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Genome-wide association study revealed candidate genes associated with leaf size in alfalfa (Medicago sativa L.)

BMC Plant Biology volume 25, Article number: 180 (2025) Cite this article

Abstract

Background

Alfalfa (Medicago sativa L.) is one of the most widely cultivated perennial leguminous forages globally, known for its high yield and quality. Leaf size plays a crucial role in influencing its photosynthetic capacity, forage yield, and quality. Therefore, understanding the genetic factors regulating leaf size is of great importance for breeding new alfalfa varieties with improved yield and quality. In this study, we performed a genome-wide association study on four leaf size-related traits in 176 alfalfa germplasm resources to identify candidate genes associated with leaf size.

Results

Phenotypic analysis revealed varying degrees of variation among the four traits, with coefficients of variation ranging from 3.43 to 36.84%. The broad sense heritability of these traits was found to be between 38.30% and 53.23%. Correlation analysis showed a significant positive correlation among the four traits (P < 0.01). The GWAS identified 39 SNPs associated with leaf size, distributed across eight chromosomes, of which 9 SNPs were linked to multiple traits. Haplotype analysis further confirmed that the number of superior alleles in each material was positively correlated with leaf area. Finally, we identified five genes near these 39 significant SNPs that are associated with leaf size or development.

Conclusion

Our findings provide new molecular markers for marker-assisted selection in alfalfa breeding programs. Moreover, this study provides a solid foundation for subsequent functional verification and genetic improvement in alfalfa.

Peer Review reports

Introduction

Alfalfa (Medicago sativa L.), commonly referred to as the “Queen of Forages”, is a globally cultivated feed crop valued for its high biomass yield and nutritional content [1, 2]. One of the main breeding objectives for alfalfa is to increase its biomass yield, as it is primarily grown for its leaves and stems. Leaves are the primary photosynthetic organs in plants, playing a crucial role in agricultural production. Photosynthesis, as the fundamental process driving plant growth, development, and organic matter accumulation, is strongly influenced by leaf size [3]. Larger leaves generally exhibit higher light interception capabilities, directly impacting crop yield. Furthermore, larger leaves also contribute to an increased transpiration area, enhancing the transpiration rate and facilitating water uptake and transport by plants, which is beneficial for maintaining overall plant health and productivity [4,5,6]. Therefore, enhancing leaf size is a feasible method to boost its overall yield. While traditional phenotypic selection has contributed to the improvement of desirable traits, the saturation of beneficial genetic variation in elite lines has limited further advances. In contrast, molecular breeding techniques, such as genome-wide association studies (GWAS) and gene editing, allow for the direct selection and aggregation of desirable genes, significantly improving breeding efficiency and reducing breeding cycles. These advances represent the future direction for alfalfa breeding [7].

The regulation of leaf size in plants involves a series of interconnected and complex events. Studies have confirmed that leaf size is ultimately determined by cell proliferation and expansion, influenced by various regulators such as plant hormones, transcription factors, and microRNAs [8]. Among these, plant hormones play a key role in leaf growth and differentiation, making them crucial targets for research aimed at enhancing crop yields and developing stress-resistant varieties [9, 10]. For example, by knocking out the PID gene that regulates auxin transport in Arabidopsis thaliana, the resulting mutants exhibited pleiotropic growth defects, including reduced leaf size. Conversely, plants overexpressing PID had significantly increased auxin content and enlarged leaves [11]. However, excessive auxin concentrations can negatively impact leaf growth by causing abnormal cell proliferation or differentiation. Similarly, gibberellins (GAs) promote leaf size by enhancing both cell proliferation and expansion. GA-deficient mutants produce smaller leaves, while plants overexpressing GA exhibit significantly larger leaves [12, 13]. Transcription factors and microRNAs also play critical roles in regulating leaf development. For instance, the three members of the growth-regulatory factor (GRF) transcription factor family (GRF1, GRF3, and GRF5) promote leaf and cotyledon growth [14]. Additionally, overexpression of miR319, which negatively regulates TCP transcription factors, and dysfunction of TCP genes themselves, can lead to phenotypes with enlarged and curled leaves [15].

