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Statistics for Testing Gene–Environment Interaction

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Environmental Factors, Genes, and the Development of Human Cancers
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Abstract

This chapter introduces a number of new gene–environment interaction measures and develop novel statistics that are based on these new gene–environment interaction measures. These new statistics are simple, less computationally intensive and easy to implement. It is hoped that these developments may open a new avenue for large-scale genome-wide gene–environment interaction analysis, deciphering the genetic and physiological meaning of gene–environment interactions and developing sophisticated statistical methods for unraveling gene–gene and gene–environment interactions leading to the development of human cancers.

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References

  • Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science. 322(5903):881–8.

    Article  PubMed  CAS  Google Scholar 

  • Amos CI, Wu X, Broderick P, et al. (2008) Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat Genet. 40:616–22.

    Article  PubMed  CAS  Google Scholar 

  • Anderson TW (1984) An introduction to multivariate statistical analysis, 2nd Edition. John Wiley & Sons, New York.

    Google Scholar 

  • Andrew AS, Mason RA, Kelsey KT, Schned AR, Marsit CJ, Nelson HH, Karagas MR (2009) DNA repair genotype interacts with arsenic exposure to increase bladder cancer risk. Toxicol Lett. 187(1):10–4.

    Google Scholar 

  • Andrieu N, Goldstein AM (1998) Epidemiologic and genetic approaches in the study of gene–environment interaction: an overview of available methods. Epidemiol Rev. 20(2):137–47.

    Article  PubMed  CAS  Google Scholar 

  • Ay N (2002) Locality of global stochastic interaction in directed acyclic networks. Neural Comput. 14:2959–80.

    Article  PubMed  Google Scholar 

  • Brennan P (2002) Gene–environment interaction and aetiology of cancer: what does it mean and how can we measure it? Carcinogenesis. 3:381–7.

    Article  Google Scholar 

  • Brillinger DR (2004) Some data analyses using mutual information. Braz J Probab Stat. 18:163–83.

    Google Scholar 

  • Bush WS, Dudek SM, Ritchie MD (2006) Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene–gene interactions. Bioinformatics. 22(17):2173–4.

    Article  PubMed  CAS  Google Scholar 

  • Chaisson MJ, Brinza D, Pevzner PA (2009) De novo fragment assembly with short mate-paired reads: does the read length matter? Genome Res. 19(2):336–46.

    Article  PubMed  CAS  Google Scholar 

  • Chatterjee N, Kalaylioglu Z, Moslehi R, Peters U, Wacholder S (2006) Powerful multilocus tests of genetic association in the presence of gene–gene and gene–environment interactions. Am J Hum Genet. 79(6):1002–100.

    Article  PubMed  CAS  Google Scholar 

  • Cheverud JM, Routman EJ (1995) Epistasis and its contribution to genetic variance components. Genetics. 139:1455–61.

    PubMed  CAS  Google Scholar 

  • Chung Y, Lee SY, Elston RC, Park T (2007) Odds ratio based multifactor-dimensionality reduction method for detecting gene–gene interactions. Bioinformatics. 23(1):71–6.

    Article  PubMed  CAS  Google Scholar 

  • Colt JS, Rothman N, Severson RK, Hartge P, Cerhan JR, Chatterjee N, Cozen W, Morton LM, De Roos AJ, Davis S, Chanock S, Wang SS (2009) Organochlorine exposure, immune gene variation, and risk of non-Hodgkin lymphoma. Blood. 113:1899–905.

    Article  PubMed  CAS  Google Scholar 

  • Cordell HJ (2009) Detecting gene–gene interactions that underlie human diseases. Nat Rev Genet. 10:392–404.

    Article  PubMed  CAS  Google Scholar 

  • Cover TM, Thomas JA (1991) Elements of information theory. John Wiley & Sons, New York.

    Book  Google Scholar 

  • Dandara C, Li DP, Walther G, Parker MI (2006) Gene–environment interaction: the role of SULT1A1 and CYP3A5 polymorphisms as risk modifiers for squamous cell carcinoma of the oesophagus. Carcinogenesis. 27:791–7.

