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Open Access

Peer-reviewed

Research Article

Genetic diversity analysis of tropical and sub-tropical maize germplasm for Striga resistance and agronomic traits with SNP markers

  • Emeline N. Dossa ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review & editing

    dossaemeline@gmail.com

    Affiliation School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg, South Africa

    ⨯
  • Hussein Shimelis,

    Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

    Affiliation School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg, South Africa

    ⨯
  • Admire I. T. Shayanowako

    Roles Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – review & editing

    Affiliation School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg, South Africa

    ⨯

Abstract

Striga hermonthica (Sh) and S. asiatica (Sa) are major parasitic weeds limiting cereal crop production and productivity in sub-Saharan Africa (SSA). Under severe infestation, Striga causes yield losses of up to 100%. Breeding for Striga-resistant maize varieties is the most effective and economical approach to controlling the parasite. Well-characterized and genetically differentiated maize germplasm is vital to developing inbred lines, hybrids, and synthetic varieties with Striga resistance and desirable product profiles. The objective of this study was to determine the genetic diversity of 130 tropical and sub-tropical maize inbred lines, hybrids, and open-pollinated varieties germplasm using phenotypic traits and single nucleotide polymorphism (SNP) markers to select Striga-resistant and complementary genotypes for breeding. The test genotypes were phenotyped with Sh and Sa infestations using a 13x10 alpha lattice design with two replications. Agro-morphological traits and Striga-resistance damage parameters were recorded under a controlled environment. Further, high-density Diversity Array Technology Sequencing-derived SNP markers were used to profile the test genotypes. Significant phenotypic differences (P<0.001) were detected among the assessed genotypes for the assessed traits. The SNP markers revealed mean gene diversity and polymorphic information content of 0.34 and 0.44, respectively, supporting the phenotypic variation of the test genotypes. Higher significant variation was recorded within populations (85%) than between populations using the analysis of molecular variance. The Structure analysis allocated the test genotypes into eight major clusters (K  =  8) in concordance with the principal coordinate analysis (PCoA). The following genetically distant inbred lines were selected, displaying good agronomic performance and Sa and Sh resistance: CML540, TZISTR25, TZISTR1248, CLHP0303, TZISTR1174, TZSTRI113, TZDEEI50, TZSTRI115, CML539, TZISTR1015, CZL99017, CML451, CML566, CLHP0343 and CML440. Genetically diverse and complementary lines were selected among the tropical and sub-tropical maize populations that will facilitate the breeding of maize varieties with Striga resistance and market-preferred traits.

Citation: Dossa EN, Shimelis H, Shayanowako AIT (2024) Genetic diversity analysis of tropical and sub-tropical maize germplasm for Striga resistance and agronomic traits with SNP markers. PLoS ONE 19(8): e0306263. https://doi.org/10.1371/journal.pone.0306263

Editor: Mehdi Rahimi, KGUT: Graduate University of Advanced Technology, ISLAMIC REPUBLIC OF IRAN

Received: February 15, 2024; Accepted: June 13, 2024; Published: August 6, 2024

Copyright: © 2024 Dossa 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 manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Background

Maize (Zea maize L., 2n = 2x = 20) is the key food security crop in sub-Saharan Africa (SSA). However, the mean maize yield in the region is low (<3 t/ha) compared with the global average of 5 to 10 t/ha (FAO, 2022). Low yields are attributable to a plethora of challenges, including biotic (e.g. field and storage pests, plant diseases, and Striga infestation) and abiotic (e.g. poor soil health, drought, and heat). Striga hermonthica (Sh) and S. asiatica (Sa) are parasitic weeds that significantly impede cereal crop production in SSA, with yield losses of up to 100% under severe infestation [1].

Striga hermonthica is prevalent in most SSA regions, notably in Western, Central, and Eastern Africa, while Sa is predominant in Southern Africa [2–4]. Maize is relatively more susceptible to both species than sorghum and pearl millet due to the co-evolution of the latter with Striga [5]. Striga extracts the host’s metabolites in exchange for phytotoxic compounds, reducing photosynthesis that causes yield loss varying from 10% to 100% [6, 7]. More than 40 million households are affected by the scourge of Striga every year across Africa [7, 8]. Several Striga control methods have been reported globally. However, the use of Striga-resistant cultivars is the most economical, sustainable, and environmentally friendly approach that can be deployed and adopted by small-holder maize producers [9]. The major components of Striga resistance/tolerance in maize are high grain yield, reduced Striga emergence, and low Striga damage symptoms [10].

