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Full Research Article

Genetic Characterization of Recently Developed Germplasm and Validation of SSR Markers Linked to Fusarium Wilt and Pigeonpea Sterility Mosaic Disease in Pigeonpea [Cajanus cajan (L.) Millsp.]

G.S. Sinchana Kashyap1, H.C. Lohithaswa1,*, M.S. Sowmya1, Banakara Santhoshkumari1, M.G. Mallikarjuna2, H.B. Shruthi3, Prakash Gangashetty3
1All India Coordinated Research Project on Pigeonpea, Zonal Agricultural Research Station, University of Agricultural Sciences, Gandhi Krishi Vigyana Kendra, Bangalore-560 065, Karnataka, India.
2Division of Genetics, Indian Council of Agricultural Research, Indian Agricultural Research Institute, New Delhi-110 012, India.
3International Crops Research Institute for the Semi-arid Tropics, Patancheru, Hyderabad-502 324, Telangana, India.

  • Submitted31-08-2024|

  • Accepted19-02-2025|

  • First Online 28-06-2025|

  • doi 10.18805/LR-5411

ABSTRACT

Background:  Pigeonpea is an important grain legume crop and a source of vegetarian protein. To meet the growing food demand, there is a pressing need to develop high-yielding disease-resistant varieties of pigeonpea. Hence, the present study was conducted to characterize the recently developed genetic material for yield and yield-attributing traits, identify the resistant sources and validate the SSR markers linked to fusarium wilt (FW) and sterility mosaic disease (SMD) resistance in pigeonpea. 

Methods: Pigeonpea genotypes were evaluated in two environments for yield and yield-related traits to assess the genetic variability parameters. The genotypes were phenotypically screened for their response to fusarium wilt and pigeonpea sterility mosaic disease under controlled conditions.

Result: High PCV, GCV, heritability and genetic advance as per cent of mean values were obtained for the traits number of primary branches plant-1, number of secondary branches plant-1, number of pods plant-1, pod bearing length and seed yield plant-1 in both environments. The diversity analysis using k-means clustering grouped the genotypes into nine clusters with high inter-cluster distance between clusters I and VIII indicating high genetic diversity between the lines present in these two clusters. The six genotypes (ICPL 15023, ICPL 15063, ICPL 19467, ICPL 19482, ICPL 19489 and ICPL 19499) showed a combined resistance response. The validation of reported linked SSR markers was done using 52 resistant and susceptible germplasm lines. Four markers, AHSSR 50, AHSSR 34, AHSSR 20 and CcM0588 and seven markers viz., ASSR 23, ASSR 229, ASSR 363, HASSR 18, HASSR 121, HASSR 128 and CcGM 03681, were able to differentiate resistant and susceptible genotypes for SMD and FW, respectively. Subsequently, one marker (AHSSR 20) for SMD and four markers (ASSR 229, HASSR 121, HASSR 128, CcGM 03681) for FW were found significantly associated with disease resistance based on single marker analysis based on t-test. The identified potential genotypes can be used as a source of resistance to SMD and FW or directly released for commercial cultivation after extensive testing.

INTRODUCTION

Pigeonpea [Cajanus cajan (L.) Millsp.] is an important multipurpose grain legume crop with a chromosome number of 2n = 22 and a genome size of 833 Mb (Dutta et al., 2011). Pigeonpea is native to India and is widely grown in the tropics and subtropics. India is the world’s top producer of pigeonpea, followed by Africa, Australia, Malawi, Tanzania, Kenya and Uganda (Sarkar et al., 2020). To meet the growing food demand, breeding for high yield remains the major breeding objective in crop improvement. In this context, the development and characterization of improved varieties that perform well under various agro-climatic conditions is crucial. It mainly depends upon the amount of genetic variability that exists in the genetic material for the traits considered (Xie et al., 2015). Understanding the genetic architecture of yield and related traits is crucial for selecting appropriate breeding strategies to intensify crop improvement programs (Yerimani et al., 2013; Saroj et al., 2013). Furthermore, evaluating genetic diversity is essential for choosing diverse parents for hybridization programs. Genetic diversity assessment employs the non-hierarchical k-means clustering method, which categorizes variability into distinct ‘k’ clusters based on k-means algorithms (Macqueen, 1967). The k-means, a centroid or distance-based algorithm, assigns points to clusters by computing distances. It creates genetically diverse clusters based on genetic distances between genotypes, facilitating the identification of diverse genotypes (Kanavi et al., 2020).
       
