Data from: AgMate: an optimal mating software versus other mate pair designing methods on long-term breeding of Pinus taeda L

Breeding objectives aim to optimize two crucial but contrasting goals of
maximizing genetic gain while managing genetic diversity. In advanced
generations, this becomes a challenge in monoecious conifer tree species
breeding programs because they suffer from inbreeding. Developing an
algorithm that maximizes genetic gain while maintaining genetic diversity
for monoecious species is imperative. While methods and algorithms for
animal breeding are well-established, an efficient algorithm suited to
monoecious species remains elusive. Towards this goal, we have adopted an
evolutionary genetic algorithm, the Differential Evolution algorithm, to
optimize mate pair designing
in Pinus taeda (loblolly pine), a widely
planted pine species in the southern USA. AgMate, an optimal mating for
monoecious species software, is a multi-functional, completely automated
optimization software. It utilizes genetic relationships and breeding
values as input to create an optimal mating list. AgMate maximizes the
genetic gain and minimizes the increase in average coancestry and
inbreeding in the proposed progeny. AgMate was more effective in
optimizing mating lists than positive assortative mating and
random mating in short-term and long-term settings. AgMate mating list
resulted in an average 93% genetic gain each cycle for ten cycles while
simultaneously minimizing the increase in coancestry to 0.086. The
framework and methods adapted for Pinus taeda are also
relevant to the breeding of other monoecious species.