https://orcid.org/0000-0003-0605-271X
Figures
Abstract
Knowledge of genetic interrelationships and grouping among pigeonpea germplasm collections is fundamental to selecting breeding parents with unique genetic constitutions. The objectives of this study were to assess the genetic diversity and genetic grouping present among 81 pigeonpea genotypes collected from Malawi, Tanzania and Kenya using 4122 single nucleotide polymorphism (SNP) markers and complementary morphological traits. The SNP markers and phenotypic traits revealed significant genetic variation among the assessed genotypes. The test genotypes were resolved into three distinct clusters based on both marker systems. The mean gene diversity and the polymorphic information content (PIC) were 0.14 and 0.11, suggesting moderate genetic differentiation among the genotypes. The analysis of molecular variance revealed that differences among populations accounted for only 2.7% of the variation, while within the population (among individuals) accounted for 97.3% of the variation. The results based on the DArT SNP genotyping complemented the phenotypic data and led to the selection of unique pigeonpea genotypes for effective breeding programs in Malawi and related agroecologies. This suggested that unique breeding populations could be created by identifying and selecting divergent individuals as parental lines. There is a need to create a new genetic variation or introgress genes from genetically unrelated parents to increase the genetic base of the current pigeonpea breeding populations.
Citation: Yohane EN, Shimelis H, Laing M, Shayanowako A (2022) Genetic diversity and grouping of pigeonpea [Cajanus cajan Millspaugh] Germplasm using SNP markers and agronomic traits. PLoS ONE 17(11): e0275060. https://doi.org/10.1371/journal.pone.0275060
Editor: Tzen-Yuh Chiang, National Cheng Kung University, TAIWAN
Received: December 9, 2020; Accepted: September 9, 2022; Published: November 3, 2022
Copyright: © 2022 Yohane et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and Supporting information files.
Funding: This research was supported by the Alliance for a Green Revolution in Africa through the African Centre for Crop Improvement (PASS030), University of KwaZulu-Natal. The funders had no role in study design, data collection and analysis, decision to publish or presentation of the manuscript.
Competing interests: The authors of this manuscript declare no conflict of interest.
Introduction
Pigeonpea is a protein-rich legume crop cultivated in more than 25 tropical and sub-tropical countries either as a sole crop or intercropped with cereals or other legumes. Pigeonpea is also a major income source for many small-scale farmers in Africa and Asia [1]. Pigeonpea has high biomass productivity making it suitable as a fodder crop [2]. Like other legume crops, pigeonpea forms symbiotic associations with nitrogen-fixing bacteria and can potentially fix between 69 to 100 kg ha-1 atmospheric nitrogen (N) [3] with a net contribution of 2 to 28 kg N ha-1 depending on genotype and environmental factors [4, 5]. Furthermore, its roots help release soil-bound phosphorus to make it available for plant growth [6]. Despite its diverse economic importance, pigeonpea is classified among underutilized and orphaned crop species.
Consequently, the production and productivity of pigeonpea are still low to attract interest from commercial and large-scale farming enterprises. The neglect of orphan crops such as pigeonpea by crop improvement research programs compared to other commodity crops such as maize, wheat, and rice has contributed to a lack of improved and high-yielding cultivars in sub-Saharan Africa (SSA). To date, very few commercially grown pigeonpea varieties are available, meeting farmer and market preferences in SSA. This includes hybrids such as ICPH 2671, ICPH 2740, and ICPH 3762 developed by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT- India) using the cytoplasmic nuclear male sterility (CMS) system [7]. Nonetheless, the crop has uncharted market potential if the quantity and quality of production are enhanced [2]. Sustainable promotion and advancement of pigeonpea will require developing and deploying improved cultivars acceptable by farmers and the entire value chain.
