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A research note on Mendelian randomization and causal inference in criminology: promises and considerations

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Abstract

Objectives

Here, we provide a brief overview of a technique that may hold promise for scholars working on key criminological and criminal justice topics.

Methods

We provide an abbreviated overview of Mendelian randomization (MR), a newer variant of instrumental variables analysis, facilitated by expanding genomic technology worldwide. Our goal is to offer readers, unacquainted with the topic, a quick checklist of key assumptions, considerations, shortcomings, and practical applications of the technique.

Results

The causal inference capabilities of the design seem poised to continue pushing modern crime science forward, assuming that careful attention is payed to key assumptions of the technique.

Conclusions

Researchers interested in causality as it relates to antisocial behaviors may benefit by the addition of MR to the toolkit alongside other data analysis tools. This strategy also offers an avenue for cross-collaboration with scientists working in other fields, thus expanding the breadth of expertise contributing to an important societal subject in crime.

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Fig. 1

Notes

  1. In a courtesy review of earlier drafts of this paper, a colleague pointed out to us a key point also worth considering. In particular, IV analysis in the social sciences is often performed by researchers utilizing macro-level data. This is important in this case because macro-level analyses often have r-squared values that tend to be considerably larger than what might be observed in either individual-level analyses or in genomic analyses.

  2. We would like to thank an anonymous reviewer for reminding us of this point, and for urging us to consider it in the revised version of the manuscript.

References

  • Adam, D. (2019). The gene-based hack that is revolutionizing epidemiology. Nature, 576(7786), 196.

    Article  Google Scholar 

  • Adams, C. D., & Boutwell, B. B. (2020). Adventurousness cuts both ways: a Mendelian randomization of adventurousness on 10 cognitive and behavioral traits. medRxiv. https://doi.org/10.1101/2020.03.12.20034918.

  • Allen, N. E., Sudlow, C., Peakman, T., & Collins, R. (2014). UK biobank data: come and get it. Science Translational Medicine, 6(224), 3–5.

    Article  Google Scholar 

  • Barnes, J. C., Boutwell, B. B., Beaver, K. M., Gibson, C. L., & Wright, J. P. (2014a). On the consequences of ignoring genetic influences in criminological research. Journal of Criminal Justice, 42(6), 471–482.

    Article  Google Scholar 

  • Barnes, J. C., Wright, J. P., Boutwell, B. B., Schwartz, J. A., Connolly, E. J., Nedelec, J. L., & Beaver, K. M. (2014b). Demonstrating the validity of twin research in criminology. Criminology, 52(4), 588–626.

    Article  Google Scholar 

  • Bowden, J., Del Greco, M. F., Minelli, C., Davey Smith, G., Sheehan, N. A., & Thompson, J. R. (2016). Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. International Journal of Epidemiology, 45(6), 1961–1974.

    Google Scholar 

  • Bowden, J., Spiller, W. M. F. D. G., Sheehan, N., Thompson, J., Minelli, C., & Smith, G. D. (2018). Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. International Journal of Epidemiology, 1–15.

  • Cochran, J. C., Barnes, J. C., Mears, D. P., & Bales, W. D. (2020). Revisiting the effect of visitation on recidivism. Justice Quarterly, 37(2), 304–331.

    Article  Google Scholar 

  • Collins, R. (2012). What makes UK Biobank special? Lancet, 379(9822), 1173–1174.

    Article  Google Scholar 

  • Core Team, R. (2013). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

    Google Scholar 

  • Davey Smith, G., & Ebrahim, S. (2003). ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? International journal of epidemiology, 32(1), 1–22.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Del Greco, M. F., Minelli, C., Sheehan, N. A., & Thompson, J. R. (2015). Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Statistics in Medicine, 34(21), 2926–2940.

    Article  Google Scholar 

  • Fry, A., Littlejohns, T. J., Sudlow, C., Doherty, N., Adamska, L., Sprosen, T., et al. (2017). Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. American Journal of Epidemiology, 186(9), 1026–1034.

    Article  Google Scholar 

  • Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford University Press.

  • Guo, G., Li, Y., Cai, T., Wang, H., & Duncan, G. J. (2015). Peer influence, genetic propensity, and binge drinking: a natural experiment and a replication. American Journal of Sociology, 121(3), 914–954.

    Article  Google Scholar 

  • Haycock, P. C., Burgess, S., Wade, K. H., Bowden, J., Relton, C., & Davey Smith, G. (2016). Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. The American journal of clinical nutrition, 103(4), 965–978.

    Article  Google Scholar 

  • Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., et al. (2018). The MR-Base platform supports systematic causal inference across the human phenome. ELife, 7, 1–29.

    Article  Google Scholar 

  • Katan, M. B. (1986). Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet, 327(8479), 507–508.

    Article  Google Scholar 

  • Kirk, D. S. (2009). A natural experiment on residential change and recidivism: lessons from Hurricane Katrina. American Sociological Review, 74(3), 484–505.

