Introduction

Maize (Zea mays L., 2n = 2x = 20) is the global food, feed and industrial cereal. In sub-Saharan Africa (SSA), the per capita consumption of maize is > 330 g/person/day [1]. The productivity of maize in SSA has remained stagnant and low (< 3 t/ha) compared to the potential productivity reaching 5 to 10 t/ha [2]. The parasitic weeds, Striga hermonthica (Del.) Benth and S. asiatica (L.) Kuntze are among the leading causes of low productivity of maize, especially in communal and small-scale maize production systems with low agricultural inputs [3, 4].

Striga hermonthica (Sh) and S. asiatica (Sa) are cosmopolitan parasitic weeds and destructive species affecting major cereals crops, including maize in SSA [3,4,5,6]. The parasites damages the host species by extracting its photoassimilates, diminishing its growth and productivity, especially under moisture stress and sub-optimal soil nutrient conditions, which are common in marginal maize production areas of SSA [7]. Under severe Striga infestation of both species, yield loss reaching 100% has been recorded in susceptible maize varieties [8]. Hence, there is a need for genetic improvement for Striga resistance and grain yield to enhance the productivity of maize and other major cereal crops in SSA.

Various Striga control strategies have been recommended, such as cultural practices (e.g., crop rotation, intercropping, catch-cropping, trap-cropping, and the application of organic and inorganic fertilizers), chemical and bio-herbicides. The control practices are used solo or in combination, which is referred to as integrated Striga management [9, 10]. Most of the described control options have limited application, and some are inaccessible and unaffordable to many smallholder farmers [8, 11, 12]. Striga-resistant varieties are the most economical and environmentally friendly option to control the parasitic weed under low-input farming systems [13, 14].

Several Striga-resistant varieties have been developed by the International Institute of Tropical Agriculture (IITA) in West Africa, where Sh is prevalent. However, only partial to moderate Striga resistance has been attained so far [15, 16], due to the low heritability of Striga resistance traits [8]. Low heritable traits are subject to genotype, environment, and genotype x environment interaction effects [17]. Molecular marker-assisted breeding tools could improve selection gains for Striga resistance and low heritable traits. Molecular markers can identify, locate, and map genes conditioning economic traits and Striga resistance. Linkage mapping has successfully identified quantitative trait loci (QTL) for complex traits using bi-parental populations [18]. However, detecting recombination events within pedigree and families is minimal in linkage mapping, leading to low mapping resolution of genetic markers and genes. Genome-wide association study (GWAS) is a cost-effective tool for discerning marker-trait association and dissecting the genetic architecture of complex quantitative traits, which can provide a high-resolution and high allelic richness. GWAS saves time in genetic analysis compared to linkage mapping for identifying QTL [19]. Various populations can be used in association mapping, including elite cultivars, landraces, wild relatives, and introductions [20, 21]. Limited studies documented QTL mapping associated with Striga resistance, yield, and yield component traits.

The first QTL associated with Striga resistance in maize were reported by Amusan [22]. The authors reported two QTL mapped on chromosome 6 using an F2 mapping population involving a cross between a susceptible (5057) and a resistant (ZD05) maize Inbred lines. The two QTL accounted for 55% of observed phenotypic variation for incompatible responses to Sh infestation on host roots. Adewale, et al. [13] used GWAS and reported three markers located close to the putative genes GRMZM2G164743 (bin 10.05), GRMZM2G060216 (bin 3.06), and GRMZM2G103085 (bin 5.07), linked to grain yield, the number of ears per plant, and Striga damage under Striga infestation. The three QTL explained 9 to 42% of the phenotypic variance for the incompatible response of Sh on the agronomic traits. Further, using QTL mapping, Badu-Apraku et al. (2020a) identified 12 QTL associated with Sh resistance traits from an F2:3 population. The authors further identified 14 other QTL and 154 candidate genes associated with Striga resistance/tolerance traits using QTL mapping [23]. Gowda, et al. [24] identified 57 SNPs significantly associated with Sh resistance indicator traits and grain yield, pin-pointing 32 candidate genes near the significant SNPs using GWAS. Recently, 22 SNP markers associated with grain yield, Striga damage, number of emerged Striga plants after planting, and ear aspect were reported by Okunlola, et al. [25] using GWAS. So far, only one study [8] reported on QTL associated with Sa resistance. The authors identified 3 SNP markers on chromosome numbers 5, 6, and 7 for total Sa plants that emerged. There is a need to identify QTL associated with Striga resistance and economic traits to guide the pace and precision of resistance breeding in maize aiming Sh and Sa.