The high sensitivity of pigeonpea to many biotic and abiotic stresses has hindered the harnessing of its production potential. Major biotic stresses of pigeonpea include Sterility mosaic disease (SMD) and Fusarium wilt (FW) which cause a yield loss of up to US$113 million (Singh et al., 2016). The sterility mosaic disease (SMD) also called “Green Plague”, is caused by an Emaravirus i.e., Pigeonpea Sterility Mosaic Virus-PPSMV which is transmitted by eriophyid mite Aceria cajani Channabasavanna in a semi-persistent manner (Kulkarni et al., 2002). This disease causes an estimated yield loss of about 26-97% if infection occurs in early stages and later infection causes sterility of plants (Kannaiyan et al., 1984). Fusarium wilt is a soil and externally seed-borne fungal disease and caused by Fusarium udum Butler, that affects the plant from seedling to the pod-setting stage resulting in wilting of plants. FW disease is markedly increasing in pigeonpea-growing states and causes an estimated loss of 29.60 to 99.90%, with variant 2 of F. udum specific to Karnataka (Ravikumar et al., 2022). Management of these diseases through conventional methods is difficult emphasizing the need for developing resistant varieties as a practical and cost-effective approach.
       
The development of resistant varieties relies on the precision mapping of multiple resistance genes using appropriate marker systems and identifying molecular markers that are closely linked to disease resistance. Despite the successful tagging of resistance to SMD and FW by various DNA marker systems in pigeonpea, their application in marker-assisted breeding remains limited (Halladakeri et al., 2023). To ensure the wider utilization of molecular markers in marker-assisted breeding, it is imperative to validate them across multiple genetic backgrounds (Bipinraj et al., 2011).
       
Thus, the present study was conducted to identify high-yielding pigeonpea lines from the recently developed germplasm carrying resistance and to validate reported linked SSR markers for FW and SMD.

MATERIALS AND METHODS

Assessment of genetic variability and genetic diversity
 
To assess the genetic variability and genetic diversity, a set of 78 newly developed pigeonpea germplasm obtained from the ICRISAT, Hyderabad, was evaluated in two different environments viz., June (normal planting, environment I) and August (late planting, environment II) in Augmented design with BRG3 and BRG5 as checks. The experiment was carried out during the rainy season of 2021 at the All India coordinated research project (AICRP) on Pigeonpea, zonal agricultural research station (ZARS), University of Agricultural Sciences, GKVK, Bengaluru. 
       
Observations were recorded on ten yield and yield-attributing traits namely, days to 50% flowering, plant height (cm), number of primary branches plant-1, number of secondary branches plant-1, number of pods plant-1, number of seeds pod-1, pod bearing length (cm), pod length (cm), test weight (g) and seed yield plant-1 (g). The data were analyzed using Augmented statistical analysis (Federer, 1956) to assess the significant differences among the genotypes. Due to unavailability of statistical package for performing pooled ANOVA for augmented design, adjusted means were derived from each environment. These blocks adjusted means from both environments were then averaged to obtain the pooled means. These pooled adjusted means were used for subsequent analysis. Descriptive statistics and genetic variability parameters, such as the phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) (Burton and Devane, 1953), heritability (H) (Hanson et al., 1956) and genetic advance as per cent of mean (GAM) (Johnson et al., 1955), were estimated for each environment using R software Version 4.1.3 with the package “augmentedRCBD.”
       
