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.

Structural Reorganization in Two Alfalfa Mitochondrial Genome Assemblies and Mitochondrial Evolution in Medicago Species.

Plant mitochondria are crucial for species evolution, phylogenetics, classification, and identification as maternal genetic material. However, the presence of numerous repetitive sequences, complex structures, and a low number of genes in the mitochondrial genome has hindered its complete assembly and related research endeavors. In this study, we assembled two mitochondrial genomes of alfalfa varieties of Zhongmu No.1 (299,123 bp) and Zhongmu No.4 (306,983 bp), based on a combination of PacBio, Illumina, and Hi-C sequences. The comparison of genome assemblies revealed that the same number of mitochondrial genes, including thirty-three protein-coding genes, sixteen tRNA genes, and three rRNA genes existed in the two varieties. Additionally, large fragments of repetitive sequences were found underlying frequent mitochondrial recombination events. We observed extensive transfer of mitochondrial fragments into the nuclear genome of Zhongmu No.4. Analysis of the cox1 and rrn18s genes in 35 Medicago accessions revealed the presence of population-level deletions and substitutions in the rrn18s gene. We propose that mitochondrial structural reorganizations may contribute to alfalfa evolution.

Identification of genetic loci associated with five agronomic traits in alfalfa using multi-environment trials.

KEY MESSAGE: The use of multi-environment trials to test yield-related traits in a diverse alfalfa panel allowed to find multiple molecular markers associated with complex agronomic traits. Yield is one of the most important target traits in alfalfa breeding; however, yield is a complex trait affected by genetic and environmental factors. In this study, we used multi-environment trials to test yield-related traits in a diverse panel composed of 200 alfalfa accessions and varieties. Phenotypic data of maturity stage measured as mean stage by count (MSC), dry matter content, plant height (PH), biomass yield (Yi), and fall dormancy (FD) were collected in three locations in Idaho, Oregon, and Washington from 2018 to 2020. Single-trial and stagewise analyses were used to obtain estimated trait means of entries by environment. The plants were genotyped using a genotyping by sequencing approach and obtained a genotypic matrix with 97,345 single nucleotide polymorphisms. Genome-wide association studies identified a total of 84 markers associated with the traits analyzed. Of those, 29 markers were in noncoding regions and 55 markers were in coding regions. Ten significant SNPs at the same locus were associated with FD and they were linked to a gene annotated as a nuclear fusion defective 4-like (NFD4). Additional SNPs associated with MSC, PH, and Yi were annotated as transcription factors such as Cysteine3Histidine (C3H), Hap3/NF-YB family, and serine/threonine-protein phosphatase 7 proteins, respectively. Our results provide insight into the genetic factors that influence alfalfa maturity, yield, and dormancy, which is helpful to speed up the genetic gain toward alfalfa yield improvement.

Genetic Mapping of Tolerance to Bacterial Stem Blight Caused by Pseudomonas syringae pv. syringae in Alfalfa (Medicago sativa L.).

The bacterial stem blight of alfalfa (Medicago sativa L.), first reported in the United States in 1904, has emerged recently as a serious disease problem in the western states. The causal agent, Pseudomonas syringae pv. syringae, promotes frost damage and disease that can reduce first harvest yields by 50%. Resistant cultivars and an understanding of host-pathogen interactions are lacking in this pathosystem. With the goal of identifying DNA markers associated with disease resistance, we developed biparental F1 mapping populations using plants from the cultivar ZG9830. Leaflets of plants in the mapping populations were inoculated with a bacterial suspension using a needleless syringe and scored for disease symptoms. Bacterial populations were measured by culture plating and using a quantitative PCR assay. Surprisingly, leaflets with few to no symptoms had bacterial loads similar to leaflets with severe disease symptoms, indicating that plants without symptoms were tolerant to the bacterium. Genotyping-by-sequencing identified 11 significant SNP markers associated with the tolerance phenotype. This is the first study to identify DNA markers associated with tolerance to P. syringae. These results provide insight into host responses and provide markers that can be used in alfalfa breeding programs to develop improved cultivars to manage the bacterial stem blight of alfalfa.

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.

Accuracy of genomic selection for alfalfa biomass yield in two full-sib populations.

Alfalfa (Medicago sativa) is one of the most important leguminous forages, widely planted in temperate and subtropical regions. As a homozygous tetraploid, its complex genetic background limits genetic improvement of biomass yield attributes through conventional breeding methods. Genomic selection (GS) could improve breeding efficiency by using high-density molecular markers that cover the whole genome to assess genomic breeding values. In this study, two full-sib F1 populations, consisting of 149 and 392 individual plants (P149 and P392), were constructed using parents with differences in yield traits, and the yield traits of the F1 populations were measured for several years in multiple environments. Comparisons of individual yields were greatly affected by environments, and the best linear unbiased prediction (BLUP) could accurately represent the original yield data. The two hybrid F1 populations were genotyped using GBS and RAD-seq techniques, respectively, and 47,367 and 161,170 SNP markers were identified. To develop yield prediction models for a single location and across locations, genotypic and phenotypic data from alfalfa yields in multiple environments were combined with various prediction models. The prediction accuracies of the F1 population, including 149 individuals, were 0.11 to 0.70, and those of the F1 population, consisting of 392 individuals, were 0.14 to 0.67. The BayesC and RF models had the highest average prediction accuracy of 0.60 for two F1 populations. The accuracy of the prediction models for P392 was higher than that of P149. By analyzing multiple prediction models, moderate prediction accuracies are obtained, although accuracies will likely decline across multiple locations. Our study provided evidence that GS can accelerate the improvement of alfalfa yield traits.

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.

Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.).

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.

Pan-transcriptome identifying master genes and regulation network in response to drought and salt stresses in Alfalfa (Medicago sativa L.).

Alfalfa is an important legume forage grown worldwide and its productivity is affected by environmental stresses such as drought and high salinity. In this work, three alfalfa germplasms with contrasting tolerances to drought and high salinity were used for unraveling the transcriptomic responses to drought and salt stresses. Twenty-one different RNA samples from different germplasm, stress conditions or tissue sources (leaf, stem and root) were extracted and sequenced using the PacBio (Iso-Seq) and the Illumina platforms to obtain full-length transcriptomic profiles. A total of 1,124,275 and 91,378 unique isoforms and genes were obtained, respectively. Comparative analysis of transcriptomes identified differentially expressed genes and isoforms as well as transcriptional and post-transcriptional modifications such as alternative splicing events, fusion genes and nonsense-mediated mRNA decay events and non-coding RNA such as circRNA and lncRNA. This is the first time to identify the diversity of circRNA and lncRNA in response to drought and high salinity in alfalfa. The analysis of weighted gene co-expression network allowed to identify master genes and isoforms that may play important roles on drought and salt stress tolerance in alfalfa. This work provides insight for understanding the mechanisms by which drought and salt stresses affect alfalfa growth at the whole genome level.

Genome-Wide Association Studies Identifying Multiple Loci Associated With Alfalfa Forage Quality.

Autotetraploid alfalfa is a major hay crop planted all over the world due to its adaptation in different environments and high quality for animal feed. However, the genetic basis of alfalfa quality is not fully understood. In this study, a diverse panel of 200 alfalfa accessions were planted in field trials using augmented experimental design at three locations in 2018 and 2019. Thirty-four quality traits were evaluated by Near Infrared Reflectance Spectroscopy (NIRS). The plants were genotyped using a genotyping by sequencing (GBS) approach and over 46,000 single nucleotide polymorphisms (SNPs) were obtained after variant calling and filtering. Genome-wide association studies (GWAS) identified 28 SNP markers associated with 16 quality traits. Among them, most of the markers were associated with fiber digestibility and protein content. Phenotypic variations were analyzed from three locations and different sets of markers were identified by GWAS when using phenotypic data from different locations, indicating that alfalfa quality traits were also affected by environmental factors. Among different sets of markers identified by location, two markers were associated with nine traits of fiber digestibility. One marker associated with lignin content was identified consistently in multiple environments. Putative candidate genes underlying fiber-related loci were identified and they are involved in the lignin and cell wall biosynthesis. The DNA markers and associated genes identified in this study will be useful for the genetic improvement of forage quality in alfalfa after the validation of the markers.

Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation.

Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50-70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green-Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

Construction of high-density genetic linkage map and mapping quantitative trait loci (QTL) for flowering time in autotetraploid alfalfa (Medicago sativa L.) using genotyping by sequencing.

