Simulation and empirical studies of genomic selection (GS) show accuracies sufficient to generate rapid genetic gains. However, with the increased popularity of GS approaches, numerous models have been proposed and no comparative analysis is available to identify the most promising ones. Using eight wheat {Triti-cum aestivum L.), barley {Hordeum vulgäre L.), Arabidopsis thaliana (L.) Heynh., and maize {Zea mays L.) datasets, the predictive ability of currently available GS models along with several machine learning methods was evaluated by comparing accuracies, the genomic estimated breeding values (GEBVs), and the marker effects for each model. While a similar level of accuracy was observed for many models, the level of overfitting varied widely as did the computation time and the distribution of marker effect estimates. Our comparisons suggested that GS in plant breeding programs could be based on a reduced set of models such as the Bayesian Lasso, weighted Bayesian shrinkage regression (wBSR, a fast version of BayesB), and random forest (RF) (a machine learning method that could capture nonadditive effects). Linear combinations of different models were tested as well as bagging and boosting methods, but they did not improve accuracy. This study also showed large differences in accuracy between subpopulations within a dataset that could not always be explained by differences in phenotypic variance and size. The broad diversity of empirical datasets tested here adds evidence that GS could increase genetic gain per unit of time and cost.

Background: Miniature inverted-repeat transposable elements (MITEs) are non-autonomous DNA-mediated transposable elements (TEs) derived from autonomous TEs. Unlike in many plants or animals, MITEs and other types of DNA-mediated TEs were previously thought to be either rare or absent in Drosophila. Most other TE families in Drosophila exist at low or intermediate copy number (around < 100 per genome).

To conserve water in arid environments, numerous plant lineages have independently evolved Crassulacean Acid Metabolism (CAM). Interestingly, Isoetes, an aquatic lycophyte, can also perform CAM as an adaptation to low CO2 availability underwater. However, little is known about the evolution of CAM in aquatic plants and the lack of genomic data has hindered comparison between aquatic and terrestrial CAM. Here, we investigate underwater CAM in Isoetes taiwanensis by generating a high-quality genome assembly and RNA-seq time course. Despite broad similarities between CAM in Isoetes and terrestrial angiosperms, we identify several key differences. Notably, Isoetes may have recruited the lesser-known ‘bacterial-type’ PEPC, along with the ‘plant-type’ exclusively used in other CAM and C4 plants for carboxylation of PEP. Furthermore, we find that circadian control of key CAM pathway genes has diverged considerably in Isoetes relative to flowering plants. This suggests the existence of more evolutionary paths to CAM than previously recognized.