crossword: A data-driven simulation language for the design of genetic-mapping experiments and breeding strategies

Abstract

The simulation of genetic systems can save time and resources by optimizing the logistics of an experiment. Current tools are difficult to use by those unfamiliar with programming, and these tools rarely address the actual genetic structure of the population under study. Here, we introduce crossword, which utilizes the widely available results of re-sequencing and genomics data to create more realistic simulations and to simplify user input. The software was written in R, making installation and implementation straightforward. Because crossword is a domain-specific language, it allows complex and unique simulations to be performed, but the language is supported by a graphical interface that guides users through functions and options. We first show crossword's utility in QTL-seq design, where its output accurately reflects empirical data. By introducing the concept of levels to reflect family relatedness, crossword is suitable to a broad range of breeding programs and crops. Using levels, we further illustrate crossword's capabilities by examining the effect of family size and number of selfing generations on phenotyping accuracy and genomic selection. Additionally, we explore the ramifications of effect polarity among parents in a mapping cross, a scenario that is common in crop genetics but often difficult to simulate. Given the ease of use and apparent realism, we anticipate crossword will quickly become a "bicycle for the [geneticist's] mind".