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Article

Genetic Diversity and Population Structure of Sweet Orange (Citrus sinensis) Germplasm in Inhambane Province, Mozambique

by
Milton Sebastião Zavale
1,2,*,
Arsénio D. Ndeve
1,*,
Winfred N. Muteti
1 and
Rogério M. Chiulele
1,3
1
Department of Crop Production, Faculty of Agronomy and Forestry Engineering, Eduardo Mondlane University, 3453 Avenida Julius Nyerere, Maputo P.O. Box 257, Mozambique
2
Mozambique Agricultural Research Institute (IIAM), Avenida das FPLM 2698, Mavalane B, Maputo P.O. Box 3658, Mozambique
3
Centre of Excellence in Agri-Food Systems and Nutrition (CE-AFSN), Eduardo Mondlane University, 5◦ Andar, Edificio Da Reitoria, Praça 25 de Junho, Maputo P.O. Box 257, Mozambique
*
Authors to whom correspondence should be addressed.
Int. J. Plant Biol. 2026, 17(9), 88; https://doi.org/10.3390/ijpb17090088
Submission received: 3 July 2026 / Revised: 2 August 2026 / Accepted: 15 August 2026 / Published: 10 September 2026
(This article belongs to the Section Plant Ecology and Biodiversity)
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Abstract

Background/Objectives: Sweet orange (Citrus sinensis (L.) Osbeck) is an economically important fruit crop that contributes substantially to food security and smallholder income in Mozambique. Despite this, the genetic diversity of the locally grown germplasm has not been characterized at the molecular level, limiting its improvement and conservation programs. This study assessed the genetic diversity and population structure of germplasm from 94 sweet orange trees sampled across four districts of Inhambane Province using DArTSeq single-nucleotide polymorphism (SNP) markers. Methods: After filtering 8111 SNPs for call rate (≥0.80) and minor allele frequency (≥0.01), 1263 markers were retained, of which 1144 were anchored to the nine chromosomes of the reference genome. Results: Sparse non-negative matrix factorization identified K = 1, indicating a single undifferentiated gene pool, supported by a smooth PCA scree with one weak axis. DAPC assigned individuals to their district only 38.3% of the time (random expectation = 25%; maximum a-score = 0.10), and the first two PCoA axes explained 10.33% of variation with complete district overlap, indicating no detectable geographic structure. Diversity was low, with observed heterozygosity (Ho = 0.247) exceeding expected heterozygosity (He = 0.138) and a negative inbreeding coefficient (Fis = −0.222). The pattern indicated a heterozygote excess consistent with the fixation of the heterozygous interspecific-hybrid genome under clonal propagation. A hierarchical analysis of molecular variance showed that differentiation among districts was negligible (0.04%), whereas 4.16% of variation was partitioned among orchards (farms) within districts, indicating that the little of the existing structure resides at the orchard level, confounded with propagation method and cultivar, rather than among districts. Most variation was partitioned within individuals (76.0%), and pairwise FST values (0.0005–0.0035) were uniformly low. Conclusions: These results indicate that the sweet orange orchards stem from a single, highly heterozygous gene pool redistributed through the exchange of seed and vegetative planting material. This underscores the need to introduce diverse external germplasm to broaden the genetic base for sustainable improvement in Mozambique.

1. Introduction

Orange (Citrus sinensis) is believed to have originated in South-East Asia, mostly in India, where there is a large natural population of Citrus gene pool [1,2]. Its genetic origin is from natural hybridization between mandarin (C. reticulata Blanco) and a pummelo (C. maxima (Burm.) Merr.) [3], and it is the most widely cultivated species in the Citrus genus, representing more than 50% of global citrus production and grown across more than 140 tropical and subtropical countries [4,5]. Sweet orange fruit is consumed fresh or processed into juice and is valued for its high content of vitamin C, flavonoids, carotenoids, and essential minerals, including potassium, magnesium, calcium, and phosphorus [6,7,8].
Global orange production covers approximately 3.41 million hectares, with an annual yield of approximately 69.85 million tonnes and a global average of 20.5 tonnes per hectare [9]. The leading producing countries are Brazil (17.6 million tonnes), China (7.8 million tonnes), India (3.9 million tonnes), Egypt (3.7 million tonnes), and the United States of America (2.3 million tonnes) [9]. In Africa, sweet orange production covers more than 537 thousand hectares, accounting for over 10.6 million tonnes annually, with Egypt and South Africa among the leading producing nations on the continent [9]. In Mozambique, sweet orange is cultivated primarily by smallholder farmers in Maputo, Inhambane, and Manica provinces [10,11], covering an estimated 2.8 thousand hectares and yielding approximately 67 thousand tonnes annually [9].
Despite its socioeconomic importance, sweet orange production faces significant biotic and abiotic constraints. Major diseases include citrus variegated chlorosis (CVC), citrus canker, leprosis, tristeza, Alternaria brown spot, and Huanglongbing (HLB), which is caused by Candidatus Liberibacter spp. and transmitted by the Asian citrus psyllid (Diaphorina citri Kuwayama) [12,13,14]. HLB has caused significant production declines in China, Brazil, the United States, and India in recent years [4,13]. In Mozambique, most orange farmers rely on locally available planting material of unknown genetic background, propagated without systematic selection. As a result, orange orchards are heterogeneous, comprising a mixture of seed-propagated trees and commercial varieties such as Washington Navel and Valencia that differ widely in yield, fruit size, color, and sweetness [10]. Additionally, although the phenotypic traits of orange fruits exhibit typical characteristics of Washington and Valencia orange types, the genetic identity of cultivated orange trees in Mozambique remains uncharacterized. This contributes to low yield and limits the implementation of targeted improvement strategies [15].
Molecular markers, including random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), inter simple sequence repeats (ISSR), simple sequence repeats (SSRs), and single-nucleotide polymorphisms (SNPs), have been used to characterize genetic diversity in citrus and other crops [15,16,17,18,19]. Among these, single-nucleotide polymorphism (SNP) markers are the most abundant form of sequence variation and are suitable for high-throughput genotyping [20]. They have been widely used in genetic diversity studies of various citrus species [20,21,22,23]. The assessment of genetic diversity is fundamental for identifying variable genotypes, establishing genetic relationships, and identifying material for use in breeding programs [24,25]. Although the genetic diversity and structure of sweet orange have been characterized in several major producing regions, molecular assessment in Mozambique remains limited. Therefore, this study aimed to determine the genetic diversity and population structure of Citrus sinensis germplasm from four districts of Inhambane province, providing a baseline for breeding and conservation strategies.

2. Materials and Methods

2.1. Plant Material and Sampling

A total of 94 sweet orange trees were sampled from four districts in Inhambane Province (25 in Zavala, 23 in Inharrime, 25 in Jangamo, and 21 in Morrumbene), which collectively represent the major orange-producing areas of Inhambane Province (Figure 1). Sampled trees were grown primarily under rainfed conditions without irrigation, fertilization, or pest management. Trees were propagated by two methods: seed (50 trees) and grafting (44 trees) (Supplementary Table S1). The main varieties cultivated in the province include Valencia and Washington Navel, grown by smallholder farmers in structured orchards and as dispersed trees. A classic hierarchical nested design was employed, and in each district, five orange orchard farms were selected. Two farms had grafted trees, and three had seed-propagated trees. Within each farm, four to five trees were randomly selected for leaf sampling.

2.2. DNA Extraction and Genotyping by Sequencing

Two fresh and young leaves were collected from the second and third nodes from the growing tips of the branches and dried in silica gel in an airtight plastic container and let to dry. After drying, ten leaf disks, 5 mm in diameter, were punched using a biopsy punch and transferred with forceps into a 96-well plate. The punch and forceps were decontaminated with 96% ethanol between samples to prevent cross-contamination. The sealed, labelled plate was shipped to SEQART AFRICA (International Livestock Research Institute, Nairobi, Kenya) for DNA extraction and genotyping. Genomic DNA was extracted from leaf tissue using the Nucleomag Plant DNA extraction kit and checked for quality and quantity under 0.8% agarose. The extracted genomic DNA was in the range of 50–100 ng/μL [26].
Reduced-representation genomic libraries were constructed using the DArTseq™ complexity reduction approach [27], which involved digestion of genomic DNA with a PstI/MseI restriction enzyme combination, ligating barcoded and common adapters, and PCR-amplifying adapter-ligated fragments. Libraries were sequenced on an Illumina NovaSeq X platform using single-read 138-cycle runs. DArTseq marker scoring was performed with DArTsoft14, an in-house pipeline implementing algorithms for SNP calling and quality assessment. SNP markers were aligned to the C. sinensis reference genome (accession GCF_022201045.2) [28], downloaded from NCBI (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_022201045.2/, accessed on 18 February 2026).