Additionally, genetic diversity was assessed through a model-based k-means clustering approach (Macqueen, 1967) using R software Version 4.1.3 and the package “factoextra”.
 
Screening for SMD and FW
 
Along with the above 78 genotypes, 24 additional lines received from ICRISAT, Hyderabad, were phenotyped for their reaction to SMD and FW. Artificial screening for SMD and FW was carried out separately during the rainy season of 2021 and summer 2022, using the leaf stapling technique (Nene et al., 1981) and root dip technique (Pande et al., 2012), respectively. BRG 3 was utilized as a resistant check for both diseases, while ICP 8863 and ICP2376 served as susceptible checks for SMD and FW, respectively. Further, the genotypes were classified into different disease response classes based on the scale provided by Singh et al., (2003) for SMD and Pande et al., (2012) for FW. The per cent disease incidence (PDI) was calculated at 15 days and seven days intervals till 60 days after planting for SMD and FW, respectively using the formula:
  
   
       
Subsequently, the third and fourth-degree statistics viz., skewness and kurtosis were estimated using R software Version 4.1.3 and the package “moments” and Shapiro-wilk’s normality test to test the nature of frequency distribution using R software Version 4.1.3 and the package “dplyr.”

Validation of reported linked SSR markers to SMD and FW
 
For validation of SSR markers, 52 germplasm lines representing the resistant and susceptible phenotypic classes based on the mean PDI were selected. The genomic DNA was extracted from two-week-old seedlings using the Cetyl-tri-methyl ammonium bromide (CTAB) method (Agbagwa et al., 2012) and quantified on 0.8% gel using a known quantity of uncut l DNA as a control. Further, the genomic DNA was diluted to 25-30 ng/ml for SSR marker analysis. 
       
For the validation study, a total of 15 and 13 reported SSR markers linked to SMD and FW resistance were utilized, respectively. The PCR reaction mixture consisted of 1 ml of stock genomic DNA, 0.3 ml Taq DNA polymerase, 1.5 ml 10X TE buffer, 0.2 ml MgCl2, 1.5 ml dNTPs and 0.5 ml of forward and reverse primer, making a total of 15 ml PCR mixture. Amplification was performed using the touchdown PCR method in a VeritiTM thermal cycler (Applied Biosystems). The amplified products were separated on a 2.5% agarose gel in 1X TBE buffer at 80V and visualized using the Bio-rad XR + gel documentation system. Based on amplicon size specific to resistant and susceptible checks, the amplified products were scored. Subsequently, single marker analysis using a t-test was conducted at each marker locus to compare the mean PDI of resistant and susceptible genotypes, aiming to identify significant differences between marker classes (p<0.05) (Darvasi et al., 1993) and to validate the markers utilizing Microsoft Excel 2019.

RESULTS AND DISCUSSION

Assessment of genetic variability for different quantitative traits
 
The analysis of variance highlighted the presence of significant genetic differences among the genotypes for all traits. The mean sum of squares due to blocks were significant for all the traits except for test weight and pod-bearing length (Table 1 and Table 2). Within and across environment variability was visualized using Box-whisker plots. The length of the box and whisker lines represents the range, while the dot inside the box signifies the mean value of the trait (Fig 1). The descriptive statistics and genetic variability parameters for each environment are given in Table 3. The genotypes ICPL 19511 (48.91 g), ICPL 19514 (40.73 g) and ICPL 19493 (39.67 g) in EI while ICPL 19494 (46 g), ICPL 15057 (46 g) and ICPL 19511 (45.05 g) in EII and the genotypes ICPL 19511 (46.87 g), ICPL 15057 (44.39 g) and ICPL 19514 (42.09 g) across environments were found to be the high yielding genotypes that were consistent with the checks (BRG3 = 39.66 g and BRG5 = 42.38 g).

Table 1: Analysis of variance for yield and yield-related traits in the environment I (EI).



Table 2: Analysis of variance for yield and yield-related traits in the environment II (EII).



