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Can education be personalised using pupils’ genetic data?
The increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How accurately polygenic scores predict an individual pupil's educational performance conditional on other phenotypic data is however not wel...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064332/ https://www.ncbi.nlm.nih.gov/pubmed/32151313 http://dx.doi.org/10.7554/eLife.49962 |
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author | Morris, Tim T Davies, Neil M Davey Smith, George |
author_facet | Morris, Tim T Davies, Neil M Davey Smith, George |
author_sort | Morris, Tim T |
collection | PubMed |
description | The increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How accurately polygenic scores predict an individual pupil's educational performance conditional on other phenotypic data is however not well understood. Using data from a UK cohort study with data linkage to national schooling records, we investigated how accurately polygenic scores for education predicted pupils’ test score achievement. We also assessed the performance of polygenic scores over and above phenotypic data that are available to schools. Across our sample, there was high overlap between the polygenic score and achievement distributions, leading to poor predictive accuracy at the individual level. Prediction of educational outcomes from polygenic scores were inferior to those from parental socioeconomic factors. Conditional on prior achievement, polygenic scores failed to accurately predict later achievement. Our results suggest that while polygenic scores can be informative for identifying group level differences, they currently have limited use for accurately predicting individual educational performance or for personalised education. |
format | Online Article Text |
id | pubmed-7064332 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-70643322020-03-11 Can education be personalised using pupils’ genetic data? Morris, Tim T Davies, Neil M Davey Smith, George eLife Genetics and Genomics The increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How accurately polygenic scores predict an individual pupil's educational performance conditional on other phenotypic data is however not well understood. Using data from a UK cohort study with data linkage to national schooling records, we investigated how accurately polygenic scores for education predicted pupils’ test score achievement. We also assessed the performance of polygenic scores over and above phenotypic data that are available to schools. Across our sample, there was high overlap between the polygenic score and achievement distributions, leading to poor predictive accuracy at the individual level. Prediction of educational outcomes from polygenic scores were inferior to those from parental socioeconomic factors. Conditional on prior achievement, polygenic scores failed to accurately predict later achievement. Our results suggest that while polygenic scores can be informative for identifying group level differences, they currently have limited use for accurately predicting individual educational performance or for personalised education. eLife Sciences Publications, Ltd 2020-03-10 /pmc/articles/PMC7064332/ /pubmed/32151313 http://dx.doi.org/10.7554/eLife.49962 Text en © 2020, Morris et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Genetics and Genomics Morris, Tim T Davies, Neil M Davey Smith, George Can education be personalised using pupils’ genetic data? |
title | Can education be personalised using pupils’ genetic data? |
title_full | Can education be personalised using pupils’ genetic data? |
title_fullStr | Can education be personalised using pupils’ genetic data? |
title_full_unstemmed | Can education be personalised using pupils’ genetic data? |
title_short | Can education be personalised using pupils’ genetic data? |
title_sort | can education be personalised using pupils’ genetic data? |
topic | Genetics and Genomics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064332/ https://www.ncbi.nlm.nih.gov/pubmed/32151313 http://dx.doi.org/10.7554/eLife.49962 |
work_keys_str_mv | AT morristimt caneducationbepersonalisedusingpupilsgeneticdata AT daviesneilm caneducationbepersonalisedusingpupilsgeneticdata AT daveysmithgeorge caneducationbepersonalisedusingpupilsgeneticdata |