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A Comparative Study of Joint-SNVs Analysis Methods and Detection of Susceptibility Genes for Gastric Cancer in Korean Population

  • Jinxiong Lv
  • Shikui Tu
  • Lei XuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10559)

Abstract

Many joint-SNVs (single-nucleotide variants) analysis methods were proposed to tackle the ‘missing heritability’ problem, which emphasizes that the joint genetic variants can explain more heritability of traits and diseases. However, there is still lack of a systematic comparison and investigation on the relative strengths and weaknesses of these methods. In this paper, we evaluated their performance on extensive simulated data generated by varying sample size, linkage disequilibrium (LD), odds ratios (OR), and minor allele frequency (MAF), which aims to cover almost all scenarios encountered in practical applications. Results indicated that a method called Statistics-space Boundary Based Test (S-space BBT) showed stronger detection power than other methods. Results on a real dataset of gastric cancer for Korean population also validate the effectiveness of the S-space BBT method.

Keywords

GWAS Sequence analysis Joint-SNVs analysis test Statistics-space Boundary based test Gastric cancer 

Notes

Acknowledgements

This work was supported by the Zhi-Yuan chair professorship start-up grant (WF220103010) from Shanghai Jiao Tong University.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Center for Cognitive Machines and Computational HealthShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong Kong SARChina

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