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

BMC Bioinformatics

Methodology Article

Sin-Ho Jung¹  Dong Hwan Kim²  Jinseog Kim³  Dae-Soon Son⁴  Taejin Ahn⁴  Insuk Sohn⁵ 

[1] Department of Biostatistics and Bioinformatics, Duke University, 27710, NC, USA;Department of Medical Oncology and Hematology, Princess Margaret Hospital, University of Toronto, Toronto, ON, Canada;Department of Statistics and Information Science, Dongguk University, 780-714, Gyeongju, Korea;In Vitro Diagnostics Lab., Bio Research Center, Samsung Advanced Institute of Technology, 449-712, Suwon, Korea;Samsung Cancer Research Institute, Samsung Medical Center, 137-710, Seoul, Korea;

关键词: Acute Myeloid Leukemia;    Genetic Model;    Recessive Model;    Multiplicative Model;    Tongue Squamous Cell Carcinoma;   

DOI  :  10.1186/1471-2105-14-58

 received in 2012-10-30, accepted in 2013-02-12,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundA 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.ResultsIn 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.ConclusionsIn 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.

【 授权许可】

Unknown   
© Kim et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. 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.