Identification of Quantitative Trait Loci for Alfalfa Forage Biomass Productivity during Drought Stress

Alfalfa genomic selection for different stress-prone growing regions

Alfalfa (Medicago sativa L.) selection for stress-prone regions has high priority for sustainable crop-livestock systems. This study assessed the genomic selection (GS) ability to predict alfalfa breeding values for drought-prone agricultural sites of Algeria, Morocco, and Argentina; managed-stress (MS) environments of Italy featuring moderate or intense drought; and one Tunisian site irrigated with moderately saline water. Additional aims were to investigate genotype × environment interaction (GEI) patterns and the effect on GS predictions of three single-nucleotide polymorphism (SNP) calling procedures, 12 statistical models that exclude or incorporate GEI, and allele dosage information. Our study included 127 genotypes from a Mediterranean reference population originated from three geographically contrasting populations, genotyped via genotyping-by-sequencing and phenotyped based on multi-year biomass dry matter yield of their dense-planted half-sib progenies. The GEI was very large, as shown by 27-fold greater additive genetic variance × environment interaction relative to the additive genetic variance and low genetic correlation for progeny yield responses across environments. The predictive ability of GS (using at least 37,969 SNP markers) exceeded 0.20 for moderate MS (representing Italian stress-prone sites) and the sites of Algeria and Argentina while being quite low for the Tunisian site and intense MS. Predictions of GS were complicated by rapid linkage disequilibrium decay. The weighted GBLUP model, GEI incorporation into GS models, and SNP calling based on a mock reference genome exhibited a predictive ability advantage for some environments. Our results support the specific breeding for each target region and suggest a positive role for GS in most regions when considering the challenges associated with phenotypic selection.

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.

Transcriptome and functional analyses reveal ERF053 from Medicago falcata as key regulator in drought resistances

Medicago falcata L. is an important legume forage grass with strong drought resistant, which could be utilized as an important gene pool in molecular breed of forage grass. In this study, M. falcata seedlings were treated with 400 mM mannitol to simulate drought stress, and the morphological and physiological changes were investigated, as well as the transcriptome changes of M. falcata seedlings at different treatment time points (0 h, 2 h, 6 h, 12 h, 24 h, 36 h and 48 h). Transcriptome analyses revealed four modules were closely related with drought response in M. falcata by WGCNA analysis, and four ERF transcription factor genes related with drought stress were identified (MfERF053, MfERF9, MfERF034 and MfRAP2.1). Among them, MfERF053 was highly expressed in roots, and MfERF053 protein showed transcriptional activation activity by transient expression in tobacco leaves. Overexpression of MfERF053 in Arabidopsis improved root growth, number of lateral roots and fresh weight under drought, salt stress and exogenous ABA treatments. Transgenic Arabidopsis over-expressing MfERF053 gene grew significantly better than the wild type under both drought stress and salt stress when grown in soil. Taken together, our strategy with transcriptome combined WGCNA analyses identified key transcription factor genes from M. falcata, and the selected MfERF053 gene was verified to be able to enhance drought and salt resistance when over-expressed in Arabidopsis.

Intraspecific Variation for Leaf Physiological and Root Morphological Adaptation to Drought Stress in Alfalfa (Medicago sativa L.)

Drought stress reduces crop biomass yield and the profitability of rainfed agricultural systems. Evaluation of populations or accessions adapted to diverse geographical and agro-climatic environments sheds light on beneficial plant responses to enhance and optimize yield in resource-limited environments. This study used the morphological and physiological characteristics of leaves and roots from two different alfalfa subspecies during progressive drought stress imposed on controlled and field conditions. Two different soils (Experiments 1 and 2) imposed water stress at different stress intensities and crop stages in the controlled environment. Algorithm-based image analysis of leaves and root systems revealed key morphological and physiological traits associated with biomass yield under stress. The Medicago sativa subspecies (ssp.) sativa population, PI478573, had smaller leaves and maintained higher chlorophyll content (CC), leaf water potential, and osmotic potential under water stress. In contrast, M. sativa ssp. varia, PI502521, had larger leaves, a robust root system, and more biomass yield. In the field study, an unmanned aerial vehicle survey revealed PI502521 to have a higher normalized difference vegetation index (vegetation cover and plant health characteristics) throughout the cropping season, whereas PI478573 values were low during the hot summer and yielded low biomass in both irrigated and rainfed treatments. RhizoVision Explorer image analysis of excavated roots revealed a smaller diameter and a narrow root angle as target traits to increase alfalfa biomass yield irrespective of water availability. Root architectural traits such as network area, solidity, volume, surface area, and maximum radius exhibited significant variation at the genotype level only under limited water availability. Different drought-adaptive strategies identified across subspecies populations will benefit the plant under varying levels of water limitation and facilitate the development of alfalfa cultivars suitable across a broad range of growing conditions. The alleles from both subspecies will enable the development of drought-tolerant alfalfa with enhanced productivity under limited water availability.

Physiological and transcriptome analyses highlight multiple pathways involved in drought stress in Medicago falcata

Medicago falcata is one of the leguminous forage crops, which grows well in arid and semiarid region. To fully investigate the mechanism of drought resistance response in M. falcata, we challenged the M. falcata plants with 30% PEG-6000, and performed physiological and transcriptome analyses. It was found that, the activities of antioxidant enzymes (eg. SOD, POD, and CAT) and soluble sugar content were all increased in the PEG-treated group, as compared to the control group. Transcriptome results showed that a total of 706 genes were differentially expressed in the PEG-treated plants in comparison with the control. Gene enrichment analyses on differentially expressed genes revealed that a number of genes in various pathway were significantly enriched, including the phenylpropanoid biosynthesis (ko00940) and glycolysis/gluconeogenesis (ko00010), indicating the involvement of these key pathways in drought response. Furthermore, the expression levels of seven differentially expressed genes were verified to be involved in drought response in M. falcata by qPCR. Taken together, these results will provide valuable information related to drought response in M. falcata and lay a foundation for molecular studies and genetic breeding of legume crops in future research.

Validation of DNA marker-assisted selection for forage biomass productivity under deficit irrigation in alfalfa

Drought and limited irrigation resources threaten agricultural sustainability in many regions of the world. Application of genomic-based breeding strategies may benefit crop variety development for these environments. Here, we provide a first report on the effect of deploying DNA marker-assisted selection (MAS) for the drought resilience quantitative trait in alfalfa (Medicago sativa L.). The goals of this study were to validate the effect of several quantitative trait loci (QTL) associated with alfalfa forage and crown-root (CR) biomass during drought and to determine their potential to improve forage yield of elite germplasm under water-limited conditions. Marker assisted selection was employed to introgress favorable or unfavorable DNA marker alleles affiliated with 10 biomass QTL into three elite backgrounds. Thirty-two populations were developed and evaluated for forage productivity over 3 yr under continuous deficit irrigation management in New Mexico, USA. Significant yield differences (ranging from -13 to 26%) were detected among some MAS-derived populations in all three elite backgrounds. Application of QTL MAS generally resulted in expected phenotypic responses within an elite genetic background that was similar to that in which the QTL were originally identified. However, relative performance of the populations varied substantially across the three genetic backgrounds. These outcomes indicate that QTL MAS can significantly affect forage productivity of elite alfalfa germplasm in drought-stressed environments. However, if biomass QTL are detected in donor germplasm that is genetically dissimilar to targeted elite populations, characterization of donor alleles may be warranted within elite backgrounds of interest to confirm their phenotypic effects prior to implementing MAS-based breeding.

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.

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.

Medicago sativa and Medicago truncatula Show Contrasting Root Metabolic Responses to Drought

Drought is an environmental stressor that affects crop yield worldwide. Understanding plant physiological responses to stress conditions is needed to secure food in future climate conditions. In this study, we applied a combination of plant physiology and metabolomic techniques to understand plant responses to progressive water deficit focusing on the root system. We chose two legume plants with contrasting tolerance to drought, the widely cultivated alfalfa Medicago sativa (Ms) and the model legume Medicago truncatula (Mt) for comparative analysis. Ms taproot (tapR) and Mt fibrous root (fibR) biomass increased during drought, while a progressive decline in water content was observed in both species. Metabolomic analysis allowed the identification of key metabolites in the different tissues tested. Under drought, carbohydrates, abscisic acid, and proline predominantly accumulated in leaves and tapRs, whereas flavonoids increased in fibRs in both species. Raffinose-family related metabolites accumulated during drought. Along with an accumulation of root sucrose in plants subjected to drought, both species showed a decrease in sucrose synthase (SUS) activity related to a reduction in the transcript level of SUS1, the main SUS gene. This study highlights the relevance of root carbon metabolism during drought conditions and provides evidence on the specific accumulation of metabolites throughout the root system.

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.

QTL mapping of flowering time and biomass yield in tetraploid alfalfa (Medicago sativa L.)