Thus far, QTL associated with Striga-resistance have been detected using a bi-parental population, which may have low mapping resolution due to the small number of accumulated recombination events [26]. The quantitative trait loci intervals found are extended over several centimorgans (cM). Compared with Sh, there is a dearth of information on major QTL for Sa resistance. Hence, this study will be the first attempt to report QTL associated with both Sa and Sh resistance for resistance breeding in SSA. The two species are the most devastating parasitic weed populations occurring in tandem over space and time in SSA, especially in East Africa. Therefore, the objective of this study was to undertake a genome-wide association analysis of grain yield and Striga resistance among 126 tropical and subtropical maize genotypes to identify genetic markers linked to resistance to Sh and Sa. The QTL associated with resistance to both parasites will be useful in developing Striga-resistant maize with wide adaptability across the SSA region.

Materials and methods

Plant material and phenotyping

The current study used a panel of 126 maize germplasm. Of these, 70 were acquired from the Striga-resistant pool developed by IITA/Nigeria, 45 were locally adapted varieties from the International Maize and Wheat Improvement Centre (CIMMYT)/Zimbabwe, and 11 were from the National Plant Genetic Resources Centre, South Africa (NPGRC)/South Africa (Supplemental Table 1). The germplasm included tropical and sub-tropical inbred lines, open-pollinated varieties (OPVs), and top-cross hybrids, as described in [27]. The 126 genotypes were rigorously phenotyped under Sa and Sh-infested conditions separately at the University of KwaZulu-Natal Controlled Environment Facilities (UKZN-CEF)/South Africa in two cropping seasons from December 2021 to April 2022 and from August 2022 to December 2022. The average maximum temperature during those seasons is between 26 and 28 °C, while the minimum is 10 °C. Each of the experiments were arranged in a 13 × 10 alpha lattice design with two replications. The UKZN-CEF is situated at the UKZN College of Agriculture, Engineering, and Science (29.62° S, 30.40° E). 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.

Grain yield and Striga resistance parameters were collected. The GY (expressed in g/plant) was determined as the weight of the grain from the ears of an individual plant after shelling, adjusted to 12.5% moisture content. Striga parameters comprised the number of emerged Sa and Sh plants 8 and 10 weeks after planting, denoted as Sect. 8 and Sect. 10. A rating of host plant damage 8 and 10 weeks after planting, designated as SDR8 and SDR10, was done using a visual 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 [28].

Genotyping

Following the plant DNA extraction protocol for DArT, genomic DNA was extracted from fresh leaves of 126 genotypes at the three-leaf stage [29]. The quality and purity of the extracted DNA were rigorously assessed using the NanoDrop 2000 spectrophotometer (ND-2000 V3.5, NanoDrop Technologies Inc) [30]. Each genotype provided a 20 µl DNA sample with concentrations between 50 and 100 ng and absorbance values ranging from 1.75 to 2.05. These samples were sent to Sequential Art (SEQAT) in Kenya (https://www.seqart.net/) for high-throughput genotyping using the DArTseq protocol. The genotyping yielded 70,197 SNPs, of which 16,000 informative SNPs were selected for further analysis after filtering out DArT loci with unknown chromosomal positions and markers with more than 20% missing data.

Data analysis for grain yield and Striga parameters

Data collected on GY and Striga parameters were subjected to Bartlet’s homogeneity of variance test prior to analysis of variance (ANOVA) using a lattice procedure, using the Deltagen platform. Genotypes mean comparison were done at the 5% significance level using Fisher’s least significance difference (LSD). Broad sense heritability (H2) was computed using DeltaGen with the following formula:

$$\:\left({\text{H}}^{2}\right)=\:\frac{{\sigma\:}^{2}g}{{\sigma\:}^{2}g+\frac{{\sigma\:}^{2}\text{s}}{ns\:\:\:}\:+\frac{{\sigma\:}^{2}\text{r}}{nr}+\frac{{\sigma\:}^{2}\text{b}}{nb}+\frac{{\sigma\:}_{\varepsilon}^{2}}{ns+nr+nb}}$$

where \(\:{\sigma\:}^{2}g,{\sigma\:}^{2}\text{s},\:{\:{\sigma\:}^{2}\text{r},\:\sigma\:}^{2}\text{b},\:\)and \(\sigma_\varepsilon^{2}\) are the variance components for genotype, season, replication, block, and the pooled error, respectively, while ns, nr, and nb are the number of seasons, replications, and blocks, in that order. The linear mixed model was best-fit by the restricted maximum likelihood and used for phenotypic data analysis.