In the post-genomic era, identifying gene functions remains a major challenge in molecular biology [16]. GWAS have become a key strategy for analyzing the genetic architecture of complex quantitative traits and exploring the functions of associated genes. GWAS involves scanning the entire genome for single nucleotide polymorphisms (SNPs) based on linkage disequilibrium (LD) and selecting SNPs that are significantly associated with phenotypic traits [17]. Using natural populations as research subjects, GWAS reduces the time required to construct mapping populations and lowers the costs of gene sequencing [18]. Consequently, GWAS has been widely applied in early-stage molecular breeding to develop trait-associated markers, which can then be used in marker-assisted breeding. This approach has been successfully employed in various crops, such as maize [19, 20], rice [21], and wheat [22]. GWAS has also been applied in alfalfa to identify molecular markers and candidate genes related to various target traits. GWAS of 109 drought-treated alfalfa germplasm identified 21 significant SNPs associated with drought resistance, including an important candidate gene, MsMYBH, which is significantly upregulated in drought-resistant varieties. Overexpression of MsMYBH resulted in increased yield and quality compared to the wild type, providing new insights into drought resistance mechanisms in alfalfa [23]. Similarly, GWAS was performed on 9 agronomic traits of 322 alfalfa materials from different regions for a period of 3 years. 42 significant markers were detected and 1 candidate gene closely related to plant height was identified. Further functional verification showed that overexpression of this gene significantly increased the height of Arabidopsis by 30% compared with the wild type [24]. In the context of leaf size in alfalfa, GWAS of 220 alfalfa materials has identified 26 significant SNPs and 6 candidate genes related to leaf size [25]. GWAS applications in alfalfa have also extended to traits such as salt tolerance [26], fall dormancy [27], verticillium wilt resistance [28] and quality-related traits [29].

Despite its agronomic importance, the genetic basis and molecular mechanisms underlying leaf size in alfalfa remain poorly understood, particularly in the context of GWAS applications Advancements in sequencing technology and reduced costs have enabled large-scale SNP detection. Additionally, the recent completion of the alfalfa genome sequencing provides a clearer genomic landscape, facilitating subsequent GWAS and candidate gene analysis [30]. In this study, we utilized 176 alfalfa germplasm resources to construct a field trial population and conducted SNP-based GWAS for four leaf size-related traits: leaf length (LL), leaf width (LW), leaf area (LA), and leaf perimeter (LP). Our aim was to identify key genetic loci controlling leaf size and provide valuable markers for molecular breeding to support the optimization of leaf size and improve alfalfa productivity.

Materials and methods

Plant materials and growth conditions

In this study, we used an association panel of 176 alfalfa germplasm materials to investigate the relationship between SNP markers and leaf size-related traits. These materials were sourced from various regions, including Asia, Africa, Europe, North America, and South America, and comprised 13 wild accessions, 72 landrace accessions, 72 cultivated accessions, and 19 accessions with unclear improvement status (Table S1). The germplasm resources were obtained from the U.S. National Plant Germplasm System online database (https://npgsweb.ars-grin.gov/gringlobal/search) and the Medium-term Germplasm Bank of China’s National Grass Germplasm Resource Center.

The experiment was conducted at the International Agricultural High-tech Industrial Park of the Chinese Academy of Agricultural Sciences, located in Langfang City, Hebei Province, China (39.59°N, 116.59°E). The region has a warm, temperate continental monsoon climate, with an average annual temperature of 11.9 °C and precipitation of 554.9 mm. The soil in the experimental area is a medium loam with a pH of 7.37 and contains 1.69% organic matter. The associated populations were asexually propagated in the greenhouse in 2021 and transplanted into the field in the spring of 2022. The previous association panel was reported in our previous study [27]. The experiment followed a completely randomized block design with three replicates and five plants per plot. Each plot consisted of five plants with a spacing of 30 cm between individual plants. The row spacing and column spacing between different genotypes were both set to 65 cm. The plot size for each genotype was approximately 1.625 m², which was manageable within the experimental field. No fertilization or irrigation was applied, and manual weeding was carried out as needed. Before phenotypic data collection, all plants were uniformly pruned to ensure even growth.