    Article  Google Scholar 

  • Lawlor, D. A., Tilling, K., & Davey Smith, G. (2016). Triangulation in aetiological epidemiology. International Journal of Epidemiology, 45(6), 1866–1886.

    Google Scholar 

  • Lee, J. J. (2012). Correlation and causation in the study of personality. European Journal of Personality, 26(4), 372–390.

    Article  Google Scholar 

  • Linnér, R. K., Biroli, P., Kong, E., Meddens, S. F. W., Wedow, R., Fontana, M. A., et al. (2019). Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nature genetics, 51(2), 245–257.

    Article  Google Scholar 

  • McGue, M., Osler, M., & Christensen, K. (2010). Causal inference and observational research: the utility of twins. Perspectives on Psychological Science, 5(5), 546–556.

    Article  Google Scholar 

  • Millard, L. A. C., Davies, N. M., Gaunt, T. R., Smith, G. D., & Tilling, K. (2018). Software application profile: PHESANT: a tool for performing automated phenome scans in UK Biobank. International Journal of Epidemiology, 47(1), 29–35.

    Article  Google Scholar 

  • Mitchell, R., Hemani, G., Dudding, T., Corbin, L., Harrison, S., & Paternoster, L. (2019). UK Biobank Genetic Data: MRC-IEU Quality Control, version 2. https://doi.org/10.5523/bris.1ovaau5sxunp2cv8rcy88688v.

    Book  Google Scholar 

  • Paternoster, L., Tilling, K., & Davey Smith, G. (2017). Genetic epidemiology and Mendelian randomization for informing disease therapeutics: conceptual and methodological challenges. PLoS Genetics, 13(10), 1–9.

    Article  Google Scholar 

  • Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect. Basic Books.

  • Rohrer, J. M. (2018). Thinking clearly about correlations and causation: graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42.

    Article  Google Scholar 

  • Sampson, R. J. (2010). Gold standard myths: observations on the experimental turn in quantitative criminology. Journal of quantitative criminology, 26(4), 489–500.

    Article  Google Scholar 

  • Sampson, R. J., Winship, C., & Knight, C. (2013). Translating causal claims: principles and strategies for policy-relevant criminology. Criminology & Public Policy, 12, 587.

    Article  Google Scholar 

  • Schmitz, L., & Conley, D. (2016). The long-term consequences of Vietnam-era conscription and genotype on smoking behavior and health. Behavior Genetics, 46(1), 43–58.

    Article  Google Scholar 

  • Sherman, L. W. (2010). An introduction to experimental criminology, In Handbook of quantitative criminology (pp. 399-436). New York: Springer.

    Book  Google Scholar 

  • Sluijs, I., Holmes, M. V., Van Der Schouw, Y. T., Beulens, J. W., Asselbergs, F. W., Huerta, J. M., ... & Boeing, H. (2015). A Mendelian randomization study of circulating uric acid and type 2 diabetes. Diabetes, 64(8), 3028–3036.

  • Tanksley, P. T., Barnes, J. C., Boutwell, B. B., Arseneault, L., Caspi, A., Danese, A., et al. (2020). Identifying psychological pathways to polyvictimization: evidence from a longitudinal cohort study of twins from the UK. Journal of Experimental Criminology, 1–31.

  • Tielbeek, J. J., Johansson, A., Polderman, T. J., Rautiainen, M. R., Jansen, P., Taylor, M., et al. (2017). Genome-wide association studies of a broad spectrum of antisocial behavior. JAMA psychiatry, 74(12), 1242–1250.

    Article  Google Scholar 

  • Tielbeek, J. J., Barnes, J. C., Popma, A., Polderman, T. J., Lee, J. J., Perry, J. R., et al. (2018). Exploring the genetic correlations of antisocial behaviour and life history traits. BJPsych open, 4(6), 467–470.

    Article  Google Scholar 

  • VanderWeele, T. (2015). Explanation in causal inference: methods for mediation and interaction. Oxford University Press.

  • VanderWeele, T. J., Tchetgen, E. J. T., & Kraft, P. (2015). Methodological challenges in Mendelian randomization. Epidemiology, 25(3), 427–435.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Applied Sciences, The University of Mississippi, 84 Dormitory Row West, University, MS, 38677-1848, USA

    Brian B. Boutwell

  2. John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, USA

    Brian B. Boutwell

  3. T.H. Chan School of Public Health, Department of Environmental Health, Program in Molecular and Integrative Physiological Sciences, Harvard University, Cambridge, MA, USA

    Charleen D. Adams

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  1. Brian B. Boutwell
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Correspondence to Brian B. Boutwell.

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Boutwell, B.B., Adams, C.D. A research note on Mendelian randomization and causal inference in criminology: promises and considerations. J Exp Criminol 18, 171–182 (2022). https://doi.org/10.1007/s11292-020-09436-9

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  • DOI: https://doi.org/10.1007/s11292-020-09436-9

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