Flowering time is an important agronomic trait of alfalfa (Medicago sativa L.). Managing flowering time can produce economic benefits for farmers. To understand the genetic basis of this trait, quantitative trait loci (QTL) mapping was conducted in a full-sib population that consisted of 392 individuals segregating based on flowering time. High density linkage maps were constructed using single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). The linkage maps contained 3,818 SNP markers on 64 linkage groups in two parents. The average marker density was 4.33 cM for Parent 1 (P1) and 1.47 cM for Parent 2 (P2). Phenotypic data for flowering time was collected for three years at one location. Twenty-eight QTLs were identified associated with flowering time. Eleven QTLs explained more than 10% of the phenotypic variation. Among them, five main effect QTLs located on linkage group (LG) 7D of P1 and five main effect QTLs located on LG 6D of P2 were identified. Three QTLs were co-located with other QTLs. The identified linked markers to QTLs could be used for marker-assisted selection in breeding programs to develop new alfalfa varieties to modulate flowering time.

Identification of genetic loci associated with forage quality in response to water deficit in autotetraploid alfalfa (Medicago sativa L.).

BACKGROUND: Alfalfa has been cultivated in many regions around the world as an important forage crop due to its nutritive value to livestock and ability to adapt to various environments. However, the genetic basis by which plasticity of quality-relevant traits influence alfalfa adaption to different water conditions remain largely unknown. RESULTS: In the present study, 198 accessions of alfalfa of the core collection for drought tolerance were evaluated for 26 forage quality traits in a field trial under an imposed deficit irrigation gradient. Regression analysis between quality traits and water stress revealed that values of fiber-related traits were negatively correlated with values of energy-related traits as water deficit increased. More than one hundred significant markers associated with forage quality under different water treatments were identified using genome-wide association studies with genotyping by sequencing. Among them, 131 markers associated with multiple traits in all the water deficit treatments. Most of the associated markers were dependent to the levels of water deficit, suggesting genetic controls for forage quality traits were dependent to the stress treatment. Twenty-four loci associated with forage quality were annotated to functional genes that may play roles in cell development or in response to water stress. CONCLUSIONS: This study addressed the genetic base of phenotypic variation of forage quality traits under water deficit. The SNP markers identified in this study will be useful in marker-assisted selection for the genetic improvement of alfalfa with enhanced drought tolerance while maintaining forage quality.

Genome-Wide Association and Prediction of Traits Related to Salt Tolerance in Autotetraploid Alfalfa (Medicago sativa L.).

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.

Construction of High-Density Linkage Maps and Identification of Quantitative Trait Loci Associated with Verticillium Wilt Resistance in Autotetraploid Alfalfa (Medicago sativa L.).

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.

Genetic Loci Associated with Salt Tolerance in Advanced Breeding Populations of Tetraploid Alfalfa Using Genome-Wide Association Studies.

Many agricultural lands in the western United States consist of soil with high concentrations of salt, which is detrimental to alfalfa ( L.) growth and production, especially in the region where water resource is limited. Developing alfalfa varieties with salt tolerance is imperative for sustainable production under increasing soil salinity. In the present study, we used advanced alfalfa breeding populations and evaluated five traits related to salt tolerance including biomass dry weight (DW) and fresh weight (FW), plant height (PH), leaf relative water content (RWC), and stomatal conductance (SC) under control and salt stress. Stress susceptibility index (SSI) of each trait and single-nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS) were used for genome-wide association studies (GWAS) to identify loci associated with salt tolerance. A total of 53 significant SNPs associated with salt tolerance were identified and they were located at 49 loci through eight chromosomes. A Basic Local Alignment Search Tool (BLAST) search of the regions surrounding the SNPs revealed 21 putative candidate genes associated with salt tolerance. The genetic architecture for traits related to salt tolerance characterized in this report could help in understanding the genetic mechanism by which salt stress affects plant growth and production in alfalfa. The markers and candidate genes identified in the present study would be useful for marker-assisted selection (MAS) in breeding salt-tolerant alfalfa after validation of the markers.

High-density linkage map construction and mapping QTL for yield and yield components in autotetraploid alfalfa using RAD-seq.

