A Comparative Study of Joint-SNVs Analysis Methods and Detection of Susceptibility Genes for Gastric Cancer in Korean Population
Refereed conference paper presented and published in conference proceedings

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AbstractMany 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.
All Author(s) ListJinxiong Lv, Shikui Tu, Lei Xu
Name of ConferenceInternational Conference on Intelligent Science and Big Data Engineering (IScIDE 2017)
Start Date of Conference22/09/2017
End Date of Conference23/09/2017
Place of ConferenceDalian
Country/Region of ConferenceChina
Proceedings TitleIntelligence Science and Big Data Engineering. IScIDE 2017
Series TitleLecture Notes in Computer Science
Volume Number10559
PublisherSpringer, Cham
Pages619 - 630
LanguagesEnglish-United States
KeywordsGWAS, Sequence analysis, Joint-SNVs analysis test, Statistics-space Boundary based test, Gastric cancer

Last updated on 2020-02-06 at 00:42