Fig 1: Box-whisker plots depicting variability in each environment and across environments for yield and yield-attributing traits in pigeonpea.



Table 3: Descriptive statistics and genetic variability parameters in environment I (EI) and environment II (EII).


       
The estimated phenotypic coefficient of variation (PCV) was higher than the genotypic coefficient of variation (GCV) across all traits. Notably, high PCV values were recorded for traits such as number of primary branches plant-1, number of secondary branches plant-1, number of pods plant-1, pod bearing length and seed yield plant-1. Similarly, high GCV values were observed for the same traits ranging from 78.64% to 99.8% in EI and 78.54% to 99.72% in EII. Additionally, higher genetic advance as per cent of mean (GAM) was observed across all traits except days to 50 % flowering (9.49 %) in EI. Higher GAM was observed for almost all the traits in EII except for days to 50 % flowering (9.48%) for which the lowest GAM was recorded and moderate GAM was observed for pod length (19.85%) and test weight (19.98).
       
Following Levene’s test to account for homogeneity of variances, the 78 genotypes were then clustered together into nine distinct clusters using non-hierarchical k means clustering. Out of nine clusters, cluster III contained the maximum number of 17 genotypes and Cluster VIII had the minimum number of three genotypes. The distribution of genotypes in nine clusters is depicted in (Fig 2) and the distribution of genotypes in different clusters is represented in Table 4.

Fig 2: k means clustering of pigeonpea genotypes based on yield and yield-attributing traits.



Table 4: Distribution of genotypes in nine clusters obtained using k means clustering.


       
The analysis of variance between the clusters revealed the presence of significant variation among clusters Table 5. The estimated cluster means along with the range within each cluster for various traits are illustrated in Fig 3. The maximum inter-cluster distance was observed between cluster I and cluster VIII (7.03), followed by cluster I and cluster VII (6.88). The minimum inter-cluster distance was observed between cluster III and cluster VI (3.40) Table 6.

Table 5: Analysis of variance for yield and yield-attributing traits between the clusters.



Fig 3: Box-whisker plots showing the variation of genotypes within each cluster for yield and yield-attributing traits.



Table 6: Intra and inter-cluster distance among the nine clusters.


 
Screening for disease resistance
 
The mean per cent disease incidence for SMD on resistant (BRG3) and susceptible (ICP 8863) checks were 0 and 100%, respectively. Similarly, for FW, average disease incidence for resistant (BRG3) and susceptible (ICP 2376) checks were 0% and 79.0%, respectively. Upon disease reaction assessment (Fig 4), 17 genotypes were grouped as resistant (0-10.0% PDI) to SMD and seven genotypes for FW. Additionally, 64 (10.1-30% PDI for SMD) and 11 (10.1-20% for FW) genotypes displayed moderately resistant reaction, while 13 genotypes were moderately susceptible (20.1-40% PDI) to FW. Furthermore, 21 (30.1-100%) and 72 (40.1-100% PDI) genotypes were susceptible to SMD and FW, respectively. Six genotypes viz., ICPL 15023, ICPL 15063, ICPL 19480, ICPL 19482, ICPL 19489 and ICPL 19499 showed combined resistance, while seven genotypes ICPL 15014, ICPL 19472, ICPL 19477, ICPL 19487, ICPL 19495, ICPL 19540 and ICPL 19529 were moderately resistant to both SMD and FW. The frequency distribution was skewed for SMD (1.37) and FW (-0.07) as indicated by the significance of Shapiro Wilk’s test for normality [SMD (p-value = 1.91 ´ 10-8) and FW (p-value = 2.31 x 10-5)].  While the kurtosis value for SMD was 4.95 and for FW it was 1.78.

Fig 4: Distribution of genotypes into different disease classes based on their response to SMD and FW.


