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Phenome-wide analysis of genome-wide polygenic scores
Genome-wide polygenic scores (GPS), which aggregate the effects of thousands of DNA variants from genome-wide association studies (GWAS), have the potential to make genetic predictions for individuals. We conducted a systematic investigation of associations between GPS and many behavioral traits, th...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4767701/ https://www.ncbi.nlm.nih.gov/pubmed/26303664 http://dx.doi.org/10.1038/mp.2015.126 |
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author | Krapohl, E Euesden, J Zabaneh, D Pingault, J-B Rimfeld, K von Stumm, S Dale, P S Breen, G O'Reilly, P F Plomin, R |
author_facet | Krapohl, E Euesden, J Zabaneh, D Pingault, J-B Rimfeld, K von Stumm, S Dale, P S Breen, G O'Reilly, P F Plomin, R |
author_sort | Krapohl, E |
collection | PubMed |
description | Genome-wide polygenic scores (GPS), which aggregate the effects of thousands of DNA variants from genome-wide association studies (GWAS), have the potential to make genetic predictions for individuals. We conducted a systematic investigation of associations between GPS and many behavioral traits, the behavioral phenome. For 3152 unrelated 16-year-old individuals representative of the United Kingdom, we created 13 GPS from the largest GWAS for psychiatric disorders (for example, schizophrenia, depression and dementia) and cognitive traits (for example, intelligence, educational attainment and intracranial volume). The behavioral phenome included 50 traits from the domains of psychopathology, personality, cognitive abilities and educational achievement. We examined phenome-wide profiles of associations for the entire distribution of each GPS and for the extremes of the GPS distributions. The cognitive GPS yielded stronger predictive power than the psychiatric GPS in our UK-representative sample of adolescents. For example, education GPS explained variation in adolescents' behavior problems (~0.6%) and in educational achievement (~2%) but psychiatric GPS were associated with neither. Despite the modest effect sizes of current GPS, quantile analyses illustrate the ability to stratify individuals by GPS and opportunities for research. For example, the highest and lowest septiles for the education GPS yielded a 0.5 s.d. difference in mean math grade and a 0.25 s.d. difference in mean behavior problems. We discuss the usefulness and limitations of GPS based on adult GWAS to predict genetic propensities earlier in development. |
format | Online Article Text |
id | pubmed-4767701 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-47677012016-09-01 Phenome-wide analysis of genome-wide polygenic scores Krapohl, E Euesden, J Zabaneh, D Pingault, J-B Rimfeld, K von Stumm, S Dale, P S Breen, G O'Reilly, P F Plomin, R Mol Psychiatry Original Article Genome-wide polygenic scores (GPS), which aggregate the effects of thousands of DNA variants from genome-wide association studies (GWAS), have the potential to make genetic predictions for individuals. We conducted a systematic investigation of associations between GPS and many behavioral traits, the behavioral phenome. For 3152 unrelated 16-year-old individuals representative of the United Kingdom, we created 13 GPS from the largest GWAS for psychiatric disorders (for example, schizophrenia, depression and dementia) and cognitive traits (for example, intelligence, educational attainment and intracranial volume). The behavioral phenome included 50 traits from the domains of psychopathology, personality, cognitive abilities and educational achievement. We examined phenome-wide profiles of associations for the entire distribution of each GPS and for the extremes of the GPS distributions. The cognitive GPS yielded stronger predictive power than the psychiatric GPS in our UK-representative sample of adolescents. For example, education GPS explained variation in adolescents' behavior problems (~0.6%) and in educational achievement (~2%) but psychiatric GPS were associated with neither. Despite the modest effect sizes of current GPS, quantile analyses illustrate the ability to stratify individuals by GPS and opportunities for research. For example, the highest and lowest septiles for the education GPS yielded a 0.5 s.d. difference in mean math grade and a 0.25 s.d. difference in mean behavior problems. We discuss the usefulness and limitations of GPS based on adult GWAS to predict genetic propensities earlier in development. Nature Publishing Group 2016-09 2015-08-25 /pmc/articles/PMC4767701/ /pubmed/26303664 http://dx.doi.org/10.1038/mp.2015.126 Text en Copyright © 2016 The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Original Article Krapohl, E Euesden, J Zabaneh, D Pingault, J-B Rimfeld, K von Stumm, S Dale, P S Breen, G O'Reilly, P F Plomin, R Phenome-wide analysis of genome-wide polygenic scores |
title | Phenome-wide analysis of genome-wide polygenic scores |
title_full | Phenome-wide analysis of genome-wide polygenic scores |
title_fullStr | Phenome-wide analysis of genome-wide polygenic scores |
title_full_unstemmed | Phenome-wide analysis of genome-wide polygenic scores |
title_short | Phenome-wide analysis of genome-wide polygenic scores |
title_sort | phenome-wide analysis of genome-wide polygenic scores |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4767701/ https://www.ncbi.nlm.nih.gov/pubmed/26303664 http://dx.doi.org/10.1038/mp.2015.126 |
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