Skip to main content

Analyzing genetic diversity in luffa and developing a Fusarium wilt-susceptible linked SNP marker through a single plant genome-wide association (sp-GWAS) study



Luffa (Luffa spp.) is an economically important crop of the Cucurbitaceae family, commonly known as sponge gourd or vegetable gourd. It is an annual cross-pollinated crop primarily found in the subtropical and tropical regions of Asia, Australia, Africa, and the Americas. Luffa serves not only as a vegetable but also exhibits medicinal properties, including anti-inflammatory, antidiabetic, and anticancer effects. Moreover, the fiber derived from luffa finds extensive applications in various fields such as biotechnology and construction. However, luffa Fusarium wilt poses a severe threat to its production, and existing control methods have proven ineffective in terms of cost-effectiveness and environmental considerations. Therefore, there is an urgent need to develop luffa varieties resistant to Fusarium wilt. Single-plant GWAS (sp-GWAS) has been demonstrated as a promising tool for the rapid and efficient identification of quantitative trait loci (QTLs) associated with target traits, as well as closely linked molecular markers.


In this study, a collection of 97 individuals from 73 luffa accessions including two major luffa species underwent single-plant GWAS to investigate luffa Fusarium wilt resistance. Utilizing the double digest restriction site associated DNA (ddRAD) method, a total of 8,919 high-quality single nucleotide polymorphisms (SNPs) were identified. The analysis revealed the potential for Fusarium wilt resistance in accessions from both luffa species. There are 6 QTLs identified from 3 traits, including the area under the disease progress curve (AUDPC), a putative disease-resistant QTL, was identified on the second chromosome of luffa. Within the region of linkage disequilibrium, a candidate gene homologous to LOC111009722, which encodes peroxidase 40 and is associated with disease resistance in Cucumis melo, was identified. Furthermore, to validate the applicability of the marker associated with resistance from sp-GWAS, an additional set of 21 individual luffa plants were tested, exhibiting 93.75% accuracy in detecting susceptible of luffa species L. aegyptiaca Mill.


In summary, these findings give a hint of genome position that may contribute to luffa wild resistance to Fusarium and can be utilized in the future luffa wilt resistant breeding programs aimed at developing wilt-resistant varieties by using the susceptible-linked SNP marker.

Peer Review reports


Luffa (Luffa spp.), also known as sponge gourd or vegetable gourd, is an annual cross-pollinated economic crop belonging to the Luffa genus within the Cucurbitaceae family [1]. Within this genus, there are five to eight species; however, only L. acutangula Roxb. and L. aegyptiaca Mill. have been domesticated for different applications, and their genome information has been revealed. Both of these species possess diploid genomes with 13 pairs of chromosomes (2n = 26), encoding approximately 25,508 proteins. The genome size is 734.6 Mb for L. acutangular and 656.19 Mb for L. aegyptiaca [2, 3].

Luffa has found wide-ranging applications, not only for food consumption with fresh fruit, but also for its dried fruit and fiber, which offer attributes like high strength, lightweight, low cost and sustainable utilization. These properties make them excellent materials for various fields including biotechnology [4], construction [5] and environmental engineering [6]. Additionally, the extracts derived from Luffa have demonstrated medicinal potential in addressing diabetes [7], inhibit inflammation [8], treating cancer [9], combating bacteria [10], and fighting fungi [11]. Overall, Luffa shows great potential to improve various aspects of our lives [12]. However, luffa production faces substantial losses due to luffa Fusarium wilt, a severe disease caused by the fungus Fusarium oxysporum (Fo). This resilient fungus can persist in the soil for an extended period, and infected plants display symptoms such as slow growth, yellowing of lower leaf, and browning of vascular bundles, ultimately leading to reduced yield and plant fatalities [13]. While several control methods are currently available, many of them are unsuitable due to cost, effectiveness, and environmental concerns. Therefore, there is an urgent imperative to breed Fusarium wilt-resistant luffa varieties for future commercial luffa production.

Genome-wide association study (GWAS) stands as a powerful and efficient tool for unraveling the intricate relationships between diverse genotypes and phenotypes. It allows for the discovery of quantitative trait loci (QTLs) and the identification of candidate genes that influence phenotypic traits. Besides, GWAS aids in the development of trait-linked markers for marker assisted selection (MAS) during the breeding process [14]. GWAS has successfully unveiled numerous disease-resistant QTLs in various crops. For instance, in maize, 18 novel head smut resistance candidate genes were identified [15], while in barley, 9 QTLs related to spot blotch resistance were revealed [16]. In soybean, a total of 27 QTLs associated with resistance to white mold were discovered [17]. Moreover, GWAS has been applied in wheat to discover novel QTLs resistant to stripe rust [18]. Furthermore, GWAS has proven to be a valuable tool in uncovering QTLs and candidate genes for Fusarium resistance in crops such as wheat [19,20,21], maize [22,23,24], rye [25], barley [26], oat [27], radishes [28], strawberry [29], and cotton [30, 31] in recent studies. However, GWAS typically requires a panel of inbred lines with multiple individuals for phenotypic analysis in crops, which necessitates time for population development. Single-plant GWAS (sp-GWAS) has been successfully applied in maize [32], wheat [33], lentil [34], and pea [35], demonstrating its ability to identify candidate genes associated with phenotype variation by performing GWAS on individual plants. This approach reduces the time and resources required to complete the GWAS [32].

In this study, we analyzed the phenotypes of 97 individuals representing 73 luffa accessions encompassing two major luffa species, L. acutangula (L) Roxb. and L. aegyptiaca Mill, in order to conduct a sp-GWAS aimed at identifying markers and candidate genes associated with three traits, including the Fusarium wilt-resistance trait. Our investigation led to the discovery of a QTL on chromosome 2, containing a candidate gene that may encode a homologue of peroxidase 40, which plays a role as disease resistance gene in Cucumis melo. Furthermore, we developed an associated SNP marker and validated its accuracy in detecting susceptible luffa plants.