 
Validation of SSR markers
 
In this study, four SSR markers (AHSSR 50, AHSSR 34, AHSSR 20 and CcM 0588) out of 15 for SMD and seven markers (ASSR 23, ASSR 229, ASSR 363, CcGM 03681, HASSR 18, HASSR 121 and HASSR 128) out of 13 for FW exhibited polymorphism. These markers effectively distinguished the resistant and susceptible genotypes based on amplicon size, as illustrated in Fig 5. To ascertain a significant association between the markers and disease response, a single-marker analysis was carried out using a t-test. The results revealed that only one marker (AHSSR 20) for SMD and four markers (ASSR 229, HASSR 121, HASSR 12, CcGM 03681) for FW effectively differentiated the germplasm lines based on p-value, as indicated in Table 7.

Fig 5: Gel pictures depicting the amplification of significantly associated markers.



Table 7: Association between the SSR markers and resistance to SMD and FW disease.


 
Yield comparison of genotypes with combined disease-resistance
 
The disease incidence of genotypes with combined resistance viz., ICPL 19499, ICPL 19489, ICPL 19482, ICPL 15023, ICPL 15063 and ICPL 19467 for SMD and FW along with the yield data are represented in Table 8. Among these genotypes, the performance of ICPL 15023 was comparable with the best check BRG3 based on the mean critical difference (5.37) across environments for grain yield.

Table 8: Performance of combined disease-resistant genotypes for yield and yield-related traits.


       
Plant breeding success hinges largely on developing High-yielding varieties that possess resistance to pests and diseases. The analysis of variance of yield and yield-related traits reflected on the presence of high genetic variability among the genotypes based on the distribution of genotypes in two environments and across environments as depicted by box whisker plots in Fig 1.  The traits days to 50% flowering, number of primary branches plant-1, test weight and seed yield plant-1 displayed the symmetrical distribution while the traits plant height, number of secondary branches plant-1, number of pods plant-1, number of seeds pod-1, pod bearing length and pod length displayed asymmetric distribution. A higher magnitude of selection response can be visualized for traits with symmetric distribution than the traits with skewed distribution (Katral et al., 2022). The estimated genetic variability parameters indicated that PCV values were relatively higher than the GCV values, suggesting the significant influence of environmental factors on trait expression (Vanniarajan et al., 2021; Parre and Raje, 2022). All the traits showed high heritability along with substantial GAM, except days to 50% flowering, implying predominance of additive gene action and facilitating effective selection for these traits with lesser environmental influence, as observed in the studies conducted by Pushpavalli et al., (2018) and Hemavathy et al., (2019).
       
The k-means clustering analysis grouped the genotypes, along with the checks, into nine clusters, revealing substantial diversity among them. The analysis of variance between these clusters further confirmed significant differences, highlighting distinctiveness among genotypes belonging to different clusters. Notably, clusters IX and V showed high divergence based on inter-cluster distance, while clusters X and III appeared less divergent and potentially related to each other. These findings align with those reported by (Kanavi et al., 2020) in green gram, Amit et al., (2022) in chickpea and Nautiyal et al., (2023) in cowpea.
 
Screening for disease resistance
 
Most genotypes displayed moderate resistance to SMD and susceptibility to FW, with fewer resistant genotypes identified for both diseases. The distribution of genotypes was skewed for both SMD (positive) and FW (negative) diseases. The positive skewness indicates dominant and complementary gene action indicating that genetic gain could be increased by intensive selection of the genotypes at the tail end. While negative skewness indicates dominant and duplicate gene action indicating that the mild selection can be applied at the tail end of genotypes for rapid improvement of genotypes. Additionally, the distribution of SMD was leptokurtic, suggesting that resistance is controlled by a few genes, while the distribution of FW was platykurtic, indicating that resistance is influenced by many genes (Bassuony et al., 2021). These findings differ from previous studies for SMD by Patil et al., (2016), Rajeswari et al., (2021) and Tharageshwari et al., (2019), which reported a higher number of genotypes in the susceptible class for SMD, albeit with a negatively skewed distribution. The negatively skewed distribution for FW was previously reported by Ashitha et al., (2016), Naik et al., (2017) and Nagaraja et al., (2016). Additionally, the root dip technique was found effective and efficient in identifying resistant genotypes based on reduced area and reduced time requirements (Ashitha et al., 2016).
 