The genetic base of maize has been enhanced by breeders at the Institute of Tropical Agriculture (IITA), the International Maize and Wheat Improvement (CIMMYT), and national breeding programs for Striga resistance and major economic traits [10]. Genetically diverse maize germplasm has been developed and dispatched by IITA and CIMMYT globally for more than three decades [11–13]. The germplasms can be phenotyped in the target production environments for selection and as parents in Striga resistance breeding programs by the public and private sectors. Genetic resources of maize selected by the breeders at IITA possess mainly S. hermonthica resistance. Conversely, CIMMYT-bred lines in East and Southern Africa display drought and heat stress tolerance. Striga asiatica is increasingly a major parasitic weed in South and East Africa due to poor soil fertility and drought stress conditions, which are conducive to the proliferation of the parasite and host susceptibility. Reportedly, both species occur in tandem in the major cereal crops [14, 15]. Breeding for Striga-resistant maize cultivars is vital for sustainable Striga management [3].

Striga-resistant maize varieties are bred with major genes conditioning Sh resistance. Gene introgression using the tropical genetic resources into locally adapted sub-tropical varieties will enable the suppression of both Sh and Sa in SSA. Well-characterized and genetically differentiated maize germplasm is vital to developing inbred lines, hybrids, and synthetic varieties with durable Striga resistance. Enhanced hybrid vigour is achieved from crosses of inbred lines from complementary heterotic groups [16, 17]. Hence, detailed information on genetic diversity, genetic interrelationships, and heterotic groups is crucial for developing maize cultivars with desirable product profiles.

Various molecular markers have been developed and applied to determine genetic diversity, population structure, quantitative trait loci (QTL), and linkage maps in maize. These include Restriction Fragment Length Polymorphism (RFLP), Random Amplified Polymorphic DNA (RAPDs), Amplified Fragment Length polymorphic (AFLPs), Single Sequence Repeats (SSR), and Single Nucleotide Polymorphisms (SNPs). SNPs have emerged as the markers of choice for genetic diversity analysis and marker-assisted breeding. This is attributed to their low cost per data point, high genomic abundance, locus specificity, co-dominance, the potential for high throughput analysis, and lower genotyping error rates [18]. SNPs can be identified using various protocols, including Genotyping by sequencing (GBS), restriction-associated DNA (RAD), complexity reduction of polymorphic sequences (CRoPS), and diversity arrays technology (DArT). DArT is a sequence-independent, high throughput, reproducible, cost-effective, and whole genome genotyping technology. DArTseq SNP markers have been routinely used in genetic diversity analysis in maize and other crops.

Results using DArTseq SNP markers enabled the selection of parents for breeding [19]. Successful genetic diversity and grouping of pigeonpea [20], cowpea [21], sorghum [22, 23] maize [24, 25] have been reported using DArTseq SNPs. Genetic diversity analysis of Striga-resistant maize populations was reported using DArTseq SNP markers. For instance, Badu-Apraku, et al. [19], Yacoubou, et al. [26], and Gasura, et al. [6] discerned the genetic diversity and population structure of maize germplasm. Zebire, et al. [27] identified suitable testers for Striga-resistant lines using DArTseq SNP markers and agronomic traits. Quantitative trait loci conditioning resistance/tolerance to S. hermonthica have been identified using this marker system [9, 28–31].

In an attempt to select novel inbred lines with Striga resistance and morpho-agronomic traits, genetically diverse tropical and sub-tropical maize genotypes were assembled by the University of KwaZulu-Natal’s African Center for Crop Improvement (ACCI) from IITA/Ibadan, CIMMYT/ Zimbabwe, and the National Plant Genetic Resources Centre (NGRC) in South Africa. The genetic diversity and the population structure of the accessions should be characterized to delineate heterotic groups for developing inbred lines, hybrids, and synthetic varieties with Striga resistance and desirable product profiles. Therefore, this study aimed to determine the genetic diversity of 130 tropical and sub-tropical maize germplasm using phenotypic traits and single nucleotide polymorphism (SNP) markers to select Striga-resistant and complementary genotypes for breeding.

Materials and methods

Plant material

A panel of 130 maize germplasm was used for this study. The test genotypes comprised 74 accessions acquired from IITA/Nigeria, 45 from CIMMYT/Zimbabwe, and 10 from the National Plant Genetic Resources Centre (NPGRC)/South Africa (Supplemental Table 1 in S1 File). The population included released tropical inbred lines, hybrids and open-pollinated varieties with Striga resistance and sub-tropical varieties bred for their agronomic performance and drought tolerance in South Africa and East Africa. Seeds of Sa were collected from Zimbabwe in 2016, while Sh seeds were collected from maize-infested fields in Kenya in 2021. The seeds were stored in airtight plastic jars at room temperature in dry conditions.