Distribution patterns of material resources, phenotypes, and correlation analysis among 73 luffa accessions

A total of 97 individuals were gathered from 73 luffa accessions. Approximately 65% of the materials were attributed to L. aegyptiaca Mill, while 34% belonged to L. acutangula (L) Roxb. These luffa materials were sourced from 11 countries, including the Philippines (34 individuals, 35%), Laos (12 individuals, 12%), Thailand (12 individuals, 12%), Taiwan (10 individuals, 10%), Vietnam (10 individuals, 10%), Malaysia (9 individuals, 9%), Indonesia (4 individuals, 4%), Bangladesh (3 individuals, 3%). Furthermore, only 1 individual each was collected from Cambodia, Uzbekistan and USA (1% of each) (Table S1).

In addition, this research documented a total of 5 distinct quantitative phenotypes and one qualitative trait across the 73 luffa accessions. The distribution and qqplot for the normal distribution test of quantitative phenotypes were presented in Fig. 1. Among these phenotypes, only hypocotyl length closely approximated a normal distribution, while the other four traits exhibited imperfect fits.

Fig. 1
figure 1

Diverse phenotype distributions were observed among the 97 luffa individuals. The distribution of distinct phenotypes was recorded for various traits including (a) hypocotyl length; (b) hypocotyl width; (c) plant height; (d) stem width; and (e) AUDPC. The smooth density curve is represented by the red line, while the qqplot located in the upper right corner of each sub-figure illustrates the univariate normality of specific phenotypes

Correlation analysis was carried out among all traits and is illustrated in Fig. 2. The results revealed significant but relatively low correlations. A negative correlation was observed between the trait AUDPC and hypocotyl width (R2 = -0.2, p-value = 0.048), while positive correlations were observed between hypocotyl length and plant height (R2 = 0.24, p-value = 0.0187), plant height and hypocotyl width (R2 = 0.23, p-value = 0.0213), as well as hypocotyl width and stem width (R2 = 0.23, p-value = 0.0231).

Fig. 2
figure 2

The correlation among all recorded phenotypes. The correlation among all phenotypes was estimated individually, displaying the R2 value in different color. Red indicates a negative correlation, while bule represents a positive correlation. The p-value for each relationship is indicated by a number at the button of each correlation square

PCA analysis and population structure detection using STRUCTURE after SNP calling

The acquisition of a total of 8,919 high-quality genome-wide SNPs were accomplished through ddRAD library sequencing, achieving a 12% breadth of coverage, and we displayed the distribution of SNPs was showed in Fig. 3. The distribution of SNPs on each chromosome showed that chromosome 4 contains the highest number of 766 SNPs, while chromosome 13 showed the least 610 SNPs. Besides, the average distance between one SNP was longest on chromosome 3 at 85.2 Kb and the shortest on chromosome 1 at 57.22 Kb, with an overall average of 75.09 Kb (Table 1).

Fig. 3
figure 3

Distribution of high-quality SNPs applied in this study. The distribution of 8,819 high-quality SNPs across 13 chromosomes of luffa is illustrated. The color represents the quantity of SNPs within specific locations on the chromosome

Table 1 Single nucleotide polymorphism (SNP) marker distribution among luffa chromosomes

The subsequent PCA analysis was executed using the complete genotypic dataset comprising 97 luffa individuals, and the results are visualized in Fig. 4. To achieve a comprehensive understanding of these accessions, the analysis was further explored in relation to luffa species (Fig. 4A), source countries (Fig. 4B) and resistance to Fusarium oxysporum (Fig. 4C) after the PCA analysis. The PCA analysis revealed three distinct groups, which predominantly corresponded to their respective luffa species (Fig. 4A). It’s worth noting that the species L. acutangular (L.) ROXB was predominantly associated with accessions from Taiwan (Fig. 4B). Moreover, a pivotal discovery was made, indicating that accessions belonging to both L. acutangular (L.) ROXB. and L. aegyptiaca Mill demonstrated resistance to Fusarium after inoculation (Fig. 4C). For a more detail perspective, among the individuals of L. aegyptiaca Mill, 9 (14.3%) demonstrated resistance to Fusarium wilt 21 days after inoculation, while in L. acutangula (L.) ROXB., 5 (15.2%) samples exhibited resistance. Additionally, out of the 9 luffa materials from Malaysia, 4 exhibited the resistance to Fusarium wilt, representing the highest ratio (44.4%) of resistant individuals, whereas samples from Laos did not display any resistance among the 12 individuals (0%) (Table S1).

Fig. 4
figure 4

The PCA analysis among 97 luffa individuals based on high-quality SNPs. PCA analysis was conducted on the group of 97 luffa individuals. To delve into the specifics of these accessions, the analysis was further explored with respect to (a) luffa species; (b) source countries; and (c) resistance to Fusarium oxysporum

These findings suggest that the collected luffa accessions exhibit significant genetic diversity, with a specific focus on the trait of resistance to Fusarium invasion. Furthermore, the population structure estimation encompassed 97 individuals, revealing a robust population stratification, as illustrated in Fig. 5, and supporting the choice of K = 2 as the basis for selection.

Fig. 5
figure 5

Population structure obtained by Structure software from 97 luffa individuals for different values of clusters (K). Population structure was conducted among 97 luffa individuals including 73 accessions by structure software with distinct clusters: (a) K = 2; (b) K = 3; and (c) K = 4. Each individual is represented by a vertical bar

GWAS results and identifying candidate genes

Five different traits, including AUDPC, hypocotyl length, hypocotyl width, plant height, and stem width, were utilized as phenotype data in GWAS, employing a general linear model (GLM) augmented by the incorporation of five PCAs to account for population structure within the collected luffa individuals. The correction level was set at p-value = 0.0001 as the criteria for identifying trait-associated QTLs. Notably, six QTLs potentially contributing to hypocotyl width (2 QTLs), plant height (3 QTLs), and AUDPC (1 QTL) were revealed and illustrated in Fig. 6. It’s evident that most of these QTLs exhibit discernible differences in the distribution of phenotypes according to SNP types, as illustrated in Fig. 7a, b, d to f. Nonetheless, it is noteworthy that one SNP (S2_41801130) associated with plant height did not exhibit such differentiation, as depicted in Fig. 7c.

