Soil salinity is a growing problem in world production agriculture. Continued improvement in crop salt tolerance will require the implementation of innovative breeding strategies such as marker-assisted selection (MAS) and genomic selection (GS). Genetic analyses for yield and vigor traits under salt stress in alfalfa breeding populations with three different phenotypic datasets was assessed. Genotype-by-sequencing (GBS) developed markers with allele dosage and phenotypic data were analyzed by genome-wide association studies (GWAS) and GS using different models. GWAS identified 27 single nucleotide polymorphism (SNP) markers associated with salt tolerance. Mapping SNPs markers against the Medicago truncatula reference genome revealed several putative candidate genes based on their roles in response to salt stress. Additionally, eight GS models were used to estimate breeding values of the training population under salt stress. Highest prediction accuracies and root mean square errors were used to determine the best prediction model. The machine learning methods (support vector machine and random forest) performance best with the prediction accuracy of 0.793 for yield. The marker loci and candidate genes identified, along with optimized GS prediction models, were shown to be useful in improvement of alfalfa with enhanced salt tolerance. DNA markers and the outcome of the GS will be made available to the alfalfa breeding community in efforts to accelerate genetic gains, in the development of biotic stress tolerant and more productive modern-day alfalfa cultivars.

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

Developing drought-resistant alfalfa (Medicago sativa L.) that maintains high biomass yield is a key breeding goal to enhance productivity in water-limited areas. In this study, 424 alfalfa breeding families were analyzed to identify molecular markers associated with biomass yield under drought stress and to predict high-merit plants. Biomass yield was measured from 18 harvests from 2020 to 2023 in a field trial with deficit irrigation. A total of 131 significant markers were associated with biomass yield, with 80 markers specifically linked to yield under drought stress; among these, 19 markers were associated with multiple harvests. Finally, genomic best linear unbiased prediction (GBLUP) was employed to obtain predictive accuracies (PAs) and genomic estimated breeding values (GEBVs). Removing low-informative SNPs [SNPs with p-values > 0.05 from the additive Genome-Wide Association (GWAS) model] for GBLUP increased PA by 47.3%. The high number of markers associated with yield under drought stress and the highest PA (0.9) represent a significant achievement in improving yield under drought stress in alfalfa.

Verticillium wilt (VW) of alfalfa is a devastating disease that causes forage yield reductions of up to 50% in the northern United States and Canada. The most effective method for controlling the disease is through the development and use of resistant varieties. To identify quantitative trait loci (QTL) for VW resistance in alfalfa, we used a full-sib population segregating for VW resistance. High-density linkage maps for both resistant and susceptible parents were constructed using single-dose alleles of single-nucleotide polymorphism markers generated by genotyping-by-sequencing. Five QTL associated with VW resistance were identified and they were in four linkage groups (4D, 6B, 6D, and 8C). Of those, three QTL (qVW-6D-1, qVW-6D-2, and qVW-8C) had higher logarithm of odds. Two putative candidates of nucleotide-binding site leucine-rich repeat disease resistance genes were identified in the QTL intervals of qVW-6D-2 and qVW-8C, respectively. The result agreed with our previous studies, in which similar resistance loci were identified in an association panel using genome-wide association. The results provide insight into the quantitative resistance to VW in alfalfa. The resistance loci and closely linked markers identified in the present study can be used in developing new alfalfa varieties with enhanced resistance to VW.

Summary: Functional analysis of large gene families in plant pathogens can be cumbersome using classical insertional mutagenesis. Additionally, Cas9 toxicity has limited the application of CRISPR–Cas9 for directed mutagenesis in bacteria. Here, we successfully applied a CRISPR interference strategy to investigate the cryptic role of the transcription activator‐like effector (tale) multigene family in several plant‐pathogenic Xanthomonas bacterial species, owing to their contribution to pathogen virulence. Single guide RNAs (sgRNAs) designed against Xanthomonas phaseoli pv manihotis tale conserved gene sequences efficiently silenced expression of all tales, with concomitant decrease in virulence and TALE‐induced host gene expression. The system is readily translatable to other Xanthomonas species infecting rice, citrus, Brassica, and cassava, silencing up to 16 tales in a given strain using a single sgRNA. Complementation with plasmid‐borne designer tales lacking the sgRNA‐targeted sequence restored molecular and virulence phenotypes in all pathosystems. Our results evidenced that X. campestris pv campestris CN08 tales are relevant for symptom development in cauliflower. They also show that the MeSWEET10a sugar transporter is surprisingly targeted by the nonvascular cassava pathogen X. cassavae, highlighting a new example of TALE functional convergence between phylogenetically distant Xanthomonas. Overall, this novel technology provides a platform for discovery and rapid functional understanding of highly conserved gene families.