Validation of SSR markers
 
The validation of reported SSR markers linked to resistance to SMD and FW was carried out to suggest efficient markers to be used in marker-assisted selection programs. A total of four and seven markers were found polymorphic and differentiated the resistant and susceptible genotypes for SMD and FW disease, respectively. The AHSSR markers exhibited polymorphism for SMD resistance, as reported by Patil et al., (2016). The CcM markers identified by Gnanesh et al., (2011), were predominantly monomorphic in our study, except for CcM0588, possibly due to their distance from the QTLs or their specificity to certain genotypes (Behera et al., 2020) or original populations (Geddam et al., 2014). Hence, before their use, these CcM markers require validation in other populations. Among all markers, the AHSSR 20 (p-value = 0.00016) marker stands out as a significant marker with a p-value <0.05. Thus, can be used for selecting (SMD) resistant genotypes as indicated by single marker analysis.
       
Seven out of 13 markers were found to be polymorphic, consistent with the findings of Bohra et al., (2017) for CcGM 03681, Patil et al., (2017) for HASSR markers and Singh et al., (2013) and Singh et al., (2016) for ASSR markers. Among these markers, only four showed significant associations with FW resistance (HASSR121 with p-value <0.001, ASSR229 with p-value <0.01, HASSR128 and CcGM03681 with p-value<0.05) based on t-test results. The effectiveness of t test to carry out single marker analysis, for establishing marker trait association was proved effective in various other studies (Diwan et al., 2022; Bhiza et al., 2015). Hence, markers that were found to be significantly associated have the potential to be utilized in marker-assisted selection for the identification of resistant genotypes, thereby serving as valuable tools in the quest to identify potential sources of resistance.

CONCLUSION

Among ten yield attributing traits studied in the newly developed germplasm lines of pigeonpea, high PCV, GCV, heritability and genetic advance as per cent mean was recorded by the traits number of primary branches plant-1, number of secondary branches plant-1, number of pods plant-1, pod bearing length and seed yield plant-1. Results from k-means clustering indicated the presence of a higher magnitude of genetic diversity. Six germplasm lines displayed combined resistance [ICPL 19499, ICPL 19489, ICPL 19482, ICPL 15023, ICPL 15063 and ICPL 19467] against SMD and FW. The line, ICPL 15023 showed resistance to both diseases and high-yielding. The markers, AHSSR 20 for SMD and ASSR 229, HASSR 121, HASSR 128 and CcGM 03681 for FW exhibited significant associations with disease response, indicating their potential for identifying resistant sources. The genotypes viz., ICPL 19511 and ICPL 15057 for yield and ICPL 15023, ICPL 15063, ICPL 19499, ICPL 19482, ICPL 19489 and ICPL 19467 with combined resistance and high grain yield could be considered for release after extensive yield trials and can also be used in breeding programs for disease resistance introgression. 

ACKNOWLEDGEMENT

The authors are grateful to the ICRISAT, Hyderabad for sharing the pigonpea germplasm used in the study.
 
Funding
 
Directorate of Research, University of Agricultural Sciences, Bangalore.
  
Authors’ contribution
 
Conceptualization of research (HC Lohithaswa); Designing of the experiments (HC Lohithaswa, Prakash Gangashetty, HB Shruthi); Execution of field/lab experiments and data collection (GS Sinchana Kashyap, HC Lohithaswa, Santoshkumari Banakara, MS Sowmya); Analysis of data and interpretation (GS Sinchana Kashyap, HC Lohithaswa, MG Mallikarjuna); Preparation of the manuscript (GS Sinchana Kashyap, Santoshkumari Banakara, MS Sowmya, HC Lohithaswa, MG Mallikarjuna, Prakash Gangashetty, HB Shruthi).

CONFLICT OF INTEREST

The authors have declared that no competing or conflict of interest exists.

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