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Socioeconomic and Genomic Roots of Verbal Ability from Current Evidence

Overview
Journal NPJ Sci Learn
Specialty Psychology
Date 2022 Sep 9
PMID 36085328
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Abstract

This research examines how the human genome and SES jointly and interactively shape verbal ability among youth in the U.S. The youth are aged 12-18 when the study starts. The research draws on findings from the latest GWAS as well as a rich set of longitudinal SES measures at individual, family and neighborhood levels from Add Health (N = 7194). Both SES and genome measures predict verbal ability well separately and jointly. More interestingly, the inclusion of both sets of predictors in the same model corrects for about 20% upward bias in the effect of the education PGS, and implies that about 20-30% of the effects of parental SES are not environmental, but parentally genomic. The three incremental Rs that measure the relative contributions of the two PGSs, the genomic component in parental SES, and the environmental component in parental SES are estimated to be about 1.5%, 1.5%, and 7.8%, respectively. The total environmental R and the total genomic R are, thus, 7.8% and 3%, respectively. These findings confirm the importance of SES environment and also pose challenges to traditional social-science research. Not only does an individual's genome have an important direct influence on verbal ability, parental genomes also influence verbal ability through parental SES. The decades-long blueprint of including SES in a model and interpreting their effects as those of SES needs to be amended accordingly. A straightforward solution is to routinely collect DNA data for large social-science studies granted that the primary purpose is to understand social and environmental influences.

Citing Articles

Social Background Effects on Educational Outcomes-New Insights from Modern Genetic Science.

Baier T, Lyngstad T Kolner Z Soz Sozpsychol. 2024; 76(3):525-545.

PMID: 39429463 PMC: 11485211. DOI: 10.1007/s11577-024-00970-2.

References
1.
Veldman K, Bultmann U, Stewart R, Ormel J, Verhulst F, Reijneveld S . Mental health problems and educational attainment in adolescence: 9-year follow-up of the TRAILS study. PLoS One. 2014; 9(7):e101751. PMC: 4105412. DOI: 10.1371/journal.pone.0101751. View

2.
Plomin R, Deary I . Genetics and intelligence differences: five special findings. Mol Psychiatry. 2014; 20(1):98-108. PMC: 4270739. DOI: 10.1038/mp.2014.105. View

3.
Akimova E, Breen R, Brazel D, Mills M . Gene-environment dependencies lead to collider bias in models with polygenic scores. Sci Rep. 2021; 11(1):9457. PMC: 8097011. DOI: 10.1038/s41598-021-89020-x. View

4.
Domingue B, Trejo S, Armstrong-Carter E, Tucker-Drob E . Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges. Sociol Sci. 2022; 7:465-486. PMC: 9455807. DOI: 10.15195/v7.a19. View

5.
Belsky D, Domingue B, Wedow R, Arseneault L, Boardman J, Caspi A . Genetic analysis of social-class mobility in five longitudinal studies. Proc Natl Acad Sci U S A. 2018; 115(31):E7275-E7284. PMC: 6077729. DOI: 10.1073/pnas.1801238115. View