Next Article in Journal
Precision Agriculture Application for Sustainable Nitrogen Management of Justicia brandegeana Using Optical Sensor Technology
Next Article in Special Issue
Effect of Species, Fertilization and Harvest Date on Microbial Composition and Mycotoxin Content in Forage
Previous Article in Journal
Drivers of Cooperation Activity in Kosovo’s Agriculture
Previous Article in Special Issue
Sowing Date Affects Maize Development and Yield in Irrigated Mediterranean Environments
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Utilization of Molecular Marker Based Genetic Diversity Patterns in Hybrid Parents to Develop Better Forage Quality Multi-Cut Hybrids in Pearl Millet

by
Govintharaj Ponnaiah
1,2,*,
Shashi Kumar Gupta
2,
Michael Blümmel
3,
Maheswaran Marappa
1,
Sumathi Pichaikannu
1,
Roma Rani Das
2 and
Abhishek Rathore
2
1
Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India
2
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Hyderabad, Telangana, India
3
International Livestock Research Institute (ILRI), Patancheru 502 324, Hyderabad, Telangana, India
*
Author to whom correspondence should be addressed.
Agriculture 2019, 9(5), 97; https://doi.org/10.3390/agriculture9050097
Submission received: 10 April 2019 / Revised: 24 April 2019 / Accepted: 24 April 2019 / Published: 3 May 2019
(This article belongs to the Special Issue Quality and Production of Forage)

Abstract

:
Genetic diversity of 130 forage-type hybrid parents of pearl millet was investigated based on multiple season data of morphological traits and two type of markers: SSRs (Simple sequence repeats) and GBS identified SNPs (Genotyping by sequencing-Single nucleotide polymorphism). Most of the seed and pollinator parents clustered into two clear-cut separate groups based on marker based genetic distance. Significant variations were found for forage related morphological traits at different cutting intervals (first and second cut) in hybrid parents. Across two cuts, crude protein (CP) varied from 11% to 15%, while in vitro organic matter digestibility (IVOMD) varied from 51% to 56%. Eighty hybrids evaluated in multi-location trial along with their parents for forage traits showed that significant heterosis can be realized for forage traits. A low but positive significant correlation found between SSR based genetic distance (GD between parents of hybrid) and heterosis for most of the forage traits indicated that SSR-based GD can be used for predicting heterosis for GFY, DFY and CP in pearl millet. An attempt was made to associate marker-based clusters with forage quality traits, to enable breeders select parents for crossing purposes in forage breeding programs.

1. Introduction

Pearl millet (Pennisetum glaucum (L.) R. Br.) is an important staple crop in the arid and semi-arid tropical regions of Asia and Africa. This crop, being a C4 species, is highly photosynthetically efficient, has a short duration coupled with significant levels of pest and disease resistance, and is tolerant to abiotic stresses (drought, heat & salinity). As such, it can be designated as a “perfect resilient crop for the future”. This crop is mainly cultivated for grain and fodder purpose in semi-arid regions of Asia and Africa, for which dual purpose cultivars are popular on farms. Apart from dual-purpose cultivars, pearl millet cultivars are also bred exclusively for forage purpose and cultivated across the globe. For instance, pearl millet forage hybrids are grown in southern USA [1,2] and in the summer season in Australia and South America [3]. Recently, it has occupied large areas under summer cultivation in north-western India and is proving to be significant source of fodder for livestock [4,5]. Also, Brazil which introduced this crop as cover crop in soybean cropping system, now cultivates 5 m ha of pearl millet for feed and forage purposes [6,7].
Non-availability of feed and fodder in sufficient amounts has been one of the major limiting components to achieving the desired level of livestock production in most of the countries in the semi-arid tropics. For instance, India faces a net deficit of 35.6% green fodder, 10.95% of dry crop residues and 44% of concentrate feeds, and it would require 1012 million tons of green fodder and 631 million tons of dry fodder by 2050. At the current level of growth in forage production, there will be a 18.4% deficit in green fodder and 13.2% deficit in dry fodder by 2050 in India [8]. To overcome this projected deficit, green forage supply should grow in India an the annual rate of 1.69%. Breeding for forage traits has not been prioritized in most of the pearl millet programs across the globe, leading to a reduced diversity for forage cultivars, with only a handful of cultivars being available for cultivation. The pearl millet forage hybrids released to date have been bred generally for single cut forage purposes, but farmers are now demanding multi-cut (2–3 cuts) forage cultivars with better forage quality to meet round the year feed requirements of livestock [9]. Forage trials of pearl millet conducted multi-locationally during summer season in India, reported 10% to 16% higher dry stover yields than sorghum and 21% to 30% higher stover yields than maize; 45% to 64% and 30% to 58% higher stover protein was reported than sorghum and maize, respectively [10]. Significant variability has been observed in previous studies for forage quality traits in pearl millet breeding materials [11]. Also, based on evaluation of pearl millet accessions for biomass traits, high variability was reported for forage traits like green forage yield (GFY), dry forage yield (DFY), stover nitrogen content, metabolizable energy (ME) and in vitro organic matter digestibility (IVOMD) [12].
To date, almost all the investigations on characterization of pearl millet (hybrid parents or other breeding materials) are either based on morphological traits or on molecular markers, and have primarily targeted grain yield and component traits [13,14,15,16,17,18,19,20]. A positive association has been reported between molecular marker-based GD and hybrid performance in pearl millet [20], while few studies have indicated a negative relationship [17,21]. Similarly, some studies found a positive relationship between molecular marker based genetic diversity and heterosis in other crops, like in maize [22,23], in rice [24,25] and in sunflower [26], while others reported no relationship between GD and heterosis in maize [27,28], in rice [29,30] and in sunflower [31]. Hence, the present study was designed to investigate genetic diversity in forage type hybrid parents based on both morphological traits and also using two different molecular marker systems (SSRs and GBS-identified SNPs markers), to reveal the relationship between marker based genetic diversity and heterosis for forage traits.

2. Materials and Methods

2.1. Plant Materials

A set of 130 hybrid parents derived from high biomass nursery (F6 and above) at ICRISAT (International Crops Research Institute for the Semi-Arid Tropics)-Patancheru, Hyderabad, Telangana, India, was investigated in this study. This included 18 seed parents and 112 pollinator parents, and all of them were derived from crosses involving diverse parents following pedigree breeding (Table S1). The seed parents were coded from FB01 (Forage B line) to FB18, while pollinator parents were coded from FP01 (Forage pollinator) to FP112. Tift 23D2B1, a maintainer of A1 CMS (cytoplasmic male sterility) bred at Tifton, Georgia [32,33] was used as a reference genotype.

2.2. DNA Isolation

Around thirty five seeds of each entry were grown in small plastic (4 inch) pots along with Tift 23D2B1 in a dark house for eight days. Approximately 100 mg of bulk leaf tissue was collected from 20 to 25 seedlings per accessions and stored immediately in a 96-well plate. DNA was isolated using NucleoSpin® 96 Plant II kit (Macherey-Nagel, Düren, Germany). Two elutions of DNA for SNP and SSR genotyping were generated. Normalization of genomic DNA to 10 ng/µL was done on 0.8% agarose gel using lamda DNA (MBI Fermentas, Hanover, MD, USA). Electrophoresis was performed in Tris acetate-EDTA buffer in a buffer tank at 90 volts for 1 h and gels were visualized by UV light using image analyzer after being stained with ethidium bromide.

2.3. Genotyping of Hybrid Parents

2.3.1. Simple Sequence Repeats

A total of 52 SSR markers (Table S2) were reported to be highly polymorphic, of which 47 were mapped earlier across 7 pearl millet linkage groups [34,35,36,37,38] and were used to genotype the set of forage type hybrid parents involved in this study.

2.3.2. Polymerase Chain Reaction

PCRs were performed using 10 µL volumes of reaction mixture, containing 2 µL of 10 ng DNA template, 0.5 µL of 1 mM dNTPs, and 0.06 µL of 0.2U Taq DNA polymerase, 1 µL of 10× Kappa Taq Polymerase buffer with MgCl2, and 1 µL of primer containing 2 pmol/µL of forward and 4 pmol/µL of reverse primer, 0.2 µL of dye either Fam, Vic, Ned and Pet. PCR amplification was carried out using step-down program in a thermal cycler (GeneAmp, PCR System 9700; Applied Biosystems, Foster City, CA, USA) using 384-well PCR plates. The amplification conditions of initial denaturation at 94 °C for 5 min, 10 cycles at 94 °C for 25 s, 64 °C (−1 °C/cycles) for 20 s, and 72 °C for 30 s, followed by 37 cycles at 56 °C for 20 s, and 72 °C for 30 s, with a final extension at 72 °C for 20 min. After amplification, PCR products were multiplexed with 1 µL of each of dye-labeled products (Fam, Vic, Ned and Pet), 7 µL of Hi-Di™ Formamide (Applied Biosystems, Foster City, California, CA, USA), 0.1 µL of the LIZ-labeled (500[–250]) internal size standard, and 3.9 µL of distilled water. The DNA fragments were size separated on an ABI 3700 automatic DNA sequencer (Perkin-Elmer/Applied Biosystems, Foster City, CA, USA) using Gene Mapper® 4.0 software and GeneScan 3.1 (Applied Biosystems, Foster City, CA, USA) was used for allele calling. AlleloBin 2.0 [39] was used to measure the accurate allele size.

