Abstract
Gene-environment interactions (GxE) play a central role in the theoretical relationship between genetic factors and complex traits. While genome wide GxE studies of human behaviors remain underutilized, in part due to methodological limitations, existing GxE research in model organisms emphasizes the importance of interpreting genetic associations within environmental contexts. In this paper, we present a framework for conducting an analysis of GxE using raw data from genome wide association studies (GWAS) and applying the techniques to analyze gene-by-age interactions for alcohol use frequency. To illustrate the effectiveness of this procedure, we calculate genetic marginal effects from a GxE GWAS analysis for an ordinal measure of alcohol use frequency from the UK Biobank dataset, treating the respondent’s age as the continuous moderating environment. The genetic marginal effects clarify the interpretation of the GxE associations and provide a direct and clear understanding of how the genetic associations vary across age (the environment). To highlight the advantages of our proposed methods for presenting GxE GWAS results, we compare the interpretation of marginal genetic effects with an interpretation that focuses narrowly on the significance of the interaction coefficients. The results imply that the genetic associations with alcohol use frequency vary considerably across ages, a conclusion that may not be obvious from the raw regression or interaction coefficients. GxE GWAS is less powerful than the standard “main effect” GWAS approach, and therefore require larger samples to detect significant moderated associations. Fortunately, the necessary sample sizes for a successful application of GxE GWAS can rely on the existing and on-going development of consortia and large-scale population-based studies.
Similar content being viewed by others
References
Aliev F, Latendresse SJ, Bacanu SA, Neale MC, Dick DM (2014) Testing for measured gene-environment interaction: problems with the use of cross-product terms and a regression model reparameterization solution. Behav Genet 44(2):165–181. https://doi.org/10.1007/s10519-014-9642-1
Allen NE, Sudlow C, Peakman T, Collins R (2014) UK biobank data: come and get it. Sci Transl Med. https://doi.org/10.1126/scitranslmed.3008601
Aschard H, Spiegelman D, Laville V, Kraft P, Wang M (2018) A test for gene-environment interaction in the presence of measurement error in the environmental variable. Genet Epidemiol 42(3):250–264. https://doi.org/10.1002/gepi.22113
Baranger DAA, Ifrah C, Prather AA, Carey CE, Corral-Frías NS, Conley ED, Hariri AR, Bogdan R (2016) PER1 rs3027172 genotype interacts with early life stress to predict problematic alcohol use, but not reward-related ventral striatum activity. Front Psychol. https://doi.org/10.3389/fpsyg.2016.00464
Bierut LJ, Goate AM, Breslau N, Johnson EO, Bertelsen S, Fox L, Agrawal A, Bucholz KK, Grucza R, Hesselbrock V, Kramer J, Kuperman S, Nurnberger J, Porjesz B, Saccone NL, Schuckit M, Tischfield J, Wang JC, Foroud T, Rice JP, Edenberg HJ (2012) ADH1B is associated with alcohol dependence and alcohol consumption in populations of European and African ancestry. Mol Psychiatry 17(4):445–450. https://doi.org/10.1038/mp.2011.124
Brambor T, Clark WR, Golder M (2006) Understanding interaction models: improving empirical analyses. Polit Anal 14:62–82
Brody GH, Beach SRH, Philibert RA, Chen YF, Murry VMB (2009) Prevention effects moderate the association of 5-HTTLPR and youth risk behavior initiation: Gene × environment hypotheses tested via a randomized prevention design. Child Dev 80(3):645–661. https://doi.org/10.1111/j.1467-8624.2009.01288.x
Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O’Connell J, Cortes A, Welsh S, Young A, Effingham M, McVean G, Leslie S, Allen N, Donnelly P, Marchini J (2018) The UK Biobank resource with deep phenotyping and genomic data. Nature 562(7726):203–209. https://doi.org/10.1038/s41586-018-0579-z
Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4(1):7. https://doi.org/10.1186/s13742-015-0047-8
Chen C, Chen C, Xue G, Dong Q, Zhao L, Zhang S (2020) Parental warmth interacts with several genes to affect executive function components: a genome-wide environment interaction study. BMC Genet 21(1):11. https://doi.org/10.1186/s12863-020-0819-8
Cho SB, Smith RL, Bucholz K, Chan G, Edenberg HJ, Hesselbrock V, Kramer J, McCutcheon VV, Nurnberger J, Schuckit M, Zang Y, Dick DM, Salvatore JE (2020) Using a developmental perspective to examine the moderating effects of marriage on heavy episodic drinking in a young adult sample enriched for risk. Dev Psychopathol. https://doi.org/10.1017/S0954579420000371
Cichon S, Craddock N, Daly M, Faraone SV, Gejman PV, Kelsoe J, Lehner T, Levinson DF, Moran AP, Sklar P, Sullivan PF (2009) A framework for interpreting genome-wide association studies of psychiatric disorders. Mol Psychiatry. https://doi.org/10.1038/mp.2008.126
Cohen J, Cohen P, West SG, Aiken LS (2003) Applied multiple regression/correlation analysis for the behavioral sciences, 3rd edn. Lawrence Erlbaum Associates Publishers, Mahwah
Colodro-Conde L, Couvy-Duchesne B, Zhu G et al (2018) A direct test of the diathesis–stress model for depression. Mol Psychiatry 23:1590–1596. https://doi.org/10.1038/mp.2017.130
Denny JC, Rutter JL, Goldstein DB, Philippakis A, Smoller JW, Jenkins G, Dishman E (2019) The “all of us” research program. N Engl J Med 381(7):668–676. https://doi.org/10.1056/NEJMsr1809937
Dick DM (2011) An interdisciplinary approach to studying gene-environment interactions: from twin studies to gene identification and back. Res Hum Dev 8(3–4):211–226
Dick DM, Bernard M, Aliev F, Viken R, Pulkkinen L, Kaprio J, Rose RJ (2009) The role of socioregional factors in moderating genetic influences on early adolescent behavior problems and alcohol use. Alcohol: Clin Exp Res 33(10):1739–1748. https://doi.org/10.1111/j.1530-0277.2009.01011.x
Dick DM, Rose RJ, Viken RJ, Kaprio J, Koskenvuo M (2001) Exploring gene-environment interactions: socioregional moderation of alcohol use. J Abnorm Psychol 110(4):625–632. https://doi.org/10.1037//0021-843x.110.4.625
Dick DM, Agrawal A, Keller MC, Adkins A, Aliev F, Monroe S, Hewitt JK, Kendler KS, Sher KJ (2015) Candidate gene-environment interaction research: reflections and recommendations. Perspect Psychol Sci 10(1):37–59. https://doi.org/10.1177/1745691614556682
Dong L, Bilbao A, Laucht M, Henriksson R, Yakovleva T, Ridinger M, Desrivieres S, Clarke TK, Lourdusamy A, Smolka MN, Cichon S, Treutlein J, Perreau S, Witt SH, Leonardi-Essmann F, Wodarz N, Peter Z, Soyka M, Schumann G (2011) Effects of the circadian rhythm gene period 1 (Per1) on psychosocial stress-induced alcohol drinking. Am J Psychiatry 168(10):1090–1098. https://doi.org/10.1176/appi.ajp.2011.10111579
Duncan LE, Keller MC (2011) A critical review of the first 10 years of candidate gene-by-environment interaction research in psychiatry. Am J Psychiatry. https://doi.org/10.1176/appi.ajp.2011.11020191
Ellenbroek BA, Van Der Kam EL, Van Der Elst MCJ, Cools AR (2005) Individual differences in drug dependence in rats: the role of genetic factors and life events. Eur J Pharmacol 526:251–258. https://doi.org/10.1016/j.ejphar.2005.09.032
Fernandes Silva L, Vangipurapu J, Kuulasmaa T, Laakso M (2019) An intronic variant in the GCKR gene is associated with multiple lipids. Sci Rep 9(1):1–9. https://doi.org/10.1038/s41598-019-46750-3
Gauderman WJ, Zhang P, Morrison JL, Lewinger JP (2013) Finding novel genes by testing G × E interactions in a genome-wide association study. Genet Epidemiol 37(6):603–613. https://doi.org/10.1002/gepi.21748
Gaziano JM, Concato J, Brophy M, Fiore L, Pyarajan S, Breeling J, Whitbourne S, Deen J, Shannon C, Humphries D, Guarino P, Aslan M, Anderson D, LaFleur R, Hammond T, Schaa K, Moser J, Huang G, Muralidhar S, Przygodzki R, O’Leary TJ (2016) Million veteran program: a mega-biobank to study genetic influences on health and disease. J Clin Epidemiol 70:214–223. https://doi.org/10.1016/j.jclinepi.2015.09.016