BACKGROUND: Alfalfa (Medicago sativa L.) is an important forage crop grown worldwide. Alfalfa is called the "queen of forage crops" due to its high forage yield and nutritional characteristics. The aim of this study was to undertake quantitative trait loci (QTL) mapping of yield and yield-related traits in an F1 population of two alfalfa varieties that differ in their yield and yield-related traits. RESULTS: We constructed a high-density linkage map using single nucleotide polymorphism (SNP) markers generated by restriction-site associated DNA sequencing (RAD-seq). The linkage map contains 4346 SNP and 119 simple sequence repeat (SSR) markers, with 32 linkage groups for each parent. The average marker distances were 3.00 and 1.32 cM, with coverages of 3455 cM and 4381 cM for paternal and maternal linkage maps, respectively. Using these maps and phenotypic data, we identified a total of 21 QTL for yield and yield components, including five for yield, five for plant height, five for branch number, and six for shoot diameter. Among them, six QTL were co-located for more than one trait. Five QTL explained more than 10% of the phenotypic variation. CONCLUSIONS: We used RAD-seq to construct a linkage map for alfalfa that greatly enhanced marker density compared to previous studies. This high-density linkage map of alfalfa is a useful reference for mapping yield-related traits. Identified yield-related loci could be used to validate their usefulness in developing markers for maker-assisted selection in breeding populations to improve yield potential in alfalfa.

Identification of Single-Nucleotide Polymorphic Loci Associated with Biomass Yield under Water Deficit in Alfalfa (Medicago sativa L.) Using Genome-Wide Sequencing and Association Mapping.

Alfalfa is a worldwide grown forage crop and is important due to its high biomass production and nutritional value. However, the production of alfalfa is challenged by adverse environmental factors such as drought and other stresses. Developing drought resistance alfalfa is an important breeding target for enhancing alfalfa productivity in arid and semi-arid regions. In the present study, we used genotyping-by-sequencing and genome-wide association to identify marker loci associated with biomass yield under drought in the field in a panel of diverse germplasm of alfalfa. A total of 28 markers at 22 genetic loci were associated with yield under water deficit, whereas only four markers associated with the same trait under well-watered condition. Comparisons of marker-trait associations between water deficit and well-watered conditions showed non-similarity except one. Most of the markers were identical across harvest periods within the treatment, although different levels of significance were found among the three harvests. The loci associated with biomass yield under water deficit located throughout all chromosomes in the alfalfa genome agreed with previous reports. Our results suggest that biomass yield under drought is a complex quantitative trait with polygenic inheritance and may involve a different mechanism compared to that of non-stress. BLAST searches of the flanking sequences of the associated loci against DNA databases revealed several stress-responsive genes linked to the drought resistance loci, including leucine-rich repeat receptor-like kinase, B3 DNA-binding domain protein, translation initiation factor IF2, and phospholipase-like protein. With further investigation, those markers closely linked to drought resistance can be used for MAS to accelerate the development of new alfalfa cultivars with improved resistance to drought and other abiotic stresses.

Genome-Wide Association Mapping of Loci Associated with Plant Growth and Forage Production under Salt Stress in Alfalfa (Medicago sativa L.).

Salinity tolerance is highly desirable to sustain alfalfa production in marginal lands that have been rendered saline. In this study, we used a diverse panel of 198 alfalfa accessions for mapping loci associated with plant growth and forage production under salt stress using genome-wide association studies (GWAS). The plants were genotyped using genotyping-by-sequencing (GBS). A greenhouse procedure was used for phenotyping four agronomic and physiological traits affected by salt stress, including dry weight (DW), plant height (PH), leaf chlorophyll content (LCC), and stomatal conductance (SC). For each trait, a stress susceptibility index (SSI) was used to evaluate plant performance under stressed and non-stressed conditions. Marker-trait association identified a total of 42 markers significantly associated with salt tolerance. They were located on all chromosomes except chromosome 2 based on the alignment of their flanking sequences to the reference genome (Medicago truncatula). Of those identified, 13 were associated with multiple traits. Several loci identified in the present study were also identified in previous reports. BLAST search revealed that 19 putative candidate genes linked to 24 significant markers. Among them, B3 DNA-binding protein, Thiaminepyrophosphokinase and IQ calmodulin-binding motif protein were identified among multiple traits in the present and previous studies. With further investigation, these markers and candidates would be useful for developing markers for marker-assisted selection in breeding programs to improve alfalfa cultivars with enhanced tolerance to salt stress.