2.3. Marker Quality Control and Filtering

Upon receipt of the genotypic data, quality control was performed using the snpReady package in R Software (version 4.5.1) [29]. Quality filtering retained SNPs with a minor allele frequency (MAF) ≥ 0.01 and a call rate > 0.80 [20,30]. Missing loci were imputed using Wright’s method.

2.4. Genetic Diversity Analysis

Genetic diversity estimates were performed for districts and propagation methods (grafted and seed-propagated trees), which included observed heterozygosity (Ho), expected heterozygosity (He), inbreeding coefficient (FIS), and polymorphic information content (PIC) [22,31,32], using the dartR package of RStudio software (version 4.5.1) [33]. Additionally, to identify unique genetic reservoirs, private alleles were identified across the 1263 SNP loci, and then the Allelic richness (Ar) was computed using the hierfstat R package [34].
Principal coordinate analysis (PCoA) was performed on a Euclidean distance matrix to visualize genetic relationships among trees and was conducted using the adegenet RStudio software package (version 4.5.1) [33].

2.5. Population Structure Analysis

Population structure was inferred using a sparse non-negative matrix factorization (sNMF), implemented in the LEA package [35]. The cross-entropy criterion was evaluated for K = 1 to 10 with 10 repetitions per K to determine the optimal number of clusters. The optimal number of genetic clusters was defined as the K value with the lowest minimum cross-entropy. To confirm the genetic structure, principal component analysis and principal coordinate analysis were performed within LEA and adegenet (version 4.5.1), respectively [33,36]. PCoA was additionally computed based on a tree propagation method (grafted or seed).

2.6. Discriminant Analysis of Principal Components (DAPC)

Discriminant analysis of principal components (DAPC) was used to determine whether the four collection districts were genetically distinguishable [37]. The number of retained principal components was assessed by cross-validation and a-score optimization [38,39]. For each PC number, the mean proportion of validation individuals correctly reassigned to their district of origin was computed, and the value with the highest assignment success was identified. The mean cross-validated assignment success and the maximum a-score were benchmarked against a 25% chance-level success.

2.7. Genetic Differentiation

The partitioning of genetic variance was assessed through hierarchical analysis of molecular variance (AMOVA) implemented in the poppr package in RStudio software (version 4.5.1) [40] with trees nested within farms and farms nested within districts. In addition, AMOVA was also performed for the propagation method. Pairwise genetic differentiation was estimated using the Weir and Cockerham [41] FST estimator in hierfstat [34]. Nei’s genetic distances were used to construct an unrooted neighbor-joining tree with ape [42].

3. Results

3.1. SNP Distribution Across C. sinensis

A total of 8111 SNPs were generated, and 1263 were retained after quality filtering for call rate (≥0.80) and minor allele frequency (MAF ≥ 0.01) (Supplementary Table S2). Of these, 1144 SNPs were anchored to the nine chromosomes of the C. sinensis reference genome (GCF_022201045.2), while 119 were Unknown. The anchored markers were distributed across all nine chromosomes but at heterogeneous densities (Figure 2; Table 1). The number of SNPs per chromosome ranged from 74 (chromosome 1) to 205 (chromosome 3). SNP density ranged from 2.98 SNPs/Mb on chromosome 1 to 4.10 SNPs/Mb on chromosome 7, with an average of 3.8 SNPs/Mb. Chromosomes 4, 5, and 7 had the highest densities (4.03 to 4.10 SNPs/Mb), whereas chromosomes 1 and 8 showed the lowest (2.98 and 3.47 SNPs/Mb, respectively).

3.2. Genetic Diversity

The mean MAF of the 1263 markers was 0.0236, while the PIC ranged from 0.0208 to 0.375, with a mean PIC of 0.0372. A total of 33 markers had a PIC above 0.25, 38 markers had a PIC ranging from 0.10 to 0.25, and 1192 markers had a PIC below 0.10 (Table 2). Within the population, observed heterozygosity (Ho = 0.247) exceeded expected heterozygosity (He = 0.138), and the inbreeding coefficient was negative (Fis = −0.222). Per-district diversity estimates were consistent across the four districts (Table 3). Observed heterozygosity ranged from 0.242 (Jangamo) to 0.250 (Zavala), and the expected heterozygosity ranged from 0.132 (Jangamo) to 0.141 (Inharrime). The inbreeding coefficient ranged from −0.355 to −0.442. Although the germplasm suggested a single gene pool, the rarefied allelic richness varied slightly among sampling sites, ranging from 1.381 (Jangamo) to 1.454 (Inharrime). Inharrime showed the highest allelic richness, expected heterozygosity, and PIC, as well as the greatest number of private alleles (Figure 3). Grafted (n = 44) and seed-propagated (n = 50) samples showed the following diversity estimates: (Grafted: Ho = 0.242, He = 0.134, Fis = −0.286, Ar = 1.576, PIC = 0.034; Seed: Ho = 0.250, He = 0.139, Fis = −0.292, Ar = 1.562, PIC = 0.038). The private alleles were 289 and 297, respectively; see Table 4.