Phenotyping

The 130 accessions were phenotyped at the University of Kwazulu-Natal Controlled Environment Facilities (UKZN-CEF) in two seasons (December 2021–April 2022, and August 2022–December 2022). The UKZN CEF is situated at the UKZN College of Agriculture, Engineering, and Science (29.62° S, 30.40° E). Treatments were laid out using a 13 x 10 alpha lattice design with two replications in each Striga-infested environment. Two weeks before planting, each pot was infested with a scoop of sand mixed with 0.03 g of 2-year-old Sa or Sh seed containing approximately 3000 Striga seeds [32]. The experimental unit consisted of 4 plastic pots of 5-L capacity, filled with a composted pine bark potting mix for each Striga infested environment. Maize and Striga parameters were used for phenotyping. Days to 50% silking (DS) was recorded as the number of days taken by 50% of the plants to silk in each plot; days to anthesis (DA), was recorded as the number of days from planting until 50% of the plants have shed pollen; anthesis-silking interval (ASI), was measured as the difference between days to 50% silking and 50% anthesis; plant height (PLHT) and ear height (EHT) were measured as the distance from the base of the plant to the height of the first tassel branch and the node bearing the upper ear, respectively; root lodging (RL) tolerance was recorded as a percentage of plants leaning more than 30° from the vertical; stalk lodging (SLG) tolerance (percentage broken at or below the highest ear node); and ear rot (EROT) was assessed as the number of rotten ears per plant. The number of ears per plant (EPP) was obtained by dividing the total number of ears per plot by the number of plants harvested. Husk cover (HUSK) was rated on a scale of 1 to 5, where 1 = husks tightly arranged and extended beyond the ear tip and 5 = ear tips exposed. Ear aspect (EASP) was recorded on a scale of 1 to 9, where 1 = clean, uniform, large, well-filled ears and 9 = ears with undesirable features. The grain yield per plant (GY/plant) adjusted to a constant moisture of 12.5% was determined as the grain weight (g) from the ears of an individual plant after shelling. This was determined by dividing the grain yield per plot by the number of plants harvested.

The Striga parameters were recorded, including the number of emerged Sa and Sh plants 8 and 10 weeks after planting, denoted as SEC8 and SEC10. Host plant damage was rated 8 and 10 weeks after planting, designated as SDR8 and SDR10 using a visual rating score of 1 to 9 where 1 = no damage, indicating normal plant growth and a high level of tolerance, and 9 = complete collapse or death of the maize plant, i.e., highly susceptible [33].

Phenotypic data analysis

Before data analysis, the ASI values were standardized and expressed in positive figures using the corrective value (cv) following [34], where cv = 1 –the smallest ASI value. Phenotypic data collected in both Sh and Sa-infested environments were subjected to Bartlet’s homogeneity of variance test prior to combined analysis of variance (ANOVA) using a lattice procedure in RStudio version 2023. 06.1 (R Core Team, 2023). Genotypes mean comparisons were made at the 5% significance level using Fisher’s least significance difference (LSD). 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 4.2.1 (R Core Team, 2018). Broad sense heritability (H2) was computed using DeltaGen [35] with the following formula: where , and are the variance components for genotypes, season, replication, block, and the pooled error, respectively, and ns, nr, and nb are the number of seasons, replications, and blocks, respectively. A hierarchical cluster was constructed using the ward D2 method in “cluster” in R package version 4.2.1 (R Core Team, 2018). Cluster analyses were conducted to classify the germplasm and study their genetic relationships.

DNA extraction and genotyping

The seeds of the 130 accessions were planted in plastic pots filled with a growing medium in a greenhouse at the University of Kwazulu-Natal. Two weeks after planting, the fresh leaves of the three leaves stage were harvested for genomic DNA extraction. Genomic DNA was extracted using the DArTseq protocol as described by Kilian, et al. [36]. DNA quality was checked for nucleic acid concentration and purity using a NanoDrop 2000 spectrophotometer (ND-2000 V3.5, NanoDrop Technologies Inc) as described by Desjardins and Conklin [37]. An estimated 20 μl of DNA sample of each genotype with concentrations between 50 and 100 ng ul-1, and absorbances ranging from 1.75 to 2.05 were submitted to Sequential art (SEQAT) (https://www.seqart.net/) in Kenya for high throughput genotyping. The Diversity Array Technology Sequencing (DArTseq) protocol was used for genotyping the samples as previously described by Elshire, et al. [38]. SNPs obtained were used for data analysis in this study.

