Wild almond relatives are valuable reservoirs of allelic variation for crop improvement and conservation, yet Kazakhstan’s wild-almond genetic resources remain poorly characterised. We conducted an exploratory SSR assessment of 80 putative individuals from four taxon-locality groups (20 per group), each representing one sampled population: Prunus ledebouriana, P. tenella, P. petunnikowii, and P. spinosissima. Of 22 nuclear simple sequence repeat loci screened for cross-taxon transferability, 15 generated reproducible profiles and were retained; their even genome-wide distribution was not verified. Across the full dataset, the mean number of alleles was 3.55, the effective number of alleles was 2.62, expected heterozygosity (He) was 0.544, and 95.0% of loci were polymorphic. Missing genotypes ranged from 0.0% to 34.7% among groups, and six loci had at least 20% missing data. AMOVA attributed 76.5% of variation to within-group differences and 23.5% to among-group differences (PhiPT = 0.235, p = 0.001). PCoA, unbiased Nei distances, UPGMA, and descriptive Bayesian clustering separated the four sampled groups. A nine-locus sensitivity analysis that excluded the six high-missing loci retained P. spinosissima as the group with the highest mean He (0.699), whereas P. petunnikowii increased from 0.471 to 0.609. Thus, the low full-panel estimate for P. petunnikowii was not robust to missing data. Because taxon identity was fully confounded with locality and the marker panel was limited, the results are interpreted as a regional marker-transferability and methodological baseline rather than as species-wide or genome-wide inference.
A. Orazov, T. Samarkhanov, A. Myrzagaliyeva et al.· International Journal of Pla...· 0 citations
Reliable identification of genomic regions controlling complex agronomic traits across variable growing seasons remains a major challenge in soybean genetics and breeding. Here, a diverse panel of 252 soybean accessions was evaluated over six consecutive growing seasons (2018–2023) for flowering time, maturity, plant height, number of seeds per plant, seed yield per plant, and thousand-seed weight. Whole-genome resequencing and variant filtering yielded 2,019,772 high-quality SNPs, and association signals were evaluated using Inclusive Integrative Input Multiple-locus Random-SNP-effect Mixed Linear Model (IIIVmrMLM), Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), and Multi-Locus Mixed Model (MLMM) together with linkage disequilibrium (LD)-based locus consolidation. Cross-model prioritization retained 21 high-confidence loci supported by all three GWAS models and distributed across 10 chromosomes. Among the identified loci, 18 overlapped or co-localized with previously reported SoyBase genes and QTLs, whereas three (q.VER2.13-1, q.YP.01-1, and q.TSW.15-1) showed no positional overlap with known genes and QTLs and were therefore considered presumably novel. These three loci were associated with flowering time, yield per plant, and thousand-seed weight, accounting for 1.60%, 2.13%, and 5.53% of phenotypic variation, respectively. Ten loci co-localized with genomic regions containing established soybean regulators, including E2, E3, GmDt2, and POWR1, support the biological plausibility of the association results. Integration of genomic position, functional annotation, and tissue-expression evidence prioritized 112 candidate genes across 18 loci. These findings provide a focused set of genomic loci and candidate genes for independent validation and further investigation of the genetic basis of soybean adaptation and yield formation under variable continental growing conditions.
A. Zatybekov, Y. Genievskaya, C. Fang et al.· Plants· 0 citations
Barley (Hordeum vulgare L.) is a major cereal in Kazakhstan, where diverse breeding material supports crop improvement. We characterized 86 two-row spring barley accessions from six breeding organizations using the Illumina Infinium 50K Barley SNP Array. Analysis of 29,920 high-quality SNPs revealed moderate diversity (He = 0.346, PIC = 0.278, Shannon = 0.752), with 80.84% of molecular variation occurring within and 19.16% among breeding organizations. A minor allele frequency-free (MAF-free) analysis showed that 95.61% of marker–allele combinations at polymorphic loci were shared by at least two organizations. Although PCA, kinship, and neighbor-joining analyses indicated extensive overlap, discriminant analysis of principal components (DAPC) cluster membership was significantly associated with breeding origin (χ2 = 106.38, Monte Carlo p = 1 × 10–5; bias-corrected Cramér’s V = 0.513), demonstrating substantial but incomplete differentiation among breeding programs. Phenotypic differentiation was evaluated using environment-adjusted genotype BLUPs. All seven traits differed significantly among five DAPC clusters. In a reduced six-trait linear discriminant analysis (LDA) excluding vegetation period, LD1 was associated most strongly with number of kernels per spike, followed by heading time, spike length, and heading-to-maturity time. Leave-one-out cross-validation (LOOCV) accuracy was 36.47%, exceeding the permutation mean of 19.83% but indicating considerable phenotypic overlap. The examined materials therefore constitute a diverse, interconnected breeding panel that may support germplasm management and parent selection and provide a genomic and phenotypic framework for future GWAS, genomic selection, and targeted validation of molecular markers within the represented collections.
Y. Genievskaya, V. Chudinov, G. Sereda et al.· International Journal of Mol...· 0 citations
Net form net blotch (NFNB), caused by
Pyrenophora teres
f.
teres
(
Ptt
), is a major constraint to barley production. However, the genetic basis of adult plant resistance (APR) and seedling resistance remains incompletely understood. This study aimed to dissect the genetic architecture of NFNB resistance in a diverse panel of 273 spring barley accessions.
APR was evaluated in two contrasting field environments in Kazakhstan, whereas seedling resistance was assessed under greenhouse conditions using two
Ptt
races. Genotyping with the 50K SNP array yielded 31,834 high-quality SNPs. Genome-wide association analyses were performed using four models – MLM, MLMM, FarmCPU, and BLINK – that accounted for population structure and kinship. Candidate genes within QTL intervals were prioritized using transcriptomic data from 16 barley tissues and co-expression network analysis.
Substantial phenotypic variation was observed, with moderate heritability for APR (
h
2
= 50.6%) and seedling resistance (
h
2
= