Researchers at the National Horticultural Research Institute (NIHORT) in Ibadan set out to assess the genetic diversity of fifteen avocado accessions conserved in the institute’s genebank. The seeds behind these accessions were originally collected from smallholder farmers across four states in south-eastern Nigeria — Imo, Enugu, Anambra and Abia — during successive collection missions, and were registered with NIHORT accession numbers upon entry into the genebank. Because avocado is highly heterozygous and predominantly outcrossing, with a protogynous–protandrous flowering system that promotes cross-pollination, the seedlings raised from these seeds represent open-pollinated half-sib progenies rather than clones of the maternal trees, and were treated as independent accessions in the analysis.

The team, led by Omolara I. Akinyoola and published in the journal Discover Plants, employed two complementary DNA marker systems: inter-simple sequence repeats (ISSR) and start codon targeted (SCoT) markers. ISSR primers amplify DNA fragments lying between simple sequence repeats in the genome and require no prior knowledge of the species’ DNA sequence, making them accessible and inexpensive. SCoT markers, first developed in rice by Collard and Mackill, target regions surrounding the start codons of genes, giving them a functional bias that often makes them more informative. After a pilot screen of twenty ISSR and fifteen SCoT primers on three representative accessions, the researchers selected five primers of each type based on band clarity, polymorphism and reproducibility.

The laboratory work began with fresh young leaflets collected from the field and preserved in liquid nitrogen. Genomic DNA was extracted from approximately 100 milligrams of leaf tissue per accession using a modified CTAB protocol, in which polyvinylpyrrolidone and 2-mercaptoethanol were added to suppress polyphenols and oxidation, and the chloroform–isoamyl alcohol extraction step was repeated twice to remove residual protein and lipid. DNA was precipitated overnight with ice-cold isopropanol, washed with 70 percent ethanol and re-suspended in buffer containing RNase A. Quality checks on 1 percent agarose gels confirmed sharp, high-molecular-weight bands with no shearing, and Nanodrop spectrophotometry gave A260/A280 purity ratios between 1.80 and 2.00 before samples were diluted to a working concentration of 30 nanograms per microlitre.

Polymerase chain reactions were carried out in 25-microlitre volumes on an Applied Biosystems GeneAmp 9700 thermal cycler. ISSR amplification used a touchdown profile in which the annealing temperature dropped from 65 to 56 degrees Celsius over ten cycles before thirty standard cycles at 55 degrees, while SCoT amplification used thirty-five cycles with a 50-degree annealing temperature. Amplified fragments were separated on 2 percent agarose gels, visualised under ultraviolet transillumination and scored manually against a 50-base-pair ladder, with faint or ambiguous bands excluded. The convergence of results across two independent marker systems, and across multiple analysis platforms, provided a form of internal validation that strengthens confidence in the findings.

The results revealed a moderate but meaningful reservoir of variation. The ten loci detected a total of 41 polymorphic alleles, averaging 4.1 alleles per locus — higher than the 3.1 alleles per locus reported in a previous avocado study using EST-SSR primers. The polymorphism information content, a measure of a locus’s discriminating power, ranged from 0.509 to 0.825 with a mean of 0.654, and gene diversity averaged 0.708 across the ten loci. Genetic similarity coefficients between accessions ranged from 0.59 to 0.84. Notably, SCoT markers outperformed ISSR markers, generating a higher mean number of alleles per locus (4.4 versus 3.8) and higher mean PIC values (0.671 versus 0.637), leading the authors to recommend the combined use of both systems rather than either alone.

When the researchers clustered the accessions using the unweighted pair group method with arithmetic mean (UPGMA) on Jaccard dissimilarities, supported by 1000 bootstrap resamplings, the fifteen accessions split into two main clusters — one containing six accessions and the other nine — with membership broadly reflecting the south-eastern states of origin. A factorial coordinate analysis performed in DARwin software resolved four geographic groups along the first two factorial axes, with groups dominated respectively by accessions from Imo, Enugu and Anambra, while the single Abia accession stood apart. Some accessions, however, were interwoven between groups, a pattern the authors attribute to informal farmer-to-farmer seed exchange across state boundaries.

The most striking result came from the analysis of molecular variance. AMOVA partitioned 99.3 percent of the genetic variation within the UPGMA clusters and only 0.7 percent between them, with a Phi statistic of 0.007 that was statistically non-significant (P = 0.395, based on 9999 random permutations). Cross-validation in a second R package yielded an essentially identical result (P = 0.390), confirming the robustness of the partitioning. In other words, the two clusters represent dissimilarity gradients within a single, largely panmictic gene pool rather than genetically isolated subpopulations. The authors attribute this to two interacting forces: avocado’s strongly outcrossing reproductive biology, which maintains high heterozygosity within genotypes, and widespread informal seed exchange among smallholder farmers, which has homogenised allele frequencies across the sampled states.

