Genome-wide genotyping data renew knowledge on genetic diversity of a worldwide alfalfa collection and give insights on genetic control of phenology traits

Optimization of high-throughput marker systems for genomic prediction in alfalfa family bulks

Alfalfa (Medicago sativa L.) is a perennial forage legume esteemed for its exceptional quality and dry matter yield (DMY); however, alfalfa has historically exhibited low genetic gain for DMY. Advances in genotyping platforms paved the way for a cost-effective application of genomic prediction in alfalfa family bulks. In this context, the optimization of marker density holds potential to reallocate resources within genomic prediction pipelines. This study aimed to (i) test two genotyping platforms for population structure discrimination and predictive ability (PA) of genomic prediction models (G-BLUP) for DMY, and (ii) explore optimal levels of marker density to predict DMY in family bulks. For this, 160 nondormant alfalfa families were phenotyped for DMY across 11 harvests and genotyped via targeted sequencing using Capture-seq with 17K probes and the DArTag 3K panel. Both platforms discriminated similarly against the population structure and resulted in comparable PA for DMY. For genotyping optimization, different levels of marker density were randomly extracted from each platform. In both cases, a plateau was achieved around 500 markers, yielding similar PA as the full set of markers. For phenotyping optimization, models with 500 markers built with data from five harvests resulted in similar PA compared to the full set of 11 harvests and full set of markers. Altogether, genotyping and phenotyping efforts were optimized in terms of number of markers and harvests. Capture-seq and DArTag yielded similar results and have the flexibility to adjust their panels to meet breeders' needs in terms of marker density.

Medicago2035: Genomes, functional genomics, and molecular breeding

Medicago, a genus in the Leguminosae or Fabaceae family, includes the most globally significant forage crops, notably alfalfa (Medicago sativa). Its close diploid relative Medicago truncatula serves as an exemplary model plant for investigating legume growth and development, as well as symbiosis with rhizobia. Over the past decade, advances in Medicago genomics have significantly deepened our understanding of the molecular regulatory mechanisms that underlie various traits. In this review, we comprehensively summarize research progress on Medicago genomics, growth and development (including compound leaf development, shoot branching, flowering time regulation, inflorescence development, floral organ development, and seed dormancy), resistance to abiotic and biotic stresses, and symbiotic nitrogen fixation with rhizobia, as well as molecular breeding. We propose avenues for molecular biology research on Medicago in the coming decade, highlighting those areas that have yet to be investigated or that remain ambiguous.

Pre-breeding in alfalfa germplasm develops highly differentiated populations, as revealed by genome-wide microhaplotype markers

Plant genebanks contain large numbers of germplasm accessions that likely harbor useful alleles or genes absent in commercial plant breeding programs. Broadening the genetic base of commercial alfalfa germplasm with these valuable genetic variations can be achieved by screening the extensive genetic diversity in germplasm collections and enabling maximal recombination among selected genotypes. In this study, we assessed the genetic diversity and differentiation of germplasm pools selected in northern U.S. latitudes (USDA Plant Hardiness Zone 7 or below) originating from Eurasian germplasm. The germplasm evaluated included four BASE populations (C0) from different geographical origins (Central Asia, Northeastern Europe, Balkans-Turkey-Black Sea, and Siberia/Mongolia), 20 cycle-one populations (C1) derived from each of the four BASE populations selected across five locations in the U.S. and Canada, and four commercial cultivars. Using a panel of 3,000 Diversity Array Technologies (DArTag) marker loci, we retrieved 2,994 target SNPs and approximately 12,000 microhaplotypes. Microhaplotypes exhibited higher genetic diversity values than target SNPs. Principal component analysis and discriminant analysis of principal components revealed significant population structure among the alfalfa populations based on geographical origin, while the check cultivars formed a central cluster. Inbreeding coefficients (FIS) ranged from - 0.1 to 0.006, with 27 out of 28 populations showing negative FIS values, indicating an excess of heterozygotes. Interpopulation genetic distances were calculated using Rho pairwise distances (FST adapted for autotetraploid species) and analysis of molecular variance (AMOVA) parameters. All BASE populations showed lower Rho values compared to C1 populations and check cultivars. AMOVA revealed that most of the genetic diversity was among individuals within populations, especially in BASE populations (92.7%). This study demonstrates that individual plants in BASE populations possess high genetic diversity, low interpopulation distances, and minimal inbreeding, characteristics that are essential for base-broadening selection. The populations developed in this project serve as valuable sources of novel alleles for North American alfalfa breeding programs, offering breeders access to diverse, regionally adapted pools for improving various alfalfa traits.

