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Identification and analysis of individuals who deviate from their genetically-predicted phenotype
Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal fact...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564121/ https://www.ncbi.nlm.nih.gov/pubmed/37733769 http://dx.doi.org/10.1371/journal.pgen.1010934 |
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author | Hawkes, Gareth Yengo, Loic Vedantam, Sailaja Marouli, Eirini Beaumont, Robin N. Tyrrell, Jessica Weedon, Michael N. Hirschhorn, Joel Frayling, Timothy M. Wood, Andrew R. |
author_facet | Hawkes, Gareth Yengo, Loic Vedantam, Sailaja Marouli, Eirini Beaumont, Robin N. Tyrrell, Jessica Weedon, Michael N. Hirschhorn, Joel Frayling, Timothy M. Wood, Andrew R. |
author_sort | Hawkes, Gareth |
collection | PubMed |
description | Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions. |
format | Online Article Text |
id | pubmed-10564121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-105641212023-10-11 Identification and analysis of individuals who deviate from their genetically-predicted phenotype Hawkes, Gareth Yengo, Loic Vedantam, Sailaja Marouli, Eirini Beaumont, Robin N. Tyrrell, Jessica Weedon, Michael N. Hirschhorn, Joel Frayling, Timothy M. Wood, Andrew R. PLoS Genet Research Article Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions. Public Library of Science 2023-09-21 /pmc/articles/PMC10564121/ /pubmed/37733769 http://dx.doi.org/10.1371/journal.pgen.1010934 Text en © 2023 Hawkes et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hawkes, Gareth Yengo, Loic Vedantam, Sailaja Marouli, Eirini Beaumont, Robin N. Tyrrell, Jessica Weedon, Michael N. Hirschhorn, Joel Frayling, Timothy M. Wood, Andrew R. Identification and analysis of individuals who deviate from their genetically-predicted phenotype |
title | Identification and analysis of individuals who deviate from their genetically-predicted phenotype |
title_full | Identification and analysis of individuals who deviate from their genetically-predicted phenotype |
title_fullStr | Identification and analysis of individuals who deviate from their genetically-predicted phenotype |
title_full_unstemmed | Identification and analysis of individuals who deviate from their genetically-predicted phenotype |
title_short | Identification and analysis of individuals who deviate from their genetically-predicted phenotype |
title_sort | identification and analysis of individuals who deviate from their genetically-predicted phenotype |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564121/ https://www.ncbi.nlm.nih.gov/pubmed/37733769 http://dx.doi.org/10.1371/journal.pgen.1010934 |
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