Genotypic data analysis

SNPs filtering.

The numerical genotyping output was used for genotypic data analysis. The initial 70197 SNPs were imputed by removing SNPs with >20% missing data and < 5% minor allele frequency (MAF) on the KDCompute server (https://kdcompute.igssafrica.org/kdcompute/). A total of 16000 informative SNP markers and 130 genotypes were used for further analysis after data imputation.

Analysis of genetic diversity parameters and genetic relationship among germplasms.

The polymorphic information content (PIC), minor allele frequency (MAF), heterozygosity (Ho), and gene diversity (GD) were calculated using RStudio version 4.3.0 (R Core Team, 2023). Analysis of molecular variance (AMOVA), inbreeding coefficient (Fis), and the genetic distance between the individuals were calculated using GenAlex version 6.5 [39].

Population structure analysis.

The clustering of the 130 genotypes was assessed using the admixture model-based clustering method in Structure software version 2.3.4 [40]. The burn-in period length and the Markov Chain Monte Carlo (MCMC) replications were set at 10,000. The Structure analysis was done for K ranging from 1 to 10 with 5 iterations at each K to determine the optimum number of clusters. The best K value was predicted following the simulation method of Evanno, et al. [41] using Structure harvester version 0.6.94 [42], and the bar plot for the optimum K was confirmed through the clustering markov packager across k (CLUMPAK) beta version [43]. Maize genotypes with inferred ancestries ≥ 70% were assigned to a different population, and those ≤ 70% were treated as admixtures. The dendrograms were generated using the genetic dissimilarity matrix using the “phylogenetics” and “evolution” procedures in RStudio version 4.3.0 (R Core Team, 2023).

Joint analysis using 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 4.3.0 (R Core Team, 2023), and the similarity of the two dendrograms was assessed using the tanglegram function developed by the “dendextend” R package (R Core Team, 2020).

3. Results

3.1 Phenotyping

Genotypic variation was significant for all the assessed traits in both Sa and Sh environments (Table 1). Under Sa-infested conditions, testing seasons had a significant effect (P<0.001) on all the traits except for EPP, PLHT, HUSK, and SEC10. Also, significant effects were noted for all traits except for EPP, PLHT, EHT, and HUSK under Sh-conditions. Block nested in replication-by-season interaction significantly affected all the assessed traits under both Sa and Sh-infested environments, except for EPP.

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Table 1. Analyse of variance and significant tests for yield components and Striga parameters of 126 maize genotypes evaluated under Striga asiatica and S. hermonthica infestations.

https://doi.org/10.1371/journal.pone.0306263.t001

Tables 2 and 3 summarize the mean performances of the top 10 inbred lines and check genotypes with high GY under Sa and Sh-infested conditions, respectively. In a Sa-infested environment, the highest variation was exhibited by PLHT, followed by ASI, with a coefficient of variation values of 426.82% and 268.88%, respectively (Table 2). Inbred lines had a mean ASI of 2.77, while the OPV and hybrid checks had mean ASI values of 1.86 and 1.77, respectively (Table 2). The mean yield of the inbred lines ranged from 0.00 g/plant (TZISTR1262) to 277.50 g/plant (CML540). Further, the mean grain yield of the hybrid checks ranged from 00.00 g/plant (Hickory/1421) to 214.00 g/plant (N.Choice/1421). Whereas the OPV checks had mean grain yields varying from 35.00 g/plant ((IWD C3 SYN*2/(White DT STR Syn)) -DT C1) to 169.50 g/plant (NC.QPM/Z.DPLO).

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Table 2. Mean values for 14 traits of 126 maize genotypes evaluated under Striga asiatica infestation, showing the top 10 inbred lines, the top 4 hybrids, and 6 OPVs based on grain yield.

https://doi.org/10.1371/journal.pone.0306263.t002

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Table 3. Mean responses for 14 traits of 126 maize genotypes evaluated under Striga hermonthica infestation, showing the top 10 inbred lines, the top 4 hybrids, and 6 OPVs.

https://doi.org/10.1371/journal.pone.0306263.t003

In a Sh-infested environment, PLHT exhibited the highest coefficient of variation of 597.49% (Table 3). The grain yield of the inbred lines varied from 0.05 g/plant (HA04A-2107-36) to 151 g/plant (CML304), with a mean of 63.89 g/plant. A mean grain yield of 79.79 g/plant was recorded for the hybrids varying from 34.75 g/plant (Kep/1421) to 133.25 g/plant (N.Choice/1421), while the OPVs recorded an overall mean yield of 70.81 g/plant varying from 33.60 g/plant (TZBSTR) to 144.25 g/plant (ZM1423) (Table 3). Low broad-sense heritability values were computed for SEC10, SDR8, and SDR10 in Sa-infested conditions. In contrast, high heritability values were recorded for all the traits except for GY (H2 = 0.02) under Sh-infested conditions.

