Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

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

Jinseog Kim1, Insuk Sohn2, Dae-Soon Son3, Dong Hwan Kim4, Taejin Ahn3 and Sin-Ho Jung5*

Author affiliations

1 Department of Statistics and Information Science, Dongguk University, Gyeongju 780-714, Korea

2 Samsung Cancer Research Institute, Samsung Medical Center, Seoul 137-710, Korea

3 In Vitro Diagnostics Lab., Bio Research Center, Samsung Advanced Institute of Technology, Suwon 449-712, Korea

4 Department of Medical Oncology and Hematology, Princess Margaret Hospital, University of Toronto, Toronto ON, Canada

5 Department of Biostatistics and Bioinformatics, Duke University, NC 27710, USA

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2013, 14:58  doi:10.1186/1471-2105-14-58

Published: 19 February 2013

Abstract

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.