    Article  PubMed  CAS  Google Scholar 

  • Frazer KA, Murray SS, Schork NJ, Topol EJ (2009) Human genetic variation and its contribution to complex traits. Nat Rev Genet. 10(4):241–51.

    Article  PubMed  CAS  Google Scholar 

  • Garcia-Closas M, Lubin JH (1999) Power and sample size calculations in case-control studies of gene–environment interactions: comments on different approaches. Am J Epidemiol. 149:689–92.

    Article  PubMed  CAS  Google Scholar 

  • Gauderman WJ (2002) Sample size requirements for matched case-control studies of gene–environment interaction. Stat Med. 21(1):35–50.

    Article  PubMed  Google Scholar 

  • Ghadirian P, Narod S, Fafard E, Costa M, Robidoux A, Nkondjock A (2009). Breast cancer risk in relation to the joint effect of BRCA mutations and diet diversity. Breast Cancer Res Treat. 117:417–22.

    Article  PubMed  CAS  Google Scholar 

  • Giarelli E, Jacobs LA (2005). Modifying cancer risk factors: the gene–environment interaction. Semin Oncol Nurs. 21:271–7.

    Article  PubMed  Google Scholar 

  • Goldstein AM, Dondon MG, Andrieu N (2006) Unconditional analyses can increase efficiency in assessing gene–environment interaction of the case-combined-control design. Int J Epidemiol. 35(4):1067–73.

    Article  PubMed  Google Scholar 

  • Goodman M, Dana Flanders W (2007) Study design options in evaluating gene–environment interactions: practical considerations for a planned case-control study of pediatric leukemia. Pediatr Blood Cancer. 48(4):375–9.

    Google Scholar 

  • Hahn LW, Ritchie MD, Moore JH (2003) Multifactor dimensionality reduction software for detecting gene–gene and gene–environment interactions. Bioinformatics. 19:376–82.

    Article  PubMed  CAS  Google Scholar 

  • Hall J, Marcel V, Bolin C, Fernet M, Tartier L, Vaslin L, Hainaut P (2009). The associations of sequence variants in DNA-repair and cell-cycle genes with cancer risk: genotype-phenotype correlations. Biochem Soc Trans. 37(Pt 3):527–33.

    Article  PubMed  CAS  Google Scholar 

  • Hansen TF, Wagner GP (2001) Modeling genetic architecture a multilinear theory of gene interaction. Theor Popul Biol. 59:61–86.

    Article  PubMed  CAS  Google Scholar 

  • 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):9362–7.

    Article  PubMed  CAS  Google Scholar 

  • Hung RJ, McKay JD, Gaborieau V, Boffetta P, Hashibe M, Zaridze D, et al. (2008). A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature. 452:633–7.

    Article  PubMed  CAS  Google Scholar 

  • Jakulin A (2005) Machine learning based on attribute interaction. Ph.D. Dissertation, University of Ljubljana, Sezana.

    Google Scholar 

  • Johnson RA, Wichern DW (2002) Applied multivariate statistical analysis, 5th Edition. Prentice Hall, Upper Saddle River, NJ.

    Google Scholar 

  • Johnson PL, Slatkin M (2007). Accounting for bias from sequencing error in population. Mol Biol Evol. 25:199–206.

    Article  PubMed  Google Scholar 

  • Joshi AD, Corral R, Siegmund KD, Haile RW, Le Marchand L, Martínez ME, Ahnen DJ, Sandler RS, Lance P, Stern MC (2009). Red meat and poultry intake, polymorphisms in the nucleotide excision repair and mismatch repair pathways and colorectal cancer risk. Carcinogenesis. 30:472–9.

    Article  PubMed  CAS  Google Scholar 

  • Kallberg H, Padyukov L, Plenge RM, Ronnelid J, Gregersen PK, et al. (2007) Gene–gene and gene–environment interactions involving HLA-DRB1, PTPN22, and smoking in two subsets of rheumatoid arthritis. Am J Hum Genet. 80:867–75.

