Abstract
The interest in performing gene–environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene–environment interaction studies and the technical challenges related to these studies—when the number of environmental or genetic risk factors is relatively small—has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene–gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to “joining” two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.
Similar content being viewed by others
References
Albert PS, Ratnasinghe D, Tangrea J, Wacholder S (2001) Limitations of the case-only design for identifying gene-environment interactions. Am J Epidemiol 154:687–693
Albrechtsen A, Castella S, Andersen G, Hansen T, Pedersen O, Nielsen R (2007) A Bayesian multilocus association method: allowing for higher-order interaction in association studies. Genetics 176:1197–1208
Amato R, Pinelli M, D’Andrea D, Miele G, Nicodemi M, Raiconi G, Cocozza S (2010) A novel approach to simulate gene-environment interactions in complex diseases. BMC Bioinformatics 11:8
Anderson CA, Soranzo N, Zeggini E, Barrett JC (2011) Synthetic associations are unlikely to account for many common disease genome-wide association signals. Plos Biol 9:e1000580
Andrieu N, Goldstein AM (2004) The case-combined-control design was efficient in detecting gene-environment interactions. J Clin Epidemiol 57:662–671
Andrieu N, Goldstein AM, Thomas DC, Langholz B (2001) Counter-matching in studies of gene-environment interaction: efficiency and feasibility. Am J Epidemiol 153:265–274
Aschard H, Hancock DB, London SJ, Kraft P (2011) Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects. Hum Hered 70:292–300
Aschard H, Chen J, Cornelis M, Chibnik L, Karlson E, Kraft P (2012) Inclusion of gene–gene and gene–environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am J Hum Genet 90(6):962–972
Balansky R, Ganchev G, Iltcheva M, Nikolov M, Steele VE, De Flora S (2012) Differential carcinogenicity of cigarette smoke in mice exposed either transplacentally, early in life or in adulthood. Int J Cancer 130:1001–1010
Bashir SA, Duffy SW (1995) Correction of risk estimates for measurement error in epidemiology. Methods Inf Med 34:503–510
Bhattacharjee S, Wang Z, Ciampa J, Kraft P, Chanock S, Yu K, Chatterjee N (2010) Using principal components of genetic variation for robust and powerful detection of gene–gene interactions in case-control and case-only studies. Am J Hum Genet 86:331–342
Bookman EB, McAllister K, Gillanders E, Wanke K, Balshaw D, Rutter J, Reedy J, Shaughnessy D, Agurs-Collins T, Paltoo D, Atienza A, Bierut L, Kraft P, Fallin MD, Perera F, Turkheimer E, Boardman J, Marazita ML, Rappaport SM, Boerwinkle E, Suomi SJ, Caporaso NE, Hertz-Picciotto I, Jacobson KC, Lowe WL, Goldman LR, Duggal P, Gunnar MR, Manolio TA, Green ED, Olster DH, Birnbaum LS (2011) Gene-environment interplay in common complex diseases: forging an integrative model-recommendations from an NIH workshop. Genet Epidemiol 35:217–225
Bouzigon E, Corda E, Aschard H, Dizier MH, Boland A, Bousquet J, Chateigner N, Gormand F, Just J, Le Moual N, Scheinmann P, Siroux V, Vervloet D, Zelenika D, Pin I, Kauffmann F, Lathrop M, Demenais F (2008) Effect of 17q21 variants and smoking exposure in early-onset asthma. N Engl J Med 359:1985–1994
Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Chapman & Hall/CRC, New York
Bureau A, Dupuis J, Falls K, Lunetta KL, Hayward B, Keith TP, Van Eerdewegh P (2005) Identifying SNPs predictive of phenotype using random forests. Genet Epidemiol 28:171–182
Bush WS, Dudek SM, Ritchie MD (2006) Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene–gene interactions. Bioinformatics 22:2173–2174
Bůžková 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
Calle ML, Urrea V, Malats N, Van Steen K (2010) mbmdr: an R package for exploring gene–gene interactions associated with binary or quantitative traits. Bioinformatics 26:2198–2199
Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM (2006) Measurement error in nonlinear models, 2nd edn. Chapman & Hall/CRC Press, Boca Raton
Cattaert T, Urrea V, Naj AC, De Lobel L, De Wit V, Fu M, Mahachie John JM, Shen H, Calle ML, Ritchie MD, Edwards TL, Van Steen K (2010) FAM-MDR: a flexible family-based multifactor dimensionality reduction technique to detect epistasis using related individuals. PLoS One 5:e10304
