Skip to main content
Log in

Using Variation in Heritability Estimates as a Test of G × E in Behavioral Research: A Brief Research Note

  • Brief Communication
  • Published:
Behavior Genetics Aims and scope Submit manuscript

Abstract

Better characterization of the sources of phenotypic variation in human behavioural traits—stemming from genetic and environmental influences—will allow for more informed decisions about how to approach a range of challenges arising from variation, ranging from societal issues to the treatment of diseases. In particular, understanding how the environment moderates genetic influence on phenotypes (i.e., genotype–environment interactions, or G × E) is a central component of the behavioral sciences. Yet, understanding of this phenomenon is lagging somewhat, due in part to the difficulties of detecting G × E. We discuss the logic behind one of the primary ways to detect G × E: comparing heritability estimates across environments. Then, we highlight some pitfalls, with an emphasis on how very strong G × E can sometimes be undetectable using this method when high heritability is present in multiple environments. We conclude by forwarding some initial, yet tentative, suggestions for how best to address to the problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Notes

  1. It is worth pointing out that debates about the nature, prevalence, and importance of G × E are not new in behavior genetics, and some of the very points we touch on here have been discussed previously. See for instance, Sesardic (1993), and the exchanges sparked from around the topic of non-additive genetic effects and reaction norms. Moreover, behavioral geneticists have, for decades, acknowledged pitfalls when testing for G × E and have been suggesting supplementary methods, so neither is this component of our paper particularly novel (see, for instance, Plomin et al. 1977). Our intention, then, is to revive interest in the topic across fields where the discussion has either faded, or has yet to take hold in general (e.g., criminology, sociology, etc.).

  2. It is worth mentioning that a biometric—or twin based—approach to testing for the presence of G × E in human data involves examining either differences in heritability estimates across environments, or differences in (raw) additive genetic variance across environments. For researchers using the approach described by Purcell (2002), it is the second strategy that is being employed.

References

  • Barnes JC, Wright JP, Boutwell BB, Schwartz JA, Connolly EJ, Nedelec JL, Beaver KM (2014) Demonstrating the validity of twin research in criminology. Criminology 52(4):588–626

    Article  Google Scholar 

  • Bradshaw AD (1965) Evolutionary significance of phenotypic plasticity in plants. Adv Gener 13:115–155

    Article  Google Scholar 

  • Carlson CS, Matise TC, North KE, Haiman CA, Fesinmeyer MD, Buyske S, Schumacher FR, Peters U, Franceschini N, Ritchie MD, Duggan DJ, Spencer KL, Dumitrescu L, Eaton CB, Thomas F, Young A, Carty C, Heiss G, Marchand LL, Crawford DC, Hindorff LA, Kooperberg CL, Page Consortium (2013) Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study. PLoS Biol 11(9):e1001661

    Article  PubMed  PubMed Central  Google Scholar 

  • Caspi A, McClay J, Moffitt TE, Mill J, Martin J, Craig IW, Taylor A, Poulton R (2002) Role of genotype in the cycle of violence in maltreated children. Science 297(5582):851–854

    Article  PubMed  Google Scholar 

  • Chabris CF, Lee JJ, Cesarini D, Benjamin DJ, Laibson DI (2015) The fourth law of behavior genetics. Curr Dir Psychol Sci 24(4):304–312

    Article  PubMed  PubMed Central  Google Scholar 

  • Conley D (2016) Socio-genomic research using genome-wide molecular data. Ann Rev Sociol 42:275–299

    Article  Google Scholar 

  • Davey Smith G, Hemani G (2014) Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Human Mol Genet 23(R1):R89–R98

    Article  Google Scholar 

  • Dick DM (2011) Gene–environment interaction in psychological traits and disorders. Annu Rev Clin Psychol 7:383–409

    Article  PubMed  PubMed Central  Google Scholar 

  • 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 168(10):1041–1049

    Article  PubMed  PubMed Central  Google Scholar 

  • Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, Harlow

    Google Scholar 

  • Ghalambor CK, McKay JK, Carroll SP, Reznick DN (2007) Adaptive versus non-adaptive plasticity and the potential for contemporary adaptation in new environments. Funct Ecol 21(3):394–407