Phenotypic data collection and analysis

Phenotypic data were collected in 2022–2024, leaves of all genotypes were sampled at the initial flowering stage (10% of the alfalfa plants have already bloomed). To ensure consistent sampling, leaves were collected from the highest branch of each seedling, specifically 3–4 nodes below the stem tip, using the middle leaflet of fully developed trifoliate leaves. A total of 15 leaves were collected from each genotype and photographed for analysis. LL, LW, LA and LP were measured using Image J software (v1.53k). Each trait is represented by year plus phenotype, such as 22LL for leaf length in 2022, and so on. The average three-year leaf length was recorded as LL-mean, as were the other three traits. Genome-wide association analysis was performed using the mean values of each trait, and the traits were abbreviated as LL, LW, LA, and LP. Statistical analyses of phenotypic data were conducted using Excel 2016, SPSS (v19.0), and R (v4.1.3) software. Pearson correlation analysis and visualization of leaf size-related traits were performed using Origin 2022 software. The broad sense heritability (H²) of each trait was calculated using the lme4 package in R, with the formula:

$$\:{H}^{2}=\frac{{V}_{g}}{{V}_{g}+\frac{{V}_{e}}{L}}\times\:100\%$$

where Vg represents genetic variance, Ve is residual variance, and L denotes the number of environments (years).

Sequencing and SNP calling

DNA was extracted from young leaves of each material using the CWBIO Plant Genome DNA Extraction Kit (Cowin Biosciences, Beijing, China) and sequenced on the BGI-Shenzhen DNBSEQ platform (BGI, Shenzhen, China), yielding approximately 36 GB of raw sequencing data per genotype. After quality control, the raw sequencing data were aligned to the reference genome of “Zhongmu-4”. SAMtools (v1.13) was used to filter multiple alignments and low-quality sequences, resulting in filtered and sorted BAM files. The final BAM files were then processed using GATK Haplotype Caller (v4.2.3.062) for mutation calling. SNP markers with a deletion rate greater than 20% and a minor allele frequency (MAF) less than 0.05% were removed, resulting in a total of 1,303,374 high-quality SNPs for the GWAS.

Genome-wide association study and haplotype analysis

Using the these high-quality SNPs, a genome-wide association study was conducted. After comparing multiple GWAS models, we selected the Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model in GAPIT3 for the final analysis due to its enhanced statistical power and computational speed. A significance threshold was set at a logarithm of the odds [LOD, -log10(P)] score of ≥ 5. GWAS results were visualized using the R package CMplot. To assess the significance of differences among alleles, a t-test was performed using the R packages broom and ggplot2, and the results were visualized using the online tool Chiplot (https://www.chiplot.online/).

Candidate gene analysis

Candidate genes were identified within a 20 kb range upstream and downstream of each significant SNP (total of 40 kb) based on the “Zhongmu-4” reference genome. The identified genes were then subjected to a Protein BLAST search using the National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/). Known genes related to leaf morphology and development were prioritized as potential candidates affecting leaf size.

Results

Phenotypic data analysis

Statistical analysis of the four traits showed that they had different degrees of phenotypic variation (Table 1). The range of LL was 0.470 cm (23LL) to 3.250 cm (24LL), LW ranged from 0.320 cm (23LW) to 1.770 cm (24LW), LA from 0.200 cm2 (23LA) to 3.910 cm² (24LA), and LP from 1.760 cm (23LP) to 8.830 cm (24LP). The coefficients of variation (CV, %) for these traits ranged from 3.43% (22LW) to 36.84% (24LA). The kurtosis and skewness values for all traits were between − 1 and 1. Additional statistics, such as the average, are presented in Table 1.

By comparing the differences between the phenotypes in the three years, it can be found that the four phenotypes in 2024 are significantly different from and better than the remaining two years (P < 0.001, Fig. 1). The phenotypes were stable between 2022 and 2023, and there was no significant difference. The mean value of each phenotype was significantly different from that of a single year (P < 0.01). Using three years of phenotypic data, the H2 of these traits was calculated. The H2 was 53.23% (LL), 42.52% (LW), 38.30% (LA) and 49.87% (LP), respectively. These results suggest that the variation in these four traits is influenced by both genetic and environmental factors in our experiment.

Table 1 Statistical analysis of phenotypic data of four traits
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Fig. 1
figure 1

The box plot showed the distribution and differences among the 4 phenotypes in 3 years. The horizontal coordinate is the phenotype under different years, and the vertical coordinate is the phenotype value. ** indicates P < 0.01; *** indicates P < 0.001

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Correlation analysis of phenotypes

The four phenotypes showed significant positive correlation at 3 years (P < 0.001, Fig. 2A-C). The Pearson correlation coefficients ranged from 0.66 to 0.98. The correlation between all phenotypes in a single year was similar, the positive correlation between LL and LW was the lowest, and the Pearson correlation coefficient between other traits was above 0.75. The mean values of the four traits at 3 years also showed a similar positive correlation with each year (P < 0.001), with Pearson correlation coefficient ranging from 0.71 to 0.96 (Fig. 2D).

Fig. 2
figure 2

Correlation analysis among the four leaf size-related phenotypes across different years. The numbers in the figure represent the Pearson correlation coefficients between the traits. The ovals illustrate the correlation, where red indicates a positive correlation and blue indicates a negative correlation. The intensity of the color reflects the strength of the correlation. **, P < 0.001

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Genome-wide association study

A total of 39 significant SNPs associated with these traits were identified by GWAS (Fig. 3; Table 2), additional details of significant SNPs are shown in Table S2. These SNPs were distributed across all eight chromosomes, with the highest number located on chromosome 3 (12 SNPs) and the lowest on chromosome 2 (1 SNP). Specifically, 9 SNPs were significantly associated with LL, 7 with LW, 15 with LA, and 8 with LP. Notably, the LOD scores for SNPs chr3__88815544 and chr6__124881480, which were associated with LP and LA respectively, exceeded 6, indicating a strong correlation with these traits. Furthermore, we observed a significant enrichment of SNPs around the 88.8 Mb region on chromosome 3 (Fig. 3A and D), suggesting this region may play a crucial role in the genetic regulation of leaf size, providing a basis for further exploration.

Table 2 Characteristics of significant SNPs associated with four leaf size-related traits in alfalfa
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Fig. 3
figure 3

Manhattan plots for the GWAS of four leaf size-related phenotypic traits. Panels A to D represent the Manhattan plots for the GWAS of LL, LW, LA, and LP, respectively. The legend indicates the density of SNPs at different chromosomal locations. The blue line represents the significance threshold

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Further analysis of the GWAS results identified a total of 9 SNPs that were associated with multiple traits (Fig. 4). Of these, seven SNPs were significantly associated with two different traits, while two SNPs were significantly associated with three traits. Specifically, SNP chr6__124881480 was significantly correlated with LL, LA, and LP, and chr3__57728314 was significantly correlated with LW, LA, and LP. The other SNPs: chr3__48598917, chr3__88812388, chr3__88815544, chr3__88816122, chr4__86792428, chr6__3139258, and chr7__75829112 were each significantly correlated with two traits. Notably, many of these SNPs, especially those associated with multiple traits, were located near the 88.8 Mb region of chromosome 3.

Fig. 4
figure 4

Quantitative analysis of SNPs significantly associated with multiple leaf size-related phenotypic traits

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Haplotype analysis

We conducted haplotype analysis on the two SNPs that were associated with three different traits. In each of the 176 materials, these two SNPs exhibited two different alleles. For chr6__124881480, the alleles were T/T and A/T, while for chr3__57728314, they were T/T and C/T. Further analysis revealed that a superior allele was present in each of the three phenotypes for these significant SNPs (Fig. 5). Specifically, the T/T allele in chr6__124881480 was significantly superior to A/T across all three phenotypes (P < 0.01). Similarly, the C/T allele in chr3__57728314 was significantly superior to T/T in all three phenotypes (P < 0.001).

Fig. 5
figure 5

Haplotype analysis of two significant SNPs. (A-C) Haplotype analysis of chr6__124881480 across three different phenotypes. (D-F) Haplotype analysis of chr3__57728314 across three different phenotypes. The x-axis represents different alleles, while the y-axis indicates the phenotypic values. ** indicates P < 0.01; *** indicates P < 0.001

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Given the presence of superior alleles, we conducted a more detailed haplotype analysis, focusing initially on LA, which is most closely related to leaf size. Using the 15 significant SNPs identified through GWAS, we performed haplotype analysis for each SNP individually. The analysis revealed that each significant SNP had a superior allele (Fig. S1). Further examination of the relationship between the number of superior alleles in each germplasm and LA showed that an increase in the number of superior alleles was associated with an improved phenotype (Fig. 6A). The correlation curve between the number of superior alleles and the phenotype had a fit of R² = 0.5211. Subsequently, we selected 12 Chinese-bred varieties from the population (as these varieties are more readily accessible in China) and evaluated their superior alleles (Fig. 6B). Among these 12 varieties, the frequencies of the 15 superior alleles ranged from 20 to 73.33%, with PI 502,646 containing a higher number of superior alleles. For the 15 superior alleles, their frequencies in the 12 varieties ranged from 8.33 to 83.33%. Notably, chr3__88812388 had only one superior allele among the 12 varieties, whereas chr6__124881480 had superior alleles in 10 of them. To increase the leaf size of alfalfa, marker-assisted selection breeding should prioritize significant SNPs with lower frequencies of superior alleles.