Chanda P, Zhang A, Brazeau D, Sucheston L, Freudenheim JL, Ambrosone C, Ramanathan M (2007) Information-theoretic metrics for visualizing gene-environment interactions. Am J Hum Genet 81:939–963
Chanda P, Sucheston L, Zhang A, Brazeau D, Freudenheim JL, Ambrosone C, Ramanathan M (2008) AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes. Genetics 180:1191–1210
Chanda P, Sucheston L, Liu S, Zhang A, Ramanathan M (2009a) Information-theoretic gene–gene and gene-environment interaction analysis of quantitative traits. BMC Genomics 10:509
Chanda P, Sucheston L, Zhang A, Ramanathan M (2009b) The interaction index, a novel information-theoretic metric for prioritizing interacting genetic variations and environmental factors. Eur J Hum Genet 17:1274–1286
Chapman J, Clayton D (2007) Detecting association using epistatic information. Genet Epidemiol 31:894–909
Chatterjee N, Carroll RJ (2005) Semiparametric maximum likelihood estimation exploiting gene-environment independence in case-control studies. Biometrika 92:399–418
Chatterjee N, Kalaylioglu Z, Carroll RJ (2005) Exploiting gene-environment independence in family-based case-control studies: increased power for detecting associations, interactions and joint effects. Genet Epidemiol 28:138–156
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 79:1002–1016
Chen J, Yu K, Hsing A, Therneau TM (2007) A partially linear tree-based regression model for assessing complex joint gene–gene and gene-environment effects. Genet Epidemiol 31:238–251
Chen YH, Chatterjee N, Carroll RJ (2008) Retrospective analysis of haplotype-based case control studies under a flexible model for gene environment association. Biostatistics 9:81–99
Chen YH, Chatterjee N, Carroll RJ (2009a) Shrinkage estimators for robust and efficient inference in haplotype-based case-control studies. J Am Stat Assoc 104:220–233
Chen YH, Lin HW, Liu HM (2009b) Two-stage Analysis for gene-environment interaction utilizing both case-only and family-based analysis. Genet Epidemiol 33:95–104
Cheng KF (2006) A maximum likelihood method for studying gene-environment interactions under conditional independence of genotype and exposure. Stat Med 25:3093–3109
Ciampa J, Yeager M, Jacobs K, Thun MJ, Gapstur S, Albanes D, Virtamo J, Weinstein SJ, Giovannucci E, Willett WC, Cancel-Tassin G, Cussenot O, Valeri A, Hunter D, Hoover R, Thomas G, Chanock S, Holmes C, Chatterjee N (2011) Application of a novel score test for genetic association incorporating gene–gene interaction suggests functionality for prostate cancer susceptibility regions. Hum Hered 72:182–193
Clayton DG (2009) Prediction and interaction in complex disease genetics: experience in type 1 diabetes. PLoS Genet 5:e1000540
Clayton D, McKeigue PM (2001) Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet 358:1356–1360
Colilla S, Nicolae D, Pluzhnikov A, Blumenthal MN, Beaty TH, Bleecker ER, Lange EM, Rich SS, Meyers DA, Ober C, Cox NJ, Asthm CSG (2003) Evidence for gene-environment interactions in a linkage study of asthma and smoking exposure. J Allergy Clin Immunol 111:840–846
Cordell HJ (2009) Estimation and testing of gene-environment interactions in family-based association studies. Genomics 93:5–9
Cordell HJ, Barratt BJ, Clayton DG (2004) Case/pseudocontrol analysis in genetic association studies: a unified framework for detection of genotype and haplotype associations, gene–gene and gene-environment interactions, and parent-of-origin effects. Genet Epidemiol 26:167–185
Cornelis MC, Agrawal A, Cole JW, Hansel NN, Barnes KC, Beaty TH, Bennett SN, Bierut LJ, Boerwinkle E, Doheny KF, Feenstra B, Feingold E, Fornage M, Haiman CA, Harris EL, Hayes MG, Heit JA, Hu FB, Kang JH, Laurie CC, Ling H, Manolio TA, Marazita ML, Mathias RA, Mirel DB, Paschall J, Pasquale LR, Pugh EW, Rice JP, Udren J, van Dam RM, Wang X, Wiggs JL, Williams K, Yu K (2010) The Gene, Environment Association Studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions. Genet Epidemiol 34:364–372
Cornelis MC, Tchetgen Tchetgen EJ, Liang L, Qi L, Chatterjee N, Hu FB, Kraft P (2011) Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Am J Epidemiol 175:191–202
Crainiceanu A, Liang KY, Crainiceanu CM (2009) Bootstrap Bayesian analysis with applications to gene-environment interaction. In: 24th International Symposium on Computer and Information Sciences, pp 649–654