2.3.3. Genotyping by Sequencing (GBS) and SNP Calling

Hybrid parents were also genotyped using the GBS method as described by Elshire et al. [40]. The genomic libraries were constructed using an ApeKI endonuclease restriction enzyme. PCR amplification of pooled amplicons was carried out before sequencing on a Illumina Hiseq2500 platform (Illumina Inc., San Diego, CA, USA).
Raw sequencing reads and barcode information were processed with the non-reference based UNEAK (Universal Network Enabled Analysis Kit) pipeline [41] implemented in TASSEL V.4 software [42] to identify SNPs. Barcodes containing reads are retained and used for SNP calling. These reads are trimmed to 64 bp from barcode side, aligned against each other and used for SNP identification. Total of 19,652 SNPs were identified across all hybrid parents in pearl millet. Identified SNPs were assigned to each hybrid parents based on the barcode sequence information. Further, the SNP data were filtered with minor allele frequency (MAF) cutoff of 0.10 (10%) and SNP with ≥25% missing data. After filtering for missing data and minor allele frequency, we obtained 7870 SNPs to conduct diversity analysis on our set of hybrid parents.

2.4. Phenotyping of Hybrid Parents

A set of 116 hybrid parents was evaluated for forage yield and quality traits in partially balanced alpha lattice design with two replications, at ICRISAT, Patancheru (18° N, 78° E, 545 m above sea level) during the summer season of 2015–2016. The plots consisted of 4 rows of 4 m in length spaced at 60 cm.
During the time of field preparation, nitrogen and phosphorous were applied as a basal dose in the form of 100 kg ha−1 of Diammonium phosphate (18% N and 46% P). Plots were fertilized equally with dosage rate of 100 kg ha−1 of urea (46% N) as top dressing, two times before first harvest. Trial was irrigated at 12 to 15 days interval to avoid any moisture stress, and crop was protected from weeds, pests, animals and diseases. Green forage of each entry in the trial was first harvested at 50 days (around boot stage of plant development) after planting by cutting at the second node from the bottom of the plant. Fresh weight of the green forage was recorded (kg) for plot and converted into t ha−1. A sub-sample (10–15 plants) of about 1 kg was collected per entry from the freshly harvested green forage and recorded for green forage weight, oven-dried for 8 h daily for three to four days at 60 °C in Campbell dryer (Campbell Industries, Inc., 3201 Dean Avenue, Des Moines, IA, USA), and weighed again (dry forage weight in kg). The dry matter concentration was determined by the ratio between the dry forage weight and green forage weight. DFY (t ha−1) on a plot basis for each entry was calculated by multiplying the green forage weight and dry matter concentration. Dried sub-samples were chopped into 10 to 15 mm pieces using a chaff cutter (Model # 230, Jyoti Ltd., Vadodara, India) and ground using a Thomas Wiley mill (Model # 4, Philadephia, PA, USA) to pass through a 1-mm screen for chemical analysis. Ground stover samples (Approximately, 40 g of sample/entry) were analyzed by Near-Infrared Reflectance Spectroscopy (NIRS) for stover nitrogen concentration (N × 6.25 equals to CP content) and IVOMD [43,44]. A second cut of forage was taken after thirty days of the first cut; GFY, DFY and stover quality traits were again recorded as described in the first cut.

2.5. Hybrid Development and Phenotypic Evaluation

Ten seed and eight pollinator parents from the set of 130 hybrid parental lines were selected based on diverse pedigrees (Table S1). This set of 18 hybrid parents effectively represented the genetic diversity of all the 130 lines, as GD of selected lines varied from 0.42 to 0.79 with a mean of 0.64, while GD of all the lines varied from 0.38 to 0.95 with a mean of 0.74. For SNPs, GD varied from 0.22 to 0.58 with a mean of 0.46 for selected hybrid parents, while GD of whole set of hybrid parents varied from 0.18 to 0.64 with a mean of 0.47. Eighty hybrids were generated by crossing them in a line × tester (10 × 8 = 80) fashion. These 80 hybrids and their parents were evaluated during summer season of 2015 at two locations [ICRISAT, Patancheru; and TNAU (Tamil Nadu Agricultural University), Coimbatore (11° N, 77° E, and 411.98 m above sea level, respectively)] for forage related morphological and quality traits. At ICRISAT, Patancheru, all entries were planted on alfisol in an alpha lattice design with three replications. Each entry was planted in 4 rows of 4 m length with rows spaced 60 cm apart and plants spaced at 10–12 cm from each other. At TNAU, Coimbatore, entries were planted in black soils with two replications. Each entry was planted in 3 rows, each of 2 m in length with rows spaced 45 cm apart. At both the locations, cultural practices were followed as described earlier under the head “phenotyping of hybrid parents”; and GFY, DFY, CP and IVOMD were again recorded as mentioned under the same heading.

2.6. Data Analysis

One hundred and thirty high biomass forage type hybrid parents (18 seed and 112 pollinator parents), including all the 116 which were phenotyped, were analyzed for SSRs. In the case of GBS-identified SNPs, eight parents were dropped due to missing information, so 122 (18 seed and 104 pollinator parents) hybrid parents were analyzed using 7870 (GBS-identified SNPs) markers. The allelic base pairs of all the markers were used to estimate the summary statistics, which include PIC, allelic richness as determined by total number of detected alleles and allele per locus, gene diversity and heterozygosity. Gene diversity, which is often referred to as expected heterozygosity, is defined as the probability that two randomly chosen alleles from the population are different. An unbiased estimator of gene diversity at the l th is D ^ l = ( 1 u = 1 k p l u 2 ) / ( 1 1 + f n ), where p l u is the frequency of the u th allele at the l th locus, n is the sample size, and f is the inbreeding depression, respectively. Occurrence of common, unique, rare and most frequent alleles were estimated using PowerMarker V3.25 software [45]. Common alleles, unique alleles and rare alleles were calculated as described by Li et al. [46] and Upadhaya et al. [47].
The genetic dissimilarities for each pair of forage type hybrid parents using simple matching coefficient matrix (SSRs) and Roger’s distance matrix (GBS-identified SNPs) were calculated with the Power Marker V3.25 software. The simple match distance takes into account both the shared 0 s (absence of a band) and shared 1 s (presence of a band) as factors that contribute to similarity between two individuals. Simple matching distance was calculated as d i j = 1 1 L l = 1 L m l π , where d i j is the dissimilarity between units i and j , L is the number of loci, π is ploidy and m l is the number of matching alleles for locus l , respectively. Rogers’s distance was calculated as D R = 1 m j m 1 2 i a j ( p i j q i j ) 2 p i j and q i j are the frequencies of i th allele at the j th locus in populations X and Y respectively, while a j is the number of alleles at the j th locus, and m is the number of loci examined. Hybrid parents (seed and pollinator) were grouped by cluster analysis using the neighbor-joining method in Power Marker V3.25 software. The pairwise Fst method was used to infer the clusters in a neighbor-joining tree. Analysis of molecular variance (AMOVA) was performed to detect mutational differences between the loci in diverse group of populations by partitioning the variation within and between the hybrid parents [48]. AMOVA analysis provided the Wright’s F statistic or Fst (Fixation index) based on genetic distance of the subgroups in both the marker systems. Association between hybrid parents were estimated with PCoA (Principal coordinate analysis) analysis using DARwin V6 software [49]. Mantel test was performed to investigate correlation between genetic distances estimated through SSRs and GBS-identified SNPs [50] based on 10,000 iterations using R software [51]. BLUP (Best Linear Unbiased Prediction) means were estimated for each of the 116 hybrid parents on the four traits evaluated and used for genetic relatedness based on forage traits using SAS v 9.4 [52].
Combined analysis of variance (ANOVA) for a hybrid estimation trial was estimated using SAS PROC MIXED [52]. The estimate of mid-parent and better parent heterosis for each trait was calculated from mean of two environments with the following formulae: Mid-parent heterosis (MPH, %) = (F1 − MP)/MP × 100; Better parent heterosis (BPH, %) = (F1 − BP)/BP × 100, where F1 (First filial hybrid) is the hybrid mean, MP (Mid-parent) is the mean of both the parents, and BP (Better parent) is the superior parent over the other parent. The Pearson correlation coefficients (r) between GD and heterosis (MPH and BPH) were estimated using across environment trait means.