Fig. 6
figure 6

Superior allele analysis. (A) Correlation analysis between the number of superior alleles in each material and LA. The x-axis represents the number of superior alleles in each material, while the y-axis indicates the LA. (B) Utilization of superior alleles of significant SNPs in 12 Chinese-bred varieties. The x-axis represents significant SNPs, and the y-axis indicates the variety numbers. Blue grids denote the presence of superior alleles, while gray grids indicate their absence

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Candidate gene analysis

Using the reference genome and LD information from “Zhongmu-4”, we identified candidate genes within a 20 kb range upstream and downstream of each of the 39 significant SNPs. Based on gene annotation and previous reports on gene function, we screened 12 genes related to plant growth and development (Table 3), of which five were specifically associated with leaf size or leaf development. These genes include polygalacturonase (Msa.H.0080710), LRR receptor-like kinase (Msa.H.0244970), LRR receptor-like kinase family protein (Msa.H.0267380), PsbD (Msa.H.0361820), and RING-type E3 ubiquitin transferase (Msa.H.0476020). Notably, two candidate genes linked to plant development were located near the 88.8 Mb region of chromosome 3: Msa.H.0165590, annotated as protein FAR1-RELATED SEQUENCE, and Msa.H.0165630, annotated as PP2A regulatory subunit TAP46. Additionally, Msa.H.0361820 is located near chr6__124881480, which is significantly associated with the three leaf size traits.

Table 3 Candidate gene analysis
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Discussion

Leaf size is a key trait that affects light interception and photosynthetic efficiency, serving as an important index of crop yield [31]. However, for alfalfa, an important forage crop, the genetic basis of leaf size has yet to be thoroughly investigated. GWAS is considered a powerful strategy for exploring the genetic basis of complex traits at the genome-wide level [32, 33]. Therefore, in the present study, we conducted GWAS to analyze the phenotypes associated with leaf size, aiming to uncover the genetic mechanisms governing leaf size variation in alfalfa.

The analysis of four traits over a period of three years revealed that there were significant differences between the trait values in 2024 and those in the other two years. This finding fully confirmed the variability of quantitative traits across different environments [34]. In order to account for environmental variation in the GWAS results, we calculated the broad-sense heritability of the four traits, which ranged from 38.30 to 53.23%. These values were generally consistent with previous studies and aligned with expectations regarding the heritability of leaf size [35, 36]. This indicates that the environmental-induced variation in leaf size remained within a normal range and was manageable for GWAS analysis. Furthermore, the variation indicates substantial phenotypic diversity among the alfalfa varieties, which providing valuable genetic potential for breeding efforts aimed at optimizing leaf traits. To minimize inter-annual variation and ensure data stability, the three-year average was employed as the basis for phenotypic analysis. In addition, the correlation analysis revealed a significant positive correlation among the four traits, which accounts for why GWAS was able to detect the same SNP across different traits.

For the GWAS of the four traits, we utilized the BLINK model, an enhanced version of FarmCPU, which offers improve statistical power and computational efficiency [37]. A total of 39 significant SNPs were identified across eight chromosomes, with nine SNPs associated with multiple traits. However, comparisons with previous GWAS and QTL studies on leaf size-related traits revealed no consistent loci [25, 38]. One possible reason for this could be that our study only identified variants within the association population. These markers are independently related to their respective traits and might be governed by distinct genetic structures and molecular mechanisms. Notably, SNPs located near the 88.8 Mb region of chromosome 3 showed significant associations with phenotypes, suggesting potential candidate loci for future studies. The findings provide new molecular markers that could potentially be utilized in marker-assisted breeding programs.

Through haplotype analysis of the significant SNPs, we observed that different alleles were associated with distinct phenotypes. Additionally, the number of superior alleles present in each material was positively correlated with phenotypic performance, further validating the robustness of our GWAS results. Haplotype breeding was carried out in pigeonpea (Cajanus cajan L.) to enhance drought tolerance by employing superior haplotypes [39]. Moreover, Haplotype assembly, recognized as a promising approach for crop improvement in the post-sequencing era, is already being utilized in various crops [40,41,42].