    Article  Google Scholar 

  • Ingleby FC, Hosken DJ, Flowers K, Hawkes MF, Lane SM, Rapkin J, Dworkin I, Hunt J (2013) Genotype-by-environment interactions for cuticular hydrrocarbon expression in Drosophila simulans. J Evolution Biol 26:94–107

    Article  Google Scholar 

  • Karlsson K, Eroukhmanoff F, Svensson EI (2010) Phenotypic plasticity in response to the social environment: effects of density and sex ratio on mating behaviour following ecotype divergence. PLoS ONE 5(9):e12755

    Article  PubMed  PubMed Central  Google Scholar 

  • Plomin R, Deary IJ (2015) Genetics and intelligence differences: five special findings. Mol Psychiatry 20(1):98–108

    Article  PubMed  Google Scholar 

  • Plomin R, DeFries JC, Loehlin JC (1977) Genotype–environment interaction and correlation in the analysis of human behavior. Psychol Bull 84(2):309

    Article  PubMed  Google Scholar 

  • Plomin R, DeFries JC, Knopik VS, Neiderheiser J (2013) Behavioral genetics, 6th edn. Worth Publishers, New York

    Google Scholar 

  • Polderman TJ, Benyamin B, De Leeuw CA, Sullivan PF, Van Bochoven A, Visscher PM, Posthuma D (2015) Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet 47(7):702–709

    Article  PubMed  Google Scholar 

  • Purcell S (2002) Variance components models for gene–environment interaction in twin analysis. Twin Res Human Genet 5(6):554–571

    Article  Google Scholar 

  • Ritchie S (2015) Intelligence: all that matters. Hodder & Stoughton, Hachette

    Google Scholar 

  • Roff DA (1997) Evolutionary quantitative genetics. Chapman and Hall, New York

    Book  Google Scholar 

  • Scarr-Salapatek S (1971) Race, social class, and IQ. Science 174(4016):1285–1295

    Article  PubMed  Google Scholar 

  • Schlichting C, Pigliucci M (1998) Phenotypic evolution: a reaction norm perspective. Sinauer Associates, Sunderland

    Google Scholar 

  • Sesardic N (1993) Heritability and causality. Philos Sci 60(3):396–418

    Article  Google Scholar 

  • Tucker-Drob EM, Bates TC (2016) Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychol Sci 27(2):138–149

    Article  PubMed  Google Scholar 

  • Turkheimer E (2000) Three laws of behavior genetics and what they mean. Curr Dir Psychol Sci 9(5):160–164

    Article  Google Scholar 

  • Via S, Gomulkiewicz R, De Jong G, Scheiner SM, Schlichting CD, Van Tienderen PH (1995) Adaptive phenotypic plasticity: consensus and controversy. Trends Ecol Evol 10:212–217

    Article  PubMed  Google Scholar 

  • West-Eberhard MJ (1989) Phenotypic plasticity and the origins of diversity. Annu Rev Ecol Syst 20:249–278

    Article  Google Scholar 

  • West-Eberhard MJ (2003) Developmental plasticity and evolution. Oxford University Press, New York

    Google Scholar 

Download references

Acknowledgements

A Spark Microgrant from Saint Louis University provided funding for this project. Feedback on earlier drafts of this manuscript (however, any errors and omissions are the product solely of the authors): RL Rodriguez, R Tinghitella, and A Burt.

Funding

This project was funded by a SPARK microgrant from Saint Louis University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kasey D. Fowler-Finn.

Ethics declarations

Conflict of interest

Kasey D. Fowler-Finn and Brian B. Boutwell declare that they have no conflict of interest.

Statement of human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

For this type of study formal consent is not required.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Edited by Matt McGue.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fowler-Finn, K.D., Boutwell, B. Using Variation in Heritability Estimates as a Test of G × E in Behavioral Research: A Brief Research Note. Behav Genet 49, 340–346 (2019). https://doi.org/10.1007/s10519-019-09948-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10519-019-09948-9

Keywords

Navigation