Epigenetic prediction of complex traits and death
BACKGROUND: Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications. RESULTS: Here, penalized regression mo...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158884/ https://www.ncbi.nlm.nih.gov/pubmed/30257690 http://dx.doi.org/10.1186/s13059-018-1514-1 |
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author | McCartney, Daniel L. Hillary, Robert F. Stevenson, Anna J. Ritchie, Stuart J. Walker, Rosie M. Zhang, Qian Morris, Stewart W. Bermingham, Mairead L. Campbell, Archie Murray, Alison D. Whalley, Heather C. Gale, Catharine R. Porteous, David J. Haley, Chris S. McRae, Allan F. Wray, Naomi R. Visscher, Peter M. McIntosh, Andrew M. Evans, Kathryn L. Deary, Ian J. Marioni, Riccardo E. |
author_facet | McCartney, Daniel L. Hillary, Robert F. Stevenson, Anna J. Ritchie, Stuart J. Walker, Rosie M. Zhang, Qian Morris, Stewart W. Bermingham, Mairead L. Campbell, Archie Murray, Alison D. Whalley, Heather C. Gale, Catharine R. Porteous, David J. Haley, Chris S. McRae, Allan F. Wray, Naomi R. Visscher, Peter M. McIntosh, Andrew M. Evans, Kathryn L. Deary, Ian J. Marioni, Riccardo E. |
author_sort | McCartney, Daniel L. |
collection | PubMed |
description | BACKGROUND: Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications. RESULTS: Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios. CONCLUSIONS: DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1514-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6158884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61588842018-10-01 Epigenetic prediction of complex traits and death McCartney, Daniel L. Hillary, Robert F. Stevenson, Anna J. Ritchie, Stuart J. Walker, Rosie M. Zhang, Qian Morris, Stewart W. Bermingham, Mairead L. Campbell, Archie Murray, Alison D. Whalley, Heather C. Gale, Catharine R. Porteous, David J. Haley, Chris S. McRae, Allan F. Wray, Naomi R. Visscher, Peter M. McIntosh, Andrew M. Evans, Kathryn L. Deary, Ian J. Marioni, Riccardo E. Genome Biol Research BACKGROUND: Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications. RESULTS: Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality (n = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios. CONCLUSIONS: DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-018-1514-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-27 /pmc/articles/PMC6158884/ /pubmed/30257690 http://dx.doi.org/10.1186/s13059-018-1514-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research McCartney, Daniel L. Hillary, Robert F. Stevenson, Anna J. Ritchie, Stuart J. Walker, Rosie M. Zhang, Qian Morris, Stewart W. Bermingham, Mairead L. Campbell, Archie Murray, Alison D. Whalley, Heather C. Gale, Catharine R. Porteous, David J. Haley, Chris S. McRae, Allan F. Wray, Naomi R. Visscher, Peter M. McIntosh, Andrew M. Evans, Kathryn L. Deary, Ian J. Marioni, Riccardo E. Epigenetic prediction of complex traits and death |
title | Epigenetic prediction of complex traits and death |
title_full | Epigenetic prediction of complex traits and death |
title_fullStr | Epigenetic prediction of complex traits and death |
title_full_unstemmed | Epigenetic prediction of complex traits and death |
title_short | Epigenetic prediction of complex traits and death |
title_sort | epigenetic prediction of complex traits and death |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158884/ https://www.ncbi.nlm.nih.gov/pubmed/30257690 http://dx.doi.org/10.1186/s13059-018-1514-1 |
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