GWAS for Drought Resilience Traits in Red Clover (Trifolium pratense L.)

Red clover (Trifolium pratense L.) is a well-appreciated grassland crop in temperate climates but suffers from increasingly frequent and severe drought periods. Molecular markers for drought resilience (DR) would benefit breeding initiatives for red clover, as would a better understanding of the genes involved in DR. Two previous studies, as follows, have: (1) identified phenotypic DR traits in a diverse set of red clover accessions; and (2) produced genotypic data using a pooled genotyping-by-sequencing (GBS) approach in the same collection. In the present study, we performed genome-wide association studies (GWAS) for DR using the available phenotypic and genotypic data. Single nucleotide polymorphism (SNP) calling was performed using GBS data and the following two red clover genome assemblies: the recent HEN-17 assembly and the Milvus assembly. SNP positions with significant associations were used to delineate flanking regions in both genome assemblies, while functional annotations were retrieved from Medicago truncatula orthologs. GWAS revealed 19 significant SNPs in the HEN-17-derived SNP set, explaining between 5.3 and 23.2% of the phenotypic variation per SNP-trait combination for DR traits. Among the genes in the SNP-flanking regions, we identified candidate genes related to cell wall structuring, genes encoding sugar-modifying proteins, an ureide permease gene, and other genes linked to stress metabolism pathways. GWAS revealed 29 SNPs in the Milvus-derived SNP set that explained substantially more phenotypic variation for DR traits, between 5.3 and 42.3% per SNP-trait combination. Candidate genes included a DEAD-box ATP-dependent RNA helicase gene, a P-loop nucleoside triphosphate hydrolase gene, a Myb/SANT-like DNA-binding domain protein, and an ubiquitin-protein ligase gene. Most accessions in this study are genetically more closely related to the Milvus genotype than to HEN-17, possibly explaining how the Milvus-derived SNP set yielded more robust associations. The Milvus-derived SNP set pinpointed 10 genomic regions that explained more than 25% of the phenotypic variation for DR traits. A possible next step could be the implementation of these SNP markers in practical breeding programs, which would help to improve DR in red clover. Candidate genes could be further characterized in future research to unravel drought stress resilience in red clover in more detail.

Assessment of Genetic Diversity in Alfalfa Using DNA Polymorphism Analysis and Statistical Tools

The cultivation of alfalfa is crucial for farmers as it is an excellent forage crop with a high nitrogen-fixing capacity, making it indispensable in crop rotations. Breeding programs face challenges in advancing more rapidly in genetic diversity to achieve a higher heterosis effect and, consequently, greater yield. In this study, we used 30 alfalfa varieties, which were used for molecular analyses by 5 ISSR primers and 13 RAPD primers. The results obtained highlighted the greater efficiency of ISSR primers in identifying genetic diversity. On the other hand, the simultaneous use of ISSR + RAPD allowed for clearer clustering of varieties that enabled more efficiently distinguishing the genetic diversity. The most efficient ISSR primer, A17, generated 31 polymorphic bands, while the most efficient RAPD primer, L-07, generated only 21 bands. Varieties such as "Pastoral" and "F1413-02" exhibited low similarity coefficients (0.39), suggesting their potential for enhancing genetic variability through crossbreeding, thereby increasing the potential of achieving a greater heterosis effect. Conversely, varieties with high similarity coefficients, such as "Cristal" and "Viking" (0.81) are less suited for this purpose. The correlation between specific markers highlights that using both ISSR and RAPD markers together offers a clear understanding of genetic diversity in alfalfa, aiding in more effective selection for crossbreeding in breeding programs.

Including marker x environment interactions improves genomic prediction in red clover (Trifolium pratense L.)

