Pulses, widely recognized as important source of vegetable protein, serve as a staple food for urban and rural populations. There are wide variety of pulse crops cultivated across the countries depending on the suitability to climatic and other edaphic conditions. Among the various pulse crops grown, Field pea (Pisum sativum L.), often known as dry pea, is an important pulse crop grown in India which is famous for its protein-rich diet. It belongs to the family Fabaceae with genome size of 4.45 gigabases (Kreplak et al., 2019).  It is also known as garden pea or green pea, when it is produced for the vegetable purpose. It is one of the oldest crops of the world, which was cultivated together with cereals like wheat and barley approximately in 9^th millennium BC (McPhee, 2003). It is originated from mediterranean region and has Ethiopia as secondary center of origin. It is a rich source of protein and carbohydrates along with minerals, vitamin A, B and C (Pandita and Pratap, 1990).  It has a prominent role in ensuring nutritional diet and plays important role in addressing malnutrition. In India, approximately 22 to 23 million hectares of land with yearly production of nearly thirteen million tonnes is under pulses. India holds sixth position in the production of field pea with the cultivation spread across 6.11 lakh hectares and production of 6.67 lakh tonnes with 1091 kg per ha productivity (IndiaStat, 2024).

Like several other crops, the productivity of field pea in India is at lower side compared to the global productivity levels due to various reasons including the narrow genetic base and limited utilization of the available variability in improving the local varieties (Kumar et al., 2004). Though yield is a complex trait depending on a number of other contributing traits and their association, there is a dire need to enhance the yield potential of the crop (Rao et al., 1990). The knowledge regarding the genetic make-up, association between yield and its attributes helps in formulating the selection criteria for improving yield, besides enhancing the scope of success of breeding programme (Sunayana et al., 2017).
       
Genetic variability plays a significant role in crop improvement, as it enables the selection of promising genotypes for enhancing yield coupled with desired quality traits. It also aids in identification of suitable parents for hybridization program (Pratap et al., 2024a). Enhancing yield is the most prioritised objective in breeding, however, development of stable high yielding noteworthy cultivars coupled with high photosynthetic efficiency, resistance to biotic and abiotic stresses, early maturity, good initial phases of growth along with high organic matter accumulation potential, high seed protein content with essential amino acids and acceptable ratio between them (Abdou et al., 1999; Tiwari et al., 2001) is the need of the hour.
       
In the era of climate change, periodic droughts and hot weather during cropping season are recurrent posing severe yield losses across crops in general and pea is not an exception for this. Crop phenology i.e., flowering, podding and maturity plays instrumental role in adaptation of crop cultivars into diverse environments (Berger et al., 2006). Earliness is an important trait in crop plants that enables genotypes to complete their life cycle earlier, particularly flowering and maturity, thereby escaping terminal stresses such as drought and heat during later growth stages. This stress-escape mechanism helps in maintaining better grain filling, yield stability and overall productivity under adverse conditions. Consequently, early-maturing genotypes are highly valuable in breeding programs aimed at improving adaptation to stress-prone environments and ensuring sustainable crop production (Jeuffroy et al., 2010). In changing climatic conditions, developing early genotypes which can avoid terminal stage drought and heat stresses making them a preferred choice under late sown conditions is in great demand. With the advent of several molecular techniques, it is indispensable to utilise those techniques in breeding programmes for enhanced efficiency, precision and to make highly informed decisions. Bulk segregants analysis (BSA) suggested by Michelmore et al., (1991) is a rapid method which involves the bulked DNA of genotypes showing extreme phenotypic values for the target trait in identifying the putatively linked markers to the trait of interest. In view of the above points, the current research was planned to isolate the promising transgressive segregants and to detect the molecular markers associated with the flowering time through BSA.

Evaluation of F₂ segregating population for morphological, phenological and productivity traits
 
The contrasting parents for the target trait, flowering time, were chosen based on two season evaluation. The selected parents, IPF 4-9 and Khanapur 10, are diverse for several other traits (Table 1) besides flowering time. The true F₁s were selfed to harvest F₂ seeds. In total, 378 F₂ seeds were generated to have an optimum population for further studies. To evaluate the phenological, morphological and productivity traits, the experiment was laid out in an un-replicated design in the experimental field of Regional Agricultural Research Station, Vijayapura, Karnataka, India during rabi 2022-23 season. Three hundred seventy eight F₂ seeds were sown along with parental genotypes. The seeds were planted with 45 cm spacing between rows and 10 cm between plants ensuring single seed per hill in the row length of 5 m. The basal dose of 25 N kg ha^-1, 60 kg P₂O₅ ha^-1 and 20 kg K₂O ha^-1 was applied to raise the healthy crop. The prescribed package for cultivation of the crop was followed to establish good crop. Need based plant protection intervention was allowed to control powdery mildew and pod borer incidence. Observations on morpho-phenological and productivity traits were recorded on each F₂ plant and the parents.


 
Genotyping for Bulk segregants analysis (BSA)
 
DNA isolation
 
Genomic DNA was extracted from leaves of all the F₂ plants and the parents using QIAGEN Plant DNeasy Plant Mini Kit, with the standard protocol mentioned in the manual. After extraction the DNA was stored at -20^oC.
 
