Gene-environment interactions in human diseases

Nature Reviews Genetics

Volumn 6, Issue 4, 2005, Pages 287-298

  Hunter, David J ^a,b  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ENVIRONMENTAL EXPOSURE; ENVIRONMENTAL FACTOR; GENETIC ANALYSIS; GENOTYPE; HAPLOTYPE; HEREDITY; HUMAN; PRIORITY JOURNAL; REVIEW; BIOLOGICAL MODEL; ENVIRONMENT; EPIDEMIOLOGY; GENETIC PREDISPOSITION; PROSPECTIVE STUDY; REPRODUCIBILITY; RISK FACTOR; SAMPLE SIZE;

ENVIRONMENT; EPIDEMIOLOGIC STUDIES; GENETIC PREDISPOSITION TO DISEASE; GENOTYPE; HUMANS; MODELS, GENETIC; PROSPECTIVE STUDIES; REPRODUCIBILITY OF RESULTS; RISK FACTORS; SAMPLE SIZE;

EID: 15944383403     PISSN: 14710056     EISSN: None     Source Type: Journal    
DOI: 10.1038/nrg1578     Document Type: Review

References (70)

1. Garrod, A. The incidence of alkaptonuria: a study in chemical individuality. Lancet 2, 1616-1620 (1902). A classical work of intuition, in which Garrod inferred the existence of inherited biochemical characteristics from family histories of alkaptonuria at the turn of the last century.
2. Green, A. & Trichopoulos, D. in Textbook of Cancer Epidemiology (eds Adami, H., Hunter, D. & Trichopoulos, D.) 281-300 (Oxford Univ. Press, Oxford, 2002).
3. Takeshita, T., Mao, X. Q. & Morimoto, K. The contribution of polymorphism in the alcohol dehydrogenase-β subunit to alcohol sensitivity in a Japanese population. Hum. Genet. 97, 409-413 (1996).
4. Thomas, D. C. Genetic epidemiology with a capital 'E'. Genet. Epidemiol. 19, 289-300 (2000).
5. Botto, L. D. & Khoury, M. J. Commentary. Facing the challenge of gene-environment interaction: the two-by-four table and beyond. Am. J. Epidemiol. 153, 1016-1020 (2001).
6. Khoury, M. J., Adams, M. J. & Flanders, W. D. An epidemiologic approach to ecogenetics. Am. J. Hum. Genet. 42, 89-95 (1988). The patterns by which genes and environment jointly determine risk were laid out using commonly accepted clinical models such as xeroderma pigmentosum and phenylketonuria.
7. Ottman, R. An epidemiologic approach to gene-environment interaction. Genet. Epidemiol. 7, 177-185 (1990).
8. Begg, C. B. & Berlin, J. A. Publication bias and dissemination of clinical research. J. Natl Cancer Inst. 81, 107-115 (1989).
9. Rothman, K. J. & Greenland, S. Modem Epidemiology 2nd edn (Lippincott-Raven, Philadelphia, 1998).
10. Rebbeck, T. R., Spitz, M. & Wu, X. Assessing the function of genetic variants in candidate gene association studies. Nature Rev. Genet. 5, 589-597 (2004).
11. Armitage, P. & Colton, T. (eds) Biostatistical Genetics and Genetic Epidemiology (John Wiley & Sons, West Sussex, 2002).
12. Narod, S. A. et al. Risk modifiers in carriers of BRCA1 mutations. Int. J. Cancer 64, 394-398 (1995).
13. Gauderman, W. J. Sample size requirements for matched case-control studies of gene-environment interaction. Stat. Med. 21, 35-50 (2002).
14. Khoury, M. J. & Flanders, W. D. Nontraditional epidemiologic approaches in the analysis of gene-environment interaction: case-control studies with no controls! Am. J. Epidemiol. 144, 207-213 (1996).
15. Adami, H. & Trichopoulos, D. in Textbook of Cancer Epidemiology (eds Adami, H., Hunter, D. & Trichopoulos, D.) 87-109 (Oxford Univ. Press, New York, 2002).
16. Wacholder S., Rothman, N. & Caporaso, N. Population stratification in epidemiologic studies of common genetic variants and cancer: quantification of bias. J. Natl Cancer Inst. 92, 1151-1158 (2000). Using simulation studies, Wacholder and colleagues showed that the fear of population stratification in well-designed associated studies was exaggerated.
17. Freedman, M. L. et al. Assessing the impact of population stratification on genetic association studies. Nature Genet. 36, 388-393 (2004).
18. Cardon, L. R. & Palmer, L. J. Population stratification and spurious allelic association. Lancet 361, 598-604 (2003).
19. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997-1004 (1999).
20. Pritchard, J. K. & Rosenberg, N. A. Use of unlinked genetic markers to detect population stratification in association studies. Am. J. Hum. Genet. 65, 220-228 (1999).
21. Morimoto, L. M., White, E. & Newcomb, P. A. Selection bias in the assessment of gene-environment interaction in case-control studies. Am. J. Epidemiol. 158, 259-263 (2003).
22. Garcia-Closas, M., Thompson, W. D. & Robins, J. M. Differential misclassification and the assessment of gene-environment interactions in case-control studies. Am J. Epidemiol. 147, 426-433 (1998).