Genome-wide analysis to decipher marker-trait associations

The initial 70,197 SNPs were imputed by removing SNPs with > 20% missing data and < 5% minor allele frequency (MAF) on the KDCompute server (https://kdcompute.seqart.net/). A filtered set of 16,000 highly informative SNP markers was used for GWAS. The 126 maize genotypes were assessed for kinship using the software Structure [31]. The parameters of the analysis were set at 10,000 burn-in periods, with 10,000 Markov chain–Monte Carlo (MCMC) repetitions after burn-in. Five iterations were run for population number (K) values of 1 to 10 to allow the selection of the replication with the highest mean value of ln likelihood. The optimum K value was determined by the ad-hoc delta K method [32]. The linkage disequilibrium (LD) decay was estimated using the LD function in TASSEL software version 5.2.92 [33], and plotted with R version 4.3.0 (R Core Team, 2022) as described by Remington, et al. [34]. To identify the possible number of loci that are associated with Striga resistance in maize, pairwise LD of the populations was estimated using squared allele frequency correlations (r2) for the DArTseq SNPs markers [30, 35]. This was achieved using the LD measure, r2 program (Version 2.1) within the KDCompute plugin system. The r2 was estimated by an LD sliding window size of 50 and a threshold of r2 set at 0.1, such that any SNP that was below 0.1 was considered to have a weak LD [36]. The SNPs significantly associated with Striga resistance were compared with the rest of the SNPs within the linkage group. The distribution pattern for the whole genome LD was visualized using graphs generated as LD heatmap from TASSEL v5.2.5. The inter-chromosomal LD among the SNPs was achieved by examining the LD data to determine whether some SNPs had an r2 equal to or above 0.1. SNPs significantly associated with the traits of interest were recorded for each chromosome pair. Analysis for the genomic regions associated with Striga resistance and grain yield, probability values, and percentage of the effect of the markers were computed using the genome association and prediction integrated tool (GAPIT) package via the KDCompute interface (https://kdcompute.seqart.net/).

The associations between Single Nucleotide Polymorphisms (SNPs) and phenotypic traits were analyzed using the Compressed Mixed Linear Model (CMLM) implemented in the GAPIT software, as outlined by Yu et al. (2006). In this model, SNP markers were treated as fixed effects and were assessed individually. The model can be expressed as: Y = Xβ + Wα + Qv + Zu + ε, where Y is the observed vector for the phenotypic records of the traits; β is the fixed-effect vector (p × 1) other than the molecular marker effects and the population structure; α is the fixed-effect vector of the molecular markers; v is the fixed-effect vector from the population structure; u is the random-effect vector from the polygenic background effect; X, W, Q, and Z are the incidence matrixes from the associated β, α, v, and u parameters; and ε is the residual effect vector, in that order [37]. The P values and false discovery rate thresholds were set at P < 1 × 10−4 and – log(p) = 3 respectively. The kinship matrix was calculated from the 16,000 markers, and the quantile-quantile (QQ) and Manhattan plots were generated using the log10 (p) value distributions of the GAPIT function in KDCompute.

The SNP(s) above the threshold value were used to indicate the genomic region associated with Striga resistance and grain yield traits. Further, the QQ plot was used to evaluate how well the model used in GWAS for this study accounted for population structure. The significant markers identified for each trait were blasted on Ensemble based on the maize genome version B73_V4.0. assembly to identify candidate genes associated with the markers using the maizeGDB (https://www.maizegdb.org/) platform.