3. Results

3.1. Genetic Diversity Indicators Based on SSRs and GBS-Identified SNPs

Fifty two SSR markers detected a total of 551 alleles in 130 hybrid parents, with an average of 10.60 alleles per locus. The number of alleles per locus ranged from 2 (Xpsmp2273) to 31 (Xpsmp2070), with three to ten alleles at 29 SSR loci (Table S3). Markers Xpsmp2068 (26), Xpsmp2081.1 (30) and Xpsmp2070 (31) had more than 20 alleles per locus. Furthermore, the average number of alleles per locus was higher for pollinator parents (9.81) than for seed parents (4.63) (Table 1). The polymorphism information content (PIC) values varied from 0.06 (Xpsmp2267) to 0.94 (Xpsmp2070) with a mean of 0.65. Pollinator parents had higher PIC values (0.63) than seed parents (0.52). The gene diversity varied from 0.06 (Xpsmp2267) to 0.94 (Xpsmp2070) with an average of 0.68. Moreover, pollinator parents showed higher gene diversity (0.66) than seed parents (0.55). The level of heterozygosity ranged from 0.0 to 0.12, with an average of 0.02. Of the 551 alleles, 129 were rare alleles (23.41%) ranging from 1 to 13, 341 were common alleles (61.89%) ranging from 1 to 23, and 81 were most frequent alleles (14.70%) ranging from 1 to 3. Genotype-specific (unique) alleles were detected in 3 seed parents and 14 pollinator parents (Table S4).
The average gene diversity was 0.48, which varied from 0.02 to 0.50 for GBS-identified SNP markers. The range of heterozygosity was 0.0 to 0.97, with an average of 0.15, while PIC varied from 0.02 to 0.38, with an average of 0.37 (Table 1).

3.2. Clustering Pattern and Genetic Relatedness between Hybrid Parents Based on Markers

The neighbor-joining tree constructed based on simple matching (SSR based) distance matrix grouped all of the seed and pollinator parents into 2 clear-cut separate groups (statistical significance values provided in Table S5) (Figure 1a), each having majority of seed and pollinator hybrid parents. These broad groups were found to be partitioned further into three and four statistically distinct clusters of seed and pollinator parents, respectively (Tables S6 and S7). Twenty three pollinator parents were found grouped in clusters dominated by seed parents, of which only 7 were found clustered with seed parents, while 16 pollinators were grouped separately. Eight B-lines were found in B-I (44%), 6 in B-II (33%) and 4 in B-III (22%) (Figure 1b and Table S1). Cluster B-II had 67% (4 out of 6) of the lines derived directly or indirectly from ICMB 89111 (ICRISAT millet B-line) in their parentage. B-III cluster had 50% (2 out of 4) of the lines sharing 81B in parentage.
Pollinator parents found grouped into four clusters (Figure 1c and Table S1), of these 45 lines in R-I (40%), 24 in R-II (21%), 30 in R-III (27%) and 13 (12%) in R-IV. Twenty six out of 45 and 16 out of 24 pollinator parents in cluster I and II respectively, had ICMS 7704 (ICRISAT millet synthetic variety) in their parentage. ICMS 7704 is an open-pollinated variety developed from six inbred lines derived from Indian × African crosses selected at Tandojam in Pakistan. Seventeen out of 30 lines in R-III cluster had progenies derived from Medium composite 94 (MC 94) in their parentage; MC 94 is a medium maturity (75–85 days) composite developed at ICRISAT. R-IV cluster had 42% (5 out of 12) of the progenies sharing a common parent ICMS 8506.
Seed and pollinator parents followed almost the same clustering pattern based on both GBS-identified SNPs and SSRs (Figure 2a and Table S1) (Statistical significance values depicting distinctness of clusters provided in Tables S8–S10). Based on SNP based clustering, thirty-three pollinators were found grouped in clusters dominated by seed parents, but 30 of them were found to be grouped separately. SNP based clustering followed the same trend of pedigree linkages as found in case of SSRs; 33% (2 out of 6) and 67% (2 out of 3) of the lines in B-II and B-III respectively, had the ICMB 89111 parent (Figure 2b), while cluster R-I and R-II had 80% and 48% of the lines with ICMS 7704 respectively, and R-III had 57% (17 out of 30) of the lines with MC 94 in the parentage (Figure 2c).
AMOVA based on SSRs showed 83.62% of the variance within seed and pollinator parents and 16.38% between seed and pollinator parents (p < 0.0001). The Fst between seed and pollinator parents for individual markers varied from −0.0126 to 0.5381 and was significant for 41 of the 52 SSRs (Table S11). Some of the markers such as Xpsmp2201 (0.2567), Xipes0105 (0.2574), Xipes0004 (0.3227), Xipes0186 (0.3351), Xpsmp2214 (0.3445), Xicmp3048 (0.4848), Xpsmp2222 (0.5294) and Xpsmp2246 (0.5381) had the highest Fst values. Following the same trend, SNPs revealed 90.11% of the variance within seed and pollinator parents and 9.89% variance was observed between seed and pollinator parents (Table S12). PCoA based on SSR markers revealed first and second principal coordinates to account for 14.57% and 6.21% of the molecular variance, respectively (Table S13 and Figure S1a), while it was 33.63% and 7.74% based on the GBS-identified SNPs (Figure S1b). The Mantel test found significant association (r = 0.35, p < 0.01) between genetic distances (Simple matching distance for SSRs vs. Roger’s distance for SNPs) estimated for hybrid parents.

3.3. Variability for Forage Traits in Hybrid Parents

A wide range was observed in hybrid parents for GFY, DFY, CP and IVOMD at different cutting intervals (Table 2). GFY of hybrid parents at first cut ranged from 15 to 29 t ha−1 with an average of 22 t ha−1, seed parents ranged from 16 to 23 t ha−1 with an average of 20 t ha−1 and pollinator parents ranged from 15 to 29 t ha−1 with an average of 22 t ha−1. In addition, DFY varied from 3 to 6 t ha−1 with an average of 4 t ha−1 for hybrid parents, from 4 to 5 t ha−1 with an average of 4 t ha−1 for seed parents and from 3 to 6 t ha−1 with an average of 4 t ha−1 for pollinator parents. At the second cut, GFY of hybrid parents ranged from 12 to 42 t ha−1 with an average of 27 t ha−1, seed parents ranged from 12 to 26 t ha−1 with an average of 20 t ha−1 and pollinator parents ranged from 16 to 42 t ha−1 with an average of 28 t ha−1. Further, DFY varied from 5 to 9 t ha−1 with an average of 6 t ha−1 for both seed and pollinator parents, from 5 to 9 t ha−1 with an average of 6 t ha−1 for seed parents and from 5 to 8 t ha−1 with an average of 6 t ha−1 for pollinator parents. CP for hybrid parents, seed and pollinator parents ranged from 11% to 15%, 12% to 15% and 11% to 14% at the first cut, whereas it was 11% to 13%, 11% to 12% and 11% to 13% for second cut. IVOMD for hybrid parents ranged from 54% to 56% at the first cut, while it ranged from 51% to 55% at the second cut. Seed parents varied from 54% to 55% at first cut and 51 to 54% at second cut, and pollinator parents varied from 54% to 56% and 51% to 55% at the first and second cuts, respectively.

3.4. Trait Association with SSRs Based Clusters

Forage quantity and quality traits associated with SSRs based clusters revealed that B-III cluster had the highest mean values for GFY and DFY at the first cut and the highest GFY at the second cut, while DFY at second cut was found highest in cluster B-I (Table 3). Meanwhile for pollinator parent clusters, R-I had highest mean for GFY at first cut and for GFY and DFY at second cut. Cluster B-II had highest CP and IVOMD in first cut; while B-III had highest CP in second cut. Cluster R-II and R-III had the highest CP in the first and second cut, respectively. For seed parental clusters, B-I had the highest IVOMD in the second cut. Cluster R-II and R-III had the highest IVOMD in the first cut and R-IV in the second cut.

3.5. ANOVA for Heterosis Estimation Trial

ANOVA for forage yield and quality (Table 4) traits showed large and highly significant variance due to locations, indicating that the materials were evaluated under diverse environments. Large and highly significant variance observed in either of the parents (seed or pollinator parent) and in hybrids for almost all the traits indicated wide genetic differences among parental lines as well as among hybrids. High significant mean squares due to “hybrids vs. parents” (except IVOMD in second cut) indicated significant heterosis in hybrids for most of the forage traits. Forage yield performance of both parental lines and hybrids (except for DFY in first cut) was significantly modified by environments. The significant and relatively large percentage of the total variation attributable to the G × E (Genotype × Environment) suggested that hybrids responded differentially to environments for forage traits.

3.6. Magnitude of Heterosis and Its Association with GD

The extent of MPH and BPH for forage quantity and quality traits over two cuts are presented in Table 5. The MPH for DFY ranged from 33% to 407%, with an average of 189%, while BPH ranged from −13% to 344%, with an average of 154% at the first cut. Similarly, MPH for IVOMD ranged from −7% to 7%, with an average of 2%, while BPH ranged from −8% to 7% at first cut. The correlation between SSR based GD and heterosis (MPH and BPH) for forage related morphological and quality traits are given in Table 6. Low positive correlation was observed between GD and MPH (r = 0.34, p < 0.05), and with BPH (r = 0.41, p < 0.05) for GFY at the second cut; GD and BPH (r = 0.33, p < 0.05) for DFY at the second cut and; GD and MPH (r = 0.26, p < 0.05) for CP at the first cut. No significant correlation was found between GD and heterosis (both MPH and BPH) for IVOMD for both the cuts. Similarly, for SNPs, significant positive relationships were found between GD and MPH (r = 0.32, p < 0.01) and with BPH (r = 0.28, p < 0.05) for DFY at the first cut.