The development of new cultivars will require understanding the existing diversity to inform breeding programs and germplasm management strategies. Knowledge of the genetic basis of yield, quality and stress tolerance is important for the genetic improvement of pigeonpea. Varshney et al. [8] reported assembled 605.78 Mb of the 833.07 Mb pigeonpea reference genome, which helps in the identification of the genetic basis of agronomically important traits to accelerate the development of improved varieties. The genome analysis predicted 48,680 genes of pigeonpea with potential genes for drought tolerance. Similarly,Singh et al. [9] identified 1,213 disease resistance genes and 152 abiotic stress tolerance genes in pigeonpea, making it a hardy crop. However, there is limited information on the magnitude of genetic diversity within the cultivated pigeonpea gene pool [10]. Knowledge of the genetic basis of yield, quality and stress tolerance is essential for the genetic improvement of pigeonpea. However, there is limited information on the magnitude of genetic diversity within the cultivated pigeonpea gene pool [10]. Morphological traits, biochemical and molecular markers have been used in genetic diversity assessments, genetic grouping and selection programs. The ICRISAT maintains 13,632 pigeonpea accessions, including landraces, cultivars, breeding materials, and wild relatives, which are important genetic resources for maintaining genetic diversity for vital morphological and agronomic traits [11]. Molecular markers are robust compared to morphological and biochemical markers in genetic diversity analysis [12]. Molecular markers offer a viable option to accelerate conventional breeding in pigeonpea or related legumes [13]. Several molecular markers have been used in genetic diversity analysis of pigeonpea, such as the restriction fragment length polymorphism (RFLP) [14], amplified fragment length polymorphism (AFLP) [15], random amplified polymorphic DNA (RAPD) [16], simple sequence repeats (SSR/microsatellites) [17] and single nucleotide polymorphism (SNP) [18]. The SNP markers derived from next-generation sequencing have been widely used because they have greater abundance throughout the genome. The automated data generation and collection make SNPs the preferred markers for all molecular breeding applications [19]. In addition, SNP markers are increasingly time, and cost-efficient to genotype large populations with relatively higher throughput [20]. High density SNP arrays and genotyping by sequencing (GBS) have become attractive genotyping tools in pigeonpea. High–density chip arrays have been developed for pigeonpea for instance 62K genic-SNP chip array for Affymetrix GeneTitan® platform called CcSNPnks’ has been developed [21], which provides an opportunity for the identification of novel QTLs for yield, nutrition quality and resistance to environmental stresses using mapping population and association mapping analysis. Thousands of SNPs detected across the genome are useful for characterizing germplasm and maker-trait association mapping. Yan et al. [22] developed a pilot diversity array technology (DArT) library for pigenonpea comprising 5,376 SNPs to analyse 96 genotypes representing 20 Cajanus species. The authors reported a narrow range of genetic diversity among the tested genotypes. More than 15000 SNPs were discovered recently across the pigeonpea genome [23].
The recently compiled diversity arrays technology (DArT) library on pigeonpea genome provides opportunities for gene discovery and developing strategies for marker-assisted selection to accelerate breeding progress in pigeonpea. Pigeonpea breeding in Malawi is lagging and is mainly focused on conventional breeding methods. Conventional breeding should be complemented with genomic-assisted selection for precision and accelerated breeding and variety release. Yohane et al. [24] reported significant genetic variation in a diverse panel of pigeonpea. However, it was established that selection was confounded by high environmental variance affecting phenotypic trait expression. Therefore, it was imperative to complement phenotypic data with molecular data to reduce environmental variance and improve genetic grouping and selection efficiency for cultivar development. Therefore, this study aimed to assess the genetic diversity and grouping among 81 pigeonpea genotypes using 4 122 single nucleotide polymorphism markers and complementary morphological traits. The results will assist in parental selection to initiate pigeonpea pre-breeding in Malawi.
Materials and methods
Plant materials
A population of 81 pigeonpea genotypes were used for this study. Test genotypes were collected from the Department of Agricultural Research Services, Lilongwe, Malawi (13°59’S 33°38’E, 1146 meter above sea level [masl]) and the National Plant Genetic Resource Centre, Lilongwe, Malawi (13°59’S 33°38’E, 1146 masl), the International Crops Research Institute for the Semi-Arid Tropics, Nairobi, Kenya 1°14’10”S 36°49’07”E 1697 masl), and Tanzania Agricultural Research Institute, Ilonga, Tanzania (6°42”S 37° 2”E 506 masl). The germplasm included landraces, breeding lines, and released cultivars obtained from different sources, as presented in Table 1. The germplasm is important for pigeonpea improvement in Malawi, and the full phenotypic and genotypic characterization of this germplasm is imperative to facilitate its utilization in breeding programs.
Genotyping
DNA extraction and DArT sequencing.
Ten seeds of each pigeonpea genotype were planted in plastic pots and allowed to grow for three weeks before DNA was extracted. Fresh leaf samples of 10 individual plants per genotype were pooled so that each genotype was well represented. Similarly, 15 pigeonpea plants per genotype were sampled and bulked DNA used for genetic analysis [25]. This was done to maintain the complex genetic information stored in a highly heterogeneous population considering the level of outcrossing in pigeonpea. The collected leaf samples were stored in a deep freezer at– 80 °C. Deoxyribonucleic acid (DNA) extraction was performed following the Diversity Arrays Technology Sequencing (DArTseq) protocol (https://www.diversityarrays.com/files/DArT). Fifty milligrams of total genomic DNA were extracted from the well-developed trifoliated leaves using the NucleoSpin Plant II kit (Macherrey-Nagel, Duren, Germany) with the Lysis Buffer I (based on the cetyl trimethylammonium bromide (CTAB) method). The DNA quality and quantity of each sample were determined on 2% agarose gel followed by quantification using a Nanodrop 2000 Spectrophotometer (ND-2000 v3.5 NanoDrop, Technologies, Inc). The DNA samples were sent to the Biosciences eastern and central Africa International Livestock Research Institution (BecA-ILRI-hub in Kenya (https://hub.africabiosciences.org/) for genotyping.
