Mathematical framework for analyzing genome-wide association study data with rational classes

A. X.C.N. Valente, Abhijit Sarkar, Joo Heon Shin, Jie Wu, Yuan Gao

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Genome-wide association studies can statistically expose genes and biological processes involved in disease mechanics. Development of algorithms that increase the statistical power of genome-wide association studies remains an important research challenge. We present a mathematical approach to analyze a genome-wide association study. The method enables the use of Rational Classes to biologically direct the search for biomarkers and thus increase statistical power. Ranking of candidate biomarkers avoids the need for a Bonferroni-type correction. A modified q-value is introduced to provide a false-discovery rate type of measure in this setting

Original languageEnglish (US)
Title of host publicationRecent Advances in Systems Biology Research
PublisherNova Science Publishers, Inc.
Pages37-48
Number of pages12
ISBN (Electronic)9781629487373
ISBN (Print)9781629487366
StatePublished - Jan 1 2014

    Fingerprint

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Valente, A. X. C. N., Sarkar, A., Shin, J. H., Wu, J., & Gao, Y. (2014). Mathematical framework for analyzing genome-wide association study data with rational classes. In Recent Advances in Systems Biology Research (pp. 37-48). Nova Science Publishers, Inc..