A genome-wide association study reveals novel loci and candidate genes associated with plant height variation in Medicago sativa.

BACKGROUND: Plant height (PH) is an important agronomic trait influenced by a complex genetic network. However, the genetic basis for the variation in PH in Medicago sativa remains largely unknown. In this study, a comprehensive genome-wide association analysis was performed to identify genomic regions associated with PH using a diverse panel of 220 accessions of M. sativa worldwide. RESULTS: Our study identified eight novel single nucleotide polymorphisms (SNPs) significantly associated with PH evaluated in five environments, explaining 8.59-12.27% of the phenotypic variance. Among these SNPs, the favorable genotype of chr6__31716285 had a low frequency of 16.4%. Msa0882400, located proximal to this SNP, was annotated as phosphate transporter 3;1, and its role in regulating alfalfa PH was supported by transcriptome and candidate gene association analysis. In addition, 21 candidate genes were annotated within the associated regions that are involved in various biological processes related to plant growth and development. CONCLUSIONS: Our findings provide new molecular markers for marker-assisted selection in M. sativa breeding programs. Furthermore, this study enhances our understanding of the underlying genetic and molecular mechanisms governing PH variations in M. sativa.

Evolutionary genomics of climatic adaptation and resilience to climate change in alfalfa.

Given the escalating impact of climate change on agriculture and food security, gaining insights into the evolutionary dynamics of climatic adaptation and uncovering climate-adapted variation can empower the breeding of climate-resilient crops to face future climate change. Alfalfa (Medicago sativa subsp. sativa), the queen of forages, shows remarkable adaptability across diverse global environments, making it an excellent model for investigating species responses to climate change. In this study, we performed population genomic analyses using genome resequencing data from 702 accessions of 24 Medicago species to unravel alfalfa's climatic adaptation and genetic susceptibility to future climate change. We found that interspecific genetic exchange has contributed to the gene pool of alfalfa, particularly enriching defense and stress-response genes. Intersubspecific introgression between M. sativa subsp. falcata (subsp. falcata) and alfalfa not only aids alfalfa's climatic adaptation but also introduces genetic burden. A total of 1671 genes were associated with climatic adaptation, and 5.7% of them were introgressions from subsp. falcata. By integrating climate-associated variants and climate data, we identified populations that are vulnerable to future climate change, particularly in higher latitudes of the Northern Hemisphere. These findings serve as a clarion call for targeted conservation initiatives and breeding efforts. We also identified pre-adaptive populations that demonstrate heightened resilience to climate fluctuations, illuminating a pathway for future breeding strategies. Collectively, this study enhances our understanding about the local adaptation mechanisms of alfalfa and facilitates the breeding of climate-resilient alfalfa cultivars, contributing to effective agricultural strategies for facing future climate change.

Transcriptome and GWAS Analyses Reveal Candidate Gene for Root Traits of Alfalfa during Germination under Salt Stress.

Alfalfa growth and production in China are negatively impacted by high salt concentrations in soils, especially in regions with limited water supplies. Few reliable genetic markers are currently available for salt tolerance selection. As a result, molecular breeding strategies targeting alfalfa are hindered. Therefore, with the continuous increase in soil salinity in agricultural lands, it is indispensable that a salt-tolerant variety of alfalfa is produced. We collected 220 alfalfa varieties around the world for resequencing and performed genome-wide association studies (GWASs). Alfalfa seeds were germinated in saline water with different concentrations of NaCl, and the phenotypic differences in several key root traits were recorded. In the phenotypic analysis, the breeding status and geographical origin strongly affected the salt tolerance of alfalfa. Forty-nine markers were significantly associated with salt tolerance, and 103 candidate genes were identified based on linkage disequilibrium. A total of 2712 differentially expressed genes were upregulated and 3570 were downregulated based on transcriptomic analyses. Some candidate genes that affected root development in the seed germination stage were identified through the combination of GWASs and transcriptome analyses. These genes could be used for molecular breeding strategies to increase alfalfa's salt tolerance and for further research on salt tolerance in general.