Results

Phenotyping for grain yield response and Striga parameters

Analysis of variance indicated significant genotypic variation (P < 0.05) for grain yield and Striga parameters in both Sa and Sh-infested environments (Table 1). Testing seasons had significant effects (P < 0.001) on GY and Striga parameters under both Striga-infested conditions except for SEC10 under Sa-infested conditions. Also, significant effects of replications in seasons were noted for the assessed traits under the two conditions, except for SDR8 and SDR10 under Sa conditions. Significant differences were also attributed to the block nested into replication-by-season interaction effect under both Sa and Sh-infested environments except for GY under Sa-infested conditions and SDR10 under Sh-infested conditions.

Table 1 Analysis of variance and significance tests for grain yield and Striga parameters of 126 maize genotypes evaluated under Striga asiatica and S. Hermonthica infested conditions
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Low broad-sense heritability values were recorded for SDR8 (< 0.10) and SDR10 (0.11) in Sa-infested conditions. In contrast, a high heritability value was recorded for Sect. 8, Sect. 10, SDR8, and SDR10 (H2 > 0.50) but low for GY (H2 = 0.02) under Sh-infested conditions (Table 2).

Table 2 Broad sense heritability estimates for grain yield and Striga resistance traits of 126 maize genotypes evaluated over two seasons, under two Striga-infested conditions and two replications
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Population structure and linkage disequilibrium

After removing markers with > 20% missing values and a marker frequency (MAF) < 5%, 16,000 SNP markers were found polymorphic and were used for GWAS. The 16,000 markers were distributed on the 10 chromosomes of the maize genome, with the highest and lowest marker densities observed on chromosome 8 (1272 SNPs) and chromosome 4 (835 SNPs), respectively. A population structure was constructed to reveal genetic relationships and aid genotype selection using the software Structure. The highest value for ΔK occurred at K = 8, showing that the genotypes could be clustered into eight sub-populations (Fig. 1A and B). The minimum coefficient for membership to a particular sub-population was 0.70. A similar trend was also observed using the Kinship analysis, where eight clusters were identified (Fig. 2).

Fig. 1
figure 1

The population structure ΔK (A) and the eight populations (B) of 126 maize genotypes resolved by the Evanno method based on 16,000 SNP markers

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The LD was estimated by calculating the squared correlation coefficient (r2) for all the 16,000 SNPs. Pairwise LD analysis between 16,000 SNP markers generated 798,675 comparisons within a physical distance extending up to 40,000,000 bp and was found to decay rapidly with the genetic distance. It was observed that LD varied along the chromosomes, with regions of high LD interspersed with regions of low LD (Fig. 3A). About 353,719 (21.52%) loci pairs were in significant LD (P < 0.001). Further, 78,157 (9.78%) were in complete LD (r2= 1), while 0.19% were at completely no LD (r2 = 0). A critical value of r2 was calculated from inter-chromosomal LD analysis and is estimated to be 0.1, beyond which LD is assumed to be caused by genetic linkage. The point at which the locally estimated scatterplot smoothing (LOESS) curve intercepts the critical r2 is determined as the average LD decay of the population. Based on these criteria, the intra-chromosomal LD decayed at 0.19 Mb for the whole genome (Fig. 3B). The significant intra-chromosomal LD (P < 0.001) ranged from 0.00 to 1.00 with an average of 0.11 Mb for the whole panel.

Fig. 2
figure 2

Kinship heatmap showing the phylogenetic relationship among 126 maize genotypes using 16,000 SNP markers. Red small squares indicate genetic relatedness, dark orange colours represent high kinship relations, while lighter colours (yellow) represent weaker relations. The dendrogram out of the matrix indicated the resulting clusters

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Fig. 3
figure 3

Plots showing linkage disequilibrium (LD) heatmap (A) and intra-chromosomal LD decay (B) for 126 maize genotypes using 16,000 SNP markers. Note: in Figure A the triangle on the right diagonal revealed the squared correlation coefficients (r2), while the P values are shown on the lower diagonal of the triangle for each marker pair. In Figure B, the horizontal blue line indicates the 95th percentile distribution of unlinked r2, while the red cover shows the locally estimated scatterplot smoothing (LOESS) illustrating the LD decay

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Marker-trait association (MTAs) under S. Asiatica and S. hermonthica-infested conditions

