Graphical summary of the article Genomic prediction through machine learning and neural networks for traits with epistasis.

The complexity of selecting for drought tolerance in cassava, influenced by multiple factors, demands innovative approaches to plant selection. This study aimed to identify cassava clones with tolerance to water stress by employing truncated selection and selection based on genomic values for population improvement and genotype evaluation per se. The Best Linear Unbiased Predictions (BLUPs), Genomic Estimated Breeding Values (GEBVs), and Genomic Estimated Genotypic Values (GETGVs) were obtained based on different prediction models via genomic selection. The selection intensity ranged from 10 to 30%. A wide range of BLUPs for agronomic traits indicate desirable genetic variability for initiating genomic selection cycles to improve cassava’s drought tolerance. SNP-based heritability (h²) and broad-sense heritabilities (H²) under water deficit were low magnitude (<0.40) for 8 to 12 agronomic traits evaluated. Genomic predictive abilities were below the levels of phenotypic heritability, varying by trait and prediction model, with the lowest and highest predictive abilities observed for starch content (0.15 – 0.22) and root length (0.34 – 0.36). Some agronomic traits of greater importance, such as fresh root yield (0.29 – 0.31) and shoot yield (0.31 – 0.32), showed good predictive ability, while dry matter content had lower predictive ability (0.16 – 0.22). The G-BLUP and RKHS methods presented higher predictive abilities, suggesting that incorporating kinship effects can be beneficial, especially in challenging environments. The selection differential based on a 15% selection intensity (62 genotypes) was higher for economically significant traits, such as starch content, shoot yield, and fresh root yield, both for population improvement (GEBVs) and for evaluating genotype’s performance per (GETGVs). The lower costs of genotyping offer advantages over conventional phenotyping, making genomic selection a promising approach to increasing genetic gains for drought tolerance in cassava and reducing the breeding cycle to at least half the conventional time.

The identification of genotypes more tolerant to water deficit is a challenge to breeding programs. In this research, our objectives were to identify and validate traits for tolerance to water deficit in eucalypts. The estimation of genotypic parameters and early selection are proposed based on mixed models, selection indexes and validation schemes. Seedlings with 110 days were grown in a greenhouse for 12 weeks, and two water deficit treatments were conducted (polyethylene glycol and water limitation). A total of 26 biomarkers were evaluated, and 15 of them were significant, exhibited adequate heritability, and used for screening: final plant height, increment in height, increment in diameter, area of mature and fully expanded leaf, nutrient contents of N, K, Ca, Mg, S, Cu, Zn, Mn and B, photosynthesis (A) and stomatal conductance (gs). Both treatments were adequate to discriminate water deficit-tolerant clones. The ranking of tolerant clones according to their phenotype in the field demonstrates the potential for early selection and is consistent with the maintenance of water-deficit-tolerance mechanisms until adulthood. There is evidence that the choice of biomarker depends on the species involved and different strategies contributing to the tolerance trait.