How to Reveal Magnitude of Gene Signals: Hierarchical Hypergeometric Complementary Cumulative Distribution Function. (October 2018)

• Record Type: Journal Article
• Authors: Kim, Bongsong
• Is Part Of: Evolutionary bioinformatics online. Volume 14(2018)
• Publication Date: 2018-10
• Subjects: Genome-wide association study -- hierarchical association coefficient algorithm -- hierarchical binary categorization -- hypergeometric complementary cumulative distribution function -- magnitude of gene signals -- quantitative trait loci; Bioinformatics -- Periodicals; Evolutionary computation -- Periodicals; Genetic programming (Computer science) -- Periodicals; Computational Biology; Evolution, Molecular; Bioinformatics; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 576.8
• DOI: 10.1177/1176934318797352
• Languages: English
• ISSNs: 1176-9343

Abstract

This article introduces a new method for genome-wide association study (GWAS), hierarchical hypergeometric complementary cumulative distribution function (HH-CCDF). Existing GWAS methods, e.g. the linear model and hierarchical association coefficient algorithm, calculate the association between a marker variable and a phenotypic variable. The ideal GWAS practice is to calculate the association between a marker variable and a gene-signal variable. If the gene-signal variable and phenotypic variable are imperfectly proportional, the existing methods do not properly reveal the magnitude of the association between the gene-signal variable and marker variable. The HH-CCDF mitigates the impact of the imperfect proportionality between the phenotypic variable and gene-signal variable and thus better reveals the magnitude of gene signals. The HH-CCDF will provide new insights into GWAS approaches from the viewpoint of revealing the magnitude of gene signals.