Summary: Millets are a class of nutrient‐rich coarse cereals with high resistance to abiotic stress; thus, they guarantee food security for people living in areas with extreme climatic conditions and provide stress‐related genetic resources for other crops. However, no platform is available to provide a comprehensive and systematic multi‐omics analysis for millets, which seriously hinders the mining of stress‐related genes and the molecular breeding of millets. Here, a free, web‐accessible, user‐friendly millets multi‐omics database platform (Milletdb, http://milletdb.novogene.com) has been developed. The Milletdb contains six millets and their one related species genomes, graph‐based pan‐genomics of pearl millet, and stress‐related multi‐omics data, which enable Milletdb to be the most complete millets multi‐omics database available. We stored GWAS (genome‐wide association study) results of 20 yield‐related trait data obtained under three environmental conditions [field (no stress), early drought and late drought] for 2 years in the database, allowing users to identify stress‐related genes that support yield improvement. Milletdb can simplify the functional genomics analysis of millets by providing users with 20 different tools (e.g., ‘Gene mapping’, ‘Co‐expression’, ‘KEGG/GO Enrichment’ analysis, etc.). On the Milletdb platform, a gene PMA1G03779.1 was identified through ‘GWAS’, which has the potential to modulate yield and respond to different environmental stresses. Using the tools provided by Milletdb, we found that the stress‐related PLATZs TFs (transcription factors) family expands in 87.5% of millet accessions and contributes to vegetative growth and abiotic stress responses. Milletdb can effectively serve researchers in the mining of key genes, genome editing and molecular breeding of millets.

Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, which were related to yield and nutrition value, respectively, using BayesA, BayesB, and BayesCπ methods. Ten-fold cross validation was used to evaluate the accuracy of GP represented by the correlation between genomic estimated breeding value (GEBV) and estimated breeding value (EBV). The accuracies ranged from 0.0021 to 0.6485 for different traits. For each trait, three GP methods displayed similar prediction accuracies. Among 15 quality traits, mineral element Ca had a moderate and the highest prediction accuracy (0.34). NDF digestibility after 48 h (NDFD 48 h) and 30 h (NDFD 30 h) and mineral element Mg had prediction accuracies varying from 0.20 to 0.25. Other traits, for example, fat and crude protein, showed low prediction accuracies (0.05 to 0.19). Among 10 agronomic traits, however, some displayed relatively high prediction accuracies. Plant height (PH) in fall (FH) had the highest prediction accuracy (0.65), followed by flowering date (FD) and plant regrowth (PR) with accuracies at 0.52 and 0.51, respectively. Leaf to stem ratio (LS), plant branch (PB), and biomass yield (BY) reached to moderate prediction accuracies ranging from 0.25 to 0.32. Our results revealed that a few agronomic traits, such as FH, FD, and PR, had relatively high prediction accuracies, therefore it is feasible to apply genomic selection (GS) for these traits in alfalfa breeding programs. Because of the limitations of population size and density of SNP markers, several traits displayed low accuracies which could be improved by a bigger reference population, higher density of SNP markers, and more powerful statistic tools.

Information on genetic diversity and population structure of a tetraploid alfalfa collection might be valuable in effective use of the genetic resources. A set of 336 worldwide genotypes of tetraploid alfalfa (Medicago sativa subsp. sativa L.) was genotyped using 85 genome-wide distributed SSR markers to reveal the genetic diversity and population structure in the alfalfa. Genetic diversity analysis identified a total of 1056 alleles across 85 marker loci. The average expected heterozygosity and polymorphism information content values were 0.677 and 0.638, respectively, showing high levels of genetic diversity in the cultivated tetraploid alfalfa germplasm. Comparison of genetic characteristics across chromosomes indicated regions of chromosomes 2 and 3 had the highest genetic diversity. A higher genetic diversity was detected in alfalfa landraces than that of wild materials and cultivars. Two populations were identified by the model-based population structure, principal coordinate and neighbor-joining analyses, corresponding to China and other parts of the world. However, lack of strictly correlation between clustering and geographic origins suggested extensive germplasm exchanges of alfalfa germplasm across diverse geographic regions. The quantitative analysis of the genetic diversity and population structure in this study could be useful for genetic and genomic analysis and utilization of the genetic variation in alfalfa breeding.

Drought is a major threat to alfalfa (Medicago sativa L.) production. The discovery of important alfalfa genes regulating drought response will facilitate breeding for drought‐resistant alfalfa cultivars. Here, we report a genome‐wide association study of drought resistance in alfalfa. We identified and functionally characterized an MYB‐like transcription factor gene (MsMYBH), which increases the drought resistance in alfalfa. Compared with the wild‐types, the biomass and forage quality were enhanced in MsMYBH overexpressed plants. Combined RNA‐seq, proteomics and chromatin immunoprecipitation analysis showed that MsMYBH can directly bind to the promoters of MsMCP1, MsMCP2, MsPRX1A and MsCARCAB to improve their expression. The outcomes of such interactions include better water balance, high photosynthetic efficiency and scavenge excess H₂O₂ in response to drought. Furthermore, an E3 ubiquitin ligase (MsWAV3) was found to induce MsMYBH degradation under long‐term drought, via the 26S proteasome pathway. Furthermore, variable‐number tandem repeats in MsMYBH promoter were characterized among a collection of germplasms, and the variation is associated with promoter activity. Collectively, our findings shed light on the functions of MsMYBH and provide a pivotal gene that could be leveraged for breeding drought‐resistant alfalfa. This discovery also offers new insights into the mechanisms of drought resistance in alfalfa.

Understanding the genetic basis underlying its agronomic traits will provide critical molecular insights for alfalfa breeding. Nine agronomic traits were characterized across 322 alfalfa genotypes in three consecutive years. Phenotypic data analysis revealed that all 9 traits were significantly influenced by genotype and were of variable heritability. A total of 44,757 SNPs was identified and uniquely physically mapped to the Medicago truncatula reference genome. Population structure analysis showed that the entire collection could be divided into two subpopulations consistent with their geographic origins. Genome‐wide association analysis identified 42 significant associated markers with agronomic traits. Two candidate genes associated with the SNPs were also identified, cloned and characterized transgenically in the model plant Arabidopsis thaliana. Overexpression of the gene MsACR11 in A. thaliana increased plant height compared with the wild‐type. Quantitative reverse transcription polymerase chain reaction analysis results showed that MsACR11 up‐regulated the transcription of GLN2, which encodes glutamine synthetase 2. The gene MsPB1 increased plant branching when overexpressed in A. thaliana. Taken together, the results of our study demonstrate that integrating GBS and GWAS can be a powerful approach for dissecting agronomic traits in alfalfa, and the results will be valuable for further characterization of candidate genes and to assist in alfalfa breeding.

Understanding how landscape factors, including suites of geographic and environmental variables, and both historical and contemporary ecological and evolutionary processes shape the distribution of genetic diversity is a primary goal of landscape and conservation genetics and may be particularly consequential for species involved in ecological restoration. In this study, we examine the factors that shape the distribution of genetic variation in a leguminous shrub (Caragana microphylla) important for restoration efforts on the Mongolian Plateau in China. This region houses several major bioclimatic gradients, and C. microphylla is an important restoration species because it stabilizes soils and prevents advancing desertification on the Inner Mongolia Plateau caused by ongoing climate change. We assembled an expansive genomic dataset, consisting of 22 microsatellite loci, four cpDNA regions, and 5788 genome-wide SNPs from ten populations of C. microphylla. We then applied ecological niche modelling and linear and non-linear regression techniques to investigate the historical and contemporary forces that explain patterns of genetic diversity and population structure in C. microphylla on the Inner Mongolia Plateau. We found strong evidence that both geographic and environmental heterogeneity contribute to genetic differentiation and that the spatial distribution of genetic diversity in C. microphylla appears to result partly from the presence of a glacial refugium at the southwestern edge of its current range. These results suggest that geographic, environmental, and historical factors have all contributed to spatial genetic variation in this ecologically important species. These results should guide restoration plans to sustain genetic diversity during plant translocations.