Genomic prediction has mostly been used in single environment contexts, largely ignoring genotype x environment interaction, which greatly affects the performance of plants. However, in the last decade, prediction models including marker x environment (MxE) interaction have been developed. We evaluated the potential of genomic prediction in red clover (Trifolium pratense L.) using field trial data from five European locations, obtained in the Horizon 2020 EUCLEG project. Three models were compared: (1) single environment (SingleEnv), (2) across environment (AcrossEnv), (3) marker x environment interaction (MxE). Annual dry matter yield (DMY) gave the highest predictive ability (PA). Joint analyses of DMY from years 1 and 2 from each location varied from 0.87 in Britain and Switzerland in year 1, to 0.40 in Serbia in year 2. Overall, crude protein (CP) was predicted poorly. PAs for date of flowering (DOF), however ranged from 0.87 to 0.67 for Britain and Switzerland, respectively. Across the three traits, the MxE model performed best and the AcrossEnv worst, demonstrating that including marker x environment effects can improve genomic prediction in red clover. Leaving out accessions from specific regions or from specific breeders' material in the cross validation tended to reduce PA, but the magnitude of reduction depended on trait, region and breeders' material, indicating that population structure contributed to the high PAs observed for DMY and DOF. Testing the genomic estimated breeding values on new phenotypic data from Sweden showed that DMY training data from Britain gave high PAs in both years (0.43-0.76), while DMY training data from Switzerland gave high PAs only for year 1 (0.70-0.87). The genomic predictions we report here underline the potential benefits of incorporating MxE interaction in multi-environment trials and could have perspectives for identifying markers with effects that are stable across environments, and markers with environment-specific effects.

Mfind: a tool for DNA barcode analysis in angiosperms and its relationship with microsatellites using a sliding window algorithm

MAIN CONCLUSION

Mfind is a tool to analyze the impact of microsatellite presence on DNA barcode specificity. We found a significant correlation between barcode entropy and microsatellite count in angiosperm. Genetic barcodes and microsatellites are some of the identification methods in taxonomy and biodiversity research. It is important to establish a relationship between microsatellite quantification and genetic information in barcodes. In order to clarify the association between the genetic information in barcodes (expressed as Shannon's Measure of Information, SMI) and microsatellites count, a total of 330,809 DNA barcodes from the BOLD database (Barcode of Life Data System) were analyzed. A parallel sliding-window algorithm was developed to compute the Shannon entropy of the barcodes, and this was compared with the quantification of microsatellites like (AT)n, (AC)n, and (AG)n. The microsatellite search method utilized an algorithm developed in the Java programming language, which systematically examined the genetic barcodes from an angiosperm database. For this purpose, a computational tool named Mfind was developed, and its search methodology is detailed. This comprehensive study revealed a broad overview of microsatellites within barcodes, unveiling an inverse correlation between the sumz of microsatellites count and barcodes information. The utilization of the Mfind tool demonstrated that the presence of microsatellites impacts the barcode information when considering entropy as a metric. This effect might be attributed to the concise length of DNA barcodes and the repetitive nature of microsatellites, resulting in a direct influence on the entropy of the barcodes.

Genome-wide association study for yield-related traits in faba bean (Vicia faba L.)

Yield is the most complex trait to improve crop production, and identifying the genetic determinants for high yield is a major issue in breeding new varieties. In faba bean (Vicia faba L.), quantitative trait loci (QTLs) have previously been detected in studies of biparental mapping populations, but the genes controlling the main trait components remain largely unknown. In this study, we investigated for the first time the genetic control of six faba bean yield-related traits: shattering (SH), pods per plant (PP), seeds per pod (SP), seeds per plant (SPL), 100-seed weight (HSW), and plot yield (PY), using a genome-wide association study (GWAS) on a worldwide collection of 352 homozygous faba bean accessions with the aim of identifying markers associated with them. Phenotyping was carried out in field trials at three locations (Spain, United Kingdom, and Serbia) over 2 years. The faba bean panel was genotyped with the Affymetrix faba bean SNP-chip yielding 22,867 SNP markers. The GWAS analysis identified 112 marker-trait associations (MTAs) in 97 candidate genes, distributed over the six faba bean chromosomes. Eight MTAs were detected in at least two environments, and five were associated with multiple traits. The next step will be to validate these candidates in different genetic backgrounds to provide resources for marker-assisted breeding of faba bean yield.