4. Discussion

The average number of alleles per locus (10.60) detected in this study was higher than earlier reports in pearl millet, 6.26 alleles per locus in 72 inbred lines [53] and 2.76 alleles per locus in 42 inbred lines [54]. These differences might be due to the lesser number of parental lines and limited number of SSRs (25 to 34) in those earlier conducted studies on grain type hybrid parents, whereas the current study had a higher number of diverse forage type hybrid parents (130) and also had a higher number of microsatellite markers (52 SSRs). However, it was still lower in comparison to other studies, like of Stich et al. [13] (16.4 alleles per locus in 145 inbred lines derived from landraces and open-pollinated varieties of West and Central Africa) and Gupta et al. [15] (12.68 alleles per locus in 379 inbred lines), which might be due to the involvement of a comparatively lesser number of hybrid parents (130) in this study. The present study detected a higher average number of alleles per locus, PIC and gene diversity in pollinator parents than seed parents, indicating that pollinator parents were genetically more diverse than seed parents. This might be due to the broader genetic base of germplasm used in the development of pollinator parents and also due to differences in the sample size (112 pollinator parents vs. 18 seed parents). These findings are in consonance with earlier investigations in pearl millet [14,15], which reported high diversity in pollinator parents than in seed parents.
Majority of seed and pollinator parents delineated into two separate groups based on SSRs. Similar kinds of clear cut separate seed and pollinator grouping patterns were reported earlier in pearl millet [14,15] and in rice [55]. Distributions of hybrid parents under study into different marker-based groups indicated presence of significant genetic diversity in the breeding materials. Also, most of parents found in the common cluster had the involvement of some common parent in the pedigree. Such genetic relatedness between hybrid parents as found in SSR based clusters was also reported earlier by Nepolean et al. [14] and Gupta et al. [15] in pearl millet hybrid parents bred for grain-type traits. The clustering pattern of hybrid parents and trend of involvement of common parents in parentage of lines in different clusters were almost similar for both type of marker systems, which might be due to significant positive correlation (r = 0.35, p < 0.01) found between parental genetic distance assessed using SSRs and GBS-identified SNPs markers. However, seed parental cluster B-III had 81B and ICMB 89111 in their parentage for SSRs and GBS-identified SNPs, respectively. Pollinator parents were grouped into four (SSRs) and five (SNPs) clusters, respectively. Pollinator parental group R-IV (SSR) had the lines sharing ICMS 8506 in parentage while SNP based R-IV had HHVBC tall in their parentage. Hence, our study suggested that any of the two marker systems, SSRs or SNPs, can be effectively used for diversity investigations for forage type pearl millet breeding materials in future. High correlation between genetic distances estimated by SSRs and SNPs was also reported earlier in sunflower [56], while no such correlation was found in maize [57,58].
The study revealed significant genetic variability for forage quantity and quality traits in hybrid parents under investigation. Several other studies have also reported a wide range of green forage yield in pearl millet [12,59,60,61,62]. Earlier, Rai et al. [63] also reported dry forage yield in hybrid parents in a range of 3 to 4 t ha−1 at the first cut. Stover CP varied from 11% to 15% and 11% to 13% at the first and second cut, respectively, which was more than the minimum (about 7%) required by rumen microbes [64]. The observed genotypic variations in crude protein in high biomass hybrid parents in this study can also help to enhance the capacity of feed intake, as suggested earlier by Van Soest [64].
The mean values observed for forage quality traits (CP and IVOMD) at the first and second cuts were higher than observed in previous studies in pearl millet [11,12,44,63,65,66] and also in several other crops, like in maize [67,68] and in sorghum [69]. Variability in IVOMD assumes high significance as a one-percent unit increase in digestibility in stover sorghum and pearl millet could result in increases in milk, meat and draught power outputs in a range from 6% to 8% [70]. Market studies on sorghum fodder also indicated that the pricing of fodder is affected by its quality traits, especially for IVOMD [71]. In hybrid parents involved in this study, stover IVOMD varied from 54% to 56% units at first cut, and from 51% to 55% in second cut. Such a wide range of IVOMD in forage type hybrid parental lines can be utilized in the breeding program for the development of quality cultivars with high digestibility.
Mean values of forage traits in SSR based clusters revealed significant variation across clusters. The study identified trait-specific clusters; such associations between marker-based clusters and forage traits might be due to a close association of markers under investigation with the specific forage trait. This study has not done association mapping between markers and forage traits, so the associations observed in this study might be the result of stratification rather than true association, but diverse hybrid parents from these trait-specific clusters can still provide inputs to breeders for utilization in forage trait-specific targeted breeding program.
Hybrids showed an average heterosis of 154% and 51% over better parent for DFY in the first and second cut, respectively, indicating that higher heterosis can be realized for forage traits in pearl millet. These results pointed that high yielding multi-cut forage type hybrids with better forage quality can be developed in pearl millet for enhancing livestock productivity [63,65]. However, a high G×E for forage traits in hybrids indicated that there is a need to breed agro-ecological specific forage type hybrids to have stable forage yields. Significant but low positive correlation was found between GD and heterosis for most of the forage quality traits in either first or second cut, suggesting that SSR-based GD can be used for predicting heterosis for GFY, DFY and CP in pearl millet. Again, SNPs based GD also found a significant association with heterosis for DFY. In contrast to our results, Gupta et al. [17] found no significant positive correlation with BPH for DFY in either of morphological or molecular distance in pearl millet. Low correlation between GD and heterosis might be due to a lack of linkage between genes for traits under investigation, unequal genome coverage, and random marker distribution in the genome [72,73].

5. Conclusions

In conclusion, this molecular marker based genetic diversity study indicated that available forage-type pearl millet hybrid parents exist as two separate gene pools, one each for seed and pollinator parents. Significant genetic diversity exists among parents for forage quantity and quality traits. The study also attempted to find associations between marker-based clustering patterns and forage quantity and quality traits, to enable breeders to generate crosses among diverse forage type hybrid parents belonging to distinct clusters and develop hybrids and parental lines with higher potential. High heterosis observed for forage quality traits indicated possibilities to develop exclusive forage type hybrids with more and better forage quality. Low positive but significant correlation between markers based genetic distance and heterosis for most of the forage traits indicated that forage traits can be predicted to some extent in pearl millet.

Supplementary Materials

The following are available online at https://www.mdpi.com/2077-0472/9/5/97/s1, Figure S1: Principal coordinate analysis of seed and pollinator parents based on (a) SSRs and (b) GBS-identified SNPs, Table S1: Pedigree of 130 forage type hybrid parents, cluster numbers based on SSR and SNP markers, and selected parents for hybrid development used in the current study, Table S2: List of markers (Forward/Reverse primer), linkage group and repeat motifs of 52 SSRs used in the study, Table S3: Allelic richness, Polymorphism Information Content (PIC), gene diversity and heterozygosity of the 52 SSRs in 18 seed parents and 112 restorer parents of forage type pearl millet, Table S4: Genotype-specific alleles present in 3 seed parents and 14 pollinator parents of forage pearl millet, Table S5: Pairwise Fst values for 130 hybrid (18 seed and 112 pollinator) parents grouped based on SSRs, Table S6: Pairwise Fst values for 18 seed parents grouped based on SSRs, Table S7: Pairwise Fst values for 112 pollinator parents grouped based on SSRs, Table S8: Pairwise Fst values for 122 hybrid (18 seed and 104 pollinator) parents grouped based on GBS-identified SNPs, Table S9: Pairwise Fst values for 18 seed parents grouped based on GBS-identified SNPs, Table S10: Pairwise Fst values for 104 pollinator parents grouped based on GBS-identified SNPs, Table S11: Significant test values for Fst from Analysis of molecular variance (AMOVA) for 52 SSRs, Table S12: Analysis of molecular variance of SSRs and GBS-identified SNPs, Table S13: Principal coordinate analysis estimated percentage of variation explained by two axes using SSRs and GBS-identified SNPs.

Author Contributions

S.K.G., M.M., S.P. and M.B. designed the experiments. A.R. and R.R.D. helped in data analysis. M.B. involved in phenotyping for forage quality traits by NIRS. P.G.G.P. and S.K.G. wrote the paper. M.M. and S.P. reviewed the manuscript. All authors read and approved the final manuscript.

Funding

Financial support of the project was provided by the ICRISAT-Pearl Millet Hybrid Parents Research Consortium (PMHPRC) and conducted under CGIAR, Research Program on Dryland Cereals to the first author to carry out this research work as a part of doctoral program.