Fig. 6
figure 6

The Manhattan plot of three luffa traits. Following the sp-GWAS analysis, a Manhattan plot was conducted for various luffa traits: (a) Hypocotyl width; (b) plant height; (c) AUDPC. In each sub-figure, the significant trait-associated SNPs were depicted, and a qq-plot of each analysis was presented in the upper left corner

Fig. 7
figure 7

Comparing recorded phenotypes based on different SNP types within trait-associated SNPs. To comprehend the distinctions attributed to different SNP types within trait-associated SNPs identified after sp-GWAS, the phenotypes of various traits were segregated. This separation aimed to uncover the impact of distinct trait-associated SNP types. Specifically: (a) hypocotyl width associated SNP S7_37420959; (b) hypocotyl width associated SNP S7_39301553; (c) plant height associated SNP S2_41801130; (d) plant height associated SNP S11_48053184; (e) plant height associated SNP S13_6069200; (f) AUDPC associated SNP S2_11917148. The p-value of each comparison is provided above the brackets, while the number of individuals associated with each SNP is indicated under different SNP types

The identification of candidate genes within each QTL was pursued within a genomic region of 10 K upstream and downstream of the trait-associated SNPs. Putative genes with annotated functions were listed and summarized in Table 2. Firstly, genes with homology to LOC127143780 and LOC111469808, which encode a SNF-1 related protein kinase regulatory subunit gamma-1-like and an F-box protein, respectively, were linked to the regulation of hypocotyl width. Additionally, genes presumed to influence plant height were discovered, with homologous to LOC111459237, LOC111809662, and LOC111009991, encoding monocopper oxidase-like protein SKU5, cold shock domain-containing protein 3-like, and U-box domain-containing protein 44-like, respectively. Furthermore, the gene homologous to LOC111009722, which encodes peroxidase 40, exhibited a notable association with the AUDPC trait.

Table 2 The trait-associated SNPs and candidate genes for different phenotypes based on GWAS analysis

Validation of AUDPC-associated SNP marker

A SNP marker potentially associated with AUDPC was developed using the position of AUDPC-associated SNP (Table S2). To determine its effectiveness in discerning susceptibility or resistance to Fusarium wilt in luffa plants, a total of 21 L. aegyptiaca Mill individuals were employed for the validation program. Phenotype assessment occurred 21 days after inoculation, with phenotype classification based on disease rank: rank 0–1 were classified as resistant, and ranks exceeding 2 were classified as susceptible. Among the 21 individuals, 16 samples exhibited a susceptible phenotype, while 5 individuals displayed a resistant phenotype after Fusarium inoculation. When subjecting the specimens to PCR for SNP typing, an accuracy of approximately 76.19% was observed when considering both susceptible and resistant phenotypes. Notably, a high accuracy (93.75) was evident in predicting susceptibility compared to resistance (Table 3).

Table 3 The accuracy of AUDPC-associated marker evaluated among 21 individuals of Luffa aegyptiaca Mill


Enhanced resistance to Fusarium wilt in robust luffa plants

The hypocotyl, as the subsequent organ emerging from the seed, facilitates increased exposure of the seedling to sunlight for photosynthesis [36]. In our study, we identified three notable yet moderately positive correlations: between hypocotyl length and plant height, between hypocotyl width and plant height, and between hypocotyl width and stem width. These findings imply that a more robust hypocotyl may result in a healthier plant. This observation aligns with the concept that hypocotyl attributes, including both length and width, play a crucial role in seedling emergence [36]. Moreover, a negative correlation was observed between AUDPC and hypocotyl width, emphasizing that a sturdy luffa plant could mitigate the impact of Fusarium invasion.

Variation in Fusarium wilt resistance across different countries in both luffa species

Resistance to Fusarium wilt was identified in both luffa species examined in this study. Specifically, a resistance rate of 14.3% was observed in L. aegyptiaca Mill, while L. acutangular (L.) ROXB. demonstrated a resistance rate of 15.2%. This discovery suggests that individuals displaying resistance within each species could potentially serve as valuable genetic material for future luffa Fusarium-resistant breeding programs.

It’s essential to emphasize that luffa samples from Malaysia exhibited the highest ratio of plants resistant to Fusarium wilt, while none of the sample from Laos showed resistance. Beyond the potential influence of our sample collection methods and the specific Fusarium species or isolates used for inoculation, which may impact luffa resistance [37], another plausible explanation for this difference could be the influence of the plants’ geographic origins. The country where luffa plants originated might significantly affect their ability to resist Fusarium wilt due to geographical isolation. This finding aligns with previous research indicating significant variations in luffa’s resistance to Fusarium wilt across different countries. For instance, there were no instances of Fusarium wilt-resistant luffa in Cambodia and Uzbekistan [38].

All these findings suggest that inclusion of hypocotyl traits and consideration of the geographical origin of the material could be promising avenues for selecting better breeding material in future luffa Fusarium-resistant breeding programs.

