Back to the future 2: the implications of germplasm structure on thebalance between short and long-term genetic gain in a changingtarget population of environments

Abstract

Plant breeding operates within a highly complex genetic landscape determined by gene effects emerging through biological networks and their interactions with the environment. This environment is not constant but subject to short-term fluctuations and long-term shifts. This significantly complicates the task of plant breeders in finding a balance between adapting their germplasm for the short- and long-term. Here we build on previous work of us that investigated the implications of genetic complexity on breeding program design, by adding an environmental dimension in the form of the E(NK) model to the simulation framework. We found that the addition of environmental interactivity and change creates greater uncertainty associated with pursuing any specific selection trajectory, as compared to a static environment. This advantages preserving genetic variability and genetic landscape exploration over quickly exposing additive variation by constraining genetic space around a particular and temporary local optimum. Nonetheless, we found that also in a dynamically changing environment, a structure in which several breeding programs explore genetic space while maintaining constant germplasm exchange, finds the best balance between short and long-term objectives, as opposed to isolated programs or one large undifferentiated program, which exclusively emphasize short respectively long-term objectives. We furthermore highlight the difficulty of exchanging germplasm to restore genetic variability with non-stationary and germplasm context dependent genetic effects. In summary we found that also with addition of environmental complexity and change, the structural features that characterized breeding operations hitherto and allowed them to navigating biological complexity apply. Namely the necessity to constraining genetic space in order for heritable additive variation to emerge. We end by arguing that optimal breeding program design depends on the level of genetic and environmental complexity, which should be appropriately reflected when modeling the long-term behavior of selection programs and the implications of specific interventions into these.