Acknowledgments

This research work was carried out as part of a Ph.D. thesis of Govintharaj, submitted to Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu. It was financially supported by the ICRISAT-Pearl Millet Hybrid Parents Research Consortium (PMHPRC) and conducted under CGIAR, Research Program on Dryland Cereals. We also acknowledge Mahender Thudi from the ICRISAT-CEG for his assistance in lab work. We also thank K.V.S. Prasad and Ramakrishna Reddy from ICRISAT-ILRI for sample preparation and NIRS analysis.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

SSRSimple sequence repeats
GBSGenotyping by sequencing
SNPSingle nucleotide polymorphism
GFYGreen forage yield
CPCrude protein
IVOMDIn vitro organic matter digestibility
NIRSNear infra-red reflectance spectroscopy
GDGenetic distance
DFYDry forage yield
MEMetabolizable energy
ICRISATInternational Crops Research Institute for the Semi-Arid Tropics
FBForage B line
FPForage pollinator
CMSCytoplasmic male sterility
PCRPolymerase chain reaction
UNEAKUniversal Network Enabled Analysis Kit
MAFMinor allele frequency
TNAUTamil Nadu Agricultural University
AMOVAAnalysis of molecular variance
FstFixation index
PCoAPrincipal coordinate analysis
BLUPBest Linear Unbiased Prediction
ANOVAAnalysis of variance
MPHMid-parent heterosis
BPHBetter parent heterosis
MPMid parent
BPBetter parent
F1First filial hybrid
PICPolymorphism information content
ICMBICRISAT millet B-line
ICMSICRISAT millet synthetic composite variety
MCMedium composite
G × EGenotype × Environment

