Faba bean (Vicia faba L.) is among the earliest domesticated crops from the Near East. Today this legume is a key protein feed and food worldwide and continues to serve an important role in culinary traditions throughout Middle East, Mediterranean region, China and Ethiopia. Adapted to a wide range of soil types, the main faba bean breeding objectives are to improve yield, resistance to biotic and abiotic stresses, seed quality and other agronomic traits. Genomic approaches aimed at enhancing faba bean breeding programs require high-quality genetic linkage maps to facilitate quantitative trait locus analysis and gene tagging for use in a marker-assisted selection. The objective of this study was to construct a reference consensus map in faba bean by joining the information from the most relevant maps reported so far in this crop.

In vitro mutagenesis is an alternative to induce genetic variation in vanilla (Vanilla planifolia Jacks. ex Andrews), which is characterized by low genetic diversity. The objective of this study was to induce somaclonal variation in V. planifolia by gamma radiation and detect it using inter-simple sequence repeat (ISSR) molecular markers. Shoots previously established in vitro were multiplied in Murashige and Skoog culture medium supplemented with 2 mg·L⁻¹ BAP (6-benzylaminopurine). Explants were irradiated with different doses (0, 20, 40, 60, 80 and 100 Gy) of ⁶⁰Co gamma rays. Survival percentage, number of shoots per explant, shoot length, number of leaves per shoot, and lethal dose (LD₅₀) were recorded after 60 d of culture. For molecular analysis, ten shoots were used for each dose and the donor plant as a control. Eight ISSR primers were selected, and 43 fragments were obtained. The percentage of polymorphism (% P) was estimated. A dendrogram based on Jaccard’s coefficient and the neighbor joining clustering method was obtained. Results showed a hormetic effect on the explants, promoting development at low dose (20 Gy) and inhibition and death at high doses (60–100 Gy). The LD₅₀ was observed at the 60 Gy. Primers UBC-808, UBC-836 and UBC-840 showed the highest % P, with 42.6%, 34.7% and 28.7%, respectively. Genetic distance analysis showed that treatments without irradiation and with irradiation presented somaclonal variation. The use of gamma rays during in vitro culture is an alternative to broaden genetic diversity for vanilla breeding.

Genomic prediction relates a set of markers to variability in observed phenotypes of cultivars and allows for the prediction of phenotypes or breeding values of genotypes on unobserved individuals. Most genomic prediction approaches predict breeding values based solely on additive effects. However, the economic value of wheat lines is not only influenced by their additive component but also encompasses a non-additive part (e.g., additive × additive epistasis interaction). In this study, genomic prediction models were implemented in three target populations of environments (TPE) in South Asia. Four models that incorporate genotype × environment interaction (G × E) and genotype × genotype (GG) were tested: Factor Analytic (FA), FA with genomic relationship matrix (FA + G), FA with epistatic relationship matrix (FA + GG), and FA with both genomic and epistatic relationship matrices (FA + G + GG). Results show that the FA + G and FA + G + GG models displayed the best and a similar performance across all tests, leading us to infer that the FA + G model effectively captures certain epistatic effects. The wheat lines tested in sites in different TPE were predicted with different precisions depending on the cross-validation employed. In general, the best prediction accuracy was obtained when some lines were observed in some sites of particular TPEs and the worse genomic prediction was observed when wheat lines were never observed in any site of one TPE.

This research evaluated the growth responses of maize plants due to the inoculation of two indole-producing Trichoderma strains in comparison to the application of two exogenous phytohormones. Either T. tomentosum (EMMFRS1C2) or T. harzianum (MichV6S3C2) were inoculated as conidia or as fungal filtrates obtained from Luria-Bertani, liquid medium, with or without the addition of L-Tryptophan (1 g L^-1). In addition, two concentrations (5 and 10 mg L^-1) of indole acetic acid (IAA) and indole butyric acid (IBA) from which 2 mL were applied per plant. Twelve treatments and six replicates distributed in a completely randomized design. Plants were maintained for 50 days in microcosms (perlite and vermiculite, 2:1 v/v), under greenhouse conditions (38ºC±5) and watered every 48 h. The best plant growth responses (height, root length, leaf area, and root, stem and total dry weight were achieved by inoculating conidia of either T. tomentosum (EMMFRS1C2) or T. harzianum (MichV6S3C2) in combination with tryptophan (1 g L^-1) when compared to control plants; but T. harzianum (MichV6S3C2) only resulted in beneficial effects in root length. Moreover, the application of fungal filtrates had similar plant growth responses than the application of either IAA or IBA at 5 mg L^-1.

Genomic prediction combines molecular and phenotypic data in a training population to predict the breeding values of individuals that have only been genotyped. The use of genomic information in breeding programs helps to increase the frequency of favorable alleles in the populations of interest. This study evaluated the performance of BLUP (Best Linear Unbiased Prediction) in predicting resistance to tan spot, spot blotch and Septoria nodorum blotch in synthetic hexaploid wheat. BLUP was implemented in single-trait and multi-trait models with three variations: (1) the pedigree relationship matrix (A-BLUP), (2) the genomic relationship matrix (G-BLUP), and (3) a combination of the two matrices (A+G BLUP). In all three diseases, the A-BLUP model had a lower performance, and the G-BLUP and A+G BLUP were statistically similar (p ≥ 0.05). The prediction accuracy with the single trait was statistically similar (p ≥ 0.05) to the multi-trait accuracy, possibly due to the low correlation of severity between the diseases.