    Article  PubMed  Google Scholar 

  • Khoury MJ, Wacholder S (2009). Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies-challenges and opportunities. Am J Epidemiol. 169:227–30.

    Article  PubMed  Google Scholar 

  • Khoury-Shakour S, Gruber SB, Lejbkowicz F, Rennert HS, Raskin L, Pinchev M, Rennert G (2008). Recreational physical activity modifies the association between a common GH1 polymorphism and colorectal cancer risk. Cancer Epidemiol Biomarkers Prev. 17: 3314–18.

    Article  PubMed  CAS  Google Scholar 

  • Klareskog L, Stolt P, Lundberg K, Kallberg H, Bengtsson C, et al. (2006) A new model for an etiology of rheumatoid arthritis: smoking may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified by citrullination. Arthritis Rheum. 54:38–46.

    Article  PubMed  CAS  Google Scholar 

  • Koopman JS (1977) Causal models and sources of interaction. Am J Epidemiol. 106:439–44.

    PubMed  CAS  Google Scholar 

  • Lehmann EL (1983) Theory of point estimation. John Wiley & Sons, New York, NY.

    Book  Google Scholar 

  • Liberman U, Puniyani A, Feldman MW (2007) On the evolution of epistasis II: a generalized Wright-Kimura framework. Theor Popul Biol. 71(2):230–8.

    Article  PubMed  Google Scholar 

  • Linn-Rasker SP, Van der Helm-Van Mil AH, Van Gaalen FA, Kloppenburg M, De Vries RR, et al. (2006) Smoking is a risk factor for anti-CCP antibodies only in rheumatoid arthritis patients who carry HLA-DRB1 shared epitope alleles. Ann Rheum Dis. 65:366–71.

    Article  PubMed  CAS  Google Scholar 

  • Liu X, Fallin MD, Kao WH (2004) Genetic dissection methods: designs used for tests of gene–environment interaction. Curr Opin Genet Dev. 14:241–5.

    Article  PubMed  CAS  Google Scholar 

  • Luan JA, Wong MY, Day NE, Wareham NJ (2001) Sample size determination for studies of gene–environment interaction. Int J Epidemiol. 30(5):1035–40.

    Article  PubMed  CAS  Google Scholar 

  • Lundberg K, Nijenhuis S, Vossenaar ER, Palmblad K, Van Venrooij WJ, et al. (2005) Citrullinated proteins have increased immunogenicity and arthritogenicity and their presence in arthritic joints correlates with disease severity. Arthritis Res Ther. 7:R458–67.

    Article  PubMed  CAS  Google Scholar 

  • Manolio TA, Bailey-Wilson JE, Collins FS (2006) Genes, environment and the value of prospective cohort studies. Nat Rev Genet. 7(10):812–20.

    Article  PubMed  CAS  Google Scholar 

  • Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet. 37:413–17.

    Article  PubMed  CAS  Google Scholar 

  • Mukherjee B, Ahn J, Gruber SB, Rennert G, Moreno V, Chatterjee N. (2008) Tests for gene environment interaction from case-control data: a novel study of type I error, power and designs. Genet Epidemiol. 32(7):615–26.

    Article  PubMed  Google Scholar 

  • Murcary CE, lewinger JP, Gauderman WJ (2009) Gene–environment interaction in genome-wide association studies. Am J Epidemiol. 169:219–26.

    Article  Google Scholar 

  • Ottman R (1996) Theoretic epidemiology. Gene–environment interaction: definitions and study designs. Prev Med. 25:764–70.

    Article  PubMed  CAS  Google Scholar 

  • Phillips PC (2008) Epistasis-the essential role of gene interactions in the structure and evolution of genetic systems. Nature Rev Genet. 9:855–67.

    Article  PubMed  CAS  Google Scholar 

  • Piegorsch WW, Weinberg CR, Taylor JA (1994) Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med. 13(2):153–62.

    Article  PubMed  CAS  Google Scholar 

  • Puniyani A, Liberman U, Feldman MW (2004) On the meaning of non-epistatic selection. Theor Popul Biol. 66:317–21.