References

  1. Burton, G. History of hybrid development in pearl millet in Tifton. In Proceedings of the First Grain Pearl Millet Symposium, Tifton, GA, USA, 17–18 January 1995; pp. 5–8. [Google Scholar]
  2. Davis, A.J.; Dale, N.M.; Ferreira, F.J. Pearl millet as an alternative feed ingredient in broiler diets. J. Appl. Poultry Res. 2003, 12, 137–144. [Google Scholar] [CrossRef]
  3. Hanna, W. Improvement of millets: Emerging trends. In Proceedings of the 2nd International Crop Science Congress, New Delhi, India; Chopra, V.L., Singh, R.B., Varma, A., Eds.; Oxford & IBH Publishing Co. Pvt. Ltd.: New Delhi, India, 1996; pp. 139–146. [Google Scholar]
  4. Reddy, A.A.; Malik, D.; Singh, I.P.; Ardeshna, N.J.; Kundu, K.K.; Rao, P.; Gupta, S.K.; Sharma, R.; Gajanan, G.N. Demand and supply for pearl millet Grain and fodder by 2020 in Western India. Agric. Situat. India 2012, 68, 635–646. [Google Scholar]
  5. Amarender Reddy, A.; Yadav, O.; Dharm Pal Malik, S.I.; Ardeshna, N.; Kundu, K.; Gupta, S.K.; Sharma, R.; Sawarganokar, G.L.; Shyam, M.; Reddy, K.S. Utilization Pattern, Demand and Supply of Pearl Millet Grain and Fodder in Western India; Working Paper Series No. 37; ICRISAT: Patancheru, India, 2013. [Google Scholar]
  6. De Assis, R.L.; de Freitas, R.S.; Mason, S.C. Pearl millet production practices in Brazil: A review. Exp. Agric. 2018, 54, 699–718. [Google Scholar] [CrossRef]
  7. Dias-Martins, A.M.; Pessanha, K.L.; Pacheco, S.; Rodrigues, J.A.S.; Carvalho, C.W.P. Potential use of pearl millet (Pennisetum glaucum (L.) R. Br.) in Brazil: Food security, processing, health benefits and nutritional products. Food Res. Int. 2018, 109, 175–186. [Google Scholar] [CrossRef]
  8. IGFRI (Indian Grassland and Fodder Research Institute). Vision 2050. 2013. Available online: http://www.igfri.res.in/ (accessed on 28 August 2018).
  9. Rai, K.; Yadav, O.; Gupta, S.K.; Mahala, R. Emerging research priorities in pearl millet. J. SAT Agric. Res. 2012, 10, 1–5. [Google Scholar]
  10. Rai, K.N.; Reddy, B.V.S.; Dakheel, A.J. Forage potential of sorghum and pearl millet adapted to dry lands. In Proceedings of the Seventh International Conference on the Development of Drylands, Tehran, Iran, 14–17 September 2003; Beltagy, K.B., Saxena, Eds.; Sustainable Development and Management of Drylands in the Twenty-First Century; International Centre for Agricultural Research in the Dry Areas: Aleppo, Syria, 2005; pp. 243–251. [Google Scholar]
  11. Bidinger, F.; Blummel, M.; Hash, C.; Choudhary, S. Genetic enhancement for superior food-feed traits in a pearl millet (Pennisetum glaucum (L.) R. Br.) variety by recurrent selection. Anim. Nutr. Feed Technol. 2010, 10, 61–68. [Google Scholar]
  12. Gupta, S.; Ghouse, S.; Atkari, D.; Blümmel, M. Pearl millet with higher stover yield and better forage quality: Identification of new germplasm and cultivars. In Proceedings of the 3rd Conference of Cereal Biotechnology and Breeding/CBB3, Berlin, Germany, 2–4 November 2015. [Google Scholar]
  13. Stich, B.; Haussmann, B.I.; Pasam, R.; Bhosale, S.; Hash, C.T.; Melchinger, A.E.; Parzies, H.K. Patterns of molecular and phenotypic diversity in pearl millet [Pennisetum glaucum (L.) R. Br.] from West and Central Africa and their relation to geographical and environmental parameters. BMC Plant Biol. 2010, 10, 216. [Google Scholar] [CrossRef]
  14. Nepolean, T.; Gupta, S.; Dwivedi, S.; Bhattacharjee, R.; Rai, K.; Hash, C. Genetic diversity in maintainer and restorer lines of pearl millet. Crop Sci. 2012, 52, 2555–2563. [Google Scholar] [CrossRef]
  15. Gupta, S.K.; Nepolean, T.; Sankar, S.M.; Rathore, A.; Das, R.R.; Rai, K.N.; Hash, C.T. Patterns of molecular diversity in current and previously developed hybrid parents of pearl millet [Pennisetum glaucum (L.) R. Br.]. Am. J. Plant Sci. 2015, 6, 1697–1712. [Google Scholar] [CrossRef]
  16. Hu, Z.; Mbacké, B.; Perumal, R.; Guèye, M.C.; Sy, O.; Bouchet, S.; Vara Prasad, P.V.; Morris, G.P. Population genomics of pearl millet (Pennisetum glaucum (L.) R. Br.): Comparative analysis of global accessions and Senegalese landraces. BMC Genom. 2015, 16, 1048. [Google Scholar] [CrossRef] [PubMed]
  17. Gupta, S.K.; Nepolean, T.; Shaikh, C.G.; Rai, K.; Hash, C.T.; Das, R.R.; Rathore, A. Phenotypic and molecular diversity-based prediction of heterosis in pearl millet (Pennisetum glaucum L. (R.) Br.). Crop J. 2018, 6, 271–281. [Google Scholar] [CrossRef]
  18. Ramya, A.R.; Ahamed, M.; Satyavathi, C.T.; Rathore, A.; Katiyar, P.; Raj, A.G.B.; Kumar, S.; Gupta, R.; Mahendrakar, M.D.; Yadav, R.S.; et al. Towards defining heterotic gene pools in pearl millet [Pennisetum glaucum (L.) R. Br.]. Front. Plant Sci. 2018, 8, 1934. [Google Scholar] [CrossRef]
  19. Sattler, F.; Sanogo, M.; Kassari, I.; Angarawai, I.; Gwadi, K.; Dodo, H.; Haussmann, B.I.G. Characterization of West and Central African accessions from a pearl millet reference collection for agro-morphological traits and Striga resistance. Plant Genet. Resour.-C 2018, 16, 260–272. [Google Scholar] [CrossRef]
  20. Singh, S.; Gupta, S.; Thudi, M.; Das, R.R.; Vemula, A.; Garg, V.; Varshney, R.K.; Rathore, A.; Pahuja, S.K.; Yadav, D.V. Genetic Diversity Patterns and Heterosis Prediction Based on SSRs and SNPs in Hybrid Parents of Pearl Millet. Crop Sci. 2018, 58, 2379–2390. [Google Scholar] [CrossRef]
  21. Chowdari, K.; Venkatachalam, S.; Davierwala, A.; Gupta, V.; Ranjekar, P.; Govila, O. Hybrid performance and genetic distance as revealed by the (GATA) 4 microsatellite and RAPD markers in pearl millet. Theor. Appl. Genet. 1998, 97, 163–169. [Google Scholar] [CrossRef]
  22. Betran, F.; Beck, D.; Bänziger, M.; Edmeades, G. Genetic analysis of inbred and hybrid grain yield under stress and nonstress environments in tropical maize. Crop Sci. 2003, 43, 807–817. [Google Scholar] [CrossRef]
  23. Wegary, D.; Vivek, B.; Labuschagne, M. Association of parental genetic distance with heterosis and specific combining ability in quality protein maize. Euphytica 2013, 191, 205–216. [Google Scholar] [CrossRef]
  24. Cai, J.; Lan, W. Using of AFLP marker to predict the hybrid yield and yield heterosis in rice. Chin. Agric. Sci. Bull. 2005, 21, 39. [Google Scholar]
  25. Zhao, Q.Y.; Zhu, Z.; Zhang, Y.D.; Zhao, L.; Chen, T.; Zhang, Q.F.; Wang, C.L. Analysis on correlation between heterosis and genetic distance based on simple sequence repeat markers in japonica rice. Chin. J. Rice Sci. 2009, 23, 141–147. [Google Scholar]
  26. Darvishzadeh, R. Phenotypic and molecular marker distance as a tool for prediction of heterosis and F1 performance in sunflower (Helianthus annuus’ L.) under well-watered and water-stressed conditions. Aust. J. Crop Sci. 2012, 6, 732. [Google Scholar]
  27. Dreisigacker, S.; Melchinger, A.; Zhang, P.; Ammar, K.; Flachenecker, C.; Hoisington, D.; Warburton, M.L. Hybrid performance and heterosis in spring bread wheat, and their relations to SSR-based genetic distances and coefficients of parentage. Euphytica 2005, 144, 51–59. [Google Scholar] [CrossRef]
  28. Ndhlela, T.; Herselman, L.; Semagn, K.; Magorokosho, C.; Mutimaamba, C.; Labuschagne, M.T. Relationships between heterosis, genetic distances and specific combining ability among CIMMYT and Zimbabwe developed maize inbred lines under stress and optimal conditions. Euphytica 2015, 204, 635–647. [Google Scholar] [CrossRef]
  29. Xie, F.; He, Z.; Esguerra, M.Q.; Qiu, F.; Ramanathan, V. Determination of heterotic groups for tropical Indica hybrid rice germplasm. Theor. Appl. Genet. 2014, 127, 407–417. [Google Scholar] [CrossRef] [PubMed]
  30. Wang, K.; Qiu, F.; Larazo, W.; dela Paz, M.A.; Xie, F. Heterotic groups of tropical indica rice germplasm. Theor. Appl. Genet. 2015, 128, 421–430. [Google Scholar] [CrossRef] [PubMed]
  31. Gvozdenović, S.; Saftić-Panković, D.; Jocić, S.; Radić, V. Correlation between heterosis and genetic distance based on SSR markers in sunflower (Helianthus annus L.). J. Agric. Sci. 2009, 54, 1–10. [Google Scholar]
  32. Burton, G.W. Pearl millets Tift 23DA and Tift 23DB released. Georgia Agric. Res. 1967, 9, 6. [Google Scholar]
  33. Burton, G.W. Registration of Pearl Millet Inbreds Tift 23B1, Tift 23A1, Tift 23DB1, and Tift 23DA11 (Reg. Nos. PL 1, PL 2, PL 3, and PL 4). Crop Sci. 1969, 9, 397. [Google Scholar] [CrossRef]
  34. Allouis, S.; Qi, X.; Lindup, S.; Gale, M.; Devos, K. Construction of a BAC library of pearl millet, Pennisetum glaucum. Theor. Appl. Genet. 2001, 102, 1200–1205. [Google Scholar] [CrossRef]
  35. Budak, H.; Pedraza, F.; Cregan, P.; Baenziger, P.; Dweikat, I. Development and utilization of SSRs to estimate the degree of genetic relationships in a collection of pearl millet germplasm. Crop Sci. 2003, 43, 2284–2290. [Google Scholar] [CrossRef]
  36. Qi, X.; Pittaway, T.; Lindup, S.; Liu, H.; Waterman, E.; Padi, F.; Hash, C.T.; Zhu, J.; Gale, M.D.; Devos, K.M. An integrated genetic map and a new set of simple sequence repeat markers for pearl millet, Pennisetum glaucum. Theor. Appl. Genet. 2004, 109, 1485–1493. [Google Scholar] [CrossRef]
  37. Rajaram, V.; Nepolean, T.; Senthilvel, S.; Varshney, R.K.; Vadez, V.; Srivastava, R.K.; Shah, T.M.; Supriya, A.; Kumar, S.; Kumari, B.R.; et al. Pearl millet [Pennisetum glaucum (L.) R. Br.] consensus linkage map constructed using four RIL mapping populations and newly developed EST-SSRs. BMC Genom. 2013, 14, 159. [Google Scholar] [CrossRef]
  38. Senthilvel, S.; Jayashree, B.; Mahalakshmi, V.; Kumar, P.S.; Nakka, S.; Nepolean, T.; Hash, C.T. Development and mapping of simple sequence repeat markers for pearl millet from data mining of expressed sequence tags. BMC Plant Biol. 2008, 8, 119. [Google Scholar] [CrossRef]
  39. Prasanth, V.; Chandra, S.; Hoisington, D.; Jayashree, B. AlleloBin: A Program for Allele Binning in Microsatellite Markers based on the Algorithm of Idury and Cardon (1997); International Crops Research Institute for the Semi-Arid Tropics (ICRISAT): New Delhi, India, 2006. [Google Scholar]
  40. Elshire, R.J.; Glaubitz, J.C.; Sun, Q.; Poland, J.A.; Kawamoto, K.; Buckler, E.S.; Mitchell, S.E. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 2011, 6, e19379. [Google Scholar] [CrossRef] [PubMed]
  41. Lu, F.; Lipka, A.E.; Glaubitz, J.; Elshire, R.; Cherney, J.H.; Casler, M.D.; Buckler, E.S.; Costich, D.E. Switchgrass genomic diversity, ploidy, and evolution: Novel insights from a network-based SNP discovery protocol. PLoS Genet. 2013, 9, e1003215. [Google Scholar] [CrossRef] [PubMed]
  42. Bradbury, P.J.; Zhang, Z.; Kroon, D.E.; Casstevens, T.M.; Ramdoss, Y.; Buckler, E.S. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 2007, 23, 2633–2635. [Google Scholar] [CrossRef] [PubMed]
  43. Bidinger, F.; Blümmel, M. Determinants of ruminant nutritional quality of pearl millet [Pennisetum glaucum (L.) R. Br.] stover: I. Effects of management alternatives on stover quality and productivity. Field Crops Res. 2007, 103, 119–128. [Google Scholar] [CrossRef]
  44. Blümmel, M.; Bidinger, F.; Hash, C. Management and cultivar effects on ruminant nutritional quality of pearl millet (Pennisetum glaucum (L.) R. Br.) stover: II. Effects of cultivar choice on stover quality and productivity. Field Crops Res. 2007, 103, 129–138. [Google Scholar] [CrossRef]
  45. Liu, K.; Muse, S.V. PowerMarker: An integrated analysis environment for genetic marker analysis. Bioinformatics 2005, 21, 2128–2129. [Google Scholar] [CrossRef] [PubMed]
  46. Li, Y.; Guan, R.; Liu, Z.; Ma, Y.; Wang, L.; Li, L.; Lin, F.; Luan, W.; Chen, P.; Yan, Z.; et al. Genetic structure and diversity of cultivated soybean (Glycine max (L.) Merr.) landraces in China. Theor. Appl. Genet. 2008, 117, 857–871. [Google Scholar] [CrossRef]
  47. Upadhyaya, H.D.; Dwivedi, S.L.; Baum, M.; Varshney, R.K.; Udupa, S.M.; Gowda, C.L.; Hoisington, D.; Singh, S. Genetic structure, diversity, and allelic richness in composite collection and reference set in chickpea (Cicer arietinum L.). BMC Plant Biol. 2008, 8, 106. [Google Scholar] [CrossRef]
  48. Excoffier, L.; Smouse, P.E.; Quattro, J.M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 1992, 131, 479–491. [Google Scholar] [PubMed]
  49. Perrier, X.; Jacquemoud-Collet, J.P. DARwin Software. Available online: https://darwin.cirad.fr/ (accessed on 11 March 2017).
  50. Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967, 27, 209–220. [Google Scholar] [PubMed]
  51. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2016; Available online: https://www.R-project.org/ (accessed on 21 July 2017).
  52. SAS Institute Inc. SAS/STAT® 14.1 User’s Guide; SAS Institute Inc.: Cary, NC, USA, 2017. [Google Scholar]
  53. Kapila, R.; Yadav, R.; Plaha, P.; Rai, K.; Yadav, O.; Hash, C.; Howarth, C.J. Genetic diversity among pearl millet maintainers using microsatellite markers. Plant Breed. 2008, 127, 33–37. [Google Scholar] [CrossRef]