Application of machine learning to explore the genomic prediction accuracy of fall dormancy in autotetraploid alfalfa.

Fall dormancy (FD) is an essential trait to overcome winter damage and for alfalfa (Medicago sativa) cultivar selection. The plant regrowth height after autumn clipping is an indirect way to evaluate FD. Transcriptomics, proteomics, and quantitative trait locus mapping have revealed crucial genes correlated with FD; however, these genes cannot predict alfalfa FD very well. Here, we conducted genomic prediction of FD using whole-genome SNP markers based on machine learning-related methods, including support vector machine (SVM) regression, and regularization-related methods, such as Lasso and ridge regression. The results showed that using SVM regression with linear kernel and the top 3000 genome-wide association study (GWAS)-associated markers achieved the highest prediction accuracy for FD of 64.1%. For plant regrowth height, the prediction accuracy was 59.0% using the 3000 GWAS-associated markers and the SVM linear model. This was better than the results using whole-genome markers (25.0%). Therefore, the method we explored for alfalfa FD prediction outperformed the other models, such as Lasso and ElasticNet. The study suggests the feasibility of using machine learning to predict FD with GWAS-associated markers, and the GWAS-associated markers combined with machine learning would benefit FD-related traits as well. Application of the methodology may provide potential targets for FD selection, which would accelerate genetic research and molecular breeding of alfalfa with optimized FD.

Identification of QTL and candidate genes associated with biomass yield and Feed Quality in response to water deficit in alfalfa (Medicago sativa L.) using linkage mapping and RNA-Seq.

Biomass yield and Feed Quality are the most important traits in alfalfa (Medicago sativa L.), which directly affect its economic value. Drought stress is one of the main limiting factors affecting alfalfa production worldwide. However, the genetic and especially the molecular mechanisms for drought tolerance in alfalfa are poorly understood. In this study, linkage mapping was performed in an F1 population by combining 12 phenotypic data (biomass yield, plant height, and 10 Feed Quality-related traits). A total of 48 significant QTLs were identified on the high-density genetic linkage maps that were constructed in our previous study. Among them, nine main QTLs, which explained more than 10% phenotypic variance, were detected for biomass yield (one), plant height (one), CP (two), ASH (one), P (two), K(one), and Mg (one). A total of 31 candidate genes were identified in the nine main QTL intervals based on the RNA-seq analysis under the drought condition. Blast-P was further performed to screen candidate genes controlling drought tolerance, and 22 functional protein candidates were finally identified. The results of the present study will be useful for improving drought tolerance of alfalfa varieties by marker-assisted selection (MAS), and provide promising candidates for further gene cloning and mechanism study.

Genome-wide identification, characterization, and expression analysis of UDP-glycosyltransferase genes associated with secondary metabolism in alfalfa (Medicago sativa L.).

Uridine diphosphate glycosyltransferases (UGTs) are enzymes that catalyze glycosylation modifications and play an essential role in regulating plant metabolism. Alfalfa (Medicago sativa L.) is the most important legume in the world due to its high yields and protein content; however, the UGT genes in alfalfa have not yet been studied. Identifying UGT genes with metabolic roles in alfalfa is essential for identifying and modifying genetic traits that are relevant to yield and quality. In this study, 90 of the 239 UGT genes identified from the alfalfa "Zhongmu No. 1" genome database were found to be related to secondary metabolism, and a series of gene family characterization analyses were conducted on each. The results demonstrated that all 90 UGT genes were unevenly distributed on eight chromosomes with few introns and that tandem duplications were the crucial driving force expanding the UGT family in alfalfa. Notably, the 90 UGT genes can be clustered into ten evolutionary groups which contain specific PSPG motifs, and genes in these ten groups have specific tissue expressions. This suggests that the UGT genes in each group could have similar glycosylation roles corresponding to analogous secondary metabolites in alfalfa. Additionally, multiple cis-acting elements found in MsUGT promoter regions, such as phytohormone and flavonoids, indicate that 90 UGT members could be induced by these features, which are also related to secondary metabolism. Therefore, our study identified 90 UGT members inten evolutionary groups that are likely related to glycosylation modifications with secondary metabolites in alfalfa. These findings help uncover pivotal regulatory mechanisms associated with secondary metabolism in plant yield and quality and contribute to genetic modification and breeding in alfalfa and other plant species.

