Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls

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dc.contributor.authorAmiri Roudbar, Mahmoud
dc.contributor.authorMohammadabadi, Mohammad Reza
dc.contributor.authorAyatollahi Mehrgardi, Ahmad
dc.contributor.authorAbdollahi-Arpanahi, Rostam
dc.contributor.authorMomen, Mehdi
dc.contributor.authorMorota, Gota
dc.contributor.authorBrito Lopes, Fernando [UNESP]
dc.contributor.authorGianola, Daniel
dc.contributor.authorRosa, Guilherme J. M.
dc.contributor.institutionEducation & Extension Organization (AREEO)
dc.contributor.institutionShahid Bahonar University of Kerman
dc.contributor.institutionUniversity of Tehran
dc.contributor.institutionUniversity of Wisconsin-Madison
dc.contributor.institutionVirginia Polytechnic Institute and State University
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2020-12-12T01:17:35Z
dc.date.available2020-12-12T01:17:35Z
dc.date.issued2020-05-01
dc.description.abstractThis 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.en
dc.description.affiliationDepartment of Animal Science Safiabad-Dezful Agricultural and Natural Resources Research and Education Center Agricultural Research Education & Extension Organization (AREEO)
dc.description.affiliationDepartment of Animal Science College of Agriculture Shahid Bahonar University of Kerman
dc.description.affiliationDepartment of Animal and Poultry Science College of Aburaihan University of Tehran, 465, Pakdasht
dc.description.affiliationDepartment of Surgical Sciences School of Veterinary Medicine University of Wisconsin-Madison
dc.description.affiliationDepartment of Animal and Poultry Sciences Virginia Polytechnic Institute and State University
dc.description.affiliationDepartment of Animal Sciences Sao Paulo State University Julio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane
dc.description.affiliationDepartment of Animal Sciences University of Wisconsin-Madison
dc.description.affiliationDepartment of Biostatistics and Medical Informatics University of Wisconsin-Madison
dc.description.affiliationUnespDepartment of Animal Sciences Sao Paulo State University Julio de Mesquita Filho (UNESP), Prof. Paulo Donato Castelane
dc.format.extent658-674
dc.identifierhttp://dx.doi.org/10.1038/s41437-020-0301-4
dc.identifier.citationHeredity, v. 124, n. 5, p. 658-674, 2020.
dc.identifier.doi10.1038/s41437-020-0301-4
dc.identifier.issn1365-2540
dc.identifier.issn0018-067X
dc.identifier.scopus2-s2.0-85081269377
dc.identifier.urihttp://hdl.handle.net/11449/198613
dc.language.isoeng
dc.relation.ispartofHeredity
dc.sourceScopus
dc.titleIntegration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controlsen
dc.typeArtigo
unesp.author.orcid0000-0002-1268-3043[2]
unesp.author.orcid0000-0003-4068-9589[4]
unesp.author.orcid0000-0002-3567-6911[6]
unesp.author.orcid0000-0001-9172-6461[9]

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