Culverhouse R, Suarez BK, Lin J, Reich T (2002) A perspective on epistasis: limits of models displaying no main effect. Am J Hum Genet 70:461–471
Culverhouse R, Klein T, Shannon W (2004) Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol 27:141–152
Dai JY, Kooperberg C, LeBlanc M, Prentice RL (2010) On two-stage hypothesis testing procedures via asymptotically independent statistics. UW Biostatistics Working Paper Series. Working Paper 367
Davis RL, Khoury MJ (2007) The emergence of biobanks: practical design considerations for large population-based studies of gene-environment interactions. Community Genet 10:181–185
Dehghan A, Dupuis J, Barbalic M, Bis JC, Eiriksdottir G, Lu C, Pellikka N, Wallaschofski H, Kettunen J, Henneman P, Baumert J, Strachan DP, Fuchsberger C, Vitart V, Wilson JF, Pare G, Naitza S, Rudock ME, Surakka I, de Geus EJ, Alizadeh BZ, Guralnik J, Shuldiner A, Tanaka T, Zee RY, Schnabel RB, Nambi V, Kavousi M, Ripatti S, Nauck M, Smith NL, Smith AV, Sundvall J, Scheet P, Liu Y, Ruokonen A, Rose LM, Larson MG, Hoogeveen RC, Freimer NB, Teumer A, Tracy RP, Launer LJ, Buring JE, Yamamoto JF, Folsom AR, Sijbrands EJ, Pankow J, Elliott P, Keaney JF, Sun W, Sarin AP, Fontes JD, Badola S, Astor BC, Hofman A, Pouta A, Werdan K, Greiser KH, Kuss O, Meyer zu Schwabedissen HE, Thiery J, Jamshidi Y, Nolte IM, Soranzo N, Spector TD, Volzke H, Parker AN, Aspelund T, Bates D, Young L, Tsui K, Siscovick DS, Guo X, Rotter JI, Uda M, Schlessinger D, Rudan I, Hicks AA, Penninx BW, Thorand B, Gieger C, Coresh J, Willemsen G, Harris TB, Uitterlinden AG, Jarvelin MR, Rice K, Radke D, Salomaa V, Willems van Dijk K, Boerwinkle E, Vasan RS, Ferrucci L, Gibson QD, Bandinelli S, Snieder H, Boomsma DI, Xiao X, Campbell H et al (2011) Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation 123:731–738
Demissie S, Cupples LA (2011) Bias due to two-stage residual-outcome regression analysis in genetic association studies. Genet Epidemiol 35:592–596
Dempfle A, Scherag A, Hein R, Beckmann L, Chang-Claude J, Schafer H (2008) Gene-environment interactions for complex traits: definitions, methodological requirements and challenges. Eur J Hum Genet 16:1164–1172
Dennis J, Hawken S, Krewski D, Birkett N, Gheorghe M, Frei J, McKeown-Eyssen G, Little J (2011) Bias in the case-only design applied to studies of gene-environment and gene–gene interaction: a systematic review and meta-analysis. Int J Epidemiol 40:1329–1341
Dizier MH, Selinger-Leneman H, Genin E (2003) Testing linkage and gene × environment interaction: comparison of different affected sib-pair methods. Genet Epidemiol 25:73–79
Doherty SP, Grabowski J, Hoffman C, Ng SP, Zelikoff JT (2009) Early life insult from cigarette smoke may be predictive of chronic diseases later in life. Biomarkers 14(Suppl 1):97–101
Duell EJ, Bracci PM, Moore JH, Burk RD, Kelsey KT, Holly EA (2008) Detecting pathway-based gene–gene and gene-environment interactions in pancreatic cancer. Cancer Epidemiol Biomarkers Prev 17:1470–1479
Dunn EC, Uddin M, Subramanian SV, Smoller JW, Galea S, Koenen KC (2011) Research review: gene-environment interaction research in youth depression—a systematic review with recommendations for future research. J Child Psychol Psychiatry 52:1223–1238
Efird JT (2005) Method for indirectly estimating gene-environment effect modification and power given only genotype frequency and odds ratio of environmental exposure. Eur J Epidemiol 20:389–393
Ege MJ, Strachan DP, Cookson WO, Moffatt MF, Gut I, Lathrop M, Kabesch M, Genuneit J, Buchele G, Sozanska B, Boznanski A, Cullinan P, Horak E, Bieli C, Braun-Fahrlander C, Heederik D, von Mutius E (2011) Gene-environment interaction for childhood asthma and exposure to farming in Central Europe. J Allergy Clin Immunol 127:138–144, 144.e1–144.e4
Elbaz A, Alperovitch A (2002) Bias in association studies resulting from gene-environment interactions and competing risks. Am J Epidemiol 155:265–272
Engelman CD, Baurley JW, Chiu YF, Joubert BR, Lewinger JP, Maenner MJ, Murcray CE, Shi G, Gauderman WJ (2009) Detecting gene-environment interactions in genome-wide association data. Genet Epidemiol 33:S68–S73
Fan R, Zhong M, Wang S, Zhang Y, Andrew A, Karagas M, Chen H, Amos CI, Xiong M, Moore JH (2011) Entropy-based information gain approaches to detect and to characterize gene–gene and gene-environment interactions/correlations of complex diseases. Genet Epidemiol 35:706–721