Phenotyping
The phenotypic evaluation of the accessions was conducted in the 2017/18 and 2018/19 growing seasons at Bvumbwe, Chitedze, and Makoka. The Bvumbwe site (15°55′ S 35°04′ E) receives an average of 1208.6 mm of rainfall per year, and the average temperature ranges between 16.2 and 24.9 °C. The Chitedze site (13°59′ S 33°38′ E) receives 811.6 mm of rainfall annually with an average temperature between 18.5 and 29.4 °C. At Makoka (15°32′ S 35°11′ E), the average rainfall received per year is 875.7 mm while the average temperature ranges between 15.6 and 28.2 °C. The full description of the sites’ environmental and edaphic conditions is presented in Yohane et al. [24].
Treatments were laid out using a 9 × 9 alpha-lattice design at each testing location. Each genotype was planted on a plot consisting of two rows. Each row was 5m in length spaced at 0.90 m apart, giving a plot size of 4.5 m2. Seeds were planted 0.75 m apart within a row. Three seeds were planted per planting station and thinned to one plant two weeks after emergence. The phenotypic data collected included qualitative and quantitative attributes (S1 Table) following the International Board for Plant Genetic Resource [26] as described in Yohane et al [24].
Phenotypic data analysis
The analysis of variance (ANOVA) of phenotypic data was presented in Yohane et al. [24]. Summary statistics of the phenotypic data were derived in SPSS version 26 [27]. Phenotypic clusters based on the dissimilarity matrix were generated using the Gower method implemented in the “cluster” and “graphics” procedures in R statistical package version 2.1.0. The final hierarchical cluster was constructed using the ward D2 method in “cluster” in R package version 2.1.0. [28].
Genotyping data analysis
DArTseq SNP delivered markers were filtered for quality control to remove bad SNPs and genotypes using the “impute” package in R software version 1.42.0 [29]. A total of 12,366 SNP markers were identified from the raw data, and after filtering markers and genotypes with > 20% missing data, 20% of heterozygosity, and the MAF value of less than 0.05 were removed, resulting in 4122 informative SNP markers and 81 genotypes that were used for analysis.
The gene diversity, minor allele frequency (MAF), polymorphic information content (PIC), and heterozygosity (Ho) were calculated using the “diveRsity” procedure in R software [30]. The analysis of molecular variance (AMOVA) was conducted using the GenAlex version 6.5 [31].
Genetic diversity and grouping.
The genetic groups of the 81 genotypes was determined using the admixture model-based clustering method in STRUCTURE Harvester [32]. The burn-in period and Markov Chain Monte Carlo (MCMC) iterations were set at 10,000 to derive the population structure based on 4124 SNP markers distributed across the pigeonpea genome. The K-value was set between 1 and 10 to generate the number of subpopulations in the genotypes. The best K-value with the highest likelihood for estimating a suitable population size for the data set was determined using the Evanno procedure [33]. The accessions with a membership probability ≤ 0.70 of a sub-population were assigned to an admixture group, and those ≥ 0.70 were assigned to a distinct population. The dendrograms were generated using the genetic dissimilarity matrix using the “phylogenetics” and “evolution” procedures in R [34].
Joint analysis of phenotypic and SNP data
Genetic groups were defined using a combination of the phenotypic and genotypic dissimilarity matrices. The joint matrix was generated by the summation of the genotypic and phenotypic dissimilarity matrices. The phenotypic dissimilarity matrix was generated using Gower’s distance matrix, while the genotypic dissimilarity matrix was based on Jaccard’s coefficients. The groups generated from the phenotypic and genotypic sets were compared using the “viridis” procedure in R version 5.0 [35], and the similarity of the two dendrograms was assessed using tanglegram function developed by the “dendextend” R package version 1.0.1 [36].
Results
Genetic diversity and grouping based on SNP markers
Genetic diversity parameters.
The SNP call rate and average reproducibility ranged from 0.34 to 0.98, and 0.9 to 0.99, respectively. Heterozygosity values varied from 0.21 to 0.23, with a mean of 0.22 (Table 2). Gene diversity ranged from 0.00 to 0.50, with a mean of 0.14. The SNP markers were moderately polymorphic, with PIC values ranging from 0.00 to 0.38 with a mean value of 0.11. The markers included the rare variants with a minimum MAF of 0.00 and common variants with a maximum MAF of 0.50 and a mean of 0.12. The inbreeding coefficient averaged -0.56, showing a high level of heterozygosity. The chromosomes 11 and 2 had the largest density of SNPs among the 11 linkage groups (S1 Fig).
Genetic relationships.
The SNPs resoved, reolved three distinct sub-populations among the 81 accessions (Fig 1A and 1B) based on the highest ΔK value at K = 3 following the Evanno method. Sub-population 1 consisted of 15% of genotypes and comprised breeding lines. Sub-population 2 had 5% of the genotypes, mainly cultivars, while sub-population 3 consisted of mainly landraces.