Grain yield and Striga parameters (SEC8, SEC10, SDR8, and SDR10) were subjected to GWAS using 16,000 SNP markers. The traits SDR8 under Sa-infested conditions and GY under Sh-infested conditions were not included in the GWAS due to their low heritability (H2 < 0.1). The GWAS results across environments are shown in Manhattan plots and Q-Q plots of P values comparing the expected − log10 p values to the observed − log10 p values in Figs. 4 and 5 for Sa and Sh environments, respectively. A total of 72 MTAs were identified at P ≤ 1 × 10−4 (Tables 3 and 4). Two MTAs were detected for GY under Sa (Table 3; Fig. 4), while seven significant MTAs were associated with the same trait under Sh-infested conditions (Table 4; Fig. 5). There were two, seven, and eleven MTAs for SEC8, SEC10, and SDR10, respectively, under Sa-infested conditions (Table 3; Fig. 4), while twelve, fourteen, seventeen, and seven MTAs were detected for the same traits, respectively under Sh conditions (Table 4; Fig. 5).

Table 3 Striga asiatica resistance-associated SNP markers and candidate genes identified in the association mapping panel of 126 maize genotypes
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Table 4 Striga hermonthica resistance-associated SNP markers and their candidate genes identified in the association mapping panel of 126 maize genotypes
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Fig. 4
figure 4

Manhattan (right) and quantile-quantile (Q-Q) (left) plots showing SNPs associated with grain yield and Striga parameters in assessing 126 maize genotypes under Striga asiatica-infested conditions and 16,000 SNP markers. The plots were drawn using the compressed mixed linear model at P < 0.001. GY = grain yield, SEC8 = Striga emergence counts 8 weeks after sowing, SEC10 = Striga emergence counts 10 weeks after sowing, and SDR10 = Striga damage rating 10 weeks after sowing

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Fig. 5
figure 5

Manhattan (right) and quantile-quantile (Q-Q) (left) plots showing SNPs associated with grain yield and Striga parameters in assessing 126 maize genotypes under Striga hermonthica-infested conditions and 16,000 SNP markers. The plots were drawn using the compressed mixed linear model at P < 0.001. SEC8 = Striga emergence counts 8 weeks after sowing, SEC10 = Striga emergence counts 10 weeks after sowing, SDR8 = Striga damage rating 8 weeks after sowing, and SDR10 = Striga damage rating 10 weeks after sowing

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The two MTAs recorded for GY under Sa-infested conditions were located on chromosomes 1 and 5 (Table 3). For SEC8, the 14 MTAs were identified on chromosomes 1 (one marker), 2 (one marker), 3 (one marker), 5 (three markers), 6 (two markers), 7 (one marker), 9 (one marker), and 10 (two markers) for Sh-infested conditions and chromosomes 10 (one marker) and 4 (one marker) for Sa-infested conditions. The 21 MTAs observed for SEC10 were on chromosomes 1 (two markers), 3 (two markers), 5 (three markers), 6 (one marker), 7 (one marker), 8 (one marker), and 10 (four markers) for Sh-infested conditions and on chromosomes 1 (two markers), 3 (two markers), 5 (one marker), seven (one marker), and 10 (ten markers) for Sa-infested conditions. SDR8 had 15 MTAs from chromosomes 1 (one marker), 2 (five markers), 3 (one marker), 4 (one marker), 5 (one marker), 6 (one marker), 7 (one marker), 8 (two markers), and 10 (two markers) in Sh-infested environment. MTAS for SDR10 were identified on chromosomes 4 (one marker), 8 (two markers), 9 (one marker), 10 (two markers), and 7 (one marker) under Sh-infested conditions and on chromosomes 1 (one marker), 2 (one marker), 3 (two markers), 5 (one marker), 6 (one marker), 7 (three markers), 8 (one marker), and 10 (one marker) for Sa-infested environment. Ten of the significant MTAs showed a negative allelic effect under Sa-infested conditions (Table 3), and six had a negative allelic effect under Sh-infested conditions (Table 4). One pleiotropic MTA was identified for SDR8 and SDR10 on chromosome 4 under Sh-infested conditions (Table 4).