Dendrograms based on phenotypic traits resolved the test genotypes into three clusters under Sa (Fig 1) and Sh (Fig 2) conditions. In a Sa-infested environment, Cluster I recorded the highest number of genotypes (91), followed by Cluster II (18), and Cluster III (17). Cluster I comprised tropical and sub-tropical genotypes from all sources. This Cluster had two sub-groups. The first sub-group is characterized by genotypes with low yield and moderate Striga resistance, whereas the second consists of genotypes with high yield and relatively high Striga resistance. Cluster II comprised 18 inbred lines mainly from IITA, while the genotypes in Cluster III were a mixture of Striga-resistant lines, drought-tolerant lines, and synthetic hybrids from IITA/Nigeria and CIMMYT/Zimbabwe. Under Sh-infested conditions, Cluster I was the largest (with 90 genotypes), followed by Cluster II (19) and Cluster III (17). Clusters I and II were composed of inbred lines from IITA and CIMMYT. Genotypes from Cluster III were from all sources; however, most were OPVs and hybrids sourced from IITA and NPGRC.

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Fig 1. Dendrogram showing genetic relatedness among the 126 maize genotypes (G1 to G126) based on phenotypic traits under Striga asiatica-infested conditions.

See Supplemental Table 2 in S1 File for the code of genotypes.

https://doi.org/10.1371/journal.pone.0306263.g001

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Fig 2. Dendrogram showing genetic relatedness among the 126 maize genotypes (G1 to G126) based on phenotypic traits under Striga hermonthica-infested conditions.

See Supplemental Table 3 in S1 File for the code of genotypes.

https://doi.org/10.1371/journal.pone.0306263.g002

3.2 Genetic analysis using SNP markers

Genetic diversity and population structure.

Table 4 summarizes the genetic diversity parameters of the biological types. The tested SNP markers were moderately polymorphic with a mean PIC value of 0.34 for the whole population, 0.33 for the inbred lines, 0.34 for the hybrids, and 0.35 for the OPVs. The whole population had a mean GD of 0.44. The OPVs exhibited the highest mean GD of 0.45 followed by the hybrids (0.44), and the inbred lines (0.42). The highest MAF was 0.37 observed among OPVs while the whole population exhibited an MAF of 0.36. The mean values of heterozygosity ranged from 0.22 to 0.28 with the highest Ho of 0.28 exhibited by the inbred lines. Overall, the level of fixation index ranged from 0.33 to 0.52. The OPVs exhibited the highest F of 0.52 followed by the hybrids (0.50).

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Table 4. Genetic diversity parameters of 126 maize genotypes assessed based on 16000 SNP markers.

https://doi.org/10.1371/journal.pone.0306263.t004

The structure analysis based on the Evanno method indicated that the highest value of ΔK was eight (Fig 3A), revealing eight main genetic clusters (Fig 3B). About 55.31% of the tested genotypes exhibited membership coefficient values ≥ 0.70. The rest, accounting for 44.69%, were considered admixtures. Sub-population II was the largest group, with 22 accessions (21.15%) representing OPVs and synthetic hybrids from IITA/Nigeria, CIMMYT/Zimbabwe, and NPGRC/South Africa. Sub-population III comprised 21 accessions (20.19%), comprising IITA/Nigeria inbred lines and hybrids. Sub-population IV composed of 19 accessions (18.26%) that were IITA hybrids and some IITA inbred lines. About 14 accessions (13.46%) were allocated to the sub-population I comprising CIMMYT/Zimbabwe inbred lines. Sub-population V constituted 10 accessions (9.61%) that were CIMMYT/Zimbabwe inbred lines. Sub-populationsVI, VII, and VIII comprised ten, five, and four accessions, respectively. Members of these populations were inbred lines from IITA/Nigeria. Principal coordinate analysis assigned the accessions to four admixture groups (Fig 4). In particular, sub-populations I and II were clustered in PC1, while sub-population V was dominant in PC2. Sub-populations VI, VII, and VIII were clustered in PC3, whereas sub-populations III and IV were dominants in PC4.