3.3. Population Structure

The samples were grouped into a single genetic population using the sNMF cross-entropy criterion, with K = 1 yielding the lowest entropy (Figure 4a). The principal component analysis scree plot was smooth, with a single weakly significant axis accounting for only a small fraction of total variation (Figure 4b). Cross-validated assignment of individuals to their district of origin succeeded only marginally above chance, at 38.3% (random expectation = 25% for four groups), and the maximum a-score across all retained principal-component numbers was 0.10 (Figure 5a,b). The DAPC scatter plot showed extensive overlap among all four districts with no clear clustering, and posterior assignment probabilities revealed widely mixed membership with few assigned individuals (Figure 5a and Figure 6). Consistently, the two PCoA coordinates (PCoA1 and PCoA2) explained 10.33% of the total variation, and individuals from all four districts overlapped (Figure 7). The propagation methods partially separated the samples along the first PCoA axis, also explaining 10.33% of the total variation (Figure 8).

3.4. Genetic Differentiation

The hierarchical AMOVA showed that among-district variation was 0.04%, whereas 4.16% of variation was among farms within districts (p = 0.001), 19.42% among individuals within farms, and 76.38% within individuals (Table 5). Pairwise FST values ranged from 0.0005 to 0. 0035 (Figure 9). The highest values were observed between Morrumbene and Zavala (0.0035) and between Jangamo and Zavala (0.0030). A total of 4.63% of variation was attributed to the propagation method (p = 0.001), with a pairwise FST of 0.0119 between the groups. The neighbor-joining tree showed very short branches with no resolved clustering (Figure 10).

4. Discussion

4.1. Citrus sinensis SNP Identification, Marker Coverage, and Genomic Distribution

The genetic diversity and population structure of sweet orange have been examined across major production regions, such as India and Iran, using a range of molecular marker systems, including SSRs and InDels [25,44]. The present study, therefore, extends molecular characterization of sweet orange to Mozambique using SNP markers to inform conservation and improvement programs.
From 8111 initial loci, 1263 high-quality SNPs were retained, of which 1144 were anchored across the nine chromosomes of the C. sinensis reference genome. This marker retention rate (15.6%) falls within the 9–22% range, aligning with previous citrus studies using similar genotyping-by-sequencing methods [20,21,45]. The uneven distribution of markers among chromosomes and per-megabase densities, ranging from 2.98 to 4.10 SNPs/Mb, reflects local differences in recombination rate, gene density, restriction-site distribution, and selection history [23,28]. The mean inter-marker spacing (265.93 kb) suggests adequate genome-wide coverage for characterizing intraspecific diversity and population structure.

4.2. Weak Population Structure Among Citrus sinensis Collection Regions

The sNMF cross-entropy criterion minimized at K = 1, indicating a single undifferentiated gene pool. The principal component analysis yielded a smoothly declining eigenvalue scree plot on which only a single axis was statistically significant. The pattern suggested a continuous gradient of relatedness rather than discrete subpopulations [36]. Discriminant analysis (DAPC), used to test whether the four collection districts were genetically distinguishable, supported the structure. Individuals were correctly assigned to their district of origin only 38.3% of the time, barely above the 25% expected by chance. The maximum a-score across all retained principal components was 0.10, indicating negligible discrimination. a-score penalizes the spurious separation that arises when too many principal components are retained; therefore, a score not exceeding 0.10 suggested that the districts are indistinguishable [38,39]. Consistently, the PCoA explained 10.33% of the total variation across its first two coordinates, and individuals from all four districts completely overlapped. The convergence of sNMF, DAPC, and PCoA findings suggested that the sweet orange orchards of Inhambane Province constitute a single, genetically homogeneous gene pool.
This pattern of high admixture and minimal differentiation aligns with previous studies of sweet orange and related citrus using SSR and CAPS-SSR markers, although the level of structuring observed here is lower [25,44]. While previous studies examined cultivars assembled from breeding collections, which may retain curated divergence, the present germplasm was sampled from smallholder farms that routinely exchange grafting and seed material and did not include certified reference cultivars or authenticated germplasm accessions. This practice may have homogenized the germplasm, leading to the observed weak differentiation. This is consistent with genome-wide evidence indicating that sweet orange cultivars share a single ancestral origin and exhibit minimal genetic divergence [45,46,47]. The weak geographic structure within Inhambane Province is therefore aligned with the narrow global base of sweet orange.

4.3. Propagation Method Associated with Genetic Variation in Citrus sinensis

Grafted and seed-propagated trees were equally diverse; therefore, their Ho, He, Fis, and PIC values were almost identical. However, they were partially separated in the PCoA and differed at a low but statistically detectable level (AMOVA among-groups = 4.63%; FST = 0.0119), exceeding the among-district values. Although the districts were genetically indistinguishable, the genetic variation was more associated with the propagation method than with geographic origin. However, propagation method may be confounded with cultivar composition, source of planting material, and farm management, as the grafted trees are likely a few named scion cultivars, while the seed-propagated trees are a genetic mix. The distinction between equal diversity and differing composition can be attributed to the contrasting reproductive biology of the propagation methods. Grafted trees are clonal copies of a few selected scions and therefore form tighter genetic clusters, whereas seed-propagated trees include occasional zygotic seedlings alongside the maternal nucellar clones, because nucellar embryony in citrus is facultative [28]. These zygotic individuals may have introduced novel allele combinations absent from the clonal grafted material, dispersing the seed-propagated group and producing the observed genetic difference. However, the present study did not distinguish between zygotic and nucellar seed-propagated trees; therefore, the observed genetic differences cannot be attributed solely to zygotic recombination. Other factors may also have contributed to genetic variation, including cultivar identity and farm management practices [48]. Both the propagation and district FST values nonetheless remained below the 0.05 threshold for low differentiation [49], confirming that the collection represents a single gene pool.

4.4. Narrow Genetic Base of Citrus sinensis

The low expected heterozygosity (He = 0.138) and PIC (0.037) observed are consistent with the narrow genetic base of C. sinensis [28,46], which originated from a single interspecific hybridization between mandarin and pummelo [3,46,50]. Narrow genetic diversity was earlier reported in a study using SNP markers on two cultivated citrus species, Citrus sinensis, with He = 0.086 and PIC = 0.072, and Citrus unshiu, with He = 0.121 and PIC = 0.10, which is supported by citrus breeding practices and domestication [20]. Likewise, previous studies using SSR and CAPS-SSR markers have also documented low intraspecific diversity in sweet orange [25,44], consistent with the species’ origin as a single, somatically mutated biotype.
In clonal sweet orange germplasm, intraspecific variation is largely attributed to spontaneous somatic mutation, which has driven the diversification of varieties such as Navel, blood, and acidless oranges [17,50,51]. These varieties share highly similar genetic backgrounds with their parental genotypes, differing mainly in the traits of interest [20]. Our findings are consistent with this. The low allelic diversity, the dominance of low-PIC markers (1192 of 1263 markers with PIC < 0.10), and the weak population structure together point to a narrow ancestral pool. The SNP filtering thresholds used in the study could substantially influence estimates of genetic diversity. However, estimates of polymorphic information content (PIC) and expected heterozygosity were consistent with those reported in previous studies that also applied MAF thresholds of 1–5% and a call rate threshold of 90% or lower [20,22]. Lower MAF thresholds may retain more rare alleles from data sets, resulting in more accurate estimates of polymorphic information content (PIC), allelic richness, and expected heterozygosity. These measures are important for understanding fine-scale patterns of local adaptation and genetic diversity and elucidating the population structure [30,52]. In addition, the observed variation cannot be attributed to somatic mutation alone, as the sampled trees included both seed-propagated and grafted individuals, and sweet orange seed can yield occasional zygotic seedlings alongside nucellar clones [28]. The variation observed here is therefore likely due to both clonal mutation and zygotic recombination. Furthermore, variation estimation in this study was limited to a single sampling time-point and on germplasm from a single province, without reference accessions or certified cultivar identity for direct comparison.