Fardo DW, Liu J, Demeo DL, Silverman EK, Vansteelandt S (2012) Gene-environment interaction testing in family-based association studies with phenotypically ascertained samples: a causal inference approach. Biostatistics 13:468–481
Ferreira T, Donnelly P, Marchini J (2007) Powerful Bayesian gene–gene interaction analysis. Am J Hum Genet S81:32
Fodor I (2002) A survey of dimension reduction techniques. LLNL technical report
Franks PW (2011) Gene × environment interactions in type 2 diabetes. Curr Diab Rep 11:552–561
Garcia-Closas M, Thompson WD, Robins JM (1998) Differential misclassification and the assessment of gene-environment interactions in case-control studies. Am J Epidemiol 147:426–433
Garcia-Closas M, Rothman N, Lubin J (1999) Misclassification in case-control studies of gene-environment interactions: assessment of bias and sample size. Cancer Epidemiol Biomarkers Prev 8:1043–1050
Gauderman WJ, Faucett CL (1997) Detection of gene-environment interactions in joint segregation and linkage analysis. Am J Hum Genet 61:1189–1199
Gauderman WJ, Thomas DC (2001) The role of interacting determinants in the localization of genes. Adv Genet 42:393–412
Gauderman WJ, Thomas DC, Murcray CE, Conti D, Li D, Lewinger JP (2010) Efficient genome-wide association testing of gene-environment interaction in case-parent trios. Am J Epidemiol 172:116–122
Geneletti S, Gallo V, Porta M, Khoury MJ, Vineis P (2011) Assessing causal relationships in genomics: from Bradford-Hill criteria to complex gene-environment interactions and directed acyclic graphs. Emerg Themes Epidemiol 8:5
Gibson G (2010) Hints of hidden heritability in GWAS. Nat Genet 42:558–560
Greenland S (2009) Interactions in epidemiology: relevance, identification, and estimation. Epidemiology 20:14–17
Gu CC, Yang WW, Kraja AT, de Las Fuentes L, Davila-Roman VG (2009) Genetic association analysis of coronary heart disease by profiling gene-environment interaction based on latent components in longitudinal endophenotypes. BMC Proc 3(Suppl 7):S86
Gunther F, Wawro N, Bammann K (2009) Neural networks for modeling gene–gene interactions in association studies. BMC Genet 10:87
Hamza TH, Chen H, Hill-Burns EM, Rhodes SL, Montimurro J, Kay DM, Tenesa A, Kusel VI, Sheehan P, Eaaswarkhanth M, Yearout D, Samii A, Roberts JW, Agarwal P, Bordelon Y, Park Y, Wang L, Gao J, Vance JM, Kendler KS, Bacanu SA, Scott WK, Ritz B, Nutt J, Factor SA, Zabetian CP, Payami H (2011) Genome-wide gene-environment study identifies glutamate receptor gene GRIN2A as a Parkinson’s disease modifier gene via interaction with coffee. PLoS Genet 7:e1002237
Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:95–108
Hoffmann TJ, Lange C, Vansteelandt S, Laird NM (2009) Gene-environment interaction tests for dichotomous traits in trios and sibships. Genet Epidemiol 33:691–699
Hothorn T, Hornik K, Zeileis A (2006) Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat 15:651–674
Hunter DJ (2005) Gene-environment interactions in human diseases. Nat Rev Genet 6:287–298
Karageorgi S, Prescott J, Wong JY, Lee IM, Buring JE, De Vivo I (2011) GSTM1 and GSTT1 copy number variation in population-based studies of endometrial cancer risk. Cancer Epidemiol Biomarkers Prev 20:1447–1452
Kazma R, Babron MC, Genin E (2011) Genetic association and gene-environment interaction: a new method for overcoming the lack of exposure information in controls. Am J Epidemiol 173:225–235
Khoury MJ, Wacholder S (2009) Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies—challenges and opportunities. Am J Epidemiol 169:227–230 (discussion 234–235)
Knox SS (2010) From ‘omics’ to complex disease: a systems biology approach to gene-environment interactions in cancer. Cancer Cell Int 10:11
Kooperberg C, Leblanc M (2008) Increasing the power of identifying gene × gene interactions in genome-wide association studies. Genet Epidemiol 32:255–263
Kraft P (2011) Population stratification bias more widespread than previously thought. Epidemiology 22:408–409
Kraft P, Yen YC, Stram DO, Morrison J, Gauderman WJ (2007) Exploiting gene-environment interaction to detect genetic associations. Hum Hered 63:111–119
Laird NM, Lange C (2006) Family-based designs in the age of large-scale gene-association studies. Nat Rev Genet 7:385–394
Lake SL, Laird NM (2004) Tests of gene-environment interaction for case-parent triads with general environmental exposures. Ann Hum Genet 68:55–64
