Jeffrey Endelman
Breeders have long appreciated the need to balance selection for short-term genetic gain with maintaining genetic variance for long-term gain. The COMA package implements selection strategies known as Optimum Contribution Selection (OCS) and Optimum Mate Allocation (OMA). OCS maximizes the average genomic-estimated breeding value (GEBV) of the parents, weighted by their contribution to the next generation. OMA maximizes the average genomic prediction of mate performance (GPMP), weighted by the contribution of each mating, which is called mate allocation. Constraints on inbreeding rate are used to ensure genetic variance is not depleted too quickly. The “C” in COMA stands for Convex because the software exploits the convex nature of the problem to efficiently find the global optimum.
Financial support for developing COMA has come from the USDA National Institute of Food and Agriculture (NIFA) Award 2020-51181-32156. Please cite the manuscript if you use it. Vignette 1 provides examples of using the software with data from the University of Wisconsin-Madison potato breeding program.
To install and load the package:
install.packages("devtools")
devtools::install_github("jendelman/COMA", build_vignettes=FALSE)
library(COMA)For a complete specification of package functions, consult the reference manual.