Candidate genes associated with grain yield and Striga resistance traits

Based on SNP genome-wide association mapping, 33 and 14 candidate genes were identified for Sh and Sa resistance, respectively. The significant SNPs associated with GY under Sh were linked to six candidate genes Zm00001eb288770, Zm00001eb014030, Zm00001eb014020, Zm00001eb381210, Zm00001eb412710, and Zm00001eb219710. However, no candidate genes were associated with GY under Sa-infested conditions. Five of the markers linked to SEC8 under Sh-infested environment flanked the regions overlapping the candidate genes Zm00001eb313080, Zm00001eb253620, Zm00001eb272670, Zm00001eb163250, and Zm00001eb060090 on chromosome 1, 5, 6, and 10. In Sa-infested environment, only one candidate gene, Zm00001eb432870, was associated with SEC8. Eight and seven putative candidate genes were detected for SEC10 under Sh and Sa-infested conditions, respectively. These genes were located on chromosomes 5, 6, 7, 8, and 10, under Sa conditions, and 1, 5, 6, 7, 8, and 10 under Sh-conditions. Under Sa-conditions, the genes associated with SEC10 were located on chromosomes 1, 3, 5, 7, and 10. Twelve genes were identified for SDR8 under Sh-infested conditions and were located on chromosomes 1, 2, 3, 4, 5, 6, 7, and 8. The significant SNPs involved in SDR10 under Sh-infested conditions were localized within the following candidate genes: Zm00001eb165170, Zm00001eb346540, Zm00001eb371500, Zm00001eb410250, Zm00001eb419180, Zm00001eb340640, and Zm00001eb307490. Out of the 12 MTAs for SDR10 in Sa-infested environment, nine were found in regions covering the following genes: Zm00001eb319510, Zm00001eb336310, Zm00001eb114280, Zm00001eb329320, Zm00001eb156390, Zm00001eb409270, and Zm00001eb217880 on chromosomes 2, 3, 5, 7, and 10.

Discussion

Phenotyping

The parasitic weeds Sh and Sa have been recognized as major constraints to maize production in SSA. Striga resistance is a polygenic trait and is subject to genotype-environment interaction effects needing accurate selection strategies. Agronomic management options (e.g. crop rotation, intercropping, catch-cropping, and trap-cropping) are often insufficient. There is a need for integrated Striga management spearheaded by resistance breeding programmes. Breeding for Striga resistance is the most economical approach to minimise yield loss of major cereal crops caused by Striga infestation in SSA. Hence, genetic markers associated with Striga resistance need to be identified to guide the pace and precision of resistance breeding in maize [38, 39]. Integrating marker-assisted and conventional breeding techniques will accelerate breeding and genetic gain of maize for Striga resistance [40]. In this study, a GWAS was conducted based on a panel of genetically diverse maize populations comprising 98 inbred lines, 21 OPVs, and 11 top cross hybrids to discern genetic loci associated with resistance to the two dominant Striga species (Supplemental Table 1).

The phenotypic evaluation revealed significant variability for both Sa and Sh resistance among the tested maize genotypes, thus confirming the availability of relevant alleles for future breeding and genetic improvement (Table 1). The broad sense heritability observed for Striga damage under Sa-infested conditions was low (SDR8 = 0.01 and SDR10 = 0.11) compared to Sh resistance (SDR8 = 0.92 and SDR10 = 0.82) (Table 2). This suggests that the genotypic variance of the Sa resistance traits was low compared to the phenotypic variance, making direct selection difficult for Sa resistance. This confirms that Sh resistance has been the focus of more research that yielded higher genetic variation than Sa in maize. The high heritability of Sh resistance suggests that the genotypic variance was relatively higher supporting phenotypic selection. Hence, Sh traits can be relatively quickly selected (Table 2). The present results agree with Olakojo and Olaoye [41], who reported low heritability of some Striga parameters and high heritability on other associated traits when assessing maize agronomic traits for yield improvement and Sa tolerance. However, the high heritability values obtained could suggest the involvement of high nonadditive genetic variance and the confounding effects of the test environments, as Striga resistance traits are known as low heritable. This implies the need to test the population across multiple growing environments to reduce the confounding effect of the test environments and the genotype-by-environment interaction effect.