    Article  PubMed  Google Scholar 

  • Qin JM, Yang L, Chen B, Wang XM, Li F, Liao PH, He L (2008) Interaction of methylenetetrahydrofolate reductase C677T, cytochrome P4502E1 polymorphism and environment factors in esophageal cancer in Kazakh population. World J Gastroenterol. 14:6986–92.

    Article  PubMed  CAS  Google Scholar 

  • Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, et al. (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet. 69:138–47.

    Article  PubMed  CAS  Google Scholar 

  • Rothman KJ, Greenland S, Walker AM (1980) Concepts of interaction. Am J Epidemiol. 112(4):467–70.

    PubMed  CAS  Google Scholar 

  • Ruwali M, Khan AJ, Shah PP, Singh AP, Pant MC, Parmar D. (2009) Cytochrome P450 2E1 and head and neck cancer: interaction with genetic and environmental risk factors. Environ Mol Mutagen. 50:473–82.

    Article  PubMed  CAS  Google Scholar 

  • Sabatti C, Risch N (2002) Homozygosity and linkage disequilibrium. Genetics. 160:1707–19.

    PubMed  Google Scholar 

  • Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol. 26(10):1135–45.

    Article  PubMed  CAS  Google Scholar 

  • Taioli E (2008). Gene–environment interaction in tobacco-related cancers. Carcinogenesis. 29:1467–74.

    Article  PubMed  CAS  Google Scholar 

  • Thorgeirsson TE, Geller F, Sulem P, et al. (2008) A variant associated with nicotine dependence, lung cancer and peripheral arterial disease. Nature. 452:638–42.

    Article  PubMed  CAS  Google Scholar 

  • Tsai MH, Tseng HC, Liu CS, Chang CL, Tsai CW, Tsou YA, Wang RF, Lin CC, Wang HC, Chiu CF, Bau DT (2009) Interaction of Exo1 genotypes and smoking habit in oral cancer in Taiwan. Oral Oncol. 45(9):e90–4.

    Article  PubMed  CAS  Google Scholar 

  • Walter SD, Holford TR (1978) Additive, multiplicative, and other models for disease risks. Am J Epidemiol. 108:341–6.

    PubMed  CAS  Google Scholar 

  • Wang K (2008) Genetic association tests in the presence of epistasis or gene–environment interaction. Genet Epidemiol. 32:606–14.

    Article  PubMed  Google Scholar 

  • Winslow RL, Boguski MS (2003) Genome informatics: current status and future prospects. Circ Res. 92:953–61.

    Article  PubMed  CAS  Google Scholar 

  • Wu X, Jin L, Xiong M. (2009) Mutual information for testing gene–environment interaction. PLoS One. 4(2):e4578.

    Article  PubMed  Google Scholar 

  • Yoon Y, Song J, Hong SH, Kim JQ (2003) Analysis of multiple single nucleotide polymorphisms of candidate genes related to coronary heart disease susceptibility by using support vector machines. Clin Chem Lab Med. 41:529–34.

    Article  PubMed  CAS  Google Scholar 

  • Zhang YW, Eom SY, Kim YD, Song YJ, Yun HY, Park JS, Youn SJ, Kim BS, Kim H, Hein DW (2009) Effects of dietary factors and the NAT2 acetylator status on gastric cancer in Koreans. Int J Cancer. 125(1):139–45.

    Article  PubMed  CAS  Google Scholar 

  • Zhou W, Liu G, Miller DP, Thurston SW, Xu LL, et al. (2002) Gene–environment interaction for the ERCC2 polymorphisms and cumulative cigarette smoking exposure in lung cancer. Cancer Res. 62(5):1377–81.

    PubMed  CAS  Google Scholar 

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Acknowledgments

M. Xiong are supported by grants from the National Institutes of Health NIAMS P01 AR052915-01A1 and NIAMS R01AR057120-01.

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Correspondence to Momiao Xiong .

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Xiong, M., Wu, X. (2010). Statistics for Testing Gene–Environment Interaction. In: Roy, D., Dorak, M. (eds) Environmental Factors, Genes, and the Development of Human Cancers. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6752-7_3

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