Selection for multiple traits is a highly challenging task for breeders due to potential unfavorable associations between characters. Fusarium head blight FHB, being one of the most relevant diseases affecting durum wheat frequently shows in this respect an unfavorable correlation with morpho-agronomical traits like plant height (PH) and heading date (HD). In this study, we used a cross-validation scheme to assess the prediction ability of the genomic predictions (GP) for FHB severity relying on genomic best linear unbiased prediction models in a diverse panel of 178 durum wheat lines evaluated across five environments. Additionally, we compared three types of approaches to include HD and PH as covariates into the analysis: (1) correcting FHB severity values before training GP models, (2) tuning the GP model parameters that included multi-trait alternatives, and (3) adjusting the genomic-based predictions by restriction indexes. Models that weighted genomic estimated breeding values (GEBV) by restriction indexes as well as models that predicted FHBms values corrected by regression-based methods were efficient alternatives in diminishing the HD trade-off, nonetheless they were also associated with large reductions in prediction ability for FHB severity. After a simulated round of genomic selection, considering HD as fixed effect in the GP model were the most suitable alternative to select a higher proportion of genotypes moderately resistant with lower-than-average HD and PH estimations. Hence, an appropriate GP model given unfavorable association between characters should combine high predictabilities and adequate reduction of undesired trade-offs.

Environmental factors like temperature and humidity are presumed to greatly influence Fusarium head blight FHB infections in wheat. Anther retention AR, on the other hand, is a morphologically neutral trait that shares a common genetic basis with FHB resistance. In this study, our aims were to: (i) Evaluate two types of corrections of FHB severity scores, namely method-1 via linear regression on flowering time (FT), and method-2 via a best-subset multiple linear regression analysis comprising FT plus accumulated thermal time variables; and (ii) assess the performance of multi-trait genomic selection (MT.GS) models for FHB severity assisted by AR. The forward prediction scenarios where GS models were trained with data from the previous years revealed average prediction accuracies (PA) of 0.28, 0.33, and 0.36 for FHB severity scores that were uncorrected or corrected by method-1 and method-2, respectively. FHB severity scores free from the influences of both environment and phenology seemed to be the most efficient trait to be predicted across different seasons. Average PA increments up to 1.9-fold were furthermore obtained for the MT.GS models, evidencing the feasibility of using AR as an assisting trait to improve the genomic selection of FHB resistance breeding lines.

Key message: FHB resistance of durum wheat was improved by introgression of Fhb1 and resistance genes from emmer wheat and by selection against adverse alleles of elite durum wheat. Durum wheat is particularly susceptible to Fusarium head blight (FHB) and breeding for resistance is impeded by the low genetic variation within the elite gene pool. To extend the genetic basis for FHB resistance in durum wheat, we analyzed 603 durum wheat lines from crosses of elite durum wheat with resistance donors carrying resistance alleles derived from Triticum aestivum, T. dicoccum and T. dicoccoides. The lines were phenotyped for FHB resistance, anthesis date, and plant height in artificially inoculated disease nurseries over 5 years. A broad variation was found for all traits, while anthesis date and plant height strongly influenced FHB severities. To correct for spurious associations, we adjusted FHB scorings for temperature fluctuations during the anthesis period and included plant height as a covariate in the analysis. This resulted in the detection of seven quantitative trait loci (QTL) affecting FHB severities. The hexaploid wheat-derived Fhb1 QTL was most significant on reducing FHB severities, highlighting its successful introgression into several durum wheat backgrounds. For two QTL on chromosomes 1B and 2B, the resistance alleles originated from the T. dicoccum line Td161 and T. dicoccoides accessions Mt. Hermon#22 and Mt. Gerizim#36, respectively. The other four QTL featured unfavorable alleles derived from elite durum wheat that increased FHB severities, with a particularly negative effect on chromosome 6A that simultaneously affected plant height and anthesis date. Therefore, in addition to pyramiding resistance genes, selecting against adverse alleles present in elite durum wheat could be a promising avenue in breeding FHB-resistant durum wheat.

Genomic selection (GS) has revolutionized breeding practices by integrating genotype and phenotype data to predict genomic estimated breeding values, offering the potential to accelerate breeding cycles and intensify and enhance early‐stage selections. This approach utilizes the concept of linkage disequilibrium (LD) between genetic markers and quantitative trait loci within populations. LD, the nonrandom association between alleles at different loci, provides valuable insights into historical recombination patterns, although it can change over time under strong selection or genetic drift. This study aimed to investigate the influence of recombination on haplotype sizes and LD, assess the impact of additive (A) versus additive + epistasis (A+I) genetic models on GS predictive ability (PA), and demonstrate how haplotype resolution in the training set (TS) impacts the PA of GS. For this, we used biparental (MP2) and multiparent (MP6–8) populations, where the main difference between them was the recombination rate. As expected, a strong correlation between LD decay and the number of recombination opportunities within populations was observed, with smaller haplotype blocks in populations experiencing more recombination. The use of A+I models increased heritability but did not improve PA. Finally, populations with smaller haplotype sizes in the TS exhibited enhanced PA. This study demonstrates the effect of haplotype size on GS accuracy, and its uniqueness lies in its focus on populations where the primary differentiating factor is haplotype size. It offers an important tool for breeders in designing GS strategies, providing valuable guidance for future breeding efforts.

La intensificación sostenible de la ganadería tropical requiere estrategias agronómicas que mejoren simultáneamente la productividad y la calidad nutricional del forraje. En este estudio, se evaluó el efecto de cinco niveles de fertilización nitrogenada (0, 50, 100, 150 y 200 kg N ha⁻¹) sobre el comportamiento agronómico, composición química y digestibilidad ruminal in vitro de Megathyrsus maximus. Se evaluó la producción de biomasa, composición química, así como la degradabilidad ruminal in vitro mediante el método de Tilley y Terry. La fertilización nitrogenada incrementó significativamente (p<0,001) la producción de biomasa seca total hasta 35,42% y la producción de proteína hasta 155,47%. El contenido de proteína cruda aumentó notablemente de 7,56% a 14,27% (88,75% de incremento), mientras que los componentes fibrosos disminuyeron significativamente. Se observó una fuerte correlación negativa entre proteína y Fibra Detergente Neutro FDN (r=-0.98), lo que permitió formular un modelo predictivo altamente robusto: Proteína = 76,152 – 0,936 × FDN (R² = 96,69%). La degradabilidad ruminal mostró que los niveles intermedios de fertilización (100-150 kg N ha⁻¹) optimizaron la degradabilidad potencial (66,20%), mientras que la dosis más alta (200 kg N ha⁻¹) maximizó la fracción inmediatamente soluble (24,28%) y la degradabilidad efectiva bajo condiciones de tránsito ruminal rápido. En conclusión, la fertilización nitrogenada transformó efectivamente un forraje de calidad proteica marginal en uno de alta calidad, siendo los niveles intermedios los más eficientes en término de balance productividad y calidad, y los niveles altos los más adecuados para sistemas intensivos de alta demanda nutricional.