BACKGROUND

The genetic and genomic basis of flowering time and biomass yield in alfalfa (Medicago sativa L.) remains poorly understood mainly due to the autopolyploid nature of the species and the lack of adequate genomic resources. We constructed linkage maps using genotyping-by-sequencing (GBS) based single dose allele (SDA) SNP and mapped alfalfa timing of flowering (TOF), spring yield (SY), and cumulative summer biomass (CSB) in a pseudo-testcross F1 population derived from a fall dormant (3010) and a non-dormant (CW 1010) cultivars. We analyzed the quantitative trait loci (QTL) to identify conserved genomic regions and detected molecular markers and potential candidate genes associated with the traits to improve alfalfa and provide genomic resources for the future studies.

RESULTS

This study showed that both fall dormant and non-dormant alfalfa cultivars harbored QTL for early and late flowering, suggesting that flowering time in alfalfa is not an indicator of its fall dormancy (FD) levels. A weak phenotypic correlation between the flowering time and fall dormancy (FD) in F1 and checks also corroborated that alfalfa FD and TOF are not the predictors of one another. The relationship between flowering time and alfalfa biomass yield was not strong, but the non-dormant had relatively more SY than dormant. Therefore, selecting superior alfalfa cultivars that are non-dormant, winter-hardy, and early flowering would allow for an early spring harvest with enhanced biomass. In this study, we found 25 QTL for TOF, 17 for SY and six QTL for CSB. Three TOF related QTL were stable and four TOF QTL were detected in the corresponding genomic locations of the flowering QTL of M. truncatula, an indication of possible evolutionarily conserved regions. The potential candidate genes for the SNP sequences of QTL regions were identified for all three traits and these genes would be potential targets for further molecular studies.

CONCLUSIONS

This research showed that variation in alfalfa flowering time after spring green up has no association with dormancy levels. Here we reported QTL, markers, and potential candidate genes associated with spring flowering time and biomass yield of alfalfa, which constitute valuable genomic resources for improving these traits via marker-assisted selection (MAS).

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.

Harnessing the Potential of Forage Legumes, Alfalfa, Soybean, and Cowpea for Sustainable Agriculture and Global Food Security

Substantial improvements in access to food and increased purchasing power are driving many people toward consuming nutrition-rich foods causing an unprecedented demand for protein food worldwide, which is expected to rise further. Forage legumes form an important source of feed for livestock and have potential to provide a sustainable solution for food and protein security. Currently, alfalfa is a commercially grown source of forage and feed in many countries. However, soybean and cowpea also have the potential to provide quality forage and fodder for animal use. The cultivation of forage legumes is under threat from changing climatic conditions, indicating the need for breeding cultivars that can sustain and acclimatize to the negative effects of climate change. Recent progress in genetic and genomic tools have facilitated the identification of quantitative trait loci and genes/alleles that can aid in developing forage cultivars through genomics-assisted breeding. Furthermore, transgenic technology can be utilized to manipulate the genetic makeup of plants to improve forage digestibility for better animal performance. In this article, we assess the genetic potential of three important legume crops, alfalfa, soybean, and cowpea in supplying quality fodder and feed for livestock. In addition, we examine the impact of climate change on forage quality and discuss efforts made in enhancing the adaptation of the plant to the abiotic stress conditions. Subsequently, we suggest the application of integrative approaches to achieve adequate forage production amid the unpredictable climatic conditions.

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.

Identification of loci controlling forage yield and nutritive value in diploid alfalfa using GBS-GWAS

We attempted to identify genomic regions controlling forage yield and nutritive value in alfalfa. Several candidate genes and associated genetic markers were identified that could potentially be useful for alfalfa breeding to more efficiently develop improved cultivars. Alfalfa is one of the most widely cultivated forage legumes worldwide and improving alfalfa forage yield and nutritive value is a major global breeding goal. Genotyping-by-sequencing (GBS) provides cost-effective molecular marker genotyping for genome-wide association studies (GWAS). Using more than 15,000 genome-wide single nucleotide polymorphisms (SNP) identified from GBS, we conducted a GWAS to investigate forage yield and nutritive value-related traits. We have detected a number of associations for all the traits evaluated and a number of associations detected were located on the Medicago truncatula genome. The SNP in a coding region of a cell wall biosynthesis gene was associated with several cell wall-related traits, and we suggest that it may be the causative polymorphism. Two other SNPs residing in meristematic development and early growth genes were found to associate with the total biomass yield. None of the SNPs associated with regrowth after harvest or with spring regrowth were mapped to the M. truncatula genome, possibly reflecting the fact that M. truncatula is an annual species related to alfalfa that typically has limited ability to regrow. The alleles we identify with the major impact on forage yield and nutritive value can be rapidly incorporated into our breeding program.

Genome-Wide Association Mapping and Genomic Selection for Alfalfa (Medicago sativa) Forage Quality Traits

Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3-0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.