23. Liotta, L. & Petricoin, E. Molecular profiling of human cancer. Nature Rev. Genet. 1, 48-56 (2000).
24. Umbach, D. M. & Weinberg, C. R. Designing and analysing case-control studies to exploit independence of genotype and exposure. Stat. Med. 16, 1731-1743 (1997).
25. Schmidt, S. & Schaid, D. J. Potential misinterpretation of the case-only study to assess gene-environment interaction. Am. J. Epidemiol. 150, 878-885 (1999).
26. Albert, P. S. Ratnasinghe, D., Tangrea, J. & Wacholder, S. Limitations of the case-only design for identifying gene-environment interactions. Am. J. Epidemiol. 154, 687-693 (2001).
27. Smith, P. G. & Day, N. E. The design of case-control studies: the influence of confounding and interaction effects. Int. J. Epidemiol. 13, 356-365 (1984).
28. Garcia-Closas, M., Rothman, N. & Lubin, J, Misclassification in case-control studies of gene-environment interactions: assessment of bias and sample size. Cancer Epidemiol. Biomarkers Prev. 8, 1043-1050 (1999). Using standard techniques, these authors pointed out the inadequate size of contemporary studies of gene-environment interactions, particularly once measurement error of the environmental variables was considered.
29. Wong, M. Y. Day, N. E., Luan, J. A. & Wareham, N. J. Estimation of magnitude in gene-environment interactions in the presence of measurement error. Stat. Med. 23, 987-998 (2004).
30. Clayton, D. & McKeigue, P. M. Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet 358, 1356-1360 (2001). A careful reconsideration of the advantages of the retrospective case-control design compared with prospective studies.
31. Sinha, R. et al. Heterocyclic amine content in beef cooked by different methods to varying degrees of doneness and gravy made from meat drippings. Food Chem. Toxicol. 36, 279-287 (1998).
32. Roberts-Thomson, I. C. et al. Diet, acetylator phenotype, and risk of colorectal neoplasia. Lancet 347, 1372-1374 (1996).
33. Chen, J. et al. A prospective study of N-acetyltransferase genotype, red meat intake, and risk of colorectal cancer. Cancer Res. 58, 3307-3311 (1998).
34. Kampman, E. et al. Meat consumption, genetic susceptibility, and colon cancer risk: a United States multicenter case-control study. Cancer Epidemiol. Biomarkers Prev. 8, 15-24 (1999).
35. Le Marchand, L. et al. Well-done red meat, metabolic phenotypes and colorectal cancer in Hawaii. Mutat Res. 506-507, 205-214 (2002).
36. Barrett, J. H. et al. Investigation of interaction between N-acetyltransferase 2 and heterocyclic amines as potential risk factors for colorectal cancer. Carcinogenesis 24, 275-282 (2003).
37. Rothman, N. et al. The use of common genetic polymorphisms to enhance the epidemiologic study of environmental carcinogens. Biochim. Biophys. Acta 1471, C1-C10 (2001).
38. Ames, B. N. Cancer prevention and diet: help from single nucleotide polymorphisms. Proc. Natl Acad. Sci. USA 96, 12216-12218 (1999).
39. Davey Smith, G. & Ebrahim, S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32, 1-22 (2003).
40. Goldstein, D. B., Tate, S. K. & Sisodiya, S. M. Pharmacogenetics goes genomic. Nature Rev. Genet. 4, 937-947 (2003).
41. Smith, M. W. et al. Contrasting genetic influence of CCR2 and CCR5 variants on HIV-1 infection and disease progression. Hemophilia Growth and Development Study (HGDS), Multicenter AIDS Cohort Study (MACS), Multicenter Hemophilia Cohort Study (MHCS), San Francisco City Cohort (SFCC), ALIVE Study. Science 277, 959-965 (1997).
42. Shaheen, F. & Collman, R. G. Co-receptor antagonists as HIV-1 entry inhibitors. Curr. Opin. Infect. Dis. 17, 7-16 (2004).
43. Colhoun, H. M., McKeigue, P. M. & Davey Smith, G. Problems of reporting genetic associations with complex outcomes. Lancet 361, 865-872 (2003).
44. Ioannidis, J. P., Rosenberg, P. S., Goedert, J. J. & O'Brien, T. R. Commentary: meta-analysis of individual participants' data in genetic epidemiology. Am. J. Epidemiol. 156, 204-210 (2002).
45. Deitz, A. C. et al. Impact of misclassification in genotype-exposure interaction studies: example of N-acetyltransferase 2 (NAT2), smoking, and bladder cancer. Cancer Epidemiol. Biomarkers Prev. 13, 1543-1546 (2004).
46. Le Marchand, L. et al. Feasibility of collecting buccal cell DNA by mail in a cohort study. Cancer Epidemiol. Biomarkers Prev. 10, 701-703 (2001).
