A genome-wide association study approach to the identification of candidate genes underlying agronomic traits in alfalfa (Medicago sativa L.)

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.

The chromosome-level assembly of the wild diploid alfalfa genome provides insights into the full landscape of genomic variations between cultivated and wild alfalfa

Alfalfa (Medicago sativa L.) is one of the most important forage legumes in the world, including autotetraploid (M. sativa ssp. sativa) and diploid alfalfa (M. sativa ssp. caerulea, progenitor of autotetraploid alfalfa). Here, we reported a high-quality genome of ZW0012 (diploid alfalfa, 769 Mb, contig N50 = 5.5 Mb), which was grouped into the Northern group in population structure analysis, suggesting that our genome assembly filled a major gap among the members of M. sativa complex. During polyploidization, large phenotypic differences occurred between diploids and tetraploids, and the genetic information underlying its massive phenotypic variations remains largely unexplored. Extensive structural variations (SVs) were identified between ZW0012 and XinJiangDaYe (an autotetraploid alfalfa with released genome). We identified 71 ZW0012-specific PAV genes and 1296 XinJiangDaYe-specific PAV genes, mainly involved in defence response, cell growth, and photosynthesis. We have verified the positive roles of MsNCR1 (a XinJiangDaYe-specific PAV gene) in nodulation using an Agrobacterium rhizobia-mediated transgenic method. We also demonstrated that MsSKIP23_1 and MsFBL23_1 (two XinJiangDaYe-specific PAV genes) regulated leaf size by transient overexpression and virus-induced gene silencing analysis. Our study provides a high-quality reference genome of an important diploid alfalfa germplasm and a valuable resource of variation landscape between diploid and autotetraploid, which will facilitate the functional gene discovery and molecular-based breeding for the cultivars in the future.

Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection

Effects of individual single-nucleotide polymorphism (SNP) markers and the size of "training" and "test" populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of "training" to "test" population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of "training" to "test" population size increased. However, the prediction accuracy of the six GS methods reduced to a range of -0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for "training" sets, but rrBLUP tended to outperform Bayesian methods in independent "test" sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.

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

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.

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.

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.

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.

A global alfalfa diversity panel reveals genomic selection signatures in Chinese varieties and genomic associations with root development

Alfalfa (Medicago sativa L.) is an important forage crop worldwide. However, little is known about the effects of breeding status and different geographical populations on alfalfa improvement. Here, we sequenced 220 alfalfa core germplasms and determined that Chinese alfalfa cultivars form an independent group, as evidenced by comparisons of F_ST values between different subgroups, suggesting that geographical origin plays an important role in group differentiation. By tracing the influence of geographical regions on the genetic diversity of alfalfa varieties in China, we identified 350 common candidate genetic regions and 548 genes under selection. We also defined 165 loci associated with 24 important traits from genome-wide association studies. Of those, 17 genomic regions closely associated with a given phenotype were under selection, with the underlying haplotypes showing significant differences between subgroups of distinct geographical origins. Based on results from expression analysis and association mapping, we propose that 6-phosphogluconolactonase (MsPGL) and a gene encoding a protein with NHL domains (MsNHL) are critical candidate genes for root growth. In conclusion, our results provide valuable information for alfalfa improvement via molecular breeding.

Comparative proteomics analysis of the responses to selenium in selenium-enriched alfalfa (Medicago sativa L.) leaves

The mass of leaves and the chlorophyll and selenium content of alfalfa can be increased by the foliar spraying of selenite. To better understand the relationship between changes in the expression of specific proteins and the various metabolic and regulatory pathways affected by selenium treatment, labeling with Tandem Mass Tags (TMT) was used as a proteomics technique to compare control leaves with those enriched with Se. A total of 8,411 proteins were identified, the expression levels of 195 of which were significantly modified, 67 significantly up-regulated and 128 significantly down-regulated. Using gene functional classification and metabolic pathway annotation, selenium treatment was found to have a significant impact on metabolic processes. The energy and substances produced by the metabolic processes of a variety of carbohydrates, lipids, and amino acids, and the metabolism of carbon may be responsible for increasing the yield of alfalfa leaves. Administration of selenium substantially influenced Se-responsive proteins, including ABC transporter G family member 36, Probable glutathione S-transferase and cysteine tRNA ligase. Selenium treatment may also enhance photosynthesis and the defense response of cells. Furthermore, protein ubiquitination also played an important role in the selenium response of alfalfa leaves. In summary, a basic analysis of the selenium response pathway in alfalfa leaves at the proteomics level was conducted, which may assist in a more detailed elucidation of selenium enrichment in alfalfa in the future.