4.5. Heterozygote Excess and the Clonal Genome

The observed heterozygosity (Ho = 0.247) substantially exceeded expected heterozygosity (He = 0.138). Correspondingly, the overall estimate of the inbreeding coefficient was negative (Fis = −0.222), and similarly negative values across all four districts (−0.355 to −0.442), indicating an excess of heterozygotes, relative to Hardy–Weinberg expectations [43,53]. Similarly, an excess of heterozygotes has been reported using SSR and InDels on several citrus species [25,54]. This excess aligns with the reproductive and propagation biology of sweet orange as an interspecific hybrid, whose genome is heterozygous at roughly half of all loci [46,55]. Under clonal propagation, whether by grafting or by nucellar seedlings, this parental heterozygosity is fixed and transmitted intact. This is because mitotic propagation bypasses the meiotic segregation that would otherwise resolve heterozygous loci into homozygotes [28,54]. At the genomic level, apomictic hybrid citrus, including sweet orange, harbor substantially more heterozygous variants than their sexually reproducing relatives, as nucellar embryony preserves the maternal heterozygous genome free from recessive selection [28]. At the population level, this is expressed as an excess of heterozygotes and a negative Fis. A pattern also reported in other clonally propagated and interspecific-hybrid crops [56,57]. The low expected heterozygosity combined with the high observed heterozygosity therefore indicates that, while the allelic base of the germplasm is narrow, its genotypic state is predominantly heterozygous.

4.6. Genetic Differentiation Among Districts

The hierarchical AMOVA indicated that most of the genetic variation was partitioned within individuals (76.38%), followed by variation among individuals among farms, reflecting genome-wide heterozygosity. Further, 4.16% of the variation was among farms within districts, indicating that farms constitute a more meaningful level of genetic differentiation than districts (0.04%), probably reflecting differences in the origin of planting material and local farmer management practices. Similar patterns were reported on Citrus jambhiri accessions in India [25]. Pairwise FST values (0.0005–0.0035) were far below the 0.05 threshold for low differentiation [49], confirming that all districts belong to a single gene pool. A previous study on sweet orange reported moderate differentiation (FST = 0.11), which may be attributed to cultivars included in the study [44]. The slight differentiation of Zavala from Jangamo and Morrumbene, and the correspondingly short branches in the neighbor-joining tree, may reflect marginally reduced exchange of planting material; however, the magnitude of the differences does not constitute meaningful genetic structuring. The low differentiation observed is consistent with the population structure analyses and indicates a single, admixed regional gene pool.

4.7. Implication for Sweet Orange Breeding and Germplasm Conservation

The low allelic diversity, high fixed heterozygosity, and the undetectable geographic genetic structure observed have implications for sweet orange improvement and conservation. The narrow allelic base suggests that it harbors little allelic novelty for genetic gain in traits such as yield, fruit quality, or disease resistance. This constraint compounds the inherent challenges of citrus breeding, which include a prolonged juvenile phase of five to ten years, high parental heterozygosity that complicates trait fixation, and a historical reliance on spontaneous or induced somatic mutation for cultivar generation [4,15,58,59].
The weak geographic structure indicates that districts do not represent distinct genetic pools, and collecting germplasm from different districts alone is unlikely to substantially broaden the genetic base. However, collecting germplasm from a larger number of farms is likely to capture more genetic diversity.
Although Inharrime showed higher allelic richness and private-allele counts, the sensitivity analysis indicated that these private alleles were rare-variant artifacts (reducing to zero at MAF ≥ 0.05), suggesting that Inharrime may not be a distinct or isolated genetic pool, which is consistent with the weak geographic structure observed across the districts.

5. Conclusions

This study provided a molecular assessment of genetic diversity and population structure in sweet orange germplasm from Inhambane Province, Mozambique, using 1263 DArTSeq SNP markers. Population structure analysis (PCA) indicated a single genetic cluster, while DAPC confirmed that the four sampling districts were not genetically distinguishable. This was further validated by the extensive district overlap in the PCoA. The germplasm formed a single, highly heterozygous gene pool characterized by low allelic diversity (He = 0.138; PIC = 0.037) and an excess of heterozygotes (Ho = 0.247; Fis = −0.222). The pattern reflects the fixation of the interspecific-hybrid genome of C. sinensis through clonal propagation. Genetic differentiation among districts was negligible (0.04%; FST = 0.0005–0.0035), indicating that the regional orchards are from a common ancestral pool redistributed through the exchange of seed and vegetative planting material. The propagation method was associated with variation in the genetic composition of the population. This association may reflect several factors, including the occurrence of occasional zygotic recombination and nuclear embryony among seed-propagated trees, cultivar identity, and farm management. The narrow genetic base is consistent with the status of sweet orange as a somatic-mutation-diversified biotype. These findings establish a molecular baseline for sweet orange in Mozambique and highlight the need to introduce diverse external germplasm, alongside cultivar-resolved characterization and the establishment of an ex situ collection.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijpb17090088/s1, Table S1: Farm distribution across sampled districts; Table S2: SNP quality filtering.

