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Prediction of a time-to-event trait using genome wide SNP data

BACKGROUND: A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. RESULTS: In this paper, we present a prediction method for time-to-event t...

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Autores principales: Kim, Jinseog, Sohn, Insuk, Son, Dae-Soon, Kim, Dong Hwan, Ahn, Taejin, Jung, Sin-Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3651372/
https://www.ncbi.nlm.nih.gov/pubmed/23418752
http://dx.doi.org/10.1186/1471-2105-14-58
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author Kim, Jinseog
Sohn, Insuk
Son, Dae-Soon
Kim, Dong Hwan
Ahn, Taejin
Jung, Sin-Ho
author_facet Kim, Jinseog
Sohn, Insuk
Son, Dae-Soon
Kim, Dong Hwan
Ahn, Taejin
Jung, Sin-Ho
author_sort Kim, Jinseog
collection PubMed
description BACKGROUND: A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. RESULTS: In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose a MaxTest associating between a time-to-event trait and a SNP accounting for its possible genetic models. The proposed MaxTest can help screen out nonprognostic SNPs and identify genetic models of prognostic SNPs. The performance of the proposed method is evaluated through simulations. CONCLUSIONS: In conjunction with the MaxTest, the proposed method provides more parsimonious prediction models but includes more prognostic SNPs than some naive prediction methods. The proposed method is demonstrated with real GWAS data.
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spelling pubmed-36513722013-05-14 Prediction of a time-to-event trait using genome wide SNP data Kim, Jinseog Sohn, Insuk Son, Dae-Soon Kim, Dong Hwan Ahn, Taejin Jung, Sin-Ho BMC Bioinformatics Methodology Article BACKGROUND: A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. RESULTS: In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose a MaxTest associating between a time-to-event trait and a SNP accounting for its possible genetic models. The proposed MaxTest can help screen out nonprognostic SNPs and identify genetic models of prognostic SNPs. The performance of the proposed method is evaluated through simulations. CONCLUSIONS: In conjunction with the MaxTest, the proposed method provides more parsimonious prediction models but includes more prognostic SNPs than some naive prediction methods. The proposed method is demonstrated with real GWAS data. BioMed Central 2013-02-19 /pmc/articles/PMC3651372/ /pubmed/23418752 http://dx.doi.org/10.1186/1471-2105-14-58 Text en Copyright © 2013 Kim et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Kim, Jinseog
Sohn, Insuk
Son, Dae-Soon
Kim, Dong Hwan
Ahn, Taejin
Jung, Sin-Ho
Prediction of a time-to-event trait using genome wide SNP data
title Prediction of a time-to-event trait using genome wide SNP data
title_full Prediction of a time-to-event trait using genome wide SNP data
title_fullStr Prediction of a time-to-event trait using genome wide SNP data
title_full_unstemmed Prediction of a time-to-event trait using genome wide SNP data
title_short Prediction of a time-to-event trait using genome wide SNP data
title_sort prediction of a time-to-event trait using genome wide snp data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3651372/
https://www.ncbi.nlm.nih.gov/pubmed/23418752
http://dx.doi.org/10.1186/1471-2105-14-58
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