Comparative studies on multivariate tests for joint-SNVs analysis and detection for bipolar disorder susceptibility genes. (2017)

• Record Type: Journal Article
• Authors: Lv, Jin-Xiong; Huang, Han-Chen; Chen, Run-Sheng; Xu, Lei
• Is Part Of: International journal of data mining and bioinformatics. Volume 17:Number 4(2017)
• Page Start: 341
• Page End: 358
• Publication Date: 2017
• Subjects: GWAS -- sequence analysis -- joint-SNVs analysis -- odds ratio -- dominant single variant -- effect direction -- minor allele frequency -- the linkage disequilibrium -- S-space boundary-based test -- bipolar disorder; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Instead of the single nucleotide variants (SNVs) analysis, many joint-SNVs analysis methods were proposed to tackle the 'missing heritability problem' in the genome-wide association studies (GWASs). In this paper, we performed a comparative study on five typical methods for joint-SNVs analysis and a recently proposed method called Statistics-space Boundary-based test (S-space BBT). For a fair and comprehensive comparison, we conducted simulation experiments by considering dominant single variant, effect direction, minor allele frequency (MAF), odds ratio (OR) and the linkage disequilibrium (LD). The results indicated that the S-space BBT not only does not swamp the significant SNV but also maintains stronger detection power under different configurations. As a result, we applied the S-space BBT to the dataset of bipolar disorder and obtained a list of biomarkers. Besides, the literature researches were conducted to validate the reliability of the results.