Population structure and linkage disequilibrium

Estimating population structure and within-group familial relatedness in maize genomic association studies is important to reduce the risks of false positives [42]. The Structure and Kinship analyses revealed population stratifications and admixtures, suggesting the need to use a robust statistical model in association analysis to control spurious MTAs [43]. The present analyses suggested eight sub-groups in the assessed tropical and sub-tropical maize population (Fig. 1), and the clustering was mainly based on genotype and geographic origin. This was expected, given that the genetic material used in the study was from diverse sources. The kinship heatmap showed different genetic relatedness in each group (Fig. 2). Hybrids and OPVs from IITA/Nigeria, NPGRC/South Africa, and CIMMYT/Zimbabwe form a distinct cluster from inbred lines from IITA/Nigeria and CIMMYT/Zimbabwe. Some clusters consisted of inbred lines from IITA (Clusters VI, VII, and VIII) or inbred lines from CIMMYT (Cluster V) (Figs. 1 and 2). The genetic distinctness of tropical inbred lines from CIMMYT lines suggests that crosses could be explored to achieve high heterosis and Striga resistance with broader adaptation.

Based on pairwise LD analysis involving 16,000 DArTSeq SNP markers, a low number of markers (9.78%) were in perfect LD (r2 = 1), which are more likely to be inherited across the whole genome. This reveals that this marker population was ideal for association mapping, as SNP markers in a strong LD provide redundant genotyping information during association analysis [44]. Furthermore, the rapid LD decay points to the high genetic diversity of the present panel and its usefulness for GWAS with a genetic mapping resolution (Fig. 3). The high genetic diversity is also associated with the genetic material that is composed of tropical and sub-tropical populations. The rapid LD decay is in agreement with previous studies in maize [45, 46] and other crops [47,48,49].

Marker-trait association for Striga resistance

For GWAS analysis, the study used CMLM model in GAPIT. The CMLM model of GAPIT was demonstrated to be more powerful and effective in association studies and can provide accurate predictions with less computing time [50]. Also, the method significantly reduces false positives [51]. The QQ plots revealed that the population structure and the phenotypic data were well distributed in both Striga-infested conditions, as the expected P-values were close to the observed P-values (Figs. 4 and 5). The plots revealed a good fit of the model. The kinship deviation at the top of the null hypothesis diagonal points to a great association between the markers and the traits (Figs. 4 and 5). The present study identified 22 MTAs for grain yield and Striga resistance traits under Sa infestations. The MTAs comprised of 2 MTAs conditioning GY, 2 MTAs conditioning SEC8, 7 MTAs conditioning SEC10, and 11 MTAs conditioning SDR10 (Table 3; Fig. 4). Fifty-seven MTAs were found for GY (7), SEC8 (12), SEC10 (14), SDR8 (17), and SDR10 (7) under Sh infestation (Table 4; Fig. 5). The marker 2,512,743 was pleiotropic for SDR8 and SDR10 for Sh resistance (Table 4) and could be useful for simultaneous selection. The SNPs were distributed across almost all chromosomes for all the assessed traits in the present study. The variable chromosome suggests that Striga resistance is a complex trait governed by polygenes. The significant markers identified in the current populations were located on chromosomes previously reported to harbour genes linked to Striga resistance traits including Striga emergence counts and Striga damage rating scores [13, 24, 52]. The significant markers for GY were located on chromosomes 1, 5, 6, 8, 9, and 10 and were associated with Sh resistance, and on chromosomes 1 and 5, they were associated with Sa resistance. These results corroborate previous studies that highlighted the importance of chromosome 10 [13], chromosome 6 [24], and chromosome 9 [52] for GY response under Sh conditions. In the present study, MTAs for Striga emergence counts were recorded on chromosome 5, previously reported to have a significant association with the same trait using the marker GRMZM2G018508 by [24], and GRMZM2G129543 and GRMZM5G823157 [52] in tropical maize lines. The same authors reported the ten chromosomes harbouring genes for Sh host damage in agreement with the present study. The markers reported in the present study for Sa resistance are novel except for the three SNPs reported by Pfunye, et al. [8] on chromosomes 5, 6, and 7 for total Striga plants emerged. There is a knowledge gap in QTL analysis associated with Sa resistance. The results revealed that different genomic regions are involved in Striga resistance (Tables 3 and 4). None of the genetic markers were associated with dual resistance of both Sa and Sh, suggesting that different genomics regions govern resistance for the two Striga species. Further association studies should be done using different populations to search for markers and genes controlling GY and Striga resistance traits under Sa and Sh infestation, as the two Striga species occur in tandem, especially in East Africa.