The genetic differentiation among the populations ranged from -0.011 to 0.002 (Table 3). The highest genetic differentiation (Fst) was observed between sub-population 1 (breeding lines) and sub-population 2 (cultivars). In contrast, the lowest Fst value was observed between sub-population 2 (cultivars) and sub-population 3 (landraces). The analysis of molecular variance (AMOVA) (Table 4) among 81 pigeonpea genotypes indicated that 2.7% of the variation was due to genetic differences among the sub-populations, while 97.3% of the variation was due to the genetic differentiation among individuals within the sub-populations.
The results obtained from genetic structure analysis based on the phylogenetic tree resolved the 81 genotypes into three groups (Fig 1). Group III was composed of a large number (45) of genotypes, followed by Group I (31) and Group II, which had the least (5) genotypes. Genotype grouping represented a mixture of landraces, breeding lines, and cultivars. However, the genotypes in Group I mainly were characterized by early maturity, while Groups II and III were composed of medium and late maturing genotypes, respectively.
Phenotyping
Genotypic variation was significant for most quantitative traits (Table 5). The earliest flowering genotypes flowered in 48 days, while the latest genotype took 195 days to flower. On average, the DTF was 111 days. Similarly, there was a wide variation in days to maturity (DTM), which exhibited a 15.64% coefficient of variation. The genotypes included short and tall plants. The shortest genotype was 0.73 m tall compared to the tallest genotype, which reached 3 m. The yield-related traits such as number of pods per plant, number of racemes per plant, number of secondary branches, and 100 seed weight also exhibited high coefficients of variation, showing their wide variability among the genotypes. The mean grain yield was 1.14 t ha-1, ranging between 0.11 t ha-1 and 3.67 t ha-1.
Using morphological attributes, the phenotypic diversity assessment [24] grouped the genotypes into three distinct clusters (Fig 3). Cluster 2 recorded the highest number (51) of genotypes, followed by Cluster 1 [37] and Cluster 3 (3). The genotypes in Cluster 1 included two landraces from Malawi; MWPLR 14 (G41) and MWPLR 24 (G26), and one collection from Tanzania, TZA 197 (G76), both with medium maturity. The genotypes in Clusters 1 and 2 were a mixture of landraces, breeding lines, and cultivars. However, genotypes in Cluster 2 were mainly medium to late maturing, which included Babati (G49), Hombolo (G78), Sauma (G81), TZA 5557 (G50), ICEAP 0673/1 (G1), MZ2/9 (G23), among others. Cluster 1 had most of the early maturing genotypes such as ICEAP 87105 (G42), ICEAP 01170 (G6), ICEAP 87091 (G58), ICEAP 01150 (G22), ICEAP 00612 (G64), ICEAP 01172/1 (G24), ICEAP 01146/01 (G18).
Combined analysis of phenotypic and genotypic data.
The phylogenetic tree generated from the phenotypic data was compared to the genotype grouping based on the SNP data (S2 Fig). The results show that only 13.5% of the accessions maintained the same position across the hierarchical clusters. There was a clear indication of the grouping patterns and membership delineated by the phenotypic and genotypic datasets. Thirty-seven genotypes representing 45.7% of the genotypes maintained their groups across the phenotypic and genotypic hierarchical clusters. Using the combined phenotypic and molecular data, genetic diversity assessment clustered the accessions into three groups (Fig 4). Groups I, II, and III comprised of 34, 7, and 40 genotypes, in that order. The genetic grouping represented a mixture of landraces, breeding lines, and cultivars.
Discussion
Preliminary evaluation of the pigeonpea germplasm revealed significant genetic variation based on phenotypic traits [18]. Such variation is essential but subject to influence by environmental conditions that confound selection. Morphological and agronomic traits are essential in germplasm preliminary description and classification for plant breeding programs [12]. Onwobiko [38] reported that both qualitative and quantitative characters could be used to establish the morphological variations in cowpea germplasm. A follow-up assessment using molecular markers is necessary to confirm the observed phenotypic divergence and grouping underlying genetic basis.
Determination of genetic diversity among genotypes, populations, and gene pools is essential to identify unique individuals as sources of genes for improving quantitative or qualitative traits. Several studies have been conducted that assessed the genetic diversity in pigeonpea using morphological descriptors [24, 39, 40], biochemical markers [12, 22, 41], and DNA-based molecular markers [8]. This study used SNP markers to elucidate genetic diversity and grouping using expected heterozygosity and the polymorphic information content (PIC). These parameters measure alternate allele representation and different allele combinations among genotypes in a breeding population [42]. The PIC values indicate the allelic diversity within individuals and the usefulness of markers for tracking between offspring and parental genotypes. The gene diversity for the haploid markers estimates the average genetic distance among individuals in the population [43]. In the present study, the PIC values ranged from 0.00 to 0.38 (Table 2), showing that the germplasm displayed various levels of allelic diversity. However, the observed average PIC value of 0.11 indicates that the overall diversity was moderate. The average PIC value observed in this study was comparable to what was previously reported in 184 pigeonpea germplasm obtained from the ICRISAT genebank [14].
