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How to Reveal Magnitude of Gene Signals: Hierarchical Hypergeometric Complementary Cumulative Distribution Function.

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.

SUBMITTER: Kim B

PROVIDER: S-EPMC6196626 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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How to Reveal Magnitude of Gene Signals: Hierarchical Hypergeometric Complementary Cumulative Distribution Function.

Kim Bongsong B

Evolutionary bioinformatics online 20181018

This article introduces a new method for genome-wide association study (GWAS), <i>hierarchical hypergeometric complementary cumulative distribution function</i> (HH-CCDF). Existing GWAS methods, e.g. the linear model and <i>hierarchical association coefficient algorithm</i>, 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 phe ...[more]

PMID: 30364489

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