Candidate genes within QTLs for different traits in Luffa

In the current study, a total of 6 QTLs were identified across 3 distinct traits, specifically hypocotyl width, plant height, and AUDPC. Initially, two homologous genes, namely LOC127143780 and LOC111469808, were detected in relation to the hypocotyl width-associated SNPs S7_37420959 and S7_39301553. The former gene encodes a SNF1-related protein kinase regulatory subunit gamma1-like, which plays a role in carbohydrate metabolism and ABA signal transduction in plants [39]. The latter homologous gene is responsible for encoding F-box proteins, constituting one of the largest protein families discovered in plants, with a diverse range of functions and broad coverage. F-box proteins are integral to various plant responses, including stress adaptation, development, regulation of plant hormones, and the biosynthesis of secondary metabolites [40]. Additionally, F-box proteins have been proven to correlate with hypocotyl elongation in Arabidopsis as well [41].

Three QTLs associated with plant height were identified, positioned on chromosome 2, 11, and 13, corresponding to SNP S2_41801130, S11_48053184, and S13_6069200, respectively. These SNPs were used to identify the associated candidate genes, which were determined as LOC111459237, LOC111809662, and LOC111009991 based on sequence homology analysis. The LOC111459237 gene encodes the monocopper oxidate-like protein SKU5, actively involved in oxidative-reductive reactions within organisms, Notably, SKU5 gene also plays a role in facilitating cell wall elongation [42, 43]. Additionally, intriguing negative correlations between SKU5 expression and both plant height and leaf area were discovered in alfalfa (Medicago sativa L.) [44]. The LOC111809662 gene is responsible for encoding the cold shock domain-containing protein 3-like, which plays a role in plant tolerance to low-temperature stress. Furthermore, it is abundantly expressed in meristematic and developmental tissues, indicating its potential involvement in plant growth and development [45]. Lastly, the final candidate gene, LOC111009991, encodes a protein housing the U-box domain, which is involved in ubiquitination processes within plants and may play an important role in the plant disease resistance network. Notably, proteins featuring the U-box domain are responsible for the degradation of misfolded proteins [46]. Moreover, the role of U-box domain-containing proteins extends to the regulation of cell proliferation and division in the Arabidopsis root meristem [47], as well as being linked to processes like cell elongation, which exert influences on diverse phenotypes, including plant height in rice [48].

The candidate gene within the AUDPC-associated QTL region is LOC111009722, which encodes peroxidase, an enzyme known for its metabolic activity against reactive oxygen species (ROS). This enzymatic role enhances plant tolerance to both biotic and abiotic stresses. Additionally, peroxidase actively participates in various physiological responses within the plant [49]. Evidence substantiate that genes encoding peroxidase play a crucial role in plant’s response to pathogen. For instance, distinct expression levels of various peroxidase isozymes can influence resistance to downy mildew disease in pearl millet [50]. Furthermore, disparities in peroxidase synthesis timing between Malvaceae species exhibiting resistance and susceptibility have been correlated with distinct responses to the pathogen Verticillium dahlia [51]. Moreover, the overexpression of rice cationic peroxidase in carrots has led to heightened resistance against necrotrophic foliar pathogen [52]. These examples collectively suggest that the candidate genes identified may contribute to the specific phenotype under consideration. Nevertheless, further analysis, including complete gene sequencing and qRT-PCR of candidate genes, is indispensable to definitively ascertain the authentic gene function and to create a functional GMO in a model plant in the future.

Validation of the AUDPC-associated SNP marker

Once QTLs are identified, the associated markers for specific traits are expected to be consistently applied for marker assisted selection (MAS) in the next step. Therefore, the accuracy of these linked markers in relation to the target trait becomes very important and necessitates assessment before their practical application. In this investigation, an AUPDC-linked SNP marker was developed and subsequently validated using a set of 21 individuals from the L. aegyptiaca Mill species. The results revealed a precision of 93.75% in detecting susceptible individuals while achieving an overall accuracy of 76.19%. However, the accuracy for identifying resistant individuals was only 20%. This outcome demonstrated robust discriminatory capability exclusively for a singular phenotype category. Similar findings have been observed in wheat, wherein linked markers to the Lr19 gene showed polymorphism solely among susceptible cultivars [53]. Another illustrative example is the SNP marker linked to HsBvm-1 in sugar beet, which exclusively manifests in tolerant varieties and distinguishes among a singular phenotype [54]. Moreover, it is plausible that recombination events between the linkage marker and the authentic trait-controlling gene contribute to the observed lack of absolute accuracy in the present investigation [55].


In this investigation, we have confirmed the presence of Fusarium wilt-resistant resources within two distinct Luffa species. Furthermore, we successfully identified a total of six QTLs associated with three distinct traits, and within the genomic regions of these QTLs, candidate genes were predicted. Notably, we developed and validated an SNP associated with AUDPC, indicating resistance to Fusarium wilt. These analyses displayed notably high accuracy in identifying susceptible individuals of the L. aegyptiaca Mill species. The acquisition of such valuable insights holds promising implications for the future cultivation of robust luffa cultivars that can effectively combat Fusarium wilt.

Materials and methods

Plant materials and leaf DNA extraction from Luffa accessions

In this study, we obtained two luffa species: L. aegyptiaca Mill and L. acutangula (L) Roxb., a total of 97 individuals, comprising 73 accessions, were acquired from the World Vegetable Center (93 individuals) and supported by KNOWN-YOU Seed Co. LTD. as a gift (4 individuals) (Table S1). Luffa plants were cultivated in the green house at NCHU (24°07′12.4"N, 120°40′32.9"E) in the year 2022. The DNA of each individual was extracted from young leaves using a modified cetyltrimethylammonium bromide (CTAB) method based on Doyle and Doyle (1987) [56].