Similarly, relatively low PIC values were reported in common bean and cowpea germplasms, respectively in Malawi and Zambia [44, 45] due to the low genetic polymorphism detected in the assessed accessions. The low PIC values obtained using SNP markers could be due to their bi-allelic nature which restricts the range of PIC values between 0 and 0.50 only [44]. Conversely, Yang et al. [22] reported high informative DArT markers with PIC values ranging from 0.002–0.50, for 232 pigeonpea accessions, including cultivated and wild species, respectively. The relatively high PIC values suggested that the sampled DArT markers were of good quality and hence can be effectively used in molecular systematics and biodiversity studies.
The negative inbreeding coefficient values indicate the presence of considerable heterozygosity in the test populations owing to the inherent outbreeding of pigeonpea. The excess heterozygosity observed in this study may have resulted from the markers deviating from the expected Mendelian ratios, referred to as “segregation disorders”. Although the actual cause of this disorder is unknown, sampling error favouring heterozygotes is among the suspected causes of excessive heterozygosity [37, 46]. However, this is not unusual, including in recombinant inbred lines of autogamous crop species. Pigeonpea has a varied rate of out-crossing (5–70%) depending on genotype, insect activities, weather conditions and their interactions [47]. This promotes inter- and intra-species crossing and the occurrence of segregation disorders [37]. Low heterozygosity values of 0.27 were reported among pigeonpea genotypes evaluated in Tanzania [48]. Conversely, the observed heterozygosity value of 0.22, GD (0.14), and MAF (0.12) (Table 2) suggested that some of the test lines were comparatively homozygous. The complete flower system of the crop renders autogamous mating system and some degree of self-pollination and homozygosity.
The presence of low levels of heterozygosity among individuals and the low frequency of rare variants in the population could present bottlenecks for breeding. Adequate genetic diversity facilitates the adaptation of populations to changes in environmental conditions [49]. High heterozygosity and rare variants provide opportunities for optimal gene recombinations during cultivar development [50]. The AMOVA revealed that much (97.3%) of the genetic variation was among individuals within the populations (Table 4), which shows that individual selection of superior genotypes as parental lines for developing breeding populations would be more effective. The low genetic divergence (2.7%) among the three populations could result from selective breeding that has increased similarity among different genotype groups such as breeding lines, elite lines, and released varieties due to common parentage. Other studies have found higher genetic variation among populations that could facilitate inter-cluster crossing during breeding population development. For instance, Obua et al. [40] found that genetic variation among soybean populations accounted for 54% of the total genetic variation. Similarly, 51% of the genetic variation was attributed to the difference among populations of a panel of common bean that consisted mostly of landraces [38]. The discrepancies in the results reported by different authors could be attributed to differences in sample sizes and origins of accessions of the same species. Nevertheless, the population used in this study exhibited moderate diversity, heterozygosity, and PIC, which could be bottlenecks for pigeonpea improvement.
The highest delta value occurred at K = 3 in the population structure analysis (Fig 1), showing that the 81-pigeonpea genotypes could be delineated into three sub-populations. The delineation of the genotypes was irrespective of the sources of collection, which indicated that gene flow had transcended geographical boundaries due to the frequent exchange of genetic resources spearheaded by international genebanks. Similarly, the dendrogram (Fig 2) grouped the genotypes into three sub-populations with no distinction among breeding lines, landraces, and cultivars. Selective breeding using elite lines from a narrow genetic base has increased similarity among cultivars and breeding lines. It is relatively easier to breed new cultivars using elite lines than landraces or wild relatives containing undesirable traits that could take continuous selection cycles. This causes a lack of divergence among different genotypes and genetic erosion for important traits. The lack of distinct grouping among test genotypes showed that there were possibly admixtures in the groups that resulted in low genetic differentiation (Fst) between the groups (Table 3). The Fst value obtained in this study was lower than 0.15, which is considered a reasonable lower threshold for genetic differentiation in pigeonpea [51]. The low Fst value shows that the clusters are not genetically divergent, and crosses should be designed based on individual phenotype and genotype data rather than inter-cluster mating.
See Table 1 for code of genotypes.
The grouping of genotypes into three clusters (Figs 2 and 3) using SNP markers and morphological traits revealed a mixture of breeding lines, landraces, and cultivars in each group. This could be attributed to the geographical proximity between the two countries, Malawi and Tanzania, where the landraces were collected. Farmers between the two countries have a long history of sharing germplasm. In addition, the breeding lines from ICRISAT were developed using some parents selected from the landraces from Tanzania and Malawi hence, the genotypes in the germplasm were likely to be related. In a related study [52], reported high similarity between the cultivars due to direct selection or selections from the crosses involving germplasm lines from ICRISAT. Similarly, Yang et al. [22] reported little variation among the cultivated pigeonpea collected in Africa and Khurshid et al. [53] reported significant genetic variation among the 30 Pakistan oilseeds Brassica cultivars based on PCR-based DNA polymorphism. The tested cultivars were clustered into two major groups and four sub-groups. However, there was a narrow genetic base among the Pakistan oilseeds Brassica cultivars.