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Fig 3.

Sub-population inference among the 126 maize genotypes based on 16000 SNPs: (A) likelihood and delta K values for different numbers of assumed clusters and (B) population structure among the 126 maize genotypes based on 16000 SNPs at K = 8.

https://doi.org/10.1371/journal.pone.0306263.g003

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Fig 4. Principal coordinate analysis clustering of the test genotypes.

See Supplemental Table 3 in S1 File for the code of genotypes.

https://doi.org/10.1371/journal.pone.0306263.g004

Genetic distance.

The inbreeding coefficient ranged from -0.06 to 0.59, with a mean of 0.34 representing the population pairs VI and VIII, and V and VII (Table 5, bottom diagonal). The pairwise genetic distance among the eight populations ranged from 0.16 to 0.48, with a mean of 0.32 (Table 5, upper diagonal). Sub-populations III and VIII, and IV and III were the most distantly related, while sub-population VII had relatively the shortest distances from sub-populations II and VI. It was noticed that the genetic distances between the sub-populations III, IV, V, VI, and VII are beyong the average. The same extent was noticed with sub-populations I, V, VI, VII, and VIII. The sub-population III consists of the genotype NC.QPM/Z.DPLO, Shesha/1421, and NC.QPM/Z.DPLO and was associated with high GY under Sa-infested conditions. Sub-population VIII and IV consisted of IITA inbred lines including TZISTR1175, TZISTR1225, TZISTR1190, TZISTR1174, and TZISTR1166 that were associated with high SDR8 and SDR10 reduction under Sa infested-conditions, and TZISTR1205 and TZSTRI108 associated with high GY under Sh-infested conditions. Nei’s genetic distance between the individuals based on the 16000 SNP markers ranged from 0.01 to 0.34 within the inbred lines with a mean of 0.18 (Supplemental Table 4 in S1 File and Table 5). TZISTR1008 and CLHP0221 had the lowest genetic distance of 0.01, while CLHP0343 and TZISTR1223 exhibited the highest genetic distance of 0.34. CLHP0343 was associated with good GY under Sa infestation and exhibited a relatively high genetic distance from all the other inbred lines. The accessions CML540, TZISTR25, TZISTR1248, CLHP0303, TZISTR1174, TZSTRI113, TZDEEI50, TZSTRI115, CML539, TZISTR1015, CZL99017, CML451, CML566, CLHP0343 and CML440 which showed high GY and reduce Striga damage under both Sa and Sh infested conditions, exhibited high and average genetic distances from each other.

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Table 5. Genetic distance (upper diagonal), and pairwise inbreeding coefficients (lower diagonal), among eight populations resulting from 130 maize genotypes based on 16000 SNP profiling.

https://doi.org/10.1371/journal.pone.0306263.t005

The analysis of molecular variance (AMOVA) showed a significant variation within populations (Table 6). The within-population variation accounted for 85% of the total variation. The variation detected among the population was low (15%).

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Table 6. Analysis of molecular variance involving 130 maize accessions based on 16000 SNP markers.

https://doi.org/10.1371/journal.pone.0306263.t006

The dendrogram based on the 16000 SNP markers clustered the accessions into three major clusters (Fig 5). The largest is Cluster III, containing mainly CIMMYT and IITA inbred lines, followed by Cluster II, consisting of admixtures of IITA and CIMMYT lines and synthetic hybrids. Cluster I form genotypes from all sources, mainly OPVs from IITA.

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Fig 5. Hierarchical cluster dendrogram showing the genetic relationships among 126 maize accessions using 16000 SNP markers.

See Supplemental Table 3 in S1 File for the code of genotypes.

https://doi.org/10.1371/journal.pone.0306263.g005

Comparison of test genotypes using phenotypic and genotypic analyses.

Figs 6 and 7 present the joint analysis that revealed three clusters for both tested conditions using the phenotypic and molecular data. Under Sa conditions, Cluster III was the largest, with 68 genotypes, followed by Cluster I (35), and Cluster II (23). Under Sh conditions, Cluster I was the largest, followed by Clusters II and III with 84, 28, and 14 genotypes, respectively.

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Fig 6. Dendrogram showing relatedness among the 126 maize genotypes under Striga asiatica-infested conditions using genotypic and phenotypic data.

See Supplemental Table 4 in S1 File for the code of genotypes.

https://doi.org/10.1371/journal.pone.0306263.g006

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Fig 7. Dendrogram showing relatedness among the 126 maize genotypes under Striga hermonthica conditions using genotypic and phenotypic data.