Lee WC, Chang CH (2006) Assessing effects of disease genes and gene-environment interactions: the case-spouse design and the counterfactual-control analysis. J Epidemiol Community Health 60:683–685
Lehr T, Yuan J, Zeumer D, Jayadev S, Ritchie MD (2011) Rule based classifier for the analysis of gene–gene and gene-environment interactions in genetic association studies. BioData Min 4:4
Lesch KP (2004) Gene-environment interaction and the genetics of depression. J Psychiatry Neurosci 29:174–184
Lettre G, Lange C, Hirschhorn JN (2007) Genetic model testing and statistical power in population-based association studies of quantitative traits. Genet Epidemiol 31:358–362
Li D, Conti DV (2009) Detecting gene-environment interactions using a combined case-only and case-control approach. Am J Epidemiol 169:497–504
Lim S, Beyene J, Greenwood CM (2005) Continuous covariates in genetic association studies of case-parent triads: gene and gene-environment interaction effects, population stratification, and power analysis. Stat Appl Genet Mol Biol 4:Article20
Lindstrom S, Yen YC, Spiegelman D, Kraft P (2009) The impact of gene-environment dependence and misclassification in genetic association studies incorporating gene-environment interactions. Hum Hered 68:171–181
Little R, Rubin D (1987) Statistical analysis with missing data. Wiley, New York
Liu X, Fallin MD, Kao WH (2004) Genetic dissection methods: designs used for tests of gene-environment interaction. Curr Opin Genet Dev 14:241–245
Lo CY, Hsieh PH, Chen HF, Su HM (2009) A maternal high-fat diet during pregnancy in rats results in a greater risk of carcinogen-induced mammary tumors in the female offspring than exposure to a high-fat diet in postnatal life. Int J Cancer 125:767–773
Lobach I, Mallick B, Carroll RJ (2011) Semiparametric Bayesian analysis of gene-environment interactions with error in measurement of environmental covariates and missing genetic data. Stat Interface 4:305–316
Lou XY, Chen GB, Yan L, Ma JZ, Mangold JE, Zhu J, Elston RC, Li MD (2008) A combinatorial approach to detecting gene–gene and gene-environment interactions in family studies. Am J Hum Genet 83:457–467
Lunetta KL, Hayward LB, Segal J, Van Eerdewegh P (2004) Screening large-scale association study data: exploiting interactions using random forests. BMC Genet 5:32
Macgregor S, Khan IA (2006) GAIA: an easy-to-use web-based application for interaction analysis of case-control data. BMC Med Genet 7:34
Maenner MJ, Denlinger LC, Langton A, Meyers KJ, Engelman CD, Skinner HG (2009) Detecting gene-by-smoking interactions in a genome-wide association study of early-onset coronary heart disease using random forests. BMC Proc 3(Suppl 7):S88
Mahachie John JM, Van Lishout F, Van Steen K (2011) Model-based multifactor dimensionality reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data. Eur J Hum Genet 19:696–703
Maity A, Carroll RJ, Mammen E, Chatterjee N (2009) Testing in semiparametric models with interaction, with applications to gene-environment interactions. J R Stat Soc Series B Stat Methodol 71:75–96
Manning AK, LaValley M, Liu CT, Rice K, An P, Liu Y, Miljkovic I, Rasmussen-Torvik L, Harris TB, Province MA, Borecki IB, Florez JC, Meigs JB, Cupples LA, Dupuis J (2011) Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet Epidemiol 35:11–18
Manolio TA, Collins FS (2007) Genes, environment, health, and disease: facing up to complexity. Hum Hered 63:63–66
Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 37:413–417
McKinney BA, Crowe JE, Guo J, Tian D (2009) Capturing the spectrum of interaction effects in genetic association studies by simulated evaporative cooling network analysis. PLoS Genet 5:e1000432
Meyer UA (2000) Pharmacogenetics and adverse drug reactions. Lancet 356:1667–1671
Mi X, Eskridge KM, George V, Wang D (2011) Structural equation modeling of gene-environment interactions in coronary heart disease. Ann Hum Genet 75:255–265
Moerkerke B, Vansteelandt S, Lange C (2010) A doubly robust test for gene-environment interaction in family-based studies of affected offspring. Biostatistics 11:213–225
Motsinger AA, Dudek SM, Hahn LW, Ritchie MD (2006) Comparison of neural network optimization approaches for studies of human genetics. Lect Notes Comput Sci 3907:103–114
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 64:685–694