See Table 1 for code of genotypes.
A joint analysis of phenotypic and genotypic data was conducted to capture the genetic variability and grouping of the test population. The comparison between the phenotypic and genotypic information showed that 45.7% (Fig 4) of the accessions evaluated maintained their membership across the phenotypic and molecular clustering, showing that the phenotypic and molecular matrices differed but were complementary. The use of both derived clusters would increase precision in selecting divergent parents, from the groups for breeding. Increased precision in selection using a combination of genotypic and phenotypic data has been previously reported in legumes such as cowpea [45]. New breeding populations can be developed by hybridization among the three divergent genetic groups, especially those that have maintained their groups, to broaden the genetic base as part of a pigeonpea pre-breeding program in Malawi.
See Table 1 for the code of genotypes.
Conclusions
The present study assessed the genetic diversity and grouping among the 81-pigeonpea accessions sourced from Malawi, Tanzania, and ICRISAT/Kenya. The genetic diversity and grouping of the test populations were confirmed using morphological traits, SNPs data, and joint analysis. The test genotypes were grouped into three genetic clusters, enabling the selection of divergent parents for hybridization and the development of new pigeonpea breeding populations in Malawi. There is a need to create a new genetic variation or introgress genes from genetically contrasting parents to harness the genetic variation in the presently assessed pigeonpea population.
Supporting information
S1 Fig. Diversity analysis data for each chromosome and whole genome data.
https://doi.org/10.1371/journal.pone.0275060.s001
(DOCX)
S2 Fig. Comparison of hierarchical cluster dendrograms based on phenotypic traits (A) and SNPs data (B) in 81 pigeonpea genotypes.
See Table 1 for the code of genotypes.
https://doi.org/10.1371/journal.pone.0275060.s002
(DOCX)
S1 Table. Descriptors for the pigeonpea qualitative and quantitative traits.
https://doi.org/10.1371/journal.pone.0275060.s003
(DOCX)
Acknowledgments
The authors acknowledge the station managers and technical staff of the Chitedze, Bvumbwe, and Makoka Research stations in Malawi for technical assistance and overall research support. The Department of Agricultural Research Services (DARS in Malawi, the Tanzanian Agricultural Research Institute (TARI), and the International Crops Research Institute for the Semi-Arid Tropics in Malawi are sincerely thanked for providing some of the germplasm used in the study. The Biosciences eastern and central Africa International Livestock Research Institution (BecA-ILRI-hub), Kenya for genotyping services.
References
- 1. Mergeai G, Kimani P, Mwang’ombe A, Olubayo F, Smith C, Audi P, et al. Survey of pigeonpea production systems, utilization and marketing in semi-arid lands of Kenya. Biotechnology, Agronomy, Society and Environment. 2001;5(3):145–53.
- 2.
Odeny DA, (editor). The potential of pigeonpea (Cajanus cajan (L.) Millsp.) in Africa. Natural resources forum; 2007: Wiley Online Library.
- 3. Rao JK, Thompson J, Sastry P, Giller K, Day J. Measurement of N 2-fixation in field-grown pigeonpea [Cajanus cajan [56] Millsp.] using 15 N-labelled fertilizer. Plant and Soil. 1987;101[37]:107–13.
- 4. Myaka FM, Sakala WD, Adu-Gyamfi JJ, Kamalongo D, Ngwira A, Odgaard R, et al. Yields and accumulations of N and P in farmer-managed intercrops of maize–pigeonpea in semi-arid Africa. Plant and soil. 2006;285(1–2):207–20.
- 5. Egbe O. Assessment of biological nitrogen fixing potentials of pigeonpea genotypes intercropped with sorghum for soil fertility improvement in Southern Guinea Savanna of Nigeria. Agro-Science. 2007;6[37]:33–45.
- 6. Noriharu A, Arihara J, Okada K, Yoshihara T, Johansen C. Phosphorus uptake by pigeon pea and its role in cropping systems of the Indian subcontinent. Science. 1990;248(4954):477–80. pmid:17815599
- 7. Saxena K. From concept to field: Evolution of hybrid pigeonpea technology in India. Indian Journal of Genetics and Plant Breeding. 2015;75(03):279–93.
- 8. Varshney RK, Chen W, Li Y, Bharti AK, Saxena RK, Schlueter JA, et al. Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers. Nature biotechnology. 2012;30[37]:83.
