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Increasing the use of functional and multimodal genetic data in social science research

Published online by Cambridge University Press:  11 September 2023

Benjamin C. Nephew Open the ORCID record for Benjamin C. Nephew [Opens in a new window] ,
Chris Murgatroyd ,
Justin J. Polcari ,
Hudson P. Santos Jr.  and
Angela C. Incollingo Rodriguez
Benjamin C. Nephew
Affiliation:
Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, USA bcnephew@aol.com jjpolicari@wpi.edu
Chris Murgatroyd
Affiliation:
School of Healthcare Science, Manchester Metropolitan University, Manchester, UK c.murgatroyd@mmu.ac.uk
Justin J. Polcari
Affiliation:
Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, USA bcnephew@aol.com jjpolicari@wpi.edu
Hudson P. Santos Jr.
Affiliation:
Department of Nursing, University of Miami, Coral Gables, FL, USA hsantos@miami.edu
Angela C. Incollingo Rodriguez
Affiliation:
Department of Psychological Science, Worcester Polytechnic Institute, Worcester, MA, USA acrodriguez@wpi.edu
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Abstract

Genetic studies in the social sciences could be augmented through the additional consideration of functional (transcriptome, methylome, metabolome) and/or multimodal genetic data when attempting to understand the genetics of social phenomena. Understanding the biological pathways linking genetics and the environment will allow scientists to better evaluate the functional importance of polygenic scores.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e223
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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References

Bartrés-Faz, D., González-Escamilla, G., Vaqué-Alcázar, L., Abellaneda-Pérez, K., Valls-Pedret, C., Ros, E., & Grothe, M. J. (2019). Characterizing the molecular architecture of cortical regions associated with high educational attainment in older individuals. The Journal of Neuroscience, 39(23), 4566. https://doi.org/10.1523/JNEUROSCI.2370-18.2019CrossRefGoogle ScholarPubMed
Gadd, D. A., Hillary, R. F., McCartney, D. L., Shi, L., Stolicyn, A., Robertson, N. A., … Marioni, R. E. (2022). Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health. Nature Communications, 13(1), 4670. https://doi.org/10.1038/s41467-022-32319-8CrossRefGoogle ScholarPubMed
Hatcher, C., Relton, C. L., Gaunt, T. R., & Richardson, T. G. (2019). Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of complex traits and disease. Translational Psychiatry, 9(1), 105. https://doi.org/10.1038/s41398-019-0437-2CrossRefGoogle ScholarPubMed
Johnson, L. A., Torres, E. R. S., Impey, S., Stevens, J. F., & Raber, J. (2017). Apolipoprotein E4 and insulin resistance interact to impair cognition and alter the epigenome and metabolome. Scientific Reports, 7(1), 43701. https://doi.org/10.1038/srep43701CrossRefGoogle ScholarPubMed
Karimi, M., Castagné, R., Delpierre, C., Albertus, G., Berger, E., Vineis, P., … Chadeau-Hyam, M. (2019). Early-life inequalities and biological ageing: A multisystem biological health score approach in Understanding Society. Journal of Epidemiology and Community Health, 73(8), 693. https://doi.org/10.1136/jech-2018-212010CrossRefGoogle Scholar
Robinson, O., Carter, A. R., Ala-Korpela, M., Casas, J. P., Chaturvedi, N., Engmann, J., … Vineis, P. (2021). Metabolic profiles of socio-economic position: A multi-cohort analysis. International Journal of Epidemiology, 50(3), 768–782. https://doi.org/10.1093/ije/dyaa188CrossRefGoogle ScholarPubMed
van Dongen, J., Bonder, M. J., Dekkers, K. F., Nivard, M. G., van Iterson, M., Willemsen, G., … consortium, B. (2018). DNA methylation signatures of educational attainment. NPJ Science of Learning, 3(1), 7. https://doi.org/10.1038/s41539-018-0020-2CrossRefGoogle ScholarPubMed
Wiedmann, M., Kuitunen-Paul, S., Basedow, L. A., Wolff, M., DiDonato, N., Franzen, J., … Golub, Y. (2022). DNA methylation changes associated with cannabis use and verbal learning performance in adolescents: An exploratory whole genome methylation study. Translational Psychiatry, 12(1), 317. https://doi.org/10.1038/s41398-022-02025-6CrossRefGoogle ScholarPubMed
Zhao, B., Shan, Y., Yang, Y., Yu, Z., Li, T., Wang, X., … Zhu, H. (2021). Transcriptome-wide association analysis of brain structures yields insights into pleiotropy with complex neuropsychiatric traits. Nature Communications, 12(1), 2878. https://doi.org/10.1038/s41467-021-23130-yCrossRefGoogle ScholarPubMed