Mukherjee B, Zhang L, Ghosh M, Sinha S (2007) Semiparametric Bayesian analysis of case-control data under conditional gene-environment independence. Biometrics 63:834–844
Mukherjee B, Ahn J, Gruber SB, Ghosh M, Chatterjee N (2010) Case-control studies of gene-environment interaction: Bayesian design and analysis. Biometrics 66:934–948
Mukherjee B, Ahn J, Gruber SB, Chatterjee N (2011) Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons. Am J Epidemiol 175:177–190
Murcray CE, Lewinger JP, Gauderman WJ (2009) Gene-environment interaction in genome-wide association studies. Am J Epidemiol 169:219–226
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 35:201–210
Ober C, Vercelli D (2011) Gene-environment interactions in human disease: nuisance or opportunity? Trends Genet 27:107–115
Paré G, Cook NR, Ridker PM, Chasman DI (2010) On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women’s Genome Health Study. PLoS Genet 6:e1000981
Park MY, Hastie T (2008) Penalized logistic regression for detecting gene interactions. Biostatistics 9:30–50
Pattin KA, White BC, Barney N, Gui J, Nelson HH, Kelsey KT, Andrew AS, Karagas MR, Moore JH (2009) A computationally efficient hypothesis testing method for epistasis analysis using multifactor dimensionality reduction. Genet Epidemiol 33:87–94
Pearce N (2011) Epidemiology in a changing world: variation, causation and ubiquitous risk factors. Int J Epidemiol 40:503–512
Pereira TV, Patsopoulos NA, Salanti G, Ioannidis JP (2009) Discovery properties of genome-wide association signals from cumulatively combined data sets. Am J Epidemiol 170:1197–1206
Pereira TV, Patsopoulos NA, Pereira AC, Krieger JE (2011) Strategies for genetic model specification in the screening of genome-wide meta-analysis signals for further replication. Int J Epidemiol 40:457–469
Phillips PC (2008) Epistasis—the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 9:855–867
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 13:153–162
Prentice RL (2011) Empirical evaluation of gene and environment interactions: methods and potential. J Natl Cancer Inst 103:1209–1210
Price AL, Zaitlen NA, Reich D, Patterson N (2010) New approaches to population stratification in genome-wide association studies. Nat Rev Genet 11:459–463
Rappaport SM, Smith MT (2010) Environment and disease risks. Science 330:460–461
Ripley B (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge
Risch N, Herrell R, Lehner T, Liang KY, Eaves L, Hoh J, Griem A, Kovacs M, Ott J, Merikangas KR (2009) Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: a meta-analysis. JAMA 301:2462–2471
Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69:138–147
Ritchie MD, Motsinger AA, Bush WS, Coffey CS, Moore JH (2007) Genetic programming neural networks: a powerful bioinformatics tool for human genetics. Appl Soft Comput 7:471–479
Rothman K, Greenland S (1998) Modern epidemiology. Lippencott-Raven, Philadelphia
Rothman KJ, Greenland S, Walker AM (1980) Concepts of interaction. Am J Epidemiol 112:467–470
Rothman K, Greenland S, Lash T (2008) Modern epidemiology, 3rd edn. Lippincott Williams & Wilkins, Philadephia
Schaid DJ (1999) Case-parents design for gene-environment interaction. Genet Epidemiol 16:261–273
Schwarz DF, Konig IR, Ziegler A (2010) On safari to random jungle: a fast implementation of random forests for high-dimensional data. Bioinformatics 26:1752–1758
Schwender H, Ruczinski I (2010) Logic regression and its extensions. Adv Genet 72:25–45
Shi M, Umbach DM, Weinberg CR (2011) Family-based gene-by-environment interaction studies revelations and remedies. Epidemiology 22:400–407
Siemiatycki J, Thomas DC (1981) Biological models and statistical interactions: an example from multistage carcinogenesis. Int J Epidemiol 10:383–387
Smith GD, Timpson N, Ebrahim S (2008) Strengthening causal inference in cardiovascular epidemiology through Mendelian randomization. Ann Med 40:524–541
Song YS, Wang F, Slatkin M (2010) General epistatic models of the risk of complex diseases. Genetics 186:1467–1473
Stern MC, Johnson LR, Bell DA, Taylor JA (2002) XPD codon 751 polymorphism, metabolism genes, smoking, and bladder cancer risk. Cancer Epidemiol Biomark Prev 11:1004–1011
Strobl C, Boulesteix AL, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics 8:25
Strobl C, Boulesteix AL, Kneib T, Augustin T, Zeileis A (2008) Conditional variable importance for random forests. BMC Bioinformatics 9:307