See Supplemental Table 4 in S1 File for the code of genotypes.

https://doi.org/10.1371/journal.pone.0306263.g007

The phylogenetic tree generated from the phenotypic data was compared to the genotype grouping based on the SNPs data (Figs 8 and 9). Only a few genotypes (21.42%) maintained their positions across the hierarchical clusters. Furthermore, the correlation between the phenotypic and genotypic dissimilarity matrices was low according to the Mantel test in Sh (r2 = 0.0009, P = 0.01) and Sa (r2 = 0.0006, P = 0.02) infested environments.

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Fig 8.

Tanglegram comparing dendrograms based on evaluation of 126 maize genotypes evaluated using phenotypic (left) and genotypic data (right) under Striga asiatica conditions. See Supplemental Table 4 in S1 File for the code of genotypes.

https://doi.org/10.1371/journal.pone.0306263.g008

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Fig 9.

Tanglegram comparing dendrograms based on evaluation of 126 maize genotypes evaluated using phenotypic (left) and genotypic data (right) under Striga hermonthica conditions. See Supplemental Table 4 in S1 File for the code of genotypes.

https://doi.org/10.1371/journal.pone.0306263.g009

4. Discussion

Genetic variation is fundamental for new or pipeline crop breeding programs. The development of open-pollinated, hybrid and synthetic maize varieties with high hybrid vigour relies on genetically contrasting parents and heterotic groups emanating from well-characterized genetic resources. The present study assessed the genetic diversity of 126 maize genotypes (Supplemental Table 1 in S1 File) sourced from IITA/Nigeria, CIMMYT/Zimbabwe, and NPGRC/South Africa using agro-morphological traits and high-density SNP markers. Morphological traits are useful in preliminary genetic diversity assessments [44] and ideotype breeding [45, 46].

In the current study, a wide variability was recorded among accessions of different sources using phenotypic traits (Table 1). Each source of genotype group presented a unique selection with specific and unique traits (Tables 2 and 3). For instance, genotypes CML540 and CML566 were higher yielders in Sa-infested environment (Table 2), while genotypes CML304 and TZSTRI101 were higher yielders in Sh-infested environment (Table 3). These genotypes are ideal candidates for Striga resistance breeding. Some of the tropical genotypes bred for Sh resistance were susceptible to Sa. This is consistent with the previous finding of Gasura, et al. [47], who reported the susceptibility to Sa of some tropical inbred lines bred for Sh resistance. Low broad sense heritability values were computed for SEC10, SDR8, and SDR10 in Sa-infested environment (Table 2), indicating that Sa-resistance has low heritability, and therefore, the phenotype was a poor measure of the genetic merit of the evaluated genotypes, which reduces the effectiveness of selection under Sa infestation. These findings differ from those of Olakojo and Olaoye [48], who reported a high heritability of Striga syndrome rating and Striga emergence count under Sa-infested conditions. Meanwhile, high heritability values were recorded for the same traits under Sh-infested conditions (Table 3). This suggests that, unlike Sa resistance, Sh resistance is highly heritable. This shows that the results would be repeatable, which is ideal for Sh resistance breeding. Kaewchumnong and Price [49] and Stanley, et al. [30] reported high heritability estimates for Striga resistance traits in a Sh-infested environment. This finding, however differs from those of Badu-Apraku, et al. [50], who recorded low heritability estimates for emerged Striga plants and Striga damage ratings. All these results suggest that the gene actions controlling Sa and Sh are not the same.

Based on phenotypic traits, the dendrogram delineated the genotypes into three major clusters subdivided into six sub-clusters under Sa-infested conditions (Fig 1), and four under Sh-conditions (Fig 2). The clusters were formed based on reaction to Sa and Sh infestations and yield components performances. This suggests the presence of considerable genetic variation among the assessed genotypes that could be used in developing Striga-resistance germplasm. Reports on the clustering of genotypes based on phenotypic traits are common in genetic studies in maize [51, 52].

