Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1093/g3journal/jkab112 http://hdl.handle.net/11449/229232 |
Resumo: | The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root mean square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (P < 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males. |
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Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human agingAgingBayesian ridge regressionReproducing kernel Hilbert spacesWhole-methylome predictionThe use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root mean square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (P < 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males.Department of Animal Science Safiabad-Dezful Agricultural and Natural Resources Research and Education Center Agricultural Research Education and Extension Organization (AREEO)Department of Animal Science Faculty of Agriculture Engineering University of KurdistanDepartment of Animal Science and Aquaculture Dalhousie UniversityDepartment of Animal Sciences Sao Paulo State University Julio de Mesquita Filho (UNESP), Prof. Paulo Donato, JaboticabalDepartment of Surgical Sciences School of Veterinary Medicine University of Wisconsin-MadisonDepartment of Animal and Dairy Sciences University of Wisconsin-MadisonDepartment of Animal Sciences Sao Paulo State University Julio de Mesquita Filho (UNESP), Prof. Paulo Donato, JaboticabalEducation and Extension Organization (AREEO)University of KurdistanDalhousie UniversityUniversidade Estadual Paulista (UNESP)University of Wisconsin-MadisonRoudbar, Mahmoud AmiriMousavi, Seyedeh FatemehArdestani, Siavash SalekLopes, Fernando Brito [UNESP]Momen, MehdiGianola, DanielKhatib, Hasan2022-04-29T08:31:21Z2022-04-29T08:31:21Z2021-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1093/g3journal/jkab112G3: Genes, Genomes, Genetics, v. 11, n. 7, 2021.2160-1836http://hdl.handle.net/11449/22923210.1093/g3journal/jkab1122-s2.0-85111573938Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengG3: Genes, Genomes, Geneticsinfo:eu-repo/semantics/openAccess2024-06-07T18:39:17Zoai:repositorio.unesp.br:11449/229232Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-06-07T18:39:17Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging |
title |
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging |
spellingShingle |
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging Roudbar, Mahmoud Amiri Aging Bayesian ridge regression Reproducing kernel Hilbert spaces Whole-methylome prediction |
title_short |
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging |
title_full |
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging |
title_fullStr |
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging |
title_full_unstemmed |
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging |
title_sort |
Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging |
author |
Roudbar, Mahmoud Amiri |
author_facet |
Roudbar, Mahmoud Amiri Mousavi, Seyedeh Fatemeh Ardestani, Siavash Salek Lopes, Fernando Brito [UNESP] Momen, Mehdi Gianola, Daniel Khatib, Hasan |
author_role |
author |
author2 |
Mousavi, Seyedeh Fatemeh Ardestani, Siavash Salek Lopes, Fernando Brito [UNESP] Momen, Mehdi Gianola, Daniel Khatib, Hasan |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Education and Extension Organization (AREEO) University of Kurdistan Dalhousie University Universidade Estadual Paulista (UNESP) University of Wisconsin-Madison |
dc.contributor.author.fl_str_mv |
Roudbar, Mahmoud Amiri Mousavi, Seyedeh Fatemeh Ardestani, Siavash Salek Lopes, Fernando Brito [UNESP] Momen, Mehdi Gianola, Daniel Khatib, Hasan |
dc.subject.por.fl_str_mv |
Aging Bayesian ridge regression Reproducing kernel Hilbert spaces Whole-methylome prediction |
topic |
Aging Bayesian ridge regression Reproducing kernel Hilbert spaces Whole-methylome prediction |
description |
The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4409 human individuals with a range of 10-101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root mean square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (P < 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-07-01 2022-04-29T08:31:21Z 2022-04-29T08:31:21Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1093/g3journal/jkab112 G3: Genes, Genomes, Genetics, v. 11, n. 7, 2021. 2160-1836 http://hdl.handle.net/11449/229232 10.1093/g3journal/jkab112 2-s2.0-85111573938 |
url |
http://dx.doi.org/10.1093/g3journal/jkab112 http://hdl.handle.net/11449/229232 |
identifier_str_mv |
G3: Genes, Genomes, Genetics, v. 11, n. 7, 2021. 2160-1836 10.1093/g3journal/jkab112 2-s2.0-85111573938 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
G3: Genes, Genomes, Genetics |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1803649309088940032 |