Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1038/s41437-020-0301-4 http://hdl.handle.net/11449/198613 |
Resumo: | This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions. |
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Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controlsThis study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions.Department of Animal Science Safiabad-Dezful Agricultural and Natural Resources Research and Education Center Agricultural Research Education & Extension Organization (AREEO)Department of Animal Science College of Agriculture Shahid Bahonar University of KermanDepartment of Animal and Poultry Science College of Aburaihan University of Tehran, 465, PakdashtDepartment of Surgical Sciences School of Veterinary Medicine University of Wisconsin-MadisonDepartment of Animal and Poultry Sciences Virginia Polytechnic Institute and State UniversityDepartment of Animal Sciences Sao Paulo State University Julio de Mesquita Filho (UNESP), Prof. Paulo Donato CastelaneDepartment of Animal Sciences University of Wisconsin-MadisonDepartment of Biostatistics and Medical Informatics University of Wisconsin-MadisonDepartment of Animal Sciences Sao Paulo State University Julio de Mesquita Filho (UNESP), Prof. Paulo Donato CastelaneEducation & Extension Organization (AREEO)Shahid Bahonar University of KermanUniversity of TehranUniversity of Wisconsin-MadisonVirginia Polytechnic Institute and State UniversityUniversidade Estadual Paulista (Unesp)Amiri Roudbar, MahmoudMohammadabadi, Mohammad RezaAyatollahi Mehrgardi, AhmadAbdollahi-Arpanahi, RostamMomen, MehdiMorota, GotaBrito Lopes, Fernando [UNESP]Gianola, DanielRosa, Guilherme J. M.2020-12-12T01:17:35Z2020-12-12T01:17:35Z2020-05-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article658-674http://dx.doi.org/10.1038/s41437-020-0301-4Heredity, v. 124, n. 5, p. 658-674, 2020.1365-25400018-067Xhttp://hdl.handle.net/11449/19861310.1038/s41437-020-0301-42-s2.0-85081269377Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengHeredityinfo:eu-repo/semantics/openAccess2021-10-22T17:27:46Zoai:repositorio.unesp.br:11449/198613Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-22T17:27:46Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls |
title |
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls |
spellingShingle |
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls Amiri Roudbar, Mahmoud |
title_short |
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls |
title_full |
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls |
title_fullStr |
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls |
title_full_unstemmed |
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls |
title_sort |
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls |
author |
Amiri Roudbar, Mahmoud |
author_facet |
Amiri Roudbar, Mahmoud Mohammadabadi, Mohammad Reza Ayatollahi Mehrgardi, Ahmad Abdollahi-Arpanahi, Rostam Momen, Mehdi Morota, Gota Brito Lopes, Fernando [UNESP] Gianola, Daniel Rosa, Guilherme J. M. |
author_role |
author |
author2 |
Mohammadabadi, Mohammad Reza Ayatollahi Mehrgardi, Ahmad Abdollahi-Arpanahi, Rostam Momen, Mehdi Morota, Gota Brito Lopes, Fernando [UNESP] Gianola, Daniel Rosa, Guilherme J. M. |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Education & Extension Organization (AREEO) Shahid Bahonar University of Kerman University of Tehran University of Wisconsin-Madison Virginia Polytechnic Institute and State University Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Amiri Roudbar, Mahmoud Mohammadabadi, Mohammad Reza Ayatollahi Mehrgardi, Ahmad Abdollahi-Arpanahi, Rostam Momen, Mehdi Morota, Gota Brito Lopes, Fernando [UNESP] Gianola, Daniel Rosa, Guilherme J. M. |
description |
This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy: (1) whole-genome prediction (WGP) using SNP marker information only, (2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers. The number of SNP and of methylation sites varied in each scenario, with either 1, 10, or 50% of these preselected based on four approaches: randomly, evenly spaced, lowest p value (genome-wide association or epigenome-wide association study), and estimated effect size using a Bayesian ridge regression (BRR) model. To remove effects of high levels of pairwise linkage disequilibrium (LD), SNPs were also preselected with an LD-pruning method. Five Bayesian regression models were studied for classification, including BRR, Bayes-A, Bayes-B, Bayes-C, and the Bayesian LASSO. Adjusting methylation profiles for cellular heterogeneity within whole blood samples had a detrimental effect on the classification ability of the models. Overall, WGMP using Bayes-B model has the best performance. In particular, selecting SNPs based on LD-pruning with 1% of the methylation sites selected based on BRR included in the model, and fitting the most significant SNP as a fixed effect was the best method for predicting disease risk with a classification accuracy of 0.975. Our results showed that multiomics data can be used to effectively predict the risk of RA and identify cases in early stages to prevent or alter disease progression via appropriate interventions. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T01:17:35Z 2020-12-12T01:17:35Z 2020-05-01 |
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/10.1038/s41437-020-0301-4 Heredity, v. 124, n. 5, p. 658-674, 2020. 1365-2540 0018-067X http://hdl.handle.net/11449/198613 10.1038/s41437-020-0301-4 2-s2.0-85081269377 |
url |
http://dx.doi.org/10.1038/s41437-020-0301-4 http://hdl.handle.net/11449/198613 |
identifier_str_mv |
Heredity, v. 124, n. 5, p. 658-674, 2020. 1365-2540 0018-067X 10.1038/s41437-020-0301-4 2-s2.0-85081269377 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Heredity |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
658-674 |
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 |
|
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1803649931149312000 |