47. Garcia-Closas, M. et al. Collection of genomic DNA from adults in epidemiological studies by buccal cytobrush and mouthwash. Cancer Epidemiol. Biomarkers Prev. 10, 687-696 (2001).
48. Collins, F. S. The case for a US prospective cohort study of genes and environment. Nature 429, 475-477 (2004).
49. Tranah, G. J., Lescault, P. J., Hunter, D. J. & De Vivo, I. Multiple displacement amplification prior to single nucleotide polymorphism genotyping in epidemiologic studies. Biotechnol. Lett. 25, 1031-1036 (2003).
50. Paez, J. G. et al. Genome coverage and sequence fidelity of φ29 polymerase-based multiple strand displacement whole genome amplification. Nucleic Acids Res. 32, e71 (2004).
51. Barker, D. L. et al. Two methods of whole-genome amplification enable accurate genotyping across a 2320-SNP linkage panel. Genome Res. 14, 901-907 (2004).
52. Ge, H., Walhout, A. J. & Vidal, M. Integrating 'omic' information: a bridge between genomics and systems biology. Trends Genet. 19, 551-560 (2003).
53. Conti, D. V., Cortessis, V., Molitor, J. & Thomas, D. C. Bayesian modeling of complex metabolic pathways. Hum. Hered. 56, 83-93 (2003). An example of an emerging interest in new statistical techniques for addressing perturbations in complex biological pathways.
54. Cai, Z. et al. Bayesian approach to discovering pathogenic SNPs in conserved protein domains. Hum. Mutat. 24, 178-184 (2004).
55. Wacholder, S., Chanock, S., Garcia-Closas, M., El Ghormli, L. & Rothman, N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J. Natl Cancer Inst. 96, 434-442 (2004).
56. Rose, G. Sick individuals and sick populations. Int. J. Epidemiol. 14, 32-38 (1985). A leader in epidemiology contended that public health could be bettered by addressing health problems at the population, rather than the individual, level.
57. Bigler, J. et al. CYP2C9 and UGT1A6 genotypes modulate the protective effect of aspirin on colon adenoma risk. Cancer Res. 61, 3566-3569 (2001).
58. Chan, A. T. et al. Genetic variants in the UGT1A6 enzyme, aspirin use, and the risk of colorectal adenoma. J. Natl Cancer Inst. (in the press).
59. National Institute on Aging/Alzheimer's Association Working Group. Apolipoprotein E genotyping in Alzhelmer's disease. Lancet 347, 1091-1095 (1996).
60. Peila, R. et al. Joint effect of the APOE gene and midlife systolic blood pressure on late-life cognitive impairment: the Honolulu-Asia aging study. Stroke 32, 2882-2889 (2001).
61. Kang, J. H., Logroscino, G., De Vivo, I., Hunter, D. & Grodstein, F. Apolipoprotein E, cardiovascular disease and cognitive function in aging women. Neurobiol. Aging 26, 475-484 (2005).
62. Rees, J. L. The genetics of sun sensitivity in humans. Am. J. Hum. Genet. 75, 739-751 (2004).
63. Chen, J., Giovannucci, E, & Hunter, D. J. MTHFR polymorphism, methyl-replete diets and the risk of colorectal carcinoma and adenoma among US men and women: an example of gene-environment interactions in colorectal tumorigenesis. J. Nutr. 129, S560-S564 (1999).
64. Bloemenkamp, K. W., Rosendaal, F. R., Heimerhorst, F. M., Buller, H. R. & Vandenbroucke, J. P. Enhancement by factor V Leiden mutation of risk of deep-vein thrombosis associated with oral contraceptives containing a third-generation progestagen. Lancet 346, 1593-1596 (1995).
65. Lehtimaki, T. et al. Association between serum lipids and apolipoprotein E phenotype is influenced by diet in a population-based sample of free-living children and young adults: the Cardiovascular Risk in Young Finns Study. J. Lipid Res. 36, 653-661 (1995).
66. Hines, L. M. et al. Genetic variation in alcohol dehydrogenase and the beneficial effect of moderate alcohol consumption on myocardial infarction. N. Engl. J. Med. 344, 549-555 (2001).
67. Memisoglu, A. et al. Interaction between a peroxisome proliferator-activated receptor-γ gene polymorphism and dietary fat intake in relation to body mass. Hum. Mol. Genet. 12, 2923-2929 (2003).
68. Maier, L. A. Genetic and exposure risks for chronic beryllium disease. Clin. Chest Med. 23, 827-839 (2002).
69. Weinshilboum, R. Thiopurine pharmacogenetics: clinical and molecular studies of thiopurine methyltransferase. Drug Metab. Dispos. 29, 601-605 (2001).
70. Israel, E. et al. The effect of polymorphisms of the β2-adrenergic receptor on the response to regular use of albuterol in asthma. Am. J. Respir. Crit. Care Med. 162, 75-80 (2000).