Qualitative estimation
 
The quality of DNA samples was verified both on UV-spectrophotometer and also on agarose gel. From the spectrophotometer, absorbance ratio at 260 and 280 nm was recorded and the samples with 1.8 ratio were considered.
 
Normalization of DNA concentration for PCR
 
Dilution of DNA was done to bring concentration to a relatively equal level (150 ng/μl) by appropriate dilutions with TE buffer. The concentration was estimated by measuring the optical density at 260 nm using an Eppendorf Bio spectrophotometer. The absorbance of the DNA samples was measured at a wavelength of 260 nm against TE buffer blank in a quartz cuvette. 2 μl of DNA aliquot was taken and volume was made up to 1 ml with TE buffer. Optical density (O.D.) was measured at 260 and 280 nm wavelength. Using the relationship of O.D. unit of 1.0 at 260 nm equivalent to 50 μg DNA per ml, the quantity of DNA was estimated by using the following formula:
 

Total quality of DNA(μg/ml) = O.D. at 260 nm ×  50 × Dilution factor

 
Molecular markers
 
To carry out the molecular studies, forty (40) SSR markers were used. The markers were carefully chosen to consider different linkage groups and to cover the genome. Lyophilised primers were synthesised from Sigma-Aldrich with the scale of 0.025 (µmole), later both stock and working solutions were prepared from that.
 
Polymerase chain reaction (PCR)
 
For amplification of the targeted genomic region based on the corresponding markers, the Gradient Eppendorf Thermocycler was employed. The nucleic acid extracted from the leaves of plants, as template DNA along with Taq DNA polymerase, Taq Buffer, dNTPs, MgCl₂, forward and reverse primers were used for cyclic amplification of DNA. The reaction mix was prepared fresh depending on the number of samples every time. Each 20 μl mix contained; Taq polymerase: 0.2 μl; Taq Buffer: 2 μl; dNTPs: 2 μl; MgCl₂: 1 μl; forward primer: 0.5 μl; reverse primer: 0.5 μl; template: 1 μl; Nuclease free water: 12.8 μl.
 
Automatic capillary electrophoresis
 
The QIAxcel Advanced high through put electrophoresis instrument which includes an array of light-emitting diodes and micro-optical collectors that latch to capillaries within QIAxcel gel cartridges was used to separate the amplicons. The fragments that migrate through a gel matrix within the capillary pass excitation, further, detection of the spots and the signal is transmitted through a photomultiplier tube to the QIAxcel ScreenGel software for data interpretation.
 
Parental polymorphic survey
 
For parental polymorphism, DNA of the parents, IPF-4-9 and Khanapur 10 was extracted and analysed. Forty SSR primers were used to survey the polymorphism. Polymorphic primers were noted. A primer was considered polymorphic, if it amplified different sized bands/amplicons across the parents.
 
Bulk segregant analysis for flowering time
 
Bulk segregant analysis (BSA) was accomplished utilizing the SSR markers having polymorphism among the parental genotypes. Further, DNA bulks of extreme flowering classes viz., extreme early and extreme late, were prepared by pooling aliquots containing equal amount of DNA (20 ng/microliter) from each of the ten early flowering and ten late flowering F₂ individuals as suggested by Michelmore et al., (1991).
 
Statistical analysis
 
The data on morphological, phenological and productivity traits was analysed using R-studio. The analysis included the determination of mean, range, variance, standard deviation and standard error following standard statistical procedures as described by Singh and Chaudhary (1977). Genetic parameters such as phenotypic, environmental and genotypic variances were estimated to dissect the genetic variability across the genotypes. The coefficients of variation were estimated to dissect the variability (Burton and Devane, 1953), while heritability was determined to comprehend the genetic contribution to phenotypic variation (Hanson et al., 1956). Genetic advances and the percentage over mean were also computed (Johnson et al., 1955). QIAxcel screen gel software was engaged for molecular characterization to conduct BSA on flowering time to report the size of amplified fragments.

Phenology of parental genotypes, F₁ and F₂
 
The genotypes used for deriving the F₂ population viz., IPF 4-9 and Khanapur 10 were contrasting for flowering time and maturity. The time of flowering of Khanapur 10, an early-flowering genotype was recorded to be 34 to 36 days, while IPF-4-9, a late-flowering one had flowering in around 58 to 60 days. The F₁ was observed to be early with 40 days to flower exhibiting dominance of earliness. Wide variability was evident in the F₂ population with average flowering time of 59 days and a range between 24 and 90 days. The parents, IPF-4-9 and Khanpur-10, matured in 114 days and 88 days, respectively, while the F1 exhibited a maturity duration of around 100 days (Table 1). Similar results were obtained by Srivastava et al., (2025).
 