Seven of the significant markers had a negative allelic effect under Sh-infested conditions and four significant MTAs showed a negative allelic effect under Sa-infested conditions (Tables 3 and 4). This implies that these genetic markers are linked with Striga resistance proper for introgression into desirable parents. A negative allelic effect indicates that the allele is associated with a more resistant phenotype in a desirable direction. On the other hand, a positive effect indicates the opposite [53]. Stably expressing SNPs are useful for maize breeders to introduce target QTL in pipeline breeding materials. In this study, markers 4,772,065 and 2,447,891 are more stable for SDR8 under Sh-infested conditions.

Forty-one and 23 candidate functional genes were identified for Striga resistance under Sh and Sa-infested environments, respectively (Tables 3 and 4). Most detected genes are involved in various cellular and metabolic processes, plant defense, and cell development. The candidate gene Zm00001eb319510 is significantly associated with SDR10 under Sa-infested conditions and encodes Oxidoreductase-like domain-containing protein. Other candidate genes, such as Zm00001eb163250, Zm00001eb295600, Zm00001eb165170, and Zm00001eb165170 associated with SEC8, SDR8, and SDR10 under Sh-infested conditions, encode Clathrin heavy chain, Golgin candidate 5, and AAA + ATPase domain-containing protein respectively. The candidate gene Zm00001eb288770 associated with GY under Sh-conditions on chromosome 6 encodes for Coronatine-insensitive protein 1. Zm00001eb288770 is linked with jasmonate production, which regulates defense against insects and pathogens, wound healing, and pollen fertility [54]. The results show the involvement of Zm00001eb288770 in maize’s tolerance response to Striga. Another putative gene Zm00001eb253620, encodes for S-adenosylmethionine carrier 1 chloroplastic/mitochondrial that acts as a precursor of ethylene (a senescence inducer) and polyamines (antisenescence molecules) and controls the regulation of senescence in plants [55]. One of the main symptoms of Striga infestation is rapid senescence. This suggests that the gene Zm00001eb253620 is vital to unlocking Striga resistance breeding. Zm00001eb409850 encodes CBM20 domain-containing protein is implicated in both glycogen metabolism and autophagy [56]. The gene model Zm00001eb219710 associated with GY under Sh-infested conditions on chromosome 8 is associated with SEC10 under the same conditions on chromosome 7. Zm00001eb165170 and Zm00001eb112030 are linked with SDR8 and SDR10 under Sh-infested conditions.

Conclusion and recommendation

The present study is the first attempt to identify genomic regions associated with the dual resistance of maize to Sa and Sh. A population of 130 maize germplasm was used, comprising tropical and sub-tropical inbred lines, OPVs, and hybrids. The study identified 72 SNPs associated with grain yield and Striga resistance parameters under Sa and Sh-infested conditions. However, markers 4,772,065 and 2,447,891 are considered more stable for SDR8 under Sh-infested conditions. No marker was linked with dual resistance to Sh and Sa. Fifty significant markers were associated with Sh resistance and 22 were linked to Sa resistance. Significant SNPs were found flanking 47 protein-coding putative genes, of which one was associated with GY, while six, sixteen, eleven, and thirteen genes linked to SEC8, SEC10, SDR8, and SDR10 were associated with Sh and Sa resistance. Some of the detected genes reportedly conditioned insect and pathogen resistance, plant cell development, variable senescence, and pollen fertility. The gene Zm00001eb219710 was pleiotropic, and conditioned GY and SEC10, while Zm00001eb165170 affected SDR8 and SDR10, and Zm00001eb112030 conditioned SDR8 and SDR10 associated with Sh resistance. The candidate markers may facilitate simultaneous selection for Sh and Sa resistance and grain yield in maize after further evaluation and introgression in breeding pipelines. Based on the current results, the two Striga species have different genetic compositions. The tested maize genotypes were resistant to Sh but not necessarily resistant to Sa. There is a need to collect separate data sets from multiple studies on the two species. This will enable to document the economic importance and genetic basis of the two Striga species to guide future research and development. We recommend resistance breeding of maize on separate genetic backgrounds for Sa and Sh resistance. This will allow for a much clearer perspective on the economic importance of each of the two major Striga species. However, gene pyramiding on a desirable genetic background of maize could harness Striga resistance against the two species.