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

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 proceeding › Chapter

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 language	English (US)
Title of host publication	Recent Advances in Systems Biology Research
Publisher	Nova Science Publishers, Inc.
Pages	37-48
Number of pages	12
ISBN (Electronic)	9781629487373
ISBN (Print)	9781629487366
State	Published - Jan 1 2014
Externally published	Yes

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)

Cite this

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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",

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AU - Valente, A. X.C.N.

AU - Sarkar, Abhijit

AU - Shin, Joo Heon

AU - Wu, Jie

AU - Gao, Yuan

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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

AB - 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

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M3 - Chapter

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BT - Recent Advances in Systems Biology Research

PB - Nova Science Publishers, Inc.

ER -

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

Abstract

ASJC Scopus subject areas

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