Evaluation of wilt symptoms after Fusarium inoculation and recording other phenotypes in luffa

A total of 97 individuals, including 73 accessions, were subjected to the evaluation of wilt symptoms, supported by Dr. Chung’s lab at NCHU. Fusarium oxysporum f. sp. luffae: (FOSULT) was applied approximately 4 weeks after germination, when 1–2 true leaves have developed. For inoculation, the luffa plant were removed from 2.5-inch growth pods and their roots were cleaned with water. Subsequently, the root were cut to retain only one-third of their original length. The cut roots were then soaked in a buffer containing Fusarium spore suspension (2.5 × 106 spores/mL) for 30 min before being replanted in pots with clean soil mixture (peat: vermiculite = 3:1) for cultivation. Symptom evaluation was conducted 7, 14, and 21 days after inoculation, and disease severity was classified on a scale from 0 to 5 [57] (where level 0 indicates no symptom, level 1 represents yellowing or wilting of cotyledons; level 2 indicates yellowing or wilting of the first true leaf; level 3 indicates the yellowing or wilting of two true leaves; level 4 indicates yellowing or wilting of at least half of plant leaf; and level 5 indicates plant death). After recording disease severity at 21 days post- inoculation, the resistance status of each individual was determined based on their disease rank: individuals with ranks ranging from 0 to 1 were classified as resistant, while those with ranks exceeding 2 were classified as susceptible. In addition, the area under the disease progress curve (AUDPC) was estimated for each individual using the following formula, where yi represents the severity of the disease level at the i-th observation, xi corresponds to the day of the i-th observation, and n indicates the total count of observations [58].


There are four traits estimated other than the AUDPC on 21 days, which including hypocotyl length, hypocotyl width, plant height, and stem width. The “mice” package [59] was applied to address missing value of these four traits through multiple imputations within the R platform.

Genotyping and SNP calling in luffa

In accordance to the experimental protocols outlined by Peterson et al. (2012) [60] and Shirasawa et al. (2016) [61] for ddRAD-seq, genomic DNA underwent digestion using two restriction enzymes, MspI (NEB) and PstI-HF (NEB). The resulting digested DNA was then ligated with adapters, subjected to size selection, and amplified through PCR to create the DNA library. Quality assurance was conducted through agarose gel electrophoresis before sequencing the library, which was performed using the HiSeq X Ten platform at GENOMICS Co. in Taiwan. The sequenced data were aligned to the reference sequence of L. aegyptiaca (ASM1713956v1) using the "mem" command in the BWA software. Subsequent analyses were carried out using GATK software (McKenna et al., 2010) [62]. To ensure the quality of SNP discovery, SNPs were filtered based on quality score > 20, a missing rate < 0.05, and a minor allele frequency > 0.05. In total, 8,919 SNPs were obtained after applying these criteria.

Population structure analysis

For population structure analysis, we utilized Structure 2.3.4 software [63]. The burn-in time and Markov Chain Monte Carlo (MCMC) parameters were both set to 1,000 and 10,000, respectively. We explored a range of K values from 2 to 8 and conducted three calculations to assess population clusters. To determine the optimal number of clusters (K), we analyzed the output results using Structure Harvester and calculated Delta K. In this analysis, we employed a total of 8,919 SNPs.

PCA analysis and GWAS study

A total of 8,919 filtered SNPs were utilized to conduct Principal Component Analysis (PCA) and Genome-Wide Association Study (GWAS). PCA was performed using the R program platform [64], and the first five principal components were chosen as covariates for the subsequent GWAS analysis. The GWAS was carried out using the general linear model (GLM) in TASSEL 5 [65] to investigate potential genetic associations with the target traits. To effectively visualize and interpret the results, we employed the qqman package [66] in R to generate Manhattan plots and boxplots, enabling us to explore the distribution and significance of genetic variants across the genome.

Development and validation of AUDPC-associated SNP marker

An AUDPC-associated SNP was identified, and a SNP marker was developed based on the flanking region sequence. If the primer's end can complement the SNP, PCR amplification can be employed to confirm the presence of the SNP type at that specific site. To validate the accuracy of this AUDPC-associated SNP marker, another population consisting of 21 luffa individuals from L. aegyptiaca Mill was selected. Expected genotypes were estimated based on the phenotypes observed 21 days after Fusarium inoculation, and observed genotypes were obtained through SNP detection using PCR. The accuracy of the marker was assessed by calculating the ratio of observed genotypes with specific phenotypes to expected genotypes with specific phenotypes.

Availability of data and materials

All datasets used and/or analyzed during the current study are available in the NCBI database with accession number PRJNA1009245.



Genome analysis toolkit


General linear model


Genome-wide association study


Markov Chain Monte Carlo


Principal component analysis


Quantitative real-time reverse transcription-polymerase chain reaction


Single plant GWAS


  1. Filipowicz N, Schaefer H, Renner SS. Revisiting Luffa (Cucurbitaceae) 25 years after C. Heiser: species boundaries and application of names tested with plastid and nuclear DNA sequences. Systematic Botany. 2014;39(1):205–15.

    Article  Google Scholar 

  2. Pootakham W, Sonthirod C, Naktang C, Nawae W, Yoocha T, Kongkachana W, Sangsrakru D, Jomchai N, U-thoomporn S, Sheedy JR. De novo assemblies of Luffa acutangula and Luffa cylindrica genomes reveal an expansion associated with substantial accumulation of transposable elements. Mol Ecol Resour. 2021;21(1):212–25.

    Article  CAS  PubMed  Google Scholar 

  3. Wu H, Zhao G, Gong H, Li J, Luo C, He X, Luo S, Zheng X, Liu X, Guo J, et al. A high-quality sponge gourd (Luffa cylindrica) genome. Hortic Res. 2020;7(1):128–128.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Adetoyese A, Aransiola E, Ademakinwa N, Bada B, Agboola F. Optimization study of bioethanol production from sponge gourd (Luffa cylindrica). Scientific African. 2020;8:e00407.

    Article  Google Scholar 

  5. Akinyemi BA, Dai C. Luffa cylindrical fibre as a natural reinforcement for cement composites: A review. Journal of Sustainable Cement-Based Materials. 2022;11(5):297–307.

    Article  CAS  Google Scholar 

  6. Anastopoulos I, Pashalidis I. Environmental applications of Luffa cylindrica-based adsorbents. J Mol Liq. 2020;319:114127.