  54. Sumanth, M.; Sumathi, P.; Vinodhana, N.; Sathya, M. Assessment of Genetic Distance Among the Inbred Lines of Pearl Millet (Pennisetum glaucum (L.) R. Br) Using SSR Markers. Int. J. Biotechnol. Allied Fields 2013, 1, 153–162. [Google Scholar]
  55. He, Z.; Xie, F.; Chen, L.; Paz, M.A.D. Genetic diversity of tropical hybrid rice germplasm measured by molecular markers. Rice Sci. 2012, 19, 193–201. [Google Scholar] [CrossRef]
  56. Filippi, C.V.; Aguirre, N.; Rivas, J.G.; Zubrzycki, J.; Puebla, A.; Cordes, D.; Moreno, M.V.; Fusari, C.M.; Alvarez, D.; Heinz, R.A.; et al. Population structure and genetic diversity characterization of a sunflower association mapping population using SSR and SNP markers. BMC Plant Biol. 2015, 15, 52. [Google Scholar] [CrossRef]
  57. Hamblin, M.T.; Warburton, M.L.; Buckler, E.S. Empirical comparison of simple sequence repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness. PLoS ONE 2007, 2, e1367. [Google Scholar] [CrossRef]
  58. Jones, E.; Sullivan, H.; Bhattramakki, D.; Smith, J. A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (Zea mays L.). Theor. Appl. Genet. 2007, 115, 361–371. [Google Scholar] [CrossRef]
  59. Akmal, M.; Naeem, M.; Nasim, S.; Shakoor, A. Performance of different pearl millet genotypes under rainfed conditions. J. Agric. Res. 1992, 30, 53–58. [Google Scholar]
  60. Byregowda, M. Performance of fodder bajra genotypes under rainfed conditions. Curr. Res.-Univ. Agric. Sci. 1990, 19, 128–129. [Google Scholar]
  61. Mohammad, D.; Hussain, A.; Khan, S.; Bhatti, M.B. Performance of new pearl millet cultivars. Pakistan J. Sci. Ind. R. 1993, 36, 261–263. [Google Scholar]
  62. Naeem, M.; Nasim, S.; Shakoor, A. Performance of new pearl millet varieties under rainfed conditions. J. Agric. Res. 1993, 31, 295–298. [Google Scholar]
  63. Rai, K.N.; Blümmel, M.; Singh, A.K.; Rao, A.S. Variability and relationships among forage yield and quality traits in pearl millet. Eur. J. Plant Sci. Biotechnol. 2012, 6, 118–124. [Google Scholar]
  64. Van Soest, P. Nitrogen metabolism. In Nutritional Ecology of the Ruminant, 2nd ed.; Comstock Publishing Associates-Cornell University Press: Ithaca, NY, USA, 1994; pp. 290–311. [Google Scholar]
  65. Blümmel, M.; Rai, K. Stover quality and grain yield relationships and heterosis effects in pearl millet. Int. Sorghum Millets Newsl. 2003, 44, 141–145. [Google Scholar]
  66. Hash, C.; Blummel, M.; Bidinger, F. Genotype x environment interactions in food-feed traits in pearl millet cultivars. Int. Sorghum Millets Newsl. 2006, 47, 153–157. [Google Scholar]
  67. Vinayan, M.; Babu, R.; Jyothsna, T.; Zaidi, P.; Blümmel, M. A note on potential candidate genomic regions with implications for maize stover fodder quality. Field Crops Res. 2013, 153, 102–106. [Google Scholar] [CrossRef]
  68. Zaidi, P.; Vinayan, M.; Blümmel, M. Genetic variability of tropical maize stover quality and the potential for genetic improvement of food-feed value in India. Field Crops Res. 2013, 153, 94–101. [Google Scholar] [CrossRef]
  69. Blümmel, M.; Deshpande, S.; Kholova, J.; Vadez, V. Introgression of staygreen QLT’s for concomitant improvement of food and fodder traits in Sorghum bicolor. Field Crops Res. 2015, 180, 228–237. [Google Scholar] [CrossRef]
  70. Kristjanson, P.; Zerbini, E.; Rao, K. Genetic Enhancement of Sorghum and Millet Residues Fed to Ruminants: An Ex Ante Assessment of Returns to Research; Impact Assessment Series No. 3; ILRI (aka ILCA and ILRAD): Nairobi, Kenya, 1999. [Google Scholar]
  71. Blümmel, M.; Rao, P.P. Economic value of sorghum stover traded as fodder for urban and peri-urban dairy production in Hyderabad, India. Int. Sorghum Millets Newsl. 2006, 47, 97–100. [Google Scholar]
  72. Melchinger, A.E.; Lee, M.; Lamkey, K.R.; Woodman, W.L. Genetic diversity for restriction fragment length polymorphisms: Relation to estimated genetic effects in maize inbreds. Crop Sci. 1990, 30, 1033–1040. [Google Scholar] [CrossRef]
  73. Charcosset, A.; Lefort-Busen, M.M.; Gallais, A. Relationship between herosis and heterozgosity at marker loci: A theoretical computation. Theor. Appl. Genet. 1991, 81, 571–575. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Neighbor joining tree based on 52 SSRs: (a) 130 seed and pollinator parents; (b) 18 seed parents; and (c) 112 pollinator parents.
Figure 1. Neighbor joining tree based on 52 SSRs: (a) 130 seed and pollinator parents; (b) 18 seed parents; and (c) 112 pollinator parents.
Agriculture 09 00097 g001aAgriculture 09 00097 g001bAgriculture 09 00097 g001c
Figure 2. Neighbor joining tree based on 7870 GBS-identified SNPs: (a) 122 seed and pollinator parents; (b) 18 seed parents; and (c) 104 pollinator parents.
Figure 2. Neighbor joining tree based on 7870 GBS-identified SNPs: (a) 122 seed and pollinator parents; (b) 18 seed parents; and (c) 104 pollinator parents.
Agriculture 09 00097 g002aAgriculture 09 00097 g002bAgriculture 09 00097 g002c
Table 1. Average of allelic richness, major allele frequency, gene diversity, heterozygosity and polymorphism information content (PIC) of the 52 SSRs and 7870 GBS-identified SNPs in hybrid parents of forage pearl millet.
Table 1. Average of allelic richness, major allele frequency, gene diversity, heterozygosity and polymorphism information content (PIC) of the 52 SSRs and 7870 GBS-identified SNPs in hybrid parents of forage pearl millet.
GroupsAllelic Richness Gene Diversity Heterozygosity PIC
SSRs
Seed parents4.63 (1.00–13.00)0.55 (0.00–0.90)0.02 (0.00–0.14)0.52 (0.00–0.89)
Pollinator parents9.81 (2.00–29.00)0.66 (0.05–0.90)0.03 (0.00–0.17)0.63 (0.05–0.93)
Seed and Pollinator parents10.6 (2.00–31.00)0.68 (0.06–0.94)0.02 (0.00–0.12)0.65 (0.06–0.94)
Standard error0.90 (0.35–0.92)0.03 (0.03–0.03)0.00 (0.01–0.01)0.03 (0.03–0.03)
GBS-Identified SNPs
Seed parents1.95 (1.00–2.00)0.37 (0.00–0.50)0.13 (0.00–1.00)0.29 (0.00–0.38)
Pollinator parents2.00 (1.00–2.00)0.48 (0.00–0.50)0.15 (0.00–0.96)0.37 0.00–0.38)
Seed and Pollinator parents2.00 (2.00–2.00)0.48 (0.02–0.50)0.15 (0.00–0.97)0.37 (0.02–0.38)
Standard error0.00 (0.00–0.00)0.00 (0.00–0.00)0.00 (0.00–0.00)0.00 (0.00–0.00)
Allelic richness, Genetic diversity, Heterozygosity and PIC value ranges for the seed and pollinator parents are given in the parentheses, respectively.
Table 2. Mean and range of forage traits of 116 hybrid parents from 2 years mean data (summer 2015 and 2016).
Table 2. Mean and range of forage traits of 116 hybrid parents from 2 years mean data (summer 2015 and 2016).
TraitsLinesFirst CutSecond Cut
MeanRangeMeanRange
Green fodder yield (t ha−1)Seed parents20.3216.30–22.8919.7912.35–26.21
Pollinator parents22.2115.18–29.1628.0315.61–42.42
Dry fodder yield (t ha−1)Seed parents4.253.63–5.196.215.23–8.99
Pollinator parents4.423.34–5.916.315.06–8.09
Crude protein (%)Seed parents12.9211.94–14.6111.8611.40–12.44
Pollinator parents12.5511.49–13.5211.7111.04–12.51
In vitro organic matter digestibility (%)Seed parents54.6653.93–55.4052.6751.16–54.26
Pollinator parents54.8153.78–55.6352.5851.01–55.04
Table 3. Mean values for forage traits in different SSR-based clusters on the basis of 2 year data (Summer season at ICRISAT, 2015 and 2016).
Table 3. Mean values for forage traits in different SSR-based clusters on the basis of 2 year data (Summer season at ICRISAT, 2015 and 2016).
LinesCluster No.First CutSecond Cut
GFY (t ha−1) DFY (t ha−1) CP (%) IVOMD (%) GFY (t ha−1) DFY (t ha−1) CP (%) IVOMD (%)
Seed parentsB-I20.14 (17.13, 22.39)4.21 (3.63, 4.46)12.75 (11.94, 13.47)54.69 (54.04, 55.40)18.72 (12.35, 22.02)6.31 (5.23, 8.99)11.82 (11.56, 12.13)52.90 (51.80, 54.26)
B-II19.67 (16.30, 22.89)4.10 (3.70, 4.54)13.07 (12.22, 14.61)54.79 (54.47, 55.03)20.53 (14.57, 26.21)6.16 (5.98, 6.43)11.82 (11.40, 12.44)52.68 (52.01, 53.70)
B-III21.63 (20.60, 22.33)4.54 (3.87, 5.19)13.02 (12.57, 13.49)54.43 (53.93, 55.06)20.84 (17.30, 23.81)6.09 (6.02, 6.25)12.01 (11.81, 12.13)52.18 (51.16, 53.30)
Pollinator parentsR-I22.81 (18.44, 27.40)4.46 (3.64, 5.61)12.40 (11.49, 13.18)54.79 (53.78, 55.56)29.73 (15.61, 37.30)6.44 (5.47, 7.69)11.67 (11.04, 12.36)52.41 (51.01, 53.83)
R-II21.22 (15.18, 25.49)4.29 (3.41, 4.80)12.75 (12.23, 13.52)54.85 (54.23, 55.47)26.36 (16.35, 34.75)6.21 (5.06, 6.94)11.69 (11.21, 12.04)52.60 (51.33, 53.69)
R-III22.24 (17.48, 29.16)4.42 (3.69, 5.91)12.64 (11.77, 13.42)54.85 (54.03, 55.63)27.99 (19.56, 42.42)6.28 (5.31, 8.09)11.78 (11.13, 12.51)52.77 (51.60, 55.04)
R-IV21.97 (19.89, 24.07)4.53 (3.34, 5.51)12.52 (11.55, 13.12)54.68 (54.21, 55.15)23.29 (18.83, 28.89)6.06 (5.17, 7.21)11.72 (11.35, 12.38)52.91 (51.85, 54.27)
Average values; minimum and maximum values in parenthesis, respectively, of the trait within the cluster, Values are in bold indicates higher mean value for the trait, GFY-Green forage yield, DFY-Dry forage yield, CP-Crude protein, IVOMD-In vitro organic matter digestibility.
Table 4. Analysis of variance for combining ability for forage related morphological and biochemical traits at first and second cut, evaluated in hybrids and parental trial at two locations in summer season of 2015.
Table 4. Analysis of variance for combining ability for forage related morphological and biochemical traits at first and second cut, evaluated in hybrids and parental trial at two locations in summer season of 2015.
Source of VariationDFGFYDFYCPIVOMD
FCǂ SCFCSCFCSCFCSCFCSC
Locations115.71 *309.26 ***277.36 ***97.57 ***116.88 ***25.07 ***562.89 ***987.69 ***
Replication (Locations)334.43 *2.636.85 ***1.273.47 *5.57 **0.296.00 **
Treatments97586.18 ***11.08 ***2.58 ***3.44 ***4.73 ***2.56 ***1.55 *3.59 ***
Parents17133.37 ***5.98 ***0.111.656.98 ***3.00 ***1.646.17 ***
(i) Females981.122.860.310.688.08 ***3.18 ***2.21 *3.71 ***
(ii) Males7421.19 ***9.15 ***1.223.02 *4.42 ***2.351.125.15 ***
(iii) Females vs. Males11264.05 ***21.50 ***141.81 ***4.37 *12.99 ***3.350.2233.7 ***
Hybrids794411.13 ***8.08 ***1.422.57 ***2.65 ***1.65 *1.44 *2.88 ***
(i) Lines986.962.16 *0.961.653.63 ***1.483.13 ***4.84 ***
(ii) Testers740.9721.82 ***1.0511.53 ***6.92 ***6.04 ***2.06 *4.94 ***
(iii) Lines × Testers63323.49 ***7.04 ***0.231.64 *2.15 ***1.041.242.17 ***
Hyb vs. Par111.46 *190.82 ***1.83 ***61.11 ***108.76 ***29.97 ***7.70 **1.77
Location × Treatments97582.76 ***6.12 ***1.091.84 ***2.48 ***1.68 *1.76 ***2.95 ***
Location × Parents17130.874.08 ***0.210.962.47 ***1.290.754.02 ***
(i) Location × Females980.943.54 ***0.080.923.29 ***0.890.414.59 ***
(ii) Location × Males740.556.03 ***0.41.281.671.651.170.82
(iii) Location × (Lines vs. Testers)112.3710.030.011.073.510.9413.35 ***
Location × Hybrids79441.225.71 ***1.032.01 ***2.32 ***1.021.332.42 ***
(i) Location × Lines981.011.410.740.962.53 **0.962.58 **2.39 *
(ii) Location × Testers740.9110.24 ***0.317.35 ***2.89 **3.80 **0.810.75
(iii) Location × (Lines × Testers)63321.295.82 ***1.141.53 *2.27 ***0.641.192.59 ***
Location × (Hybrids vs. Parents1128.05 ***45.02 ***79.69 ***6.21 **12.76 ***34.65 ***50.91 ***16.97 ***
DF-Degree of freedom; First cut; ǂ Second cut, GFY-Green forage yield (t ha−1), DFY-Dry forage yield (t ha−1), CP-Crude protein (%), IVOMD-In vitro organic matter digestibility (%). *, **, *** Significant at 0.05, 0.01 and 0.001 level, respectively.
Table 5. Summary of MPH and BPH for forage linked traits.
Table 5. Summary of MPH and BPH for forage linked traits.
TraitsCutting IntervalsMid-Parent Heterosis (%)Better-Parent Heterosis (%)
MinimumMaximumAverageMinimumMaximumAverage
GFYFirst cut20.6115.758.2−8.274.432.6
Second cut−22.2378.1103.8−37.0301.951.5
DFYFirst cut33.1406.5189.1−13.1344.3154.2
Second cut−15.5290.093.2−29.7248.250.7
CPFirst cut−23.69.8−9.5−27.88.8−14.4
Second cut−24.17.2−7.8−30.74.5−12.8
IVOMDFirst cut−6.87.11.8−8.26.70.1
Second cut−7.55.90.0−10.83.4−2.9
GFY-Green forage yield (t ha−1), DFY-Dry forage yield (t ha−1), CP-Crude protein (%), IVOMD-In vitro organic matter digestibility (%).
Table 6. Correlation between genetic distance (GD) between parents measured using SSRs and SNPs and heterosis (Mid-parent heterosis (MPH) and Better-parent heterosis (BPH)) for forage yield and quality traits in pearl millet.
Table 6. Correlation between genetic distance (GD) between parents measured using SSRs and SNPs and heterosis (Mid-parent heterosis (MPH) and Better-parent heterosis (BPH)) for forage yield and quality traits in pearl millet.
TraitsCutting IntervalsCorrelation Coefficient between GD and MPHCorrelation Coefficient between GD and BPH
SSRsSNPsSSRsSNPs
Green forage yield (GFY, t ha−1)First cut0.11−0.020.12−0.15
Second cut0.34 *0.150.41 *0.13
Dry forage yield (DFY, t ha−1)First cut−0.100.32 **−0.140.28 *
Second cut0.250.190.33 *0.19
Crude protein (CP, %)First cut0.26 *0.040.20−0.12
Second cut−0.05−0.22−0.02−0.19
In vitro organic matter digestibility (IVOMD, %)First cut0.110.000.18−0.01
Second cut−0.180.06−0.170.11
*, ** Significant at 0.05 and 0.01 level, respectively.