- 9. Singh NK, Gupta DK, Jayaswal PK, Mahato AK, Dutta S, Singh S, et al. The first draft of the pigeonpea genome sequence. Journal of plant biochemistry and biotechnology. 2012;21[37]:98–112. pmid:24431589
- 10. Saxena K, Sawargaonkar S. First information on heterotic groups in pigeonpea [Cajanus cajan [56] Millsp.]. Euphytica. 2014;200[37]:187–96.
- 11. Upadhyaya HD, Reddy K, Sharma S, Varshney RK, Bhattacharjee R, Singh S, et al. Pigeonpea composite collection and identification of germplasm for use in crop improvement programmes. Plant Genetic Resources. 2011;9[37]:97–108.
- 12. Zavinon F, Adoukonou-Sagbadja H, Ahoton L, Vodouhe R, Ahanhanzo C. Quantitative Analysis, Distribution and traditional management of pigeon pea [Cajanus cajan [56] Millsp.] landraces’ diversity in Southern Benin. European Scientific Journal 2018;14(9):184.
- 13. Choudhary A, Kumar S, Patil B, Sharma M, Kemal S, Ontagodi T, et al. Narrowing yield gaps through genetic improvement for Fusarium wilt resistance in three pulse crops of the semi-arid tropics. SABRAO Journal of Breeding and Genetics. 2013;45(03):341–70.
- 14. Sivaramakrishnan S, Kannan S, Reddy L. Diversity in selected wild and cultivated species of pigeonpea using RFLP of mtDNA. Euphytica. 2002;125[37]:21–8.
- 15. Pati PG, Datta S, Agbagwa IO, Singh IP, Soren KR, Das A, et al. Using AFLP-RGA markers to assess genetic diversity among pigeon pea (Cajanus cajan) genotypes in relation to major diseases. Acta Botanica Brasilica. 2014;28[37]:198–205.
- 16. Malviya N, Yadav D. RAPD analysis among pigeon pea [Cajanus cajan [56] Millsp.] cultivars for their genetic diversity. Genetic Engineering and Biotechnology Journal. 2010;1:1–9.
- 17. Sarkar B, Chakravarthy V, Varalaxmi Y, Yadav S, Vanaja M, Maheswari M. Genetic diversity among Pigeonpea (Cajanus cajan L. Millsp.) genotypes using genic SSRs with putative function for drought tolerance. International Journal of Current Microbiology Applied Scince. 2017;6:1804–14.
- 18. Saxena RK, Von Wettberg E, Upadhyaya HD, Sanchez V, Songok S, Saxena K, et al. Genetic diversity and demographic history of Cajanus spp. illustrated from genome-wide SNPs. PLoS One. 2014;9[37]:e88568. pmid:24533111
- 19. Varshney R, Penmetsa R, Dutta S, Kulwal P, Saxena R, Datta S, et al. Pigeonpea genomics initiative (PGI): an international effort to improve crop productivity of pigeonpea (Cajanus cajan L.). Molecular Breeding. 2010;26(3):393–408. pmid:20976284
- 20. Jaccoud D, Peng K, Feinstein D, Kilian A. Diversity arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Research. 2001;29(4):e25–e. pmid:11160945
- 21. Singh S, Mahato AK, Jayaswal PK, Singh N, Dheer M, Goel P, et al. A 62K genic-SNP chip array for genetic studies and breeding applications in pigeonpea (Cajanus cajan L. Millsp.). Scientific reports. 2020;10[37]:1–14. pmid:32188919
- 22. Yang S, Pang W, Ash G, Harper J, Carling J, Wenzl P, et al. Low level of genetic diversity in cultivated pigeonpea compared to its wild relatives is revealed by diversity arrays technology. Theoretical and Applied Genetics 2006;113(4):585–95. pmid:16845522
- 23. Varshney RK. Exciting journey of 10 years from genomes to fields and markets: some success stories of genomics-assisted breeding in chickpea, pigeonpea and groundnut. Plant Science. 2016;242:98–107. pmid:26566828
- 24. Yohane EN, Shimelis H, Laing M, Mathew I, Shayanowako A. Phenotypic Divergence Analysis in Pigeonpea [Cajanus cajan [56] Millspaugh] Germplasm Accessions. Agronomy. 2020;10(11):1682.
- 25. Allan V, Vetriventhan M, Senthil R, Geetha S, Deshpande S, Rathore A, et al. Genome-wide DArTSeq genotyping and phenotypic based assessment of within and among accessions diversity and effective sample size in the diverse sorghum, pearl millet, and pigeonpea landraces. Frontiers in Plant Science. 2020;11:1878. pmid:33381130
- 26.
IBPGR. Descriptors for pigeonpea [Cajanus cajan [56] Millsp.]. Italy, Rome: International Board of Plant Genetic Resouces; 1993.
- 27.
IBM SPSS Statistics for Windows, Version 26.0. [Internet]. IBM Corp. 2019.
- 28.
Maechler M, Rousseeuw P, Struyf A, Hubert M, Hornik K. cluster: Cluster analysis basics and extensions. R package version 2.1. 0. 2019.
