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EBglmnet: a comprehensive R package for sparse generalized linear regression models.

Anhui Huang1, Dianting Liu1

  • 1Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146, USA.

Bioinformatics (Oxford, England)
|May 7, 2016
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Summary
This summary is machine-generated.

EBglmnet is a new R package for generalized linear models using empirical Bayesian methods. It offers improved performance over standard lasso and elastic net for genotype-phenotype association studies with many covariates.

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Area of Science:

  • Genetics
  • Statistical Modeling
  • Bioinformatics

Background:

  • High-throughput technologies enable dense genetic marker data in population analysis.
  • Traditional methods like lasso and elastic net face challenges with high-dimensional data where covariates exceed sample size.

Purpose of the Study:

  • Introduce EBglmnet, an R package for generalized linear models.
  • Implement empirical Bayesian priors, specifically lasso (EBlasso) and elastic net (EBEN).
  • Provide a tool for statistical modeling in genetics and population analysis.

Main Methods:

  • Developed EBglmnet as an R package.
  • Integrated empirical Bayesian lasso (EBlasso) and elastic net (EBEN) priors.
  • Utilized generalized linear models for statistical analysis.

Main Results:

  • EBlasso and EBEN demonstrated superior performance compared to standard lasso and elastic net.
  • Outperformance was observed in inferring sparse genotype and phenotype associations.
  • The methods are effective when the number of covariates is substantially larger than the sample size.

Conclusions:

  • EBglmnet is a valuable R package for statistical modeling in genetics.
  • The empirical Bayesian approach offers advantages for high-dimensional genetic data analysis.
  • EBglmnet enhances the inference of genotype-phenotype associations.