Combining QTL mapping and RNA-Seq Unravels candidate genes for Alfalfa (Medicago sativa L.) leaf development.

BACKGROUND: Leaf size affects crop canopy morphology and photosynthetic efficiency, which can influence forage yield and quality. It is of great significance to mine the key genes controlling leaf development for breeding new alfalfa varieties. In this study, we mapped leaf length (LL), leaf width (LW), and leaf area (LA) in an F1 mapping population derived from a cultivar named ZhongmuNo.1 with larger leaf area and a landrace named Cangzhou with smaller leaf area. RESULTS: This study showed that the larger LW was more conducive to increasing LA. A total of 24 significant quantitative trait loci (QTL) associated with leaf size were identified on both the paternal and maternal linkage maps. Among them, nine QTL explained about 11.50-22.45% phenotypic variation. RNA-seq analysis identified 2,443 leaf-specific genes and 3,770 differentially expressed genes. Combining QTL mapping, RNA-seq alalysis, and qRT-PCR, we identified seven candidate genes associated with leaf development in five major QTL regions. CONCLUSION: Our study will provide a theoretical basis for marker-assisted breeding and lay a foundation for further revealing molecular mechanism of leaf development in alfalfa.

A Genome-Wide Association Study Coupled With a Transcriptomic Analysis Reveals the Genetic Loci and Candidate Genes Governing the Flowering Time in Alfalfa (Medicago sativa L.).

The transition to flowering at the right time is very important for adapting to local conditions and maximizing alfalfa yield. However, the understanding of the genetic basis of the alfalfa flowering time remains limited. There are few reliable genes or markers for selection, which hinders progress in genetic research and molecular breeding of this trait in alfalfa. We sequenced 220 alfalfa cultivars and conducted a genome-wide association study (GWAS) involving 875,023 single-nucleotide polymorphisms (SNPs). The phenotypic analysis showed that the breeding status and geographical origin strongly influenced the alfalfa flowering time. Our GWAS revealed 63 loci significantly related to the flowering time. Ninety-five candidate genes were detected at these SNP loci within 40 kb (20 kb up- and downstream). Thirty-six percent of the candidate genes are involved in development and pollen tube growth, indicating that these genes are key genetic mechanisms of alfalfa growth and development. The transcriptomic analysis showed that 1,924, 2,405, and 3,779 differentially expressed genes (DEGs) were upregulated across the three growth stages, while 1,651, 2,613, and 4,730 DEGs were downregulated across the stages. Combining the results of our GWAS and transcriptome analysis, in total, 38 candidate genes (7 differentially expressed during the bud stage, 13 differentially expressed during the initial flowering stage, and 18 differentially expressed during the full flowering stage) were identified. Two SNPs located in the upstream region of the Msa0888690 gene (which is involved in isop renoids) were significantly related to flowering. The two significant SNPs within the upstream region of Msa0888690 existed as four different haplotypes in this panel. The genes identified in this study represent a series of candidate targets for further research investigating the alfalfa flowering time and could be used for alfalfa molecular breeding.

RAD-Seq-Based High-Density Linkage Maps Construction and Quantitative Trait Loci Mapping of Flowering Time Trait in Alfalfa (Medicago sativa L.).