The study is not without limitations, which the authors acknowledge candidly. The sample of fifteen accessions, while reflecting the current holdings of the NIHORT avocado germplasm at the time of sampling, is modest for inferring fine-scale population structure and limits the statistical power of subgroup analyses. Marker-trait associations were not pursued, and the dominant nature of ISSR and SCoT markers means each accession had to be treated as a single haploid genotype. Future work, the team suggests, should integrate SNP-based platforms such as genotyping-by-sequencing with phenotypic and biochemical fruit characterisation, to link the genetic variation documented here to agronomically relevant traits such as fruit quality, pest resistance and climate resilience.

Even so, the significance of the work extends well beyond a single genebank. Nigeria’s avocado industry is growing rapidly and holds considerable potential in the global market, but it faces threats from pests including the avocado lace bug, the Persea mite and the western avocado leaf roller, as well as diseases such as anthracnose and avocado black streak. The moderate diversity documented in the NIHORT collection provides a baseline molecular dataset for future curation, hybridisation and selection, and underscores a broader truth for crop science: genetic diversity is the raw currency of breeding, and knowing exactly what a genebank holds is the first step towards spending it wisely. The identified variation can now be deployed in breeding programmes aimed at developing better varieties with desirable traits, while the finding that most variation lies within rather than between geographic groupings offers a caution against assuming that provenance alone guarantees genetic distinctiveness in conserved germplasm.

Subject of Research: Genetic diversity assessment of avocado germplasm using ISSR and SCoT molecular markers

Article Title: Genetic diversity assessment of avocado (Persea americana Mill.) germplasm from NIHORT genebank

Article References: Akinyoola, O. I., Olagunju, Y. O., Matthew, J. O., Akin-Idowu, P. E., & Ajayi, E. O. (2026). Genetic diversity assessment of avocado (Persea americana Mill.) germplasm from NIHORT genebank. Discover Plants, 3(1), Article 414. https://doi.org/10.1007/s44372-026-00866-9

Image Credits: AI Generated

DOI: 10.1007/s44372-026-00866-9

Keywords: avocado, Persea americana, genetic diversity, ISSR markers, SCoT markers, germplasm, genebank, Nigeria, NIHORT, molecular markers, AMOVA, plant breeding

The scale of the investigation is what sets it apart. The team evaluated 1,984 spring wheat genotypes drawn from 65 countries, spanning Africa, the Americas, Asia, Europe, and Oceania, with Asia contributing the largest share at 1,040 accessions. Each genotype was tested for seedling-stage resistance to aggressive isolates of both rust fungi using detached-leaf assays conducted in greenhouse batches. Rather than relying on slow, subjective visual scoring, the researchers employed Macrobot, a semi-automated high-throughput phenotyping platform that images infected leaf segments and quantifies the percentage of diseased tissue with the BluVision software. Up to seven leaf segments per genotype served as biological replicates, and the platform handled the resulting mountain of samples with standardized, non-destructive measurement after an eight-day incubation for leaf rust and a fifteen-day incubation for yellow rust.

To convert raw images into reliable trait values, the team fitted linear mixed models that accounted for experiment and tray effects, flagged outliers through residual analysis, and calculated best linear unbiased estimates for each genotype. Broad-sense heritability came out at 0.54 for leaf rust resistance, indicating a strong genetic component, and 0.38 for yellow rust, where a longer incubation period, variable inoculation efficiency, and differences in symptom development inflated residual variance. The distributions of infection levels were continuous and largely unimodal, a pattern consistent with quantitative, polygenic inheritance rather than a handful of major genes acting alone. Notably, 1,323 genotypes outperformed the susceptible control for leaf rust and 1,287 did so for yellow rust, while 875 accessions showed reduced infection against both pathogens simultaneously, even though the overall correlation between the two resistance traits was weak and positive at just 0.14.

On the genotyping side, the researchers used a two-enzyme genotyping-by-sequencing protocol with PstI and MspI, sequencing barcoded sample pools on an Illumina NovaSeq 6000 to an average of 2.5 million reads per genotype. After aligning reads to the Chinese Spring RefSeq V2.1 wheat genome, imputing missing data with Beagle, and filtering out markers with high missingness, low minor allele frequency, or excessive heterozygosity, the final working set contained 90,283 genome-wide markers. Population structure analysis with the STRUCTURE software and the Evanno delta-K method pointed to three ancestral subpopulations. The first cluster was dominated by accessions from Southern and Eastern Asia, particularly India and China; the second was led by Iranian germplasm; and the third, comprising just over half the panel, was spread more evenly across Europe, Asia, the Americas, Africa, and Australia. This geographic breadth is precisely what makes genebank panels so valuable for resistance discovery.

The heart of the study was a genome-wide association study run through four complementary statistical frameworks: TASSEL and GAPIT with compressed mixed linear models, the multilocus FarmCPU algorithm, and GenABEL. The rationale for this quadruple screening is methodological rigor. Single-locus and multilocus models each carry their own biases, and spurious associations can arise from population structure or kinship. By requiring that a marker-trait association be detected by all four methods, the team deliberately traded sensitivity for specificity, retaining only the most defensible signals. A linkage disequilibrium decay analysis across all 21 chromosomes, which yielded an average decay distance of roughly 2.6 million base pairs, defined the quantitative trait locus intervals around each significant peak marker.

The consensus screen delivered six reliable associations for each disease. For leaf rust, the shared markers mapped to chromosomes 3B, 4B, 4D, 5A, and 7D, with two independent peaks on chromosome 4D, and none of these six loci overlapped previously reported resistance regions, marking them as entirely novel. For yellow rust, the six consensus markers sat on chromosomes 1D, 2B, 4A, 5B, and 6B, with three markers on 6B, and four of the yellow rust loci co-localized with resistance regions reported in earlier studies, including work on the adult-plant gene Yr86 on chromosome 1D and recent genomics-driven discoveries on 6B. The agreement between the new data and these prior findings strengthens confidence in the stable, reproducible nature of those loci, while the completely fresh leaf rust regions expand the catalog of available resistance targets.

Within the linkage-disequilibrium-defined intervals, the team searched for high-confidence candidate genes using functional annotations, gene ontology terms, and transcriptome evidence. Among the markers shared across all four association models, six candidate genes emerged for each disease. The yellow rust candidates include a cysteine-rich receptor-like kinase on chromosome 4A, a family of pattern-recognition proteins known to trigger defense signaling against P. striiformis, and an NBS-LRR immune receptor on chromosome 6B, a classic intracellular guard protein that initiates oxidative bursts, kinase cascades, and hypersensitive responses. The leaf rust candidates include a basic helix-loop-helix transcription factor on chromosome 5A, positioned to regulate hormone signaling and defense gene expression, and a receptor kinase 1 homologous to the well-characterized Lr10 resistance pathway. The authors are careful to note that association mapping alone demonstrates proximity, not causality, so fine mapping and functional validation remain essential next steps.

Beyond the loci themselves, the study identified elite germplasm ready for breeding use. Comparing allele effects from the association output with phenotypic performance, the researchers pinpointed six accessions, originating from Europe, Asia, and the Americas, that carry multiple favorable alleles for both rust diseases. These genotypes stack several previously unknown resistance quantitative trait loci and represent ready-made donors for pyramiding programs. Because race-specific major genes tend to be overcome within years as pathogen populations mutate and recombine, combining multiple resistance loci, especially ones never before deployed, is considered one of the most effective strategies for extending the durability of resistance in the field. The six multi-resistant accessions demonstrate that favorable alleles are distributed across continents and can be combined from diverse genetic backgrounds.

The authors also acknowledge the limitations of their design. Phenotyping occurred exclusively at the seedling stage under controlled greenhouse conditions using single isolates of each pathogen, so the detected loci primarily reflect seedling resistance, and their performance against the broader pathogen diversity found in farmers’ fields remains to be tested. The lower heritability for yellow rust means some smaller-effect loci may have gone undetected. Complementary multi-isolate experiments and adult-plant field trials will be needed to confirm the stability and breadth of the newly discovered regions. Even so, the study delivers a template for modern pre-breeding: automate phenotyping at genebank scale, genotype with sequencing-based markers, and triangulate associations across multiple statistical models to strip away method-dependent noise. As rust pathogens continue their evolutionary arms race against wheat, the millions of accessions sitting in cold storage around the world may hold the resistance genes of tomorrow, and this work shows how to find them efficiently and reliably.

Subject of Research: Genome-wide association mapping of leaf rust and yellow rust resistance in spring wheat genebank genetic resources

Article Title: Unlocking the potential of spring wheat genetic resources: uncovering resistance sources against leaf rust and yellow rust

Article References: Unlocking the potential of spring wheat genetic resources: uncovering resistance sources against leaf rust and yellow rust. (n.d.). https://doi.org/10.1007/s00122-026-05365-9

Image Credits: AI Generated

DOI: 10.1007/s00122-026-05365-9

Keywords: wheat, leaf rust, yellow rust, genome-wide association study, genebank, disease resistance, genotyping-by-sequencing, plant breeding, Puccinia triticina, Puccinia striiformis, candidate genes, high-throughput phenotyping