Variability parameters
 
There was a significant variability for the target trait, flowering time, in the test population. The days to flower initiation ranged between 24 to 90 days with an average of 59.8 days. The trait exhibited low GCV (8.31%) and low PCV (8.4%) with high heritability (98.02%). High GA (30.44%) with high (50.91%) GAM was noticed for the trait. The mean days to maturity was 120.87 falling under the range of 73 to 127 days. Both GCV and PCV were found to be low with 1.72 per cent and 2.17 per cent respectively. Moderate GA (10.75%) with low (8.89%) GAM and high heritability (62.59%) were noticed (Table 2). Similarly, days to first flowering and plant height were reported to exhibit high heritability and genetic advance by Umesh et al., (2024).

There was a significant variability for yield per plant across the genotypes ranging between 0.11g and 98.04g with a mean of 13.38g. All the genetic parameters viz., GCV (73.23%), PCV (75.88%), heritability (93.14%) and GAM (197.7%) were high with moderate (26.44%) GA. The study showed high GCV and PCV for number of pods per axil, number of primary branches per plant, number of pods per plant, hundred seed weight, number of seeds per pod and seed yield per plant. These results are in line with investigations of Singh et al., (2012), Khan et al., (2017), Pratap et al. (2024) and Manimozhi et al., (2022). 


       
The observations on heritability and genetic advance for days to flowering, plant height, number of pods per axil, number of primary branches per plant, number of pods per plant, number of seeds per pod, hundred seed weight and seed yield per plant showed high heritability coupled with high genetic advance as reported by Lal et al., (2011), Pratap et al. (2024), Manimozhi et al., (2022), Jaiswal et al., (2013) and Jagadeesh et al., (2023).
 
Isolation of transgressive segregants
 
Transgressive segregation is a most desirable phenomenon in breeding wherein the genotypes with outlying phenotypic trait values are isolated. The outliers in the direction of desirability are crucial for morpho-phenological and productivity traits. The transgressive segregants for the phenology were observed based on the trait values. The data on all the genotypes was observed carefully and the number along with per cent transgressive segregants was worked out. For days to maturity, the genotypes outlying the parental values were 337 accounting for 89.15 per cent in both the directions. The screening for transgressive segregants for other traits concurrent with early flowering gave valuable insights and worthy genotypes. Across the genotypes in the population, 250 exhibited transgressive segregation for number of seeds per pod accounting for 66.14 per cent. Plant height showed 204 transgressive segregants which accounted for 53.97 per cent of transgressive segregation. Days to flower initiation was next in order with 193 segregants showing transgressive segregation which accounted to 51.06 per cent (Table 3).



The transgressive segregants for phenological traits were categorised into distinct classes viz., Early flowering with early maturity; early flowering with late maturity; late flowering with early maturity and late flowering with late maturity with 07, 27, 05 and 60 genotypes respectively (Table 4,5,6).






 
Parental polymorphism using SSR markers
 
To detect the polymorphism between parents, molecular analysis was carried out using 40 SSR markers to detect the markers which can distinguish the parents, IPF-4-9 (late) and Khanapur 10 (early) genotypically through polymorphic bands. Out of those, twenty-six were polymorphic between the parents indicating high degree of polymorphism of SSR markers and reasonable number of markers for downstream BSA.
 
Bulked segregant analysis
 
For BSA, the two bulks of the extreme phenotypic values for the phenology (early and late) were used as per Michelmore et al., (1991). The bulks prepared by mixing equal quantity of genomic DNA from ten extreme genotypes each were subjected for genotyping using polymorphic markers.  Primer pairs that distinguished for the bulks by distinct banding pattern between the early and late bulks for flowering time were carefully observed and recorded (Fig 1).


       
Among the polymorphic SSR markers (26) between the parents of the cross, two markers viz., AB 23 and AA 206 were able to clearly differentiate extreme early and late F₂ bulks during BSA analysis (Fig 2 and 3). These two markers revealed the linkage with flowering time and usefulness in selection of early flowering genotypes. The extent of transgressive segregation for the phenology was highly encouraging to isolate desired genotypes in the F₂ population, further, detection of two markers associated with the flowering time is noteworthy and highly useful in pea breeding for earliness especially under the changing climatic conditions to avoid terminal stage drought and/ or heat stresses and suitability for late sown conditions. Nunavath et al., 2022 reported similar findings using BSA wherein two markers, TA64 and TA142 were reported to be putatively linked to efl3 locus governing early flowering in chickpea. Other reports on markers linked to important trait include, Uma et al., (2016) who reported SSR marker linked to resistance for rust disease in cowpea and Kumar et al., (2021) reported SSR markers linked with high Fe and Zn content.

In field pea, it is vital to identify the early genotypes in view of climate vagaries. In this regard, the genetic population developed through the cross between two contrasting parents, IPF-4-9 and Khanapur 10 showcased substantial genetic variability across morphological, phenological and productivity traits. Transgressive segregation for early flowering, early maturity and also for other traits was evident. It was noteworthy that successful identification of the transgressive segregants for earliness coupled with high productivity was possible. Further, the application of bulked segregant analysis, a rapid method to identify the putatively linked markers proved to be efficient and the analysis yielded two markers associated with flowering time. Upon, validation, these markers could potentially serve a significant role in facilitating marker assisted breeding for earliness in field pea.

The authors declare that they have no conflict of interest.