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Why it is hard to find genes associated with social science traits: Theoretical and empirical considerations

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  • Chabris, C.F.
  • Lee, J.J.
  • Benjamin, D.J.
  • Beauchamp, J.P.
  • Glaeser, E.L.
  • Borst, G.
  • Pinker, S.
  • Laibson, D.I.

Abstract

Objectives. We explain why traits of interest to behavioral scientists may have a genetic architecture featuring hundreds or thousands of loci with tiny individual effects rather than a few with large effects and why such an architecture makes it difficult to find robust associations between traits and genes. Methods. We conducted a genome-wide association study at 2 sites, Harvard University and Union College, measuring more than 100 physical and behavioral traits with a sample size typical of candidate gene studies. We evaluated predictions that alleles with large effect sizes would be rare and most traits of interest to social science are likely characterized by a lack of strong directional selection. We also carried out a theoretical analysis of the genetic architecture of traits based on R.A. Fisher's geometric model of natural selection and empirical analyses of the effects of selection bias and phenotype measurement stability on the results of genetic association studies. Results. Although we replicated several known genetic associations with physical traits, we found only 2 associations with behavioral traits that met the nominal genome-wide significance threshold, indicating that physical and behavioral traits are mainly affected by numerous genes with small effects. Conclusions. The challenge for social science genomics is the likelihood that genes are connected to behavioral variation by lengthy, nonlinear, interactive causal chains, and unraveling these chains requires allying with personal genomics to take advantage of the potential for large sample sizes as well as continuing with traditional epidemiological studies.

Suggested Citation

  • Chabris, C.F. & Lee, J.J. & Benjamin, D.J. & Beauchamp, J.P. & Glaeser, E.L. & Borst, G. & Pinker, S. & Laibson, D.I., 2013. "Why it is hard to find genes associated with social science traits: Theoretical and empirical considerations," American Journal of Public Health, American Public Health Association, vol. 103(SUPPL.1), pages 152-166.
  • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2013.301327_0
    DOI: 10.2105/AJPH.2013.301327
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    Cited by:

    1. John Beshears & James J. Choi & David Laibson & Brigitte C. Madrian & Katherine L. Milkman, 2015. "The Effect of Providing Peer Information on Retirement Savings Decisions," Journal of Finance, American Finance Association, vol. 70(3), pages 1161-1201, June.
    2. Pietro Biroli & Titus J. Galama & Stephanie von Hinke & Hans van Kippersluis & Cornelius A. Rietveld & Kevin Thom, 2022. "The Economics and Econometrics of Gene-Environment Interplay," Papers 2203.00729, arXiv.org.
    3. Steven F. Lehrer & Weili Ding, 2017. "Are genetic markers of interest for economic research?," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 6(1), pages 1-23, December.
    4. Lee, James J. & McGue, Matt & Iacono, William G. & Michael, Andrew M. & Chabris, Christopher F., 2019. "The causal influence of brain size on human intelligence: Evidence from within-family phenotypic associations and GWAS modeling," Intelligence, Elsevier, vol. 75(C), pages 48-58.
    5. Chabris, C. F. & Lee, J. J. & Cesarini, D. & Benjamin, D. J. & Laibson, David I., 2015. "The Fourth Law of Behavior Genetics," Scholarly Articles 30780203, Harvard University Department of Economics.
    6. C. Justin Cook & Jason M. Fletcher, 2018. "High-school genetic diversity and later-life student outcomes: micro-level evidence from the Wisconsin Longitudinal Study," Journal of Economic Growth, Springer, vol. 23(3), pages 307-339, September.

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