Skip to main content
SpringerLink
Log in

Replication of a Gene–Environment Interaction Via Multimodel Inference: Additive-Genetic Variance in Adolescents’ General Cognitive Ability Increases with Family-of-Origin Socioeconomic Status

  • Original Research
  • Published:
Behavior Genetics Aims and scope Submit manuscript

Abstract

The present study of general cognitive ability attempts to replicate and extend previous investigations of a biometric moderator, family-of-origin socioeconomic status (SES), in a sample of 2,494 pairs of adolescent twins, non-twin biological siblings, and adoptive siblings assessed with individually administered IQ tests. We hypothesized that SES would covary positively with additive-genetic variance and negatively with shared-environmental variance. Important potential confounds unaddressed in some past studies, such as twin-specific effects, assortative mating, and differential heritability by trait level, were found to be negligible. In our main analysis, we compared models by their sample-size corrected AIC, and base our statistical inference on model-averaged point estimates and standard errors. Additive-genetic variance increased with SES—an effect that was statistically significant and robust to model specification. We found no evidence that SES moderated shared-environmental influence. We attempt to explain the inconsistent replication record of these effects, and provide suggestions for future research.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. We are grateful to two anonymous referees for calling to our attention the points made in this paragraph concerning stability of SES.

  2. See (Tucker-Drob et al. 2009) and McCallum and Mar (1995) for discussion of how quadratic trends may be mistaken for multiplicative interactions.

  3. We consider effect sizes and their interval estimates to be more scientifically interesting and informative than hypothesis tests. However, our confidence intervals only have a marginal 95 % coverage probability; their joint coverage probability is presumably smaller. Also, not every free parameter we estimated is an easily interpretable effect size, and further, the null hypothesis is indeed of interest and somewhat plausible for certain parameters. We therefore report p-values as well, and when making decisions about null hypotheses, compare them to the conventional significance level of \(\alpha =0.05\). P values are also easier than confidence intervals for the reader to adjust for “multiple testing.” We report 17 of them altogether. A Bonferroni correction would certainly be conservative, but skeptical readers are free to hold our results to its standard of \(\alpha =0.0029\).

  4. Readers certainly can think of models we could have fitted, but did not. Some readers may be interested in Table S3 (Online Resource), which, for the sake of completeness, reports point estimates and standard errors from a post hoc, “full” model in which all parameters under consideration were freely estimated.

  5. Unfortunately, several important primary sources by Akaike are inaccessible to us, due to being conference presentations or being written in Japanese. We do not cite sources we cannot read. Here, we rely on secondary sources by Burnham and Anderson (2001, 2002, 2004) and Pawitan (2013).

  6. It may be objected that basing inference about a parameter only upon those models in which it is freely estimated ignores evidence about the parameter conveyed by those models in which it is fixed. If one’s objective is regression prediction rather than inference, Burnham and Anderson (2002) do recommend calculating the model-averaged regression coefficient from models in which it is fixed, as well as those in which it is free. However, as Bartels (1997, footnote 11) points out, a model-averaged estimate computed in this way will not have a normal sampling distribution, which complicates its use for statistical inference.

References

  • Azen R, Budescu DV (2003) The dominance analysis approach for comparing predictors in multiple regression. Psychol Methods 8(2):129–148. doi:10.1037/1082-989X.8.2.129

    Article  PubMed  Google Scholar 

  • Bartels LM (1997) Specification uncertainty and model averaging. Am J Polit Sci 41(2):641–674

    Article  Google Scholar 

  • Bates TC, Lewis GJ, Weiss A (2013) Childhood socioeconomic status amplifies genetic effects on adult intelligence. Psychol Sci 24(10):2111–2116. doi:10.1177/0956797613488394

  • Boker S, Neale M, Maes H, Wilde M et al. (2011) OpenMx: an open source extended structural equation modeling framework. Psychometrika, 76(2), 306–317. doi: 10.1007/S11336-010-9200-6. Software and documentation available at http://openmx.psyc.virginia.edu/

  • Bouchard TJ (2004) Genetic influence on human psychological traits: a survey. Curr Dir Psychol Sci 13(4):148–151

    Article  Google Scholar 

  • Bouchard TJ, McGue M (1981) Familial studies of intelligence: a review. Science 212(4498):1055–1059

    Article  PubMed  Google Scholar 

  • Bouchard TJ, McGue M (2003) Genetic and environmental influences on human psychological differences. J Neurobiol 54:4–45

    Article  PubMed  Google Scholar 

  • Braveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, Posner S (2005) Socioeconomic status in health research: One size does not fit all. J Am Med Assoc 294(22):2879–2888

    Article  Google Scholar 

  • Breiman L (1992) The little bootstrap and other methods for dimensionality selection in regression: x-fixed prediction error. Journal of the American Statistical Association 87(419):738–754

    Article  Google Scholar 

  • Bronfenbrenner U, Ceci SJ (1994) Nature-nurture reconceptualized in developmental perspective: a bioecological model. Psychol Rev 101(4):568–586

    Article  PubMed  Google Scholar 

  • Browne MW (2000) Cross-validation methods. J Math Psychol 44:108–132. doi:10.1006/jmps.1999.1279

    Article  PubMed  Google Scholar 

  • Burnham KP, Anderson DR (2001) Kullback-Leibler information as a basis for strong inference in ecological studies. Wildl Res 28:111–119

    Article  Google Scholar 

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New York

    Google Scholar 

  • Burnham KP, Anderson DR (2004) Multimodel inference: Understanding AIC and BIC in model selection. Sociol Methods Res 33(2):261–304. doi:10.1177/0049124104268644

    Article  Google Scholar 

  • Cherny SS, Cardon LR, Fulker DW, DeFries JC (1992) Differential heritability across levels of cognitive ability. Behav Genet 22(2):153–162

    Article  PubMed  Google Scholar 

  • Deary IJ, Spinath FM, Bates TC (2006) Genetics of intelligence. Eur J Hum Genet 14:690–700. doi:10.1038/sj.ejhg.5201588

    Article  PubMed  Google Scholar 

  • DeFries JC, Fulker DW (1985) Multiple regression analysis of twin data. Behav Genet 15(5):467–473

    Article  PubMed  Google Scholar 

  • DeFries JC, Fulker DW (1988) Multiple regression analysis of twin data: Etiology of deviant scores versus individual differences. Acta Geneticae Medicae et Gemellologiae 37:205–216

    PubMed  Google Scholar 

  • Evans GW (2004) The environment of childhood poverty. Am Psychol 59(2):77–92. doi:10.1037/0003-066X.59.2.77

    Article  PubMed  Google Scholar 

  • Fischbein S (1980) IQ and social class. Intelligence 4:51–63

    Article  Google Scholar 

  • Galton F (1869). Hereditary genius: an inquiry into its laws and consequences. London: MacMillan & Co. Retrieved from http://galton.org/

  • Grant MD, Kremen WS, Jacobson KC et al (2010) Does parental education have a moderating effect on the genetic and environmental influences of general cognitive ability in early adulthood? Behav Genet 40:438–446. doi:10.1007/s10519-010-9351-3

    Article  PubMed  Google Scholar 

  • Hanscombe KB, Trzaskowski M, Haworth CMA, Davis OSP, Dale PS, Plomin R (2012) Socioeconomic status (SES) and children’s intelligence (IQ): in a UK-representative sample SES moderates the environmental, not genetic, effect on IQ. PLoS ONE 7(2):e30320. doi:10.1371/journal.pone.0030320

    Article  PubMed Central  PubMed  Google Scholar 

  • Harden KP, Turkheimer E, Loehlin JC (2007) Genotype by environment interaction in adolescents’ cognitive aptitude. Behav Genet 37:273–283. doi:10.1007/s10519-006-9113-4

    Article  PubMed Central  PubMed  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer Science + Business Media, New York. doi:10.1007/b94608

    Book  Google Scholar 

  • Hollingshead AB (1957) Two factor index of social position. Hollingshead, New Haven

    Google Scholar 

  • Hurvich CM, Tsai C-L (1989) Regression and time series model selection in small samples. Biometrika 76(2):297–307

    Article  Google Scholar 

  • Iacono WG, McGue M (2002) Minnesota twin family study. Twin Res 5(5):482–487

    Article  PubMed  Google Scholar 

  • Iacono WG, Carlson SR, Taylor J, Elkins IJ, McGue M (1999) Behavioral disinhibition and the development of substance-use disorders: findings from the Minnesota twin family study. Dev Psychopathol 11:869–900

    Article  PubMed  Google Scholar 

  • Kapetanios G, Labhard V, Price S (2008) Forecasting using Bayesian and information-theoretic model-averaging: An application to U.K. inflation. J Bus Econ Stat 26(1):33–41. doi:10.1198/073500107000000232

    Article  Google Scholar 

  • Keyes MA, Malone SM, Elkins IJ, Legrand LN, McGue M, Iacono WG (2009) The enrichment study of the Minnesota twin family study: increasing the yield of twin families at high risk for externalizing psychopathology. Twin Res Human Genet 12(5):489–501

    Article  Google Scholar 

  • Kirkpatrick RM, McGue M, Iacono WG (2009) Shared-environmental contributions to high cognitive ability. Behav Genet 39:406–416. doi:10.1007/s10519-009-9265-0

    Article  PubMed Central  PubMed  Google Scholar 

  • Kirkpatrick RM, McGue M, Iacono WG, Miller MB, Basu S, Pankratz N (2014) Low-frequency copy-number variants and general cognitive ability: no evidence of association. Intelligence 42:98–106. doi:10.1016/j.intell.2013.11.005

    Article  PubMed Central  PubMed  Google Scholar 

  • Kohler HP, Rodgers JL (2001) DF-analyses of heritability with double-entry twin data: asymptotic standard errors and efficient estimation. Behav Genet 31(2):179–191

    Article  PubMed  Google Scholar 

  • Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86

    Article  Google Scholar 

  • Loehlin JC, Harden KP, Turkheimer E (2009) The effect of assumptions about parental assortative mating and genotype-income correlation on estimates of genotype-environment interaction in the National Merit Twin Study. Behav Genet 39:165–169. doi:10.1007/s10519-008-9253-9

    Article  PubMed Central  PubMed  Google Scholar 

  • Lukacs PM, Burnham KP, Anderson DR (2009) Model selection bias and Freedman’s paradox. Ann Inst Stat Math 62:117–125. doi:10.1007/s10463-009-0234-4

    Article  Google Scholar 

  • McCallum RC, Mar CM (1995) Distinguishing between moderator and quadratic effects in multiple regression. Psychol Bull 118(3):405–421

    Article  Google Scholar 

  • McGue M, Bouchard TJ (1984) Adjustment of twin data for the effects of age and sex. Behav Genet 14(4):325–343

    Article  PubMed  Google Scholar 

  • McGue M, Keyes M, Sharma A, Elkins I, Legrand L, Johnson W, Iacono WG (2007) The environments of adopted and non-adopted youth: Evidence on range restriction from the Sibling Interaction and Behavior Study (SIBS). Behav Genet 37:449–462. doi:10.1007/s10519-007-9142-7

    Article  PubMed  Google Scholar 

  • Myrianthopolous NC, French KS (1968) An application of the U.S. Bureau of the Census socioeconomic index to a large, diversified patient population. Soc Sci Med 2:283–299

    Article  Google Scholar 

  • Pawitan Y (2013) In all likelihood: statistical modelling and inference using likelihood. Oxford University Press, Oxford

    Google Scholar 

  • Plomin R, DeFries JC, Loehlin JC (1977) Genotype-environment interaction and correlation in the analysis of human behavior. Psychol Bull 84(2):309–322

    Article  PubMed  Google Scholar 

  • Price TS, Jaffee SR (2008) Effects of the family environment: Gene-environment interaction and passive gene-environment correlation. Dev Psychol 44(2):305–315. doi:10.1037/0012-1649.44.2.305

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Rathouz PJ, Van Hulle CA, Rodgers JL, Waldman ID, Lahey BB (2008) Specification, testing, and interpretation of gene-by-measured environment interaction models in the presence of gene-environment correlation. Behav Genet 38:301–315. doi:10.1007/s10519-008-9193-4

    Article  PubMed Central  PubMed  Google Scholar 

  • Rijsdijk FV, Vernon PA, Boomsma DI (2002) Application of hierarchical genetic models to Raven and WAIS subtests: a Dutch twin study. Behav Genet 32(3):199–210

    Article  PubMed  Google Scholar 

  • Rodgers JL, Kohler HP (2005) Reformulating and simplifying the DF analysis model. Behav Genet 35(2):211–217

    Article  Google Scholar 

  • Rodgers JL, McGue M (1994) A simple algebraic demonstration of the validity of Defries-Fulker analysis in unselected samples with multiple kinship levels. Behav Genet 24(3):259–262

    Article  PubMed  Google Scholar 

  • Rowe DC, Jacobson KC, van den Oord EJCG (1999) Genetic and environmental influences on vocabulary IQ: parental educational level as moderator. Child Dev 70(5):1151–1162

    Article  PubMed  Google Scholar 

  • Scarr S (1992) Developmental theories for the 1990s: development and individual differences. Child Dev 63:1–19

    Article  PubMed  Google Scholar 

  • Scarr S, Weinberg RA (1978) The influence of “family background” on intellectual attainment. Am Sociol Rev 43(5):674–692

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Shao J (1997) An asymptotic theory for linear model selection. Stat Sin 7:221–264

    Google Scholar 

  • Spearman C (1904) “General intelligence”, objectively determined and measured. Am J Psychol 15(2):201–292

    Article  Google Scholar 

  • Stone M (1977). An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion. J R Stat Soc Ser B (Methodol), 39(1):44–47

  • Tucker-Drob EM, Harden KP, Turkheimer E (2009) Combining nonlinear biometric and psychometric models of cognitive abilities. Behav Genet 39:461–471. doi:10.1007/s10519-009-9288-6

    Article  PubMed Central  PubMed  Google Scholar 

  • Turkheimer E, Haley A, Waldron M, D’Onofrio B, Gottesman II (2003) Socioeconomic status modifies heritability of IQ in young children. Psychol Sci 14(6):623–628

    Article  PubMed  Google Scholar 

  • Uher R, Dragomirecka E, Papezova H (2006) Use of socioeconomic status in health research. J Am Med Assoc 295(15):1770

    Article  Google Scholar 

  • Van den Ooord EJCG, Rowe DC (1998) An examination of genotype-environment interactions for academic achievement in an U.S. National Longitudinal Survey. Intelligence 25(3):205–228

    Article  Google Scholar 

  • Van der Sluis S, Willemsen G, de Geus EJC, Boomsma DI, Posthuma D (2008) Gene-environment interaction in adults’ IQ scores: measures of past and present environment. Behav Genet 38:348–360. doi:10.1007/s10519-008-9212-5

    Article  PubMed Central  PubMed  Google Scholar 

  • Van der Sluis S, Posthuma D, Dolan CV (2012) A note on false positives and power in G × E modelling of twin data. Behav Genet 42:170–186. doi:10.1007/s10519-011-9480-3

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

This research was supported in part by USPHS Grants from the National Institute on Alcohol Abuse and Alcoholism (AA09367 and AA11886), the National Institute on Drug Abuse (DA05147, DA13240, and DA024417), and the National Institute on Mental Health (MH066140). The first author (RMK) was supported by a Doctoral Dissertation Fellowship from the University of Minnesota Graduate School and by grant DA026119 from the National Institute on Drug Abuse. The authors acknowledge the assistance of Niels G. Waller and Saonli Basu, who provided helpful comments on an early draft of this paper. The first author gives his special thanks to Scott I. Vrieze and Joshua D. Isen for thought-provoking discussion of model-selection and of the main effects of SES, respectively.

Conflict of interest

Robert M. Kirkpatrick, Matt McGue, and William G. Iacono declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

The MTFS and SIBS studies were reviewed and approved by the Institutional Review Board at the University of Minnesota. Written informed assent or consent was obtained from all participants, with parents providing written consent for their minor children.

Author information

Author notes

  1. Robert M. Kirkpatrick

    Present address: Virginia Institute for Psychiatric & Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, 23298-0126, USA

Authors and Affiliations

  1. Department of Psychology, University of Minnesota, 75 E. River Rd, Minneapolis, MN, 55455, USA

    Robert M. Kirkpatrick, Matt McGue & William G. Iacono

Authors
  1. Robert M. Kirkpatrick
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matt McGue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William G. Iacono
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robert M. Kirkpatrick.

Additional information

Edited by Danielle Posthuma.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 111 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kirkpatrick, R.M., McGue, M. & Iacono, W.G. Replication of a Gene–Environment Interaction Via Multimodel Inference: Additive-Genetic Variance in Adolescents’ General Cognitive Ability Increases with Family-of-Origin Socioeconomic Status. Behav Genet 45, 200–214 (2015). https://doi.org/10.1007/s10519-014-9698-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10519-014-9698-y

Keywords

Search

Navigation