Share and Cite

MDPI and ACS Style

Ponnaiah, G.; Gupta, S.K.; Blümmel, M.; Marappa, M.; Pichaikannu, S.; Das, R.R.; Rathore, A. Utilization of Molecular Marker Based Genetic Diversity Patterns in Hybrid Parents to Develop Better Forage Quality Multi-Cut Hybrids in Pearl Millet. Agriculture 2019, 9, 97. https://doi.org/10.3390/agriculture9050097

AMA Style

Ponnaiah G, Gupta SK, Blümmel M, Marappa M, Pichaikannu S, Das RR, Rathore A. Utilization of Molecular Marker Based Genetic Diversity Patterns in Hybrid Parents to Develop Better Forage Quality Multi-Cut Hybrids in Pearl Millet. Agriculture. 2019; 9(5):97. https://doi.org/10.3390/agriculture9050097

Chicago/Turabian Style

Ponnaiah, Govintharaj, Shashi Kumar Gupta, Michael Blümmel, Maheswaran Marappa, Sumathi Pichaikannu, Roma Rani Das, and Abhishek Rathore. 2019. "Utilization of Molecular Marker Based Genetic Diversity Patterns in Hybrid Parents to Develop Better Forage Quality Multi-Cut Hybrids in Pearl Millet" Agriculture 9, no. 5: 97. https://doi.org/10.3390/agriculture9050097

APA Style

Ponnaiah, G., Gupta, S. K., Blümmel, M., Marappa, M., Pichaikannu, S., Das, R. R., & Rathore, A. (2019). Utilization of Molecular Marker Based Genetic Diversity Patterns in Hybrid Parents to Develop Better Forage Quality Multi-Cut Hybrids in Pearl Millet. Agriculture, 9(5), 97. https://doi.org/10.3390/agriculture9050097

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop