Morpho-biochemical characterization and molecular marker based genetic diversity of pearl millet (Pennisetum glaucum (L.) R. Br.)

Morphological characters

The panicles were covered with glassine bags to prevent cross-pollination by outside pollen and to collect self-seeds. For phenotyping of grain based traits, physiologically mature panicles were collected, dried under sunlight, and then manually threshed in bulk.

The experimental material was evaluated for eight morphological traits viz., days to 50% flowering, plant height, panicle diameter (PD, cm), panicle length (PL, cm), number of productive tillers, grain yield, days to maturity, and 1,000 grain weight. Except for days to 50% flowering and days to maturity, which were recorded on a plot basis during the study, data on the above traits were collected from randomly tagged five competitive plants in each genotype in both replications. PD was measured with Vernier calipers.

Biochemical characters

Before biochemical analysis, grains were cleaned followed by hot air oven drying (80 °C for 24 h). Dried grains (10 g) were powdered manually. For mineral analysis, 0.5 g flour was processed in 11 mL of nitric acid (69%) and one mL of H₂O₂. The digestions were carried out in HVT50 vessels using rotor 12HVT50 in Multiwave GO/Multiwave GO plus (Anton Paar GmbH, Graz, Austria). For microwave digestion the initial temperature was kept at 180 °C with a ramp of 20 min and a hold period of 12 min. While the second round of digestion was performed with a ramp time of 10 mins at 70 °C temperature and a holding time of 5 mins. Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES (model 7000DV; Perkin Elmer, Waltham, MA, USA, wintab32 software ver. 5.1)) was used to determine the mineral content (iron (Fe), zinc (Zn), calcium (Ca), copper (Cu), and manganese (Mn)) in grain after diluting the digested mixture to a volume of 50 ml using distilled water. The flow rate in a peristaltic pump was 1.5ml per min. From the acid extract, potassium (K) content was quantified using a flame photometer (Jackson, 1973), while the vanadate-molybdate method of Jackson (1973) was used to estimate the phosphorus (P). Total phenols were estimated using the Folin-Ciocalteau reagent as Malik & Singh (1980) and reading was measured at 730 nm using a spectrophotometer. Soxhlet extraction was performed to estimate the crude oil content, and semimicro-Kjeldahl was employed to determine the crude protein content.

Molecular marker study

Genomic DNA was extracted from tender leaves as per Mace et al. (2003). Genotyping was done using simple sequence repeat (SSR) and sequence-related amplified polymorphism (SRAP) markers. For SSR marker profiling, markers from the PMES series (Zala et al., 2017) were amplified in SensoQuest Thermocycler (Göttingen, Germany). The SSR-PCR reaction conditions were as follows: 94 °C (initial denaturation) for 5 min., followed by 35 cycles of 94 °C for 45 s, X°C (primer specific) for 45 s, 72 °C for 45 s, and 72 °C for 7 mins (final extension). The SRAP amplification was as follows: 94 °C (initial denaturation) for 5 mins, followed by 5 cycles of 94 °C for 30 s, 35 °C for 45 s, and 72 °C for 90 s, followed by 35 cycles of 94 °C for 30 s, X°C (primer specific) for 45 s, 72 °C for 60 s and 72 °C for 10 min (final extension). An agarose gel (3%) was used to resolve PCR products.

Statistical analysis

The mean value of traits was figured out, and analysis of variance (ANOVA) was performed in accordance with Panse & Sukhatme (1978) in Microsoft Excel 2013. A phenotypic trait-based dissimilarity matrix was constructed using Manhattan coefficients with Numerical Taxonomy and Multivariate Analysis System (NTSYSpc 2.0; Rohlf, 1998). The amplified products of SSR and SRAP markers were scored in 1 (presence) and 0(absence) fashion. Polymorphism information content (PIC), Multiplex ratio (MR), effective multiplex ratio (EMR) marker index (MI) and resolving power (Rp) value were estimated following Sharma et al. (2016) in Microsoft Excel 2013. In NTSYSpc 2.0, The SIMQUAL program used Jaccard’s similarity (J) coefficient to compute the genetic similarity between genotypes, SAHN clustering method was used to construct the unweighted pair group method with arithmetic mean (UPGMA) dendrogram (Sneath & Sokal, 1973).

Character variance analysis

Nutrient-dense genotypes

Genotypes dense in multiple nutrients can directly be released as a variety after evaluating their yield performance over the locations for multiple years. Such genotypes can be exploited in a hybridization program. In the current study, the top eight nutrient-dense genotypes flowered in 34–74 days and had 5.71–9.39 g TGW (Table 3). Top genotypes had Fe content of 61.07–77.38 (ICMR 08666) ppm, Zn content of 45.11–55.48 (IC 139900) ppm, Mn content of 16.2–17.63 (ICMR 08444) ppm, Ca content of 230.5-256 (ICMR 12888) ppm, Cu content of14.32–22.59 (GHB 558) ppm, K content of 52.3–60.15(ICMR 10999) ppm, and P content of 33.28–36.72 (ICMR 08666). IC 139900 was superior for both Fe (71.22 ppm) and Zn (55.48 ppm). Genotype ICMR 08666 was dense for Fe, Zn, K and P. Out of eight high-Fe genotypes, only two genotypes had >75 ppm. Thus current experiment also identified the best genotypes that had a higher content of multiple nutrients. Earlier, Govindaraj et al. (2020) also identified genotypes having a high content of multiple nutrients.

Phenotypic diversity analysis

Phenotypic diversity is important for pearl millet breeding. The interactions between the genome and all of its growing micro- and mega-environments lead to the phenotype of the plant (Fasoula, Ioannides & Omirou, 2020). The mean value of each trait was used to generate the Manhattan dissimilarity coefficient and dendrogram (Sokal, 1958). The genotypes were divided into nine major clusters based on the Manhattan dissimilarity coefficient. Earlier, Shashibhushan, Kumar & Kondi (2022) also generated eight clusters of 40 pearl millet genotypes using phenotypic data (Fig. 1). In current study, the average dissimilarity value among genotypes was calculated to be 0.16, demonstrating modest phenotypic variability (Table 4). The dissimilarity between genotypes ranged from 0.08 (IC 139899 and ICMR 07888) to 0.27 (Nandi 75 and ICMR 07222) for the respective pair of genotypes.

Cluster I comprise seven genotypes, characterized by high values of DFF, days to maturity, lipid, potassium and low values of NPT. Cluster II consists of 25 genotypes. Cluster III contains four genotypes, namely ICMR 10222, Nandi 75, ICMR 12111, and ICMR 10888. This cluster is characterized by more GY, days to maturity and TGW. Cluster IV has four genotypes, namely ICMR 08999, ICMR 11888, ICMR 139900 and ICMR 07777. This cluster is characterized by more lipid content, Mn content, and days to maturity. Cluster V has three genotypes (IC 332716, GHB 558, ICMR 07444) which are characterized by low values of head diameter and lipid content. Cluster VI consists of two genotypes (ICMR 08111 and ICMR 12333). This cluster is characterized by more productive tillers per plant with low content of Zn. Cluster VII has only one genotype (ICMR 12555) which has high values for characters like days to maturity, protein content, Mn content and TGW (Table 4).

Molecular marker based diversity

Forty-eight genotypes of pearl millet were analyzed using SSRs and SRAP markers (Table 5). During the experiment, a total of 50 SSR markers were screened for amplification. Out of 50 markers, 30 (60%) primers showed amplification. Of these 30 SSR markers, 12 (37.5%) markers were found polymorphic. These 12 polymorphic SSRs markers generated 65 amplicons. The molecular weight of the amplicon ranged from 85 bp (PMES 190) to 292 bp (PMES 171). In previous reports with PMES-series SSR markers, Zala et al. (2017) recorded amplicon size from 101 to 285 bp. The number of polymorphic bands/amplicons per SSR marker ranged from 2 to 13, with a mean of 6.71. All SSR amplicons were found polymorphic. The PIC value, the informativeness of a primer, for each marker was computed for the estimation of marker allelic variation considering the allele frequencies in studied genotypes. The mean PIC of SSR markers was 0.28 though it varied from 0.132 (PMES 171) to 0.499 (PMES 173). This range was comparable with Zala et al. (2017) where PIC was between 0.188–0.375.

The Rp was estimated considering the proportion of genotypes containing the amplicon. The primer that might best differentiate the cultivar can easily be identified by the value of the Rp and PIC. In the current study, Rp varied from 1.625 (PMES 162) to 2.375 (PMES 168), with an average of 1.98. Mean Rp was between 0.154 (PMES 171) to 1.000 (PMES 173). The PI ranged from 0.998 (PMES 173) to 1.963 (PMES 168), though the mean PI value was 1.420. MI is considered to be an inclusive measure of the efficiency to detect polymorphism. The SSR MI was 19.092.

In the case of the SRAP markers, of 25 SRAP, six (24%) were polymorphic. The polymorphic SRAPs amplified 119 amplicons. The product size for SRAPs ranged from 94 (Em2+Me2) to 1357 bp (Em6+Me3). The polymorphic bands ranged from 10 (Em6+Me3) to 34 (Em2+Me2), with a mean of 21.67. Liu et al. (2008) observed a polymorphic band detected with each ranging from 6 to 17, with an average of 11.76. Bhatt et al. (2017) had a band size from 120 to 500 bp in cumin.This suggested that in different crops SRAP amplicon size will be highly variable. PIC oscillated from 0.224 (Em5+Me4)—0.324 (Em6+Me2), with an average of 0.26. Bhatt et al. (2017) reported a PIC value (0.34).

The mean PI of SRAP markers was 5.69, through the maximum PI was for Em6+Me2 (8.108) and the lowest value for Em6+Me3 (2.335). Rp ranged from 4.625 (Em5+Me4) to 13.250 (Em6+Me2), with an average value of 8.27. Mean Rp was between 0.272 (Em5+Me4)—0.542 (Em6+Me3) with an average of 0.40. Liu et al. (2008) stated higher RP values ranged from 2.229 to 8.457 with an average of 4.927. The fraction of polymorphism, MR, EMR and MI for SRAPs are 1.00, 10.00, 10.00, and 15.65, respectively (Table 6).

Inter-genotype genetic relationship

Forty-eight pearl millet genotypes were divided into seven major groups by the dendrogram created using pooled data from SSR and SRAP markers based on Jaccard’s similarity matrix. Clusters I, II, III, IV, V, VI and VII had one, one, 11, eight , three, 22, and two genotypes, respectively (Fig. 2). In the study of Nehra et al. (2017), with SSR markers, 49 accessions were clustered into eight core clusters. Kumar et al. (2020b) alienated 18 lines into three clusters in pearl millet using 74 SSRs. In the current study, the inter-accession genetic coefficient of similarity ranged from 0.616 to 0.877 while the average similarity was 0.743. ICMR 098888 and GHB 905 has a genetic distance (0.384) indicative that both genotypes are having moderate genetic difference level and can be crossed to create a bi-parental mapping population. The minimum genetic distance (0.123) was between IC 139899 and IC 332727, demonstrating that these accessions have more similarity in SSR locus. Moreover, based on diversity results, breeders can select diverse genotypes for combining ability and heterosis analysis for traits studied in the current study.