    Article  CAS  Google Scholar 

  7. Arise RO, Idi JJ, Mic-Braimoh IM, Korode E, Ahmed RN, Osemwegie O. In vitro Angiotesin-1-converting enzyme, α-amylase and α-glucosidase inhibitory and antioxidant activities of Luffa cylindrical (L.) M. Roem seed protein hydrolysate. Heliyon. 2019;5(5):e01634.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Han Y, Zhang X, Qi R, Li X, Gao Y, Zou Z, Cai R, Qi Y. Lucyoside B, a triterpenoid saponin from Luffa cylindrica, inhibits the production of inflammatory mediators via both nuclear factor-κB and activator protein-1 pathways in activated macrophages. J Funct Foods. 2020;69:103941.

    Article  CAS  Google Scholar 

  9. Abdel-Salam IM, Awadein NE-S, Ashour M. Cytotoxicity of Luffa cylindrica (L.) M. Roem. extract against circulating cancer stem cells in hepatocellular carcinoma. Journal of Ethnopharmacology. 2019;229:89–96.

    Article  CAS  PubMed  Google Scholar 

  10. Scalia RA, Dolci JEL, Ueda SMY, Sassagawa SM. In vitro antimicrobial activity of Luffa operculata. Braz J Otorhinolaryngol. 2015;81:422–30.

    Article  PubMed  Google Scholar 

  11. Parkash A, Ng T, Tso W. Isolation and characterization of luffacylin, a ribosome inactivating peptide with anti-fungal activity from sponge gourd (Luffa cylindrica) seeds. Peptides. 2002;23(6):1019–24.

    Article  CAS  PubMed  Google Scholar 

  12. Srinivas C, Devi DN, Murthy KN, Mohan CD, Lakshmeesha T, Singh B, Kalagatur NK, Niranjana S, Hashem A, Alqarawi AA. Fusarium oxysporum f. sp. lycopersici causal agent of vascular wilt disease of tomato: Biology to diversity–A review. Saudi J Biol Sciences. 2019;26(7):1315–24.

    Article  CAS  Google Scholar 

  13. Namisy A, Huang JH, Rakha M, Hong CF, Chung WH. Resistance to Fusarium oxysporum f. sp. luffae in Luffa Germplasm Despite Hypocotyl Colonization. Plant Dis. 2022;107:1993–2001.

    Article  Google Scholar 

  14. He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci. 2014;5:484.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang M, Yan J, Zhao J, Song W, Zhang X, Xiao Y, Zheng Y. Genome-wide association study (GWAS) of resistance to head smut in maize. Plant Sci. 2012;196:125–31.

    Article  CAS  PubMed  Google Scholar 

  16. Gyawali S, Chao S, Vaish SS, Singh SP, Rehman S, Vishwakarma SR, Verma RPS. Genome wide association studies (GWAS) of spot blotch resistance at the seedling and the adult plant stages in a collection of spring barley. Mol Breeding. 2018;38:1–14.

    Article  CAS  Google Scholar 

  17. Wen Z, Tan R, Zhang S, Collins PJ, Yuan J, Du W, Gu C, Ou S, Song Q, An YQC. Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean. Plant Biotechnol J. 2018;16(11):1825–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zegeye H, Rasheed A, Makdis F, Badebo A, Ogbonnaya FC. Genome-wide association mapping for seedling and adult plant resistance to stripe rust in synthetic hexaploid wheat. PLoS ONE. 2014;9(8): e105593.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhu Z, Chen L, Zhang W, Yang L, Zhu W, Li J, Liu Y, Tong H, Fu L, Liu J. Genome-wide association analysis of Fusarium head blight resistance in Chinese elite wheat lines. Front Plant Sci. 2020;11:206.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Malosetti M, Zwep LB, Forrest K, van Eeuwijk FA, Dieters M. Lessons from a GWAS study of a wheat pre-breeding program: pyramiding resistance alleles to Fusarium crown rot. Theor Appl Genet. 2021;134:897–908.

    Article  CAS  PubMed  Google Scholar 

  21. Hu W, Gao D, Wu H, Liu J, Zhang C, Wang J, Jiang Z, Liu Y, Li D, Zhang Y. Genome-wide association mapping revealed syntenic loci QFhb-4AL and QFhb-5DL for Fusarium head blight resistance in common wheat (Triticum aestivum L.). BMC Plant Biology. 2020;20(1):1–13.

    Article  Google Scholar 

  22. Ju M, Zhou Z, Mu C, Zhang X, Gao J, Liang Y, Chen J, Wu Y, Li X, Wang S. Dissecting the genetic architecture of Fusarium verticillioides seed rot resistance in maize by combining QTL mapping and genome-wide association analysis. Sci Rep. 2017;7(1):1–11.

    Article  Google Scholar 

  23. Lanubile A, Maschietto V, Borrelli VM, Stagnati L, Logrieco AF, Marocco A. Molecular basis of resistance to Fusarium ear rot in maize. Front Plant Sci. 2017;8:1774.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Zila CT, Ogut F, Romay MC, Gardner CA, Buckler ES, Holland JB. Genome-wide association study of fusarium ear rot disease in the USA maize inbred line collection. BMC Plant Biol. 2014;14:1–15.

    Article  Google Scholar 

  25. Gaikpa DS, Koch S, Fromme FJ, Siekmann D, Würschum T, Miedaner T. Genome-wide association mapping and genomic prediction of Fusarium head blight resistance, heading stage and plant height in winter rye (Secale cereale). Plant Breeding. 2020;139(3):508–20.

    Article  CAS  Google Scholar 

  26. Massman J, Cooper B, Horsley R, Neate S, Dill-Macky R, Chao S, Dong Y, Schwarz P, Muehlbauer G, Smith K. Genome-wide association mapping of Fusarium head blight resistance in contemporary barley breeding germplasm. Mol Breeding. 2011;27:439–54.

