An active learning tool for quantitative genetics instruction using R and shiny

Abstract

Basic quantitative and population genetics topics are typically taught in introductory plant breeding courses and are critical for success in upper-level study. Active learning, including simulations and games, may be useful for instruction of these concepts, which rely heavily on theory and may be more challenging for students. The statistical computing language R is now routinely used in the analysis of plant breeding experiments, but the command-line interface of the language may be unsuitable for an introductory course. Here we describe qgshiny (quantitative genetics in shiny), an interactive application for performing simulations to understand basic theory in quantitative and population genetics. The initial version of the application includes modules on three core topics in quantitative genetics: randomly mating populations, genetic variance, and response to selection. Students can specify parameters and initiate simulations to assess their impact on responses such as allele frequency, genetic variance, and genetic gain, which together can be used to reinforce more general learning objectives. Feedback collected from students after engaging with the application suggests this tool can have a positive impact on student learning. The application is bundled in an R package, qgshiny, which is available through the Comprehensive R Archive Network (CRAN), on GitHub (https://github.com/neyhartj/qgshiny), or interactively through the shinyapps.io platform (http://neyhartj.shinyapps.io/qgshiny).