Struchalin MV, Dehghan A, Witteman JC, van Duijn C, Aulchenko YS (2010) Variance heterogeneity analysis for detection of potentially interacting genetic loci: method and its limitations. BMC Genet 11:92
Takeuchi F, Kobayashi S, Ogihara T, Fujioka A, Kato N (2011) Detection of common single nucleotide polymorphisms synthesizing quantitative trait association of rarer causal variants. Genome Res 21:1122–1130
Tan P, Steinbach M, Kumar V (2006) Introduction to Data Mining. Addison-Wesley, Reading
Tan Y-D, Fornage M, George V. Xu H (2007) Parent–child pair design for detecting gene–environment interactions in complex diseases. Hum Genet 121:745–757
Tanck MW, Jukema JW, Zwinderman AH (2006) Simultaneous estimation of gene–gene and gene-environment interactions for numerous loci using double penalized log-likelihood. Genet Epidemiol 30:645–651
Tchetgen Tchetgen EJ, Robins J (2010) The semiparametric case-only estimator. Biometrics 66:1138–1144
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 22:257–261
Tchetgen Tchetgen EJ, VanderWeele TJ (2012) Robustness of measures of interaction to unmeasured confounding. Harvard University Biostatistics Working Paper Series Working Paper 89
Thomas DC (2000) Case-parents design for gene-environment interaction by Schaid. Genet Epidemiol 19:461–463
Thomas D (2010a) Gene–environment-wide association studies: emerging approaches. Nat Rev Genet 11:259–272
Thomas D (2010b) Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies. Annu Rev Public Health 31:21–36
Thompson WD (1991) Effect modification and the limits of biological inference from epidemiologic data. J Clin Epidemiol 44:221–232
Tryon R (1939) Cluster analysis. McGraw-Hill, New-York
Tung L, Gordon D, Finch SJ (2007) The impact of genotype misclassification errors on the power to detect a gene-environment interaction using cox proportional hazards modeling. Hum Hered 63:101–110
Tweel I, Schipper M (2004) Sequential tests for gene-environment interactions in matched case-control studies. Stat Med 23:3755–3771
Tzeng JY, Zhang DW, Pongpanich M, Smith C, McCarthy MI, Sale MM, Worrall BB, Hsu FC, Thomas DC, Sullivan PF (2011) Studying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression. Am J Hum Genet 89:277–288
Uher R (2008) Gene-environment interaction: overcoming methodological challenges. Novartis Found Symp 293:13–26 (discussion 26–30, 68–70)
Umbach DM, Weinberg CR (1997) Designing and analysing case-control studies to exploit independence of genotype and exposure. Stat Med 16:1731–1743
Umbach DM, Weinberg CR (2000) The use of case-parent triads to study joint effects of genotype and exposure. Am J Hum Genet 66:251–261
van der Sluis S, Dolan CV, Neale MC, Posthuma D (2008) A general test for gene-environment interaction in sib pair-based association analysis of quantitative traits. Behav Genet 38:372–389
Van Lishout F, Cattaert T, Mahachie John M, Gusareva E, Urrea V, Cleynen I, Théatre E, Charloteaux B, Calle M, Wehenkel L, Van Steen K (2011) An efficient algorithm to perform multiple testing in epistasis screening
Van Steen K (2012) Travelling the world of gene–gene interactions. Brief Bioinform 13:1–19
Vansteelandt S, Demeo DL, Lasky-Su J, Smoller JW, Murphy AJ, McQueen M, Schneiter K, Celedon JC, Weiss ST, Silverman EK, Lange C (2008) Testing and estimating gene-environment interactions in family-based association studies. Biometrics 64:458–467
Vercelli D (2010) Gene-environment interactions in asthma and allergy: the end of the beginning? Curr Opin Allergy Clin Immunol 10:145–148
Visscher PM, Yang J, Goddard ME (2010) A commentary on ‘common SNPs explain a large proportion of the heritability for human height’ by Yang et al. (2010). Twin Res Hum Genet 13:517–524
Wakefield J, De Vocht F, Hung RJ (2010) Bayesian mixture modeling of gene-environment and gene–gene interactions. Genet Epidemiol 34:16–25
Wang LY, Lee WC (2008) Population stratification bias in the case-only study for gene-environment interactions. Am J Epidemiol 168:197–201
Wang T, Ho G, Ye K, Strickler H, Elston RC (2009) A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped. Genet Epidemiol 33(1):6–15
Wang WY, Barratt BJ, Clayton DG, Todd JA (2005) Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6:109–118
Weinberg CR, Umbach DM (2000) Choosing a retrospective design to assess joint genetic and environmental contributions to risk. Am J Epidemiol 152:197–203