    Article  Google Scholar 

  27. Haikka H, Manninen O, Hautsalo J, Pietilä L, Jalli M, Veteläinen M. Genome-wide association study and genomic prediction for Fusarium graminearum resistance traits in nordic oat (Avena sativa L.). Agronomy. 2020;10(2):174.

    Article  CAS  Google Scholar 

  28. Lee ON, Koo H, Yu JW, Park HY. Genotyping-by-sequencing-based genome-wide association studies of Fusarium wilt resistance in radishes (Raphanus sativus L.). Genes. 2021;12(6):858.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pincot DD, Poorten TJ, Hardigan MA, Harshman JM, Acharya CB, Cole GS, Gordon TR, Stueven M, Edger PP, Knapp SJ. Genome-wide association mapping uncovers Fw1, a dominant gene conferring resistance to Fusarium wilt in strawberry. G3: Genes, Genomes. Genetics. 2018;8(5):1817–28.

    CAS  Google Scholar 

  30. Zhu Y, Thyssen GN, Abdelraheem A, Teng Z, Fang DD, Jenkins JN, McCarty JC, Wedegaertner T, Hake K, Zhang J. A GWAS identified a major QTL for resistance to Fusarium wilt (Fusarium oxysporum f. sp. vasinfectum) race 4 in a MAGIC population of Upland cotton and a meta-analysis of QTLs for Fusarium wilt resistance. Theoretical and Applied Genetics. 2022;135(7):2297–312.

    Article  CAS  PubMed  Google Scholar 

  31. Abdelraheem A, Elassbli H, Zhu Y, Kuraparthy V, Hinze L, Stelly D, Wedegaertner T, Zhang J. A genome-wide association study uncovers consistent quantitative trait loci for resistance to Verticillium wilt and Fusarium wilt race 4 in the US Upland cotton. Theor Appl Genet. 2020;133:563–77.

    Article  CAS  PubMed  Google Scholar 

  32. Gyawali A, Shrestha V, Guill KE, Flint-Garcia S, Beissinger TM. Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs. BMC Plant Biol. 2019;19:1–15.

    Article  CAS  Google Scholar 

  33. Crain J, Wang X, Evers B, Poland J. Evaluation of field-based single plant phenotyping for wheat breeding. The Plant Phenome Journal. 2022;5(1):e20045.

    Article  Google Scholar 

  34. Heineck GC, Altendorf KR, Coyne CJ, Ma Y, McGee R, Porter LD. Phenotypic and Genetic Characterization of the Lentil Single Plant-Derived Core Collection for Resistance to Root Rot Caused by Fusarium avenaceum. Phytopathology. 2022;112(9):1979–87.

    Article  CAS  PubMed  Google Scholar 

  35. Martins LB, Balint-Kurti P, Reberg-Horton SC. Genome-wide association study for morphological traits and resistance to Peryonella pinodes in the USDA pea single plant plus collection. G3. 2022;12(9):jkac168.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Nelson MN, Nesi N, Barrero JM, Fletcher AL, Greaves IK, Hughes T, Laperche A, Snowdon R, Rebetzke GJ, Kirkegaard JA. Strategies to improve field establishment of canola: a review. Adv Agron. 2022;175:133–77.

    Article  Google Scholar 

  37. Bindal S, Sheu ZM, Kenyon L, Taher D, Rakha M. Novel sources of resistance to fusarium wilt in Luffa species. Front Plant Sci. 2023;14:1116006.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Namisy A, Huang JH, Rakha M, Hong CF, Chung WH. Resistance to Fusarium oxysporum f. sp. luffae in Luffa Germplasm Despite Hypocotyl Colonization. Plant Disease. 2023;107:1993–2001 PDIS-08–22–1986-RE.

    Article  CAS  PubMed  Google Scholar 

  39. Jiao C, Gu Z. iTRAQ-based analysis of proteins involved in secondary metabolism in response to ABA in soybean sprouts. Food Res Int. 2019;116:878–82.

    Article  CAS  PubMed  Google Scholar 

  40. Franciosini A, Lombardi B, Iafrate S, Pecce V, Mele G, Lupacchini L, Rinaldi G, Kondou Y, Gusmaroli G, Aki S. The Arabidopsis COP9 signalosome interacting F-box kelch 1 protein forms an SCF ubiquitin ligase and regulates hypocotyl elongation. Mol Plant. 2013;6(5):1616–29.

    Article  CAS  PubMed  Google Scholar 

  41. Hu Z, Keçeli MA, Piisilä M, Li J, Survila M, Heino P, Brader G, Palva ET, Li J. F-box protein AFB4 plays a crucial role in plant growth, development and innate immunity. Cell Res. 2012;22(4):777–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ye X, Chen XF, Cai LY, Lai NW, Deng CL, Guo JX, Yang LT, Chen LS. Molecular and physiological mechanisms underlying magnesium-deficiency-induced enlargement, cracking and lignification of Citrus sinensis leaf veins. Tree Physiol. 2020;40(9):1277–91.

    Article  CAS  PubMed  Google Scholar 

  43. Liu Y, Ma L, Cao D, Gong Z, Fan J, Hu H, Jin X: Investigation of cell wall proteins of C. sinensis leaves by combining cell wall proteomics and N-glycoproteomics. BMC Plant Biology 2021, 21:1–15.

    Article  CAS  Google Scholar 

  44. Du HQ, Xu ZX, Fang WP, Feng CS, Lou ZG, Yuan HY. Expression Regulation of Monocopper Oxidase-like Protein SKU5 Gene and its Role in Alfalfa Fall Dormancy. Acta Agrestia Sinica. 2019;27(6):1494.