Genome-Wide Identification of GRAS Gene Family and Their Responses to Abiotic Stress in Medicago sativa

Alfalfa (Medicago sativa) is a high-quality legume forage crop worldwide, and alfalfa production is often threatened by abiotic environmental stresses. GRAS proteins are important transcription factors that play a vital role in plant development, as well as in response to environmental stress. In this study, the availability of alfalfa genome "Zhongmu No.1" allowed us to identify 51 GRAS family members, i.e., MsGRAS. MsGRAS proteins could be classified into nine subgroups with distinct conserved domains, and tandem and segmental duplications were observed as an expansion strategy of this gene family. In RNA-Seq analysis, 14 MsGRAS genes were not expressed in the leaf or root, 6 GRAS genes in 3 differentially expressed gene clusters were involved in the salinity stress response in the leaf. Moreover, qRT-PCR results confirmed that MsGRAS51 expression was induced under drought stress and hormone treatments (ABA, GA and IAA) but down-regulated in salinity stress. Collectively, our genome-wide characterization, evolutionary, and expression analysis suggested that the MsGRAS proteins might play crucial roles in response to abiotic stresses and hormonal cues in alfalfa. For the breeding of alfalfa, it provided important information on stress resistance and functional studies on MsGRAS and hormone signaling.

Genome-Wide Association Analysis Coupled With Transcriptome Analysis Reveals Candidate Genes Related to Salt Stress in Alfalfa (Medicago sativa L.)

Salt stress is the main abiotic factor affecting alfalfa yield and quality. However, knowledge of the genetic basis of the salt stress response in alfalfa is still limited. Here, a genome-wide association study (GWAS) involving 875,023 single-nucleotide polymorphisms (SNPs) was conducted on 220 alfalfa varieties under both normal and salt-stress conditions. Phenotypic analysis showed that breeding status and geographical origin play important roles in the alfalfa salt stress response. For germination ability under salt stress, a total of 15 significant SNPs explaining 9%-14% of the phenotypic variation were identified. For tolerance to salt stress in the seedling stage, a total of 18 significant SNPs explaining 12%-23% of the phenotypic variation were identified. Transcriptome analysis revealed 2,097 and 812 differentially expressed genes (DEGs) that were upregulated and 2,445 and 928 DEGs that were downregulated in the leaves and roots, respectively, under salt stress. Among these DEGs, many encoding transcription factors (TFs) were found, including MYB-, CBF-, NAC-, and bZIP-encoding genes. Combining the results of our GWAS analysis and transcriptome analysis, we identified a total of eight candidate genes (five candidate genes for tolerance to salt stress and three candidate genes for germination ability under salt stress). Two SNPs located within the upstream region of MsAUX28, which encodes an auxin response protein, were significantly associated with tolerance to salt stress. The two significant SNPs within the upstream region of MsAUX28 existed as three different haplotypes in this panel. Hap 1 (G/G, A/A) was under selection in the alfalfa domestication and improvement process.

Genome-Wide Identification, Expression Analysis and Functional Study of CCT Gene Family in Medicago truncatula

The control of flowering time has an important impact on biomass and the environmental adaption of legumes. The CCT (CO, COL and TOC1) gene family was elucidated to participate in the molecular regulation of flowering in plants. We identified 36 CCT genes in the M. truncatula genome and they were classified into three distinct subfamilies, PRR (7), COL (11) and CMF (18). Synteny and phylogenetic analyses revealed that CCT genes occurred before the differentiation of monocot and dicot, and CCT orthologous genes might have diversified among plants. The diverse spatial-temporal expression profiles indicated that MtCCT genes could be key regulators in flowering time, as well as in the development of seeds and nodules in M. truncatula. Notably, 22 MtCCT genes with typical circadian rhythmic variations suggested their different responses to light. The response to various hormones of MtCCT genes demonstrated that they participate in plant growth and development via varied hormones dependent pathways. Moreover, six MtCCT genes were dramatically induced by salinity and dehydration treatments, illustrating their vital roles in the prevention of abiotic injury. Collectively, our study provides valuable information for the in-depth investigation of the molecular mechanism of flowering time in M. truncatula, and it also provides candidate genes for alfalfa molecular breeding with ideal flowering time.