gene-environment interactions. Hum Hered. 2011;72(2):98-109.
19. Lin X, Lee S, Christiani DC, Lin X. Test for interactions between a genetic marker set and environment in generalized linear models. Biostatistics. 2013;14(4):667-81.
20. Jiao S, Hsu L, Bzieau S, et al. SBERIA: Set-based gene-environment interaction test for rare and common variants in complex diseases. Genet Epidemiol. 2013;37(5):452-64.
21. Tang H, Wei P, Duell EJ, et al. 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. 2014;35:1039-45.
22. Tang H, Wei P, Duell EJ, et al. Genes-environment interactions in obesity-and diabetes-associated pancreatic cancer: a GWAS data analysis. Cancer Epidemiol Biomarkers Prev. 2014;23(1):98-106.
23. Tukey JW. One degree of freedom for non-additivity. Biometrics. 1949;5(3):232-42.
24. Chatterjee N, Kalaylioglu Z, Moslehi R, Peters U, Wacholder S. Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions. Am J Hum Genet. 2006;79(6):1002-16.
25. Han SS, Rosenberg PS, Garcia-Closas M, et al. Likelihood ratio test for detecting gene (G)-environment (E) interactions under an additive risk model exploiting G-E independence for case-control data. Am J Epidemiol. 2012;176(11):1060-7.
26. Cordell HJ. Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet. 2009;10(6):392-404.
27. Wei P, Tang H, Li D. Insights into pancreatic cancer etiology from pathway analysis of genome-wide association study data. PLoS One. 2012;7(10):e46887.
28. Pan W. Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genet Epidemiol. 2009;33(6):497-507.
29. Pan W. Relationship between genomic distance-based regression and kernel machine regression for multi-marker association testing. Genet Epidemiol. 2011;35:211-6.
30. Goeman JJ, van de Geer S, van Houwelingen HC. Testing against a high dimensional alternative. J R Stat Soc B. 2006;68:477-93.
31. Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89(l):82-93.
32. Jensen LJ, Kuhn M, Stark M, et al. STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009;37(Database issue):D412-6.
33. de Bakker PI, Yelensky R, Pe’er I, Gabriel SB, Daly MJ, Altshuler D. Efficiency and power in genetic association studies. Nat Genet. 2005;37(11):1217-23.
34. Wang K, Li M, Hakonarson H. Analysing biological pathways in genome-wide association studies. Nat Rev Genet. 2010;11(12):843-54.
35. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661-78.
36. Sun YV. Integration of biological networks and pathways with genetic association studies. Hum Genet. 2012;131(10):1677-86.
37. American Cancer Society. Cancer Facts and Figures 2013. Atlanta: American Cancer Society; 2013.
38. Li D, Morris JS, Liu J, et al. Body mass index and risk, age of onset, and survival in patients with pancreatic cancer. JAMA. 2009;301(24):2553-62.
39. Wei P, Tang H, Li D. Functional logistic regression approach to detecting gene by longitudinal environmental exposure interaction in a case-control study. Genet Epidemiol. 2014;38(7):638-51.
40. Amundadottir L, Kraft P, Stolzenberg-Solomon RZ, et al. Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer. Nat Genet. 2009;41(9):986-90.
41. Petersen GM, Amundadottir L, Fuchs CS, et al. A genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22.1, lq32.1 and 5pl5.33. Nat Genet. 2010;42(3):224-8.
42. McGuigan FE, Ralston SH. Single nucleotide polymorphism detection: Allelic discrimination using TaqMan. Psychiatr Genet. 2002;12(3):1336.
43. Luo L, Boerwinkle E, Xiong M. Association studies for next-generation sequencing. Genome Res. 2011;21(7):1099-108.
44. Zhang F, Boerwinkle E, Xiong M. Epistasis analysis for quantitative traits by functional regression model. Genome Res. 2014;24(6):989-98.
45. Wang T, Ho G, Ye K, Strickler H, Elston RC. A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped. Genet Epidemiol. 2009;33(1):6-15.
46. Mukherjee B, Ahn J, Gruber SB, Chatterjee N. Testing gene-environment interaction in large-scale case-control association studies: Possible choices and comparisons. Am J Epidemiol. 2012;175(3):177190.
47. Satagopan JM, Elston RC. Evaluation of removable statistical interaction for binary traits. Stat Med. 2013;32(7):1164-90.