Author Contributions

Conceptualization, M.S.Z. and A.D.N.; methodology, validation, M.S.Z., A.D.N. and W.N.M.; data curation, M.S.Z., A.D.N. and W.N.M.; formal analysis, M.S.Z. and W.N.M.; investigation, M.S.Z.; resources, R.M.C.; writing—original draft, M.S.Z.; writing—review and editing, M.S.Z., W.N.M. and A.D.N.; supervision, A.D.N.; project administration, M.S.Z. and R.M.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research work was supported through a scholarship awarded to Milton Sebastião Zavale by the Centre of Excellence in Agri-Food Systems and Nutrition (CE-AFSN) of Eduardo Mondlane University, Mozambique, under the World Bank—African Center of Excellence (ACE) II Funded Project grant number E089-MZ.

Data Availability Statement

The data supporting the results in this study are included in this article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The study was part of the M.Sc. research work of the first author (M.Z.), and he is grateful to Eduardo Mondlane University for providing him the opportunity to conduct postgraduate research. He would also like to express his gratitude to the Centre of Excellence in Agri-Food Systems and Nutrition (CE-AFSN) for providing a scholarship and enabling research environment during his M.Sc. studies. The authors also thank the Serviços Distritais de Actividades Económicas (SDAEs) of the Zavala, Inharrime, Jangamo, and Morrumbene districts for identifying and making contact with local farmers during the fieldwork. The acknowledgments are extended to the SEQART AFRICA (Nairobi, Kenya) genotyping platform for its high-quality genotyping services. In addition, we would like to thank Belmiro dos Santos André (IIAM, Mozambique), for providing maps of the study sites.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution map of sampled orange tree orchards in Inhambane province.
Figure 1. Distribution map of sampled orange tree orchards in Inhambane province.
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Figure 2. Density of the 1144 chromosome-anchored SNP markers along the nine Citrus sinensis chromosomes, shown as the number of SNPs per 1 Mb window.
Figure 2. Density of the 1144 chromosome-anchored SNP markers along the nine Citrus sinensis chromosomes, shown as the number of SNPs per 1 Mb window.
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Figure 3. Number of private alleles per district (descriptive).
Figure 3. Number of private alleles per district (descriptive).
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Figure 4. Population structure inference: (a) sNMF cross-entropy as a function of the number of clusters (K = 1–10), minimized at K = 1; (b) principal component analysis scree plot showing the proportion of variance explained by each axis.
Figure 4. Population structure inference: (a) sNMF cross-entropy as a function of the number of clusters (K = 1–10), minimized at K = 1; (b) principal component analysis scree plot showing the proportion of variance explained by each axis.
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Figure 5. Discriminant analysis of principal components (DAPC) of the four districts: (a) discriminant scatter; (b) a-score optimization across retained principal components (maximum a-score = 0.10).
Figure 5. Discriminant analysis of principal components (DAPC) of the four districts: (a) discriminant scatter; (b) a-score optimization across retained principal components (maximum a-score = 0.10).
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Figure 6. DAPC posterior membership probabilities for each individual, grouped by collection district.
Figure 6. DAPC posterior membership probabilities for each individual, grouped by collection district.
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Figure 7. Principal coordinate analysis (PCoA) of 94 Citrus sinensis individuals based on 1144 SNPs; axes show the percentage of variation explained.
Figure 7. Principal coordinate analysis (PCoA) of 94 Citrus sinensis individuals based on 1144 SNPs; axes show the percentage of variation explained.
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Figure 8. Principal coordinate analysis (PCoA) of 94 Citrus sinensis individuals based on the propagation method.
Figure 8. Principal coordinate analysis (PCoA) of 94 Citrus sinensis individuals based on the propagation method.
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Figure 9. Heatmap of pairwise genetic differentiation (FST; [41]) among the four districts.