Among the 12 genes identified as related to plant growth and development in this study, five were specifically linked to leaf size or leaf development. The gene Msa.H.0080710 is annotated as polygalacturonase. The PGX2 gene which encodes polygalacturonase was identified and it was found that its activation led to longer hypocotyls and larger rosette leaves suggesting a role in cell expansion [43]. Msa.H.0244970 and Msa.H.0267380 are annotated as LRR receptor-like kinases (LRR-RLK), which are the largest subgroup of receptor-like kinases in plants. These genes regulate various processes, including morphogenesis, disease resistance, and stress responses [44]. Notably, BRI1, a member of the LRR-RLK family, has been shown to increase leaf size and accelerate flowering [45]. The gene Msa.H.0361820, annotated as PsbD, plays a critical role in wheat leaf development [46]. Msa.H.0476020 encodes a RING-type E3 ubiquitin ligase, which is involved in plant growth, development, and defense responses [47]. It was identified that TIE1-ASSOCIATED RING-TYPE E3 LIGASE1 (TEAR1) functions as a regulator of leaf development. For instance, TEAR1 promotes the degradation of TIE1, a repressor of CIN-like TCP transcription factors, which are critical for leaf development [48]. In addition to these key genes, other candidate genes identified in this study were found to participate in various stages of plant growth and development. These genes may provide valuable insights for regulating alfalfa leaf size and optimizing its genetic potential.

Conclusion

In summary, this study identified 39 significant SNPs associated with leaf size traits in 176 alfalfa germplasm resources through a GWAS. The region near 88.8 Mb on chromosome 3, as well as two significant SNPs (chr6__124881480 and chr3__57728314), were found to be particularly noteworthy. We also screened five candidate genes related to leaf size near these significant SNPs. The findings of this study aim to provide molecular markers for GWAS and support molecular breeding efforts to optimize leaf size and improve alfalfa productivity.

Data availability

All raw sequence data were upload to the National Genomics Data Center (NGDC, https://bigd.big.ac.cn/) under BioProject PRJCA018485 and NCBI Sequence Read Archive (SRA) under Bioproject PRJNA995892.

Abbreviations

BLINK:

bayesian-information and linkage-disequilibrium iteratively nested keyway

CV:

coefficient of variation

GAs:

gibberellins

GWAS:

genome-wide association study

H2 :

broad sense heritability

LA:

leaf area

LD:

linkage disequilibrium

LL:

leaf length

LOD:

logarithm of the odds

LP:

leaf perimeter

LW:

leaf width

MAF:

minor allele frequency

MAS:

marker assisted selection

pH:

potential of hydrogen

SNP:

single nucleotide polymorphism

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Acknowledgements

We thank all members of the Forage Breeding and Cultivation Technology Innovation Team for their help and support with field material planting and management.

Funding

This work was supported by the Ordos Science and Technology Plan (2022EEDSKJZDZX011), the major demonstration project of “The Open Competition” for Seed Industry Science and Technology Innovation in Inner Mongolia (No. 2022JBGS0016), the Key Projects in Science and Technology of Inner Mongolia (2021ZD0031) and the Project of Science and Technology Innovation 2030 (2023ZD04060).

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Author notes

  1. Ming Xu and Yanchao Xu contributed equally to this work.

Authors and Affiliations

  1. Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China

    Ming Xu, Yanchao Xu, Hao Liu, Qingchuan Yang, Ruicai Long, Lin Chen & Fei He

  2. Cangzhou Academy of Agriculture and Forestry Sciences, Cangzhou, 061001, China

    Qingsong Liu

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  1. Ming Xu
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Contributions

L.C. and F.H. conceived and designed the experiments. M.X. and Y.X. analyzed the data and wrote the main manuscript text. M.X., Y.X., H.L. and Q.L. collected phenotypic data. Q.Y. and R.L. revised and edited the manuscript. All authors contributed to the article and approved the submitted version.

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Correspondence to Lin Chen or Fei He.

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Xu, M., Xu, Y., Liu, H. et al. Genome-wide association study revealed candidate genes associated with leaf size in alfalfa (Medicago sativa L.). BMC Plant Biol 25, 180 (2025). https://doi.org/10.1186/s12870-025-06170-0

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BMC Plant Biology

ISSN: 1471-2229

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