The Genetic Architecture of Economic and Political Preferences

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The Genetic Architecture of Economic and Political Preferences

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Title: The Genetic Architecture of Economic and Political Preferences
Author: Benjamin, Daniel J.; Cesarini, David; van der Loos, Matthijs J. H. M.; Dawes, Christopher T.; Koellinger, Philipp D.; Magnusson, Patrik K. E.; Chabris, Christopher F.; Conley, Dalton; Laibson, David I.; Johannesson, Magnus; Visscher, Peter M.

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Citation: Benjamin, Daniel J., David Cesarini, Matthijs J. H. M. van der Loos, Christopher T. Dawes, Philipp D. Koellinger, Patrik K. E. Magnusson, Christopher F. Chabris, et al. 2012. The genetic architecture of economic and political preferences. Proceedings of the National Academy of Sciences 109(21): 8026-8031.
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Abstract: Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic–based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.
Published Version: doi:10.1073/pnas.1120666109
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