- 29.
Hastie T, Tibshirani R, Balasubramanian N, Chu G. impute: Imputation for microarray data. R package version 1.42. 0. 2017.
- 30. Keenan K, McGinnity P, Cross TF, Crozier WW, Prodöhl PA. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods in ecology and evolution. 2013;4(8):782–8.
- 31. Peakall R, Smouse PE. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular ecology notes. 2006;6[37]:288–95.
- 32. Earl DA. vonHoldt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation genetics resources. 2012;4[37]:359–61.
- 33. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology. 2005;14(8):2611–20. pmid:15969739
- 34. Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20[37]:289–90. pmid:14734327
- 35.
Garnier S, Ross N, Rudis B, Sciaini M, Scherer C. viridis: Default Color Maps from ‘matplotlib’. R package version 05. 2018;1.
- 36. Galili T. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics. 2015;31[37]:3718–20.
- 37. Knox M, Ellis T. Excess heterozygosity contributes to genetic map expansion in pea recombinant inbred populations. Genetics. 2002;162[37]:861–73. pmid:12399396
- 38. Onwubiko N. Phenotypic variability in Cowpea (Vigna unguiculate L. Walp) genotypes assessed with quantitative and qualitative characters. SABRAO J Breed Genet. 2020;52(2):191–201.
- 39. Upadhyaya H, Reddy K, Gowda C, Singh S. Phenotypic diversity in the pigeonpea (Cajanus cajan) core collection. Genetic Resources and Crop Evolution. 2007;54[37]:1167–84.
- 40. Manyasa E, Silim S, Christiansen J. Variability patterns in Ugandan pigeonpea landraces. Journal of SAT Agricultural Research. 2009;7:1–9.
- 41. Bohra A, Jha R, Pandey G, Patil PG, Saxena RK, Singh IP, et al. New hypervariable SSR markers for diversity analysis, hybrid purity testing and trait mapping in pigeonpea [Cajanus cajan [56] Millspaugh]. Frontiers in Plant Science. 2017;8(377):1–15. pmid:28408910
- 42. Kumar J, Abbo S. Genetics of flowering time in chickpea and its bearing on productivity in semiarid environments. Advances in Agronomy. 2001;72:107–38.
- 43. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics. 1978;89(3):583–90. pmid:17248844
- 44. Nkhata W, Shimelis H, Melis R, Chirwa R, Mzengeza T, Mathew I, et al. Population structure and genetic diversity analyses of common bean germplasm collections of East and Southern Africa using morphological traits and high-density SNP markers. PLoS One. 2020;15(12):e0243238. pmid:33338076
- 45. Nkhoma N, Shimelis H, Laing M, Shayanowako A, Mathew I. Assessing the genetic diversity of cowpea [Vigna unguiculata [56] Walp.] germplasm collections using phenotypic traits and SNP markers. 2020;21:110. pmid:32948123
- 46. Truong SK, McCormick RF, Morishige DT, Mullet JE. Resolution of genetic map expansion caused by excess heterozygosity in plant recombinant inbred populations. G3: Genes, Genomes, Genetics. 2014;4(10):1963–9. pmid:25128435
- 47. Saxena K, Sharma D. Pigeonpea: genetics. The pigeonpea. 1990:137–57.
- 48. Obua T, Sserumaga JP, Opiyo SO, Tukamuhabwa P, Odong TL, Mutuku J, et al. Genetic Diversity and Population Structure Analysis of Tropical Soybean (Glycine Max (L.) Merrill) using single Nucleotide Polymorphic Marker. Global journal of science frontier research.2020;20(6D).2249–4626. https://journalofscience.org/index.php/GJSFR/article/view/2746.
- 49. Markert JA, Champlin DM, Gutjahr-Gobell R, Grear JS, Kuhn A, McGreevy TJ, et al. Population genetic diversity and fitness in multiple environments. BMC evolutionary biology. 2010;10[37]:1–13. pmid:20609254
- 50. Imai‐Okazaki A, Li Y, Horpaopan S, Riazalhosseini Y, Garshasbi M, Mosse YP, et al. Heterozygosity mapping for human dominant trait variants. Human Mutation. 2019;40[37]:996–1004. pmid:31018026
- 51.
Frankham R, Ballou SEJD, Briscoe DA, Ballou JD. Introduction to conservation genetics: Cambridge university press; 2002.
- 52. Choudhury PR, Singh I, George B, Verma A, Singh N. Assessment of genetic diversity of pigeonpea cultivars using RAPD analysis. Biologia Plantarum. 2008;52[37]:648–53.
- 53. Khurshid H, Arshad M, Khan MA, Ali N, Shinwari ZK, Rabbani MA. Genetic structure of pakistani oilseed brassica cultivars revealed by morphometric and microsatellite markers. Pakistan Journal of Botany. 2019;51(4):1331–40.