Grigorenko EL, Bick J, Campbell DJ, Lewine G, Abrams J, Nguyen V, Chang JT (2016) The trilogy of GxE: conceptualization, operationalization, and application. Developmental Psychopathology. Wiley, Hoboken, pp 1–52
Guo G, Li Y, Wang H, Cai T, Duncan GJ (2015) Peer influence, genetic propensity, and binge drinking: a natural experiment and a replication. Am J Sociol 121(3):914–954. https://doi.org/10.1086/683224
Harden KP, Hill JE, Turkheimer E, Emery RE (2008) Gene-environment correlation and interaction in peer effects on adolescent alcohol and tobacco use. Behav Genet 38(4):339–347. https://doi.org/10.1007/s10519-008-9202-7
Hutter CM, Mechanic LE, Chatterjee N, Kraft P, Gillanders EM, Gene-Environment Think Tank NCI (2013) Gene-environment interactions in cancer epidemiology: a national cancer institute think tank report. Genet Epidemiol 37(7):643–657. https://doi.org/10.1002/gepi.21756
Jiao S, Peters U, Berndt S, Bézieau S, Brenner H, Campbell PT, Chan AT, Chang-Claude J, Lemire M, Newcomb PA, Potter JD, Slattery ML, Woods MO, Hsu L (2015) Powerful set-based gene-environment interaction testing framework for complex diseases. Genet Epidemiol 39(8):609–618. https://doi.org/10.1002/gepi.21908
Joreskog K, Sorbom D (1993) New features in PRELIS 2. Scientific Software International, Chicago
Kaprio J (2012) Twins and the mystery of missing heritability. The contribution of gene-environment interactions. J Intern Med. https://doi.org/10.1111/j.1365-2796.2012.02587.x
Kaufman J, Yang BZ, Douglas-Palumberi H, Crouse-Artus M, Lipschitz D, Krystal JH, Gelernter J (2007) Genetic and environmental predictors of early alcohol use. Biol Psychiatry 61(11):1228–1234. https://doi.org/10.1016/j.biopsych.2006.06.039
Keller MC (2014) Gene× environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution. Biol Psychiatry 75(1):18–24
Kiive E, Laas K, Vaht M, Veidebaum T, Harro J (2017) Stressful life events increase aggression and alcohol use in young carriers of the GABRA2 rs279826/rs279858 A-allele. Eur Neuropsychopharmacol 27(8):816–827. https://doi.org/10.1016/j.euroneuro.2017.02.003
Kim J, Park A (2018) A systematic review: candidate gene and environment interaction on alcohol use and misuse among adolescents and young adults. Am J Addict. https://doi.org/10.1111/ajad.12755
Laucht M, Treutlein J, Schmid B, Blomeyer D, Becker K, Buchmann AF, Banaschewski T, Schmidt MH, Esser G, Jennen-Steinmetz C, Rietschel M, Zimmermann US, Banaschewski T (2009) Impact of psychosocial adversity on alcohol intake in young adults: moderation by the LL genotype of the serotonin transporter polymorphism. Biol Psychiatry 66(2):102–109. https://doi.org/10.1016/j.biopsych.2009.02.010
Laville V, Bentley AR, Privé F, Zhu X, Gauderman J, Winkler TW, Province M, Rao DC, Aschard H (2018) VarExp: estimating variance explained by genome-wide GxE summary statistics. Bioinformatics 34(19):3412–3414. https://doi.org/10.1093/bioinformatics/bty379
Li JJ, Cho SB, Salvatore JE, Edenberg HJ, Agrawal A, Chorlian DB, Porjesz B, Hesselbrock V, COGA Investigators, Dick DM (2017) The impact of peer substance use and polygenic risk on trajectories of heavy episodic drinking across adolescence and emerging adulthood. Alcohol Clin Exp Res 41(1):65–75. https://doi.org/10.1111/acer.13282
Little J, Sharp L, Khoury MJ, Bradley L, Gwinn M (2005) The epidemiologic approach to pharmacogenomics. Amer J Pharmacogenomics 5(1):1–20
Liu CY, Maity A, Lin X, Wright RO, Christiani DC (2012) Design and analysis issues in gene and environment studies environmental health: a global access science source. BioMed Central. https://doi.org/10.1186/1476-069X-11-93
Maher B (2008) Personal genomes: the case of the missing heritability. Nature. https://doi.org/10.1038/456018a
Manolio TA, Bailey-Wilson JE, Collins FS (2006) Genes, environment and the value of prospective cohort studies. Nat Rev Genet. https://doi.org/10.1038/nrg1919
Marjoram P, Zubair A, Nuzhdin SV (2014) Post-GWAS: where next More samples, more SNPs or more biology. Heredity (Edinb). https://doi.org/10.1038/hdy.2013.52
Mather K, Jinks JL (1982) Biometrical genetics. The study of continuous variation. Chapman Hall, London
Mayhew AJ, Meyre D (2017) Assessing the heritability of complex traits in humans: methodological challenges and opportunities. Curr Genom 18(4):332. https://doi.org/10.2174/1389202918666170307161450
McAllister K, Mechanic LE, Amos C, Aschard H, Blair IA, Chatterjee N, Conti D, Gauderman WJ, Hsu L, Hutter CM, Jankowska MM, Kerr J, Kraft P, Montgomery SB, Mukherjee B, Papanicolaou GJ, Patel CJ, Ritchie MD, Ritz BR, Thomas DC, Wei P, Witte JS (2017) Current challenges and new opportunities for gene-environment interaction studies of complex diseases. Am J Epidemiol 186:753–761. https://doi.org/10.1093/aje/kwx227
Mies GW, Verweij K, Treur JL, Ligthart L, Fedko IO, Hottenga JJ, Willemsen G, Bartels M, Boomsma DI, Vink JM (2018) Polygenic risk for alcohol consumption and its association with alcohol-related phenotypes: do stress and life satisfaction moderate these relationships? Drug Alcohol Depend 183:7–12. https://doi.org/10.1016/j.drugalcdep.2017.10.018
Neale ZE, Kuo SIC, Dick DM (2020) A systematic review of gene-by-intervention studies of alcohol and other substance use. Dev Psychopathol. https://doi.org/10.1017/S0954579420000590
Onukwugha E, Bergtold J, Jain R (2014) A primer on marginal effects—part I: theory and formulae. Pharmacoeconomics 33(1):25–30. https://doi.org/10.1007/s40273-014-0210-6
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(6):1–10. https://doi.org/10.1371/journal.pgen.1000981
Pasman JA, Verweij KJH, Vink JM (2019) Systematic review of polygenic gene-environment interaction in tobacco, alcohol, and cannabis use. Behav Genet. https://doi.org/10.1007/s10519-019-09958-7
Polimanti R, Kaufman J, Zhao H, Kranzler HR, Ursano RJ, Kessler RC, Stein MB, Gelernter J (2018) Trauma exposure interacts with the genetic risk of bipolar disorder in alcohol misuse of US soldiers. Acta Psychiatr Scand 137(2):148–156. https://doi.org/10.1111/acps.12843
Pritikin J, Neale M, Prom-Wormley EC, Clark SL, Verhulst B (2021) Gw-sem 2.0: enhancy efficiency, flexibility, and accessibility. Behav Genet. https://doi.org/10.1007/s10519-021-10043-1
Purcell SM, Wray NR, Stone JL, Visscher PM, O’Donovan MC, Sullivan PF, Sklar P (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460(7256):748–752. https://doi.org/10.1038/nature08185
Rose RJ, Dick DM (2004) Gene-environment interplay in adolescent drinking behavior. Alcohol Res Health 28(4):222
Rose RJ, Dick DM, Viken RJ, Kaprio J (2001) Gene-environment interaction in patterns of adolescent drinking: regional residency moderates longitudinal influences on alcohol use. Alcohol: Clin Exp Res 25(5):637–643. https://doi.org/10.1111/j.1530-0277.2001.tb02261.x
Salvatore JE, Aliev F, Edwards AC, Evans DM, Macleod J, Hickman M, Lewis G, Kendler KS, Loukola A, Korhonen T, Latvala A, Rose RJ, Kaprio J, Dick DM (2014) Polygenic scores predict alcohol problems in an independent sample and show moderation by the environment. Genes 5(2):330–346. https://doi.org/10.3390/genes5020330
Salvatore JE, Savage JE, Barr P, Wolen AR, Aliev F, Vuoksimaa E, Latvala A, Pulkkinen L, Rose RJ, Kaprio J, Dick DM (2018) Incorporating functional genomic information to enhance polygenic signal and identify variants involved in gene-by-environment interaction for young adult alcohol problems. Alcohol Clin Exp Res 42(2):413–423. https://doi.org/10.1111/acer.13551
Sartor CE, Agrawal A, Lynskey MT, Bucholz KK, Heath AC (2008) Genetic and environmental influences on the rate of progression to alcohol dependence in young women. Alcohol: Clin Exp Res 32(4):632–638. https://doi.org/10.1111/j.1530-0277.2008.00621.x
Schumann G, Liu C, O’Reilly P, Gao H, Song P, Xu B, Ruggeri B, Amin N, Jia T, Preis S, Lepe MS, Akira S, Barbieri C, Baumeister S, Cauchi S, Clarke T-K, Enroth S, Fischer K, Hällfors J et al (2016) KLB is associated with alcohol drinking, and its gene product β-Klotho is necessary for FGF21 regulation of alcohol preference. Proc Natl Acad Sci USA 113(50):14372–14377. https://doi.org/10.1073/pnas.1611243113
Shi H, Kichaev G, Pasaniuc B (2016) Contrasting the genetic architecture of 30 complex traits from summary association data. Am J Hum Genet 99(1):139–153. https://doi.org/10.1016/j.ajhg.2016.05.013
Su YR, Di CZ, Hsu L (2017) A unified powerful set-based test for sequencing data analysis of GxE interactions. Biostatistics 18(1):119–131. https://doi.org/10.1093/biostatistics/kxw034
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. https://doi.org/10.1371/journal.pmed.1001779
Tallis GM (1962) The maximum likelihood estimation of correlation from contingency tables. Biometrics 18(3):342. https://doi.org/10.2307/2527476
Thomas D (2010a) Gene-environment-wide association studies: emerging approaches. Nat Rev Genet. https://doi.org/10.1038/nrg2764
Thomas D (2010b) Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies. Annu Rev Public Health. https://doi.org/10.1146/annurev.publhealth.012809.103619
Turner S (2018) qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. J Open Source Softw 3(25):731. https://doi.org/10.1101/005165
Verhulst B, Maes HH, Neale MC (2017) GW-SEM: a statistical package to conduct genome-wide structural equation modeling. Behav Genet 47(3):345–359. https://doi.org/10.1007/s10519-017-9842-6
Verhulst B, Neale MC (2021) Best practices for binary and ordinal data analyses. Behav Genet. https://doi.org/10.1007/s10519-020-10031-x
Vrieze SI, McGue M, Iacono WG (2012) The interplay of genes and adolescent development in substance use disorders: leveraging findings from GWAS meta-analyses to test developmental hypotheses about nicotine consumption. Hum Genet. https://doi.org/10.1007/s00439-012-1167-1
Widaman KF, Helm JL, Castro-Schilo L, Pluess M, Stallings MC, Belsky J (2012) Distinguishing ordinal and disordinal interactions. Psychol Methods 17(4):615–622. https://doi.org/10.1037/a0030003
Xue A, Jiang L, Zhu Z, Wray NR, Visscher PM, Zeng J, Yang J (2021) Genome-wide analyses of behavioural traits are subject to bias by misreports and longitudinal changes. Nat Commun. https://doi.org/10.1038/s41467-020-20237-6
Young-Wolff KC, Enoch MA, Prescott CA (2011) The influence of gene-environment interactions on alcohol consumption and alcohol use disorders: a comprehensive review. Clin Psychol Rev. https://doi.org/10.1016/j.cpr.2011.03.005
Zhang J, Sha Q, Hao H, Zhang S, Gao XR, Wang X (2019) Test gene-environment interactions for multiple traits in sequencing association studies. Hum Hered 84(4–5):170–196. https://doi.org/10.1159/000506008
Acknowledgement
This work was supported by 5R01AA015416-09 and R01AA018333S1.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Brad Verhulst, Joshua N. Pritikin, James Clifford, and Elizabeth C. Prom-Wormley declare that they have no conflicts of interest related to the publication of this article.
Ethical approval
The data used for the demonstration section of this study were obtained from the UK Biobank (application number 40967) and involved secondary data analysis. As no identifying information was transferred, the data was not deemed “Human Subjects Data”, and appropriate human subjects waivers were obtained by the authors.
Human and animal rights and informed consent
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The data that were analyzed were completely de-identified, and thus were not considered human subjects data under the NIH regulations. A human subjects exemption for the project was received from Texas A&M University.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Edited by Sarah Medland.
Rights and permissions
About this article
Cite this article
Verhulst, B., Pritikin, J.N., Clifford, J. et al. Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data. Behav Genet 51, 358–373 (2021). https://doi.org/10.1007/s10519-021-10058-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10519-021-10058-8