Compared with morphological traits, molecular markers are independent of environmental effects and can provide additional and accurate information for assessing genetic diversity [53, 54]. This study used SNP markers to assess the genetic diversity of tropical and sub-tropical maize germplasm. The test germplasm exhibited a high heterozygosity of 0.26 (Table 4), suggesting that alternative alleles were represented in the population. The inbred lines exhibited the highest heterozygosity estimates. The observed heterozygosity in the inbred lines (28%) exceeded the expectations (6.25%) for inbred lines derived after four generations of selfing needing continuous selfing, given that the inbred lines are relatively in the early generation of inbreeding [55]. The PIC and GD values were useful to assess the population’s genetic diversity to identify divergent parental lines for breeding programs. The mean PIC and GD values were 0.34 and 0.44, respectively, for the whole population, and the same trend was observed for the inbred lines, the hybrid checks, and the OPV checks (Table 4). This shows that the 16000 SNP markers in this study were polymorphic to distinguish the test population, inbred lines, and checks. The PIC value corresponds to the ability of the test markers to detect the polymorphism among individuals of the population [56]. The PIC values in this study are higher compared to PIC values reported in some of the past related studies. Adu, et al. [17] reported PIC values within the range of 0.01 to 0.38 using 15,047 SNP markers on 94 maize inbred lines. Badu-Apraku, et al. [19] reported PIC values ranging from 0.029 to 0.37 with a mean of 0.21 using 9642 SNP markers. The mean PIC values observed in this study are comparable to Yang, et al. [57]. The mean GD of the population in this study (0.44) was similar to the one reported by Eschholz, et al. [58] when using SSR markers. Yacoubou, et al. [26] reported a gene diversity value of 0.44 in early-generation maize lines. According to the formula of Anderson, et al. [59], the theoretical maximum gene diversity for bi-allelic markers is 0.50. This signifies that the gene diversity obtained in this study was high, suggesting a significant genetic segregation in the test population in this study. Genetic diversity reflects the population’s genetic constitution and its adaptability in various environments [60].

The genetic differentiation recorded in this study ranged from 0.16 to 0.48 (Table 5). According to Wright [61] an Fst of 0–0.005 indicates low, 0.05–0.15 moderate, 0.15–0.25 high, and above 0.25 very significant genetic differentiations. The Fst value in the present study is indicative of high genetic differentiation among the heterotic groups, which was expected. This result is confirmed by the high rate of inbreeding coefficient, reflecting a low level of genetic identity for the populations in this study. Genetic differentiation occurs when there is restricted gene flow between populations [62]. The high genetic differentiation observed in this study agrees with previous studies in maize [63, 64].

The analysis of molecular variance is a suitable criterion for assessing the overall diversity distribution within and among populations. The AMOVA results in this study showed a higher level of genetic variation within populations than among populations of the test genotypes (Table 6), which supports the high genetic differentiation. Related findings were reported by Leng, et al. [65] and Mathiang, et al. [66]. Based on phenotyping, the test genotypes were resolved into six clusters under Sa-infested (Fig 1) conditions and four clusters under Sh-infested conditions (Fig 2). The model-based population structure analysis (Fig 3), principal coordinate analysis (Fig 4), and neighbour-joining cluster analysis (Fig 5) revealed the presence of eight groups, which is fairly consistent with pedigree information and with putative heterotic groups. This is supported by the very low and significant correlation exhibited by the phenotypic and genotypic distance matrices, revealing the discordance between the two matrices. The discordance between phenotypic and genotypic matrices is partially attributed to the environment effect on the phenotypic trait’s expression [21]. Other studies reported inconsistency between phenotypic and genotypic matrices [54, 67].

Conclusion

The results of the present study revealed significant phenotypic and molecular diversity of the tropical and sub-tropical maize populations. Significant differences were recorded for all the assessed quantitative traits. The SNPs used in this study revealed the genetic variation among the test population. The mean gene diversity and polymorphic information content were 0.34 and 0.44, respectively, reflecting a moderate level of genetic variation among the test genotypes when assessed using SNP markers. The overall mean genetic distance among the inbred lines was 0.18, ranging from 0.01 to 0.34. Divergent parents were selected for hybridization and the development of new Striga-resistant varieties in SSA. The following genetically distant genotypes were selected, displaying good agronomic performance and Sa and Sh resistance: CML540, TZISTR25, TZISTR1248, CLHP0303, TZISTR1174, TZSTRI113, TZDEEI50, TZSTRI115, CML539, TZISTR1015, CZL99017, CML451, CML566, CLHP0343 and CML440. Genetically diverse and complementary lines were selected among the tropical and sub-tropical maize populations that will facilitate the breeding of maize varieties with Striga resistance and market-preferred traits. Both molecular and morphological features are useful and will facilitate the selection and breeding process for Striga resistance in maize.

Acknowledgments

This work was supported by the African Centre for Crop Improvement (ACCI) and the Organisation for Women in Science for the Developing World (OWSD).

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