Alfalfa (Medicago sativa L.) is a perennial forage crop known as the "Queen of Forages." To dissect the genetic mechanism of flowering time (FT) in alfalfa, high-density linkage maps were constructed for both parents of an F1 mapping population derived from a cross between Cangzhou (P1) and ZhongmuNO.1 (P2), consisting of 150 progenies. The FT showed a transgressive segregation pattern in the mapping population. A total of 13,773 single-nucleotide polymorphism markers was obtained by using restriction-site associated DNA sequencing and distributed on 64 linkage groups, with a total length of 3,780.49 and 4,113.45 cM and an average marker interval of 0.58 and 0.59 cM for P1 and P2 parent, respectively. Quantitative trait loci (QTL) analyses were performed using the least square means of each year as well as the best linear unbiased prediction values across 4 years. Sixteen QTLs for FT were detected for P1 and 22 QTLs for P2, accounting for 1.40-16.04% of FT variation. RNA-Seq analysis at three flowering stages identified 5,039, 7,058, and 7,996 genes that were differentially expressed between two parents, respectively. Based on QTL mapping, DEGs analysis, and functional annotation, seven candidate genes associated with flowering time were finally detected. This study discovered QTLs and candidate genes for alfalfa FT, making it a useful resource for breeding studies on this essential crop.

Genome Assembly of Alfalfa Cultivar Zhongmu-4 and Identification of SNPs Associated with Agronomic Traits.

Alfalfa (Medicago sativa L.) is the most important legume forage crop worldwide with high nutritional value and yield. For a long time, the breeding of alfalfa was hampered by lacking reliable information on the autotetraploid genome and molecular markers linked to important agronomic traits. We herein reported the de novo assembly of the allele-aware chromosome-level genome of Zhongmu-4, a cultivar widely cultivated in China, and a comprehensive database of genomic variations based on resequencing of 220 germplasms. Approximate 2.74 Gb contigs (N50 of 2.06 Mb), accounting for 88.39% of the estimated genome, were assembled, and 2.56 Gb contigs were anchored to 32 pseudo-chromosomes. A total of 34,922 allelic genes were identified from the allele-aware genome. We observed the expansion of gene families, especially those related to the nitrogen metabolism, and the increase of repetitive elements including transposable elements, which probably resulted in the increase of Zhongmu-4 genome compared with Medicago truncatula. Population structure analysis revealed that the accessions from Asia and South America had relatively lower genetic diversity than those from Europe, suggesting that geography may influence alfalfa genetic divergence during local adaption. Genome-wide association studies identified 101 single nucleotide polymorphisms (SNPs) associated with 27 agronomic traits. Two candidate genes were predicted to be correlated with fall dormancy and salt response. We believe that the allele-aware chromosome-level genome sequence of Zhongmu-4 combined with the resequencing data of the diverse alfalfa germplasms will facilitate genetic research and genomics-assisted breeding in variety improvement of alfalfa.

Identification of Genetic Loci Associated With Crude Protein Content and Fiber Composition in Alfalfa (Medicago sativa L.) Using QTL Mapping.

Forage quality determined mainly by protein content and fiber composition has a crucial influence on digestibility and nutrition intake for animal feeding. To explore the genetic basis of quality traits, we conducted QTL mapping based on the phenotypic data of crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and lignin of an F1 alfalfa population generated by crossing of two alfalfa parents with significant difference in quality. In total, 83 QTLs were identified with contribution to the phenotypic variation (PVE) ranging from 1.45 to 14.35%. Among them, 47 QTLs interacted significantly with environment and 12 QTLs were associated with more than one trait. Epistatic effect was also detected for 73 pairs of QTLs with PVE of 1.08-14.06%. The results suggested that the inheritance of quality-related traits was jointly affected by additive, epistasis and environment. In addition, 83.33% of the co-localized QTLs were shared by ADF and NDF with the same genetic direction, while the additive effect of crude protein-associated QTLs was opposite to that fiber composition on the same locus, suggesting that the loci may antagonistically contribute to protein content and fiber composition. Further analysis of a QTL related to all the three traits of fiber composition (qNDF1C, qADF1C-2, and qlignin1C-2) showed that five candidate genes were homologs of cellulose synthase-like protein A1 in Medicago truncatula, indicating the potential role in fiber synthesis. For the protein-associated loci we identified, qCP4C-1 was located in the shortest region (chr 4.3 39.3-39.4 Mb), and two of the seven corresponding genes in this region were predicted to be E3 ubiquitin-protein ligase in protein metabolism. Therefore, our results provide some reliable regions significantly associated with alfalfa quality, and identification of the key genes would facilitate marker-assisted selection for favorable alleles in breeding program of alfalfa quality improvement.