Whittemore AS (2007) Assessing environmental modifiers of disease risk associated with rare mutations. Hum Hered 63:134–143
Wild CP (2005) Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev 14:1847–1850
Willis-Owen SA, Valdar W (2009) Deciphering gene-environment interactions through mouse models of allergic asthma. J Allergy Clin Immunol 123:14–23 (quiz 24–25)
Witte JS, Gauderman WJ, Thomas DC (1999) Asymptotic bias and efficiency in case-control studies of candidate genes and gene-environment interactions: basic family designs. Am J Epidemiol 149:693–705
Wong MY, Day NE, Luan JA, Chan KP, Wareham NJ (2003) The detection of gene-environment interaction for continuous traits: should we deal with measurement error by bigger studies or better measurement? Int J Epidemiol 32:51–57
Wong MY, Day NE, Luan JA, Wareham NJ (2004) Estimation of magnitude in gene-environment interactions in the presence of measurement error. Stat Med 23:987–998
Wray NR, Purcell SM, Visscher PM (2011) Synthetic associations created by rare variants do not explain most GWAS results. Plos Biol 9:e1000579
Wright AF, Carothers AD, Campbell H (2002) Gene-environment interactions–the BioBank UK study. Pharmacogenomics J 2:75–82
Wu X, Jin L, Xiong M (2009) Mutual information for testing gene-environment interaction. PLoS One 4:e4578
Wu C, Hu Z, He Z, Jia W, Wang F, Zhou Y, Liu Z, Zhan Q, Liu Y, Yu D, Zhai K, Chang J, Qiao Y, Jin G, Liu Z, Shen Y, Guo C, Fu J, Miao X, Tan W, Shen H, Ke Y, Zeng Y, Wu T, Lin D (2011) Genome-wide association study identifies three new susceptibility loci for esophageal squamous-cell carcinoma in Chinese populations. Nat Genet 43:679–684
Wyszynski DF, Diehl SR (2001) The mother-only method (MOM) to detect maternal gene–environment interactions. Paediatr Perinat Epidemiol 15:317–318
Yang Q, Khoury MJ (1997) Evolving methods in genetic epidemiology. III. Gene-environment interaction in epidemiologic research. Epidemiol Rev 19:33–43
Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW, Goddard ME, Visscher PM (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42:565–569
Yoshida M, Koike A (2011) SNPInterForest: a new method for detecting epistatic interactions. BMC Bioinformatics 12:469
Yu K, Wacholder S, Wheeler W, Wang Z, Caporaso N, Landi MT, Liang F (2012) A flexible bayesian model for studying gene-environment interaction. PLoS Genet 8:e1002482
Zhai R, Zhao Y, Liu G, Ter-Minassian M, Wu IC, Wang Z, Su L, Asomaning K, Chen F, Kulke MH, Lin X, Heist RS, Wain JC, Christiani DC (2011) Interactions between environmental factors and polymorphisms in angiogenesis pathway genes in esophageal adenocarcinoma risk: a case-only study. Cancer 118:804–811
Zhang Y, Liu JS (2007) Bayesian inference of epistatic interactions in case-control studies. Nat Genet 39:1167–1173
Zhang L, Mukherjee B, Ghosh M, Gruber S, Moreno V (2008) Accounting for error due to misclassification of exposures in case-control studies of gene-environment interaction. Stat Med 27:2756–2783
Zhang Y, Jiang B, Zhu J, Liu JS (2011) Bayesian models for detecting epistatic interactions from genetic data. Ann Hum Genet 75:183–193
Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci USA 109:1193–1198
Acknowledgments
H. Aschard and P. Kraft were supported by grant NIH/NIDDK-R21 DK084529. B. Maus and K. Van Steen acknowledge research opportunities offered by the Belgian Network BioMAGNet (Bioinformatics and Modeling: from Genomes to Networks), funded by the Interuniversity Attraction Poles Program (Phase VI/4), initiated by the Belgian State, Science Policy Office. Their work was also supported in part by the IST Program of the European Community, under the PASCAL2 Network of Excellence (Pattern Analysis, Statistical Modeling and Computational Learning), IST-2007-216886. E.J. Duell was supported by the Spanish Ministry of Health (ISCIII RETICC RD06/0020). The scientific responsibility for this work rests with its authors.
Conflict of interest
The authors declare that they have no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Additional information
S. Lutz and B. Maus contributed equally to this work.
Rights and permissions
About this article
Cite this article
Aschard, H., Lutz, S., Maus, B. et al. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet 131, 1591–1613 (2012). https://doi.org/10.1007/s00439-012-1192-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00439-012-1192-0