articlePLoS ONEJun 6, 2016GOLD OA

Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer

University of Wisconsin–Madison

Indexed incrossrefdoaj

Abstract

Most traits of agronomic importance are quantitative in nature, and genetic markers have been used for decades to dissect such traits. Recently, genomic selection has earned attention as next generation sequencing technologies became feasible for major and minor crops. Mixed models have become a key tool for fitting genomic selection models, but most current genomic selection software can only include a single variance component other than the error, making hybrid prediction using additive, dominance and epistatic effects unfeasible for species displaying heterotic effects. Moreover, Likelihood-based software for fitting mixed models with multiple random effects that allows the user to specify the…

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Topics & keywords

Keywords
  • Covariance
  • Epistasis
  • Mixed model
  • Selection (genetic algorithm)
  • Computer science
  • Biology
  • Bayesian probability
  • Software
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