Figure 9. Heatmap of pairwise genetic differentiation (FST; [41]) among the four districts.
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Figure 10. Unrooted neighbor-joining tree of the four districts based on Nei’s genetic distances [43]. The numbers represent districts: 1 (Inharrime), 2 (Jangamo), 3 (Morrumbene), and 4 (Zavala).
Figure 10. Unrooted neighbor-joining tree of the four districts based on Nei’s genetic distances [43]. The numbers represent districts: 1 (Inharrime), 2 (Jangamo), 3 (Morrumbene), and 4 (Zavala).
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Table 1. Chromosomal distribution of the 1263 retained SNP markers across the Citrus sinensis reference genome (GCF_022201045.2).
Table 1. Chromosomal distribution of the 1263 retained SNP markers across the Citrus sinensis reference genome (GCF_022201045.2).
ChromosomeLength (Mbp)No. of. SNPsMarker Distance (Kbp)SNP per Mbp
124.8574335.812.98
232.94127259.393.86
352.31205255.163.92
429.63120246.94.05
539157248.44.03
626.18100261.773.82
729.49121243.754.1
830.58106288.513.47
934134253.723.94
Unknown 119--
Total298.981263-34.17
Average 127.11265.933.8
Table 2. Single-population genetic diversity estimates for the 94 pooled Citrus sinensis trees, with bootstrapped 95% confidence intervals.
Table 2. Single-population genetic diversity estimates for the 94 pooled Citrus sinensis trees, with bootstrapped 95% confidence intervals.
MetricEstimate95% CI Lower95% CI Upper
Observed heterozygosity (Ho)0.2470.2260.269
Expected heterozygosity (He)0.1380.1270.149
Inbreeding coefficient (Fis)−0.222−0.244−0.202
Polymorphic Information Content (PIC)0.0372——
Minor Allele Frequency (MAF) 0.0236
Table 3. Per-district genetic diversity estimates of Citrus sinensis (descriptive). Ho, observed heterozygosity; He, expected heterozygosity; Fis, inbreeding coefficient; Ar, rarefied allelic richness; PIC, polymorphic information content.
Table 3. Per-district genetic diversity estimates of Citrus sinensis (descriptive). Ho, observed heterozygosity; He, expected heterozygosity; Fis, inbreeding coefficient; Ar, rarefied allelic richness; PIC, polymorphic information content.
DistrictNHoHeFisArPIC
Inharrime230.2490.141−0.3551.4540.040
Jangamo250.2420.132−0.4421.3810.029
Morrumbene210.2460.137−0.4001.4310.035
Zavala250.2500.140−0.3801.4330.039
Table 4. Genetic diversity of C. sinensis grouped by propagation method (grafted versus seed-propagated).
Table 4. Genetic diversity of C. sinensis grouped by propagation method (grafted versus seed-propagated).
GroupNHoHeFisArPICPrivate Alleles
Grafted440.2420.134−0.2861.5760.034289
Seed500.2500.139−0.2921.5620.038297
Table 5. Analysis of molecular variance (AMOVA) for 94 Citrus sinensis individuals partitioned among and within four collection districts.
Table 5. Analysis of molecular variance (AMOVA) for 94 Citrus sinensis individuals partitioned among and within four collection districts.
Source of VariationVariance% Variationp-Value
Among districts0.0210.040.001
Among farms within districts2.1294.160.001
Among individuals within farms9.94519.420.001
Within individuals39.11776.380.402
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Zavale, M.S.; Ndeve, A.D.; Muteti, W.N.; Chiulele, R.M. Genetic Diversity and Population Structure of Sweet Orange (Citrus sinensis) Germplasm in Inhambane Province, Mozambique. Int. J. Plant Biol. 2026, 17, 88. https://doi.org/10.3390/ijpb17090088

AMA Style

Zavale MS, Ndeve AD, Muteti WN, Chiulele RM. Genetic Diversity and Population Structure of Sweet Orange (Citrus sinensis) Germplasm in Inhambane Province, Mozambique. International Journal of Plant Biology. 2026; 17(9):88. https://doi.org/10.3390/ijpb17090088

Chicago/Turabian Style

Zavale, Milton Sebastião, Arsénio D. Ndeve, Winfred N. Muteti, and Rogério M. Chiulele. 2026. "Genetic Diversity and Population Structure of Sweet Orange (Citrus sinensis) Germplasm in Inhambane Province, Mozambique" International Journal of Plant Biology 17, no. 9: 88. https://doi.org/10.3390/ijpb17090088

APA Style

Zavale, M. S., Ndeve, A. D., Muteti, W. N., & Chiulele, R. M. (2026). Genetic Diversity and Population Structure of Sweet Orange (Citrus sinensis) Germplasm in Inhambane Province, Mozambique. International Journal of Plant Biology, 17(9), 88. https://doi.org/10.3390/ijpb17090088

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