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The production of within-family inequality: Insights and implications of integrating genetic data

The integration of genetic data within large-scale social and health surveys provides new opportunities to test long-standing theories of parental investments in children and within-family inequality. Genetic predictors, called polygenic scores, allow novel assessments of young children's abili...

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Autores principales: Fletcher, Jason M, Wu, Yuchang, Zhao, Zijie, Lu, Qiongshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10139699/
https://www.ncbi.nlm.nih.gov/pubmed/37124401
http://dx.doi.org/10.1093/pnasnexus/pgad121
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author Fletcher, Jason M
Wu, Yuchang
Zhao, Zijie
Lu, Qiongshi
author_facet Fletcher, Jason M
Wu, Yuchang
Zhao, Zijie
Lu, Qiongshi
author_sort Fletcher, Jason M
collection PubMed
description The integration of genetic data within large-scale social and health surveys provides new opportunities to test long-standing theories of parental investments in children and within-family inequality. Genetic predictors, called polygenic scores, allow novel assessments of young children's abilities that are uncontaminated by parental investments, and family-based samples allow indirect tests of whether children's abilities are reinforced or compensated. We use over 16,000 sibling pairs from the UK Biobank to test whether the relative ranking of siblings’ polygenic scores for educational attainment is consequential for actual attainments. We find evidence consistent with compensatory processes, on average, where the association between genotype and phenotype of educational attainment is reduced by over 20% for the higher-ranked sibling compared to the lower-ranked sibling. These effects are most pronounced in high socioeconomic status areas. We find no evidence that similar processes hold in the case of height or for relatives who are not full biological siblings (e.g. cousins). Our results provide a new use of polygenic scores to understand processes that generate within-family inequalities and also suggest important caveats to causal interpretations the effects of polygenic scores using sibling difference designs. Future work should seek to replicate these findings in other data and contexts.
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spelling pubmed-101396992023-04-28 The production of within-family inequality: Insights and implications of integrating genetic data Fletcher, Jason M Wu, Yuchang Zhao, Zijie Lu, Qiongshi PNAS Nexus Social and Political Sciences The integration of genetic data within large-scale social and health surveys provides new opportunities to test long-standing theories of parental investments in children and within-family inequality. Genetic predictors, called polygenic scores, allow novel assessments of young children's abilities that are uncontaminated by parental investments, and family-based samples allow indirect tests of whether children's abilities are reinforced or compensated. We use over 16,000 sibling pairs from the UK Biobank to test whether the relative ranking of siblings’ polygenic scores for educational attainment is consequential for actual attainments. We find evidence consistent with compensatory processes, on average, where the association between genotype and phenotype of educational attainment is reduced by over 20% for the higher-ranked sibling compared to the lower-ranked sibling. These effects are most pronounced in high socioeconomic status areas. We find no evidence that similar processes hold in the case of height or for relatives who are not full biological siblings (e.g. cousins). Our results provide a new use of polygenic scores to understand processes that generate within-family inequalities and also suggest important caveats to causal interpretations the effects of polygenic scores using sibling difference designs. Future work should seek to replicate these findings in other data and contexts. Oxford University Press 2023-04-12 /pmc/articles/PMC10139699/ /pubmed/37124401 http://dx.doi.org/10.1093/pnasnexus/pgad121 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Social and Political Sciences
Fletcher, Jason M
Wu, Yuchang
Zhao, Zijie
Lu, Qiongshi
The production of within-family inequality: Insights and implications of integrating genetic data
title The production of within-family inequality: Insights and implications of integrating genetic data
title_full The production of within-family inequality: Insights and implications of integrating genetic data
title_fullStr The production of within-family inequality: Insights and implications of integrating genetic data
title_full_unstemmed The production of within-family inequality: Insights and implications of integrating genetic data
title_short The production of within-family inequality: Insights and implications of integrating genetic data
title_sort production of within-family inequality: insights and implications of integrating genetic data
topic Social and Political Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10139699/
https://www.ncbi.nlm.nih.gov/pubmed/37124401
http://dx.doi.org/10.1093/pnasnexus/pgad121
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