    CAS  Google Scholar 

  45. Sasaki K, Imai R. Pleiotropic roles of cold shock domain proteins in plants. Front Plant Sci. 2012;2:116.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Hatakeyama S, Kei-ichi IN. U-box proteins as a new family of ubiquitin ligases. Biochem Biophys Res Commun. 2003;302(4):635–45.

    Article  CAS  PubMed  Google Scholar 

  47. Kinoshita A, Ten Hove CA, Tabata R, Yamada M, Shimizu N, Ishida T, Yamaguchi K, Shigenobu S, Takebayashi Y, Iuchi S. A plant U-box protein, PUB4, regulates asymmetric cell division and cell proliferation in the root meristem. Development. 2015;142(3):444–53.

    Article  CAS  PubMed  Google Scholar 

  48. Wang N, Xing Y, Lou Q, Feng P, Liu S, Zhu M, Yin W, Fang S, Lin Y, Zhang T. Dwarf and short grain 1, encoding a putative U-box protein regulates cell division and elongation in rice. J Plant Physiol. 2017;209:84–94.

    Article  CAS  PubMed  Google Scholar 

  49. Pandey VP, Awasthi M, Singh S, Tiwari S, Dwivedi UN. A comprehensive review on function and application of plant peroxidases. Biochem Anal Biochem. 2017;6(1):308.

    Article  Google Scholar 

  50. Shivakumar P, Geetha H, Shetty H. Peroxidase activity and isozyme analysis of pearl millet seedlings and their implications in downy mildew disease resistance. Plant Sci. 2003;164(1):85–93.

    Article  CAS  Google Scholar 

  51. Golubenko Z, Akhunov A, Khashimova N, Beresneva Y, Mustakimova E, Ibragimov F, Abdurashidova N, Stipanovic R. Induction of peroxidase as a disease resistance response in resistant (Hibiscus trionum) and susceptible (Althea armeniaca) species in the family Malvaceae. Phytoparasitica. 2007;35:401–13.

    Article  CAS  Google Scholar 

  52. Wally O, Punja Z: Enhanced disease resistance in transgenic carrot (Daucus carota L.) plants over-expressing a rice cationic peroxidase. Planta 2010, 232:1229–1239.

    Article  CAS  PubMed  Google Scholar 

  53. Gupta SK, Charpe A, Prabhu KV, Haque QMR. Identification and validation of molecular markers linked to the leaf rust resistance gene Lr19 in wheat. Theor Appl Genet. 2006;113:1027–36.

    Article  CAS  PubMed  Google Scholar 

  54. Stevanato P, Trebbi D, Panella L, Richardson K, Broccanello C, Pakish L, Fenwick AL, Saccomani M. Identification and validation of a SNP marker linked to the gene HsBvm-1 for nematode resistance in sugar beet. Plant Mol Biol Report. 2015;33:474–9.

    Article  CAS  Google Scholar 

  55. Singh L, Anderson JA, Chen J, Gill BS, Tiwari VK, Rawat N. Development and validation of a perfect KASP marker for Fusarium head blight resistance gene Fhb1 in wheat. The Plant Pathology Journal. 2019;35(3):200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Doyle JJ, Doyle JL. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem Bullet. 1987;19(1):11–5.

    Google Scholar 

  57. Gao P, Liu S, Zhu Q, Luan F. Marker-assisted selection of Fusarium wilt-resistant and gynoecious melon (Cucumis melo L.). Genetics and Molecular Research. 2015;14(4):16255–64.

    Article  CAS  PubMed  Google Scholar 

  58. Jeger M, Viljanen-Rollinson S. The use of the area under the disease-progress curve (AUDPC) to assess quantitative disease resistance in crop cultivars. Theor Appl Genet. 2001;102:32–40.

    Article  Google Scholar 

  59. Van Buure S, Oudshoorn CG. Multivariate imputation by chained equations. Leiden: TNO; 2000.

    Google Scholar 

  60. Peterson BK, Weber JN, Kay EH, Fisher HS, Hoekstra HE. Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PLoS ONE. 2012;7(5):e37135.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Shirasawa K, Hirakawa H, Isobe S. Analytical workflow of double-digest restriction site-associated DNA sequencing based on empirical and in silico optimization in tomato. DNA Res. 2016;23(2):145–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611–20.

    Article  CAS  PubMed  Google Scholar 

  64. R Core Team RR. A language and environment for statistical computing. 2013.

    Google Scholar 

  65. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23(19):2633–5.

    Article  CAS  PubMed  Google Scholar 

  66. Turner SD. qqman: an R package for visualizing GWAS results using QQ and manhattan plots. Biorxiv. 2014;14:005165.

    Google Scholar 

Download references


We thank KNOWN-YOU SEED Co. LTD. for their support in providing 4 luffa accessions as a gift for this study.


This work was supported by the National Science and Technology Council, Taiwan (grant no.: MOST 111–2313-B-005–024). The funding body did not play a role in the design research, measurements, data analysis, and drafted the manuscript, but just provide the financial support.

Author information

Authors and Affiliations



Y.D.L. performed Fusarium inoculation and phenotyping of luffa individuals, conducted PCA and sp-GWAS, and identified the candidate genes. Y.C.L. contributed to caring for the luffa plants in the green house and validating AUDPC-associated SNP; Y.X.J helped with the growth and maintenance of luffa phenotypes. A.N. contributed to providing the Fusarium source and assisting with inoculation; W.H.C helped determine the disease level of symptoms after Fusarium inoculation and advised on recording it; Y.H.S contributed to the development of the ddRAD library system; S.Y.C. conceived research plans, composed the article with assistance from all authors, completed the writing, and is serving as the corresponding author for communication.

Corresponding author

Correspondence to Shu-Yun Chen.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, YD., Liu, YC., Jiang, YX. et al. Analyzing genetic diversity in luffa and developing a Fusarium wilt-susceptible linked SNP marker through a single plant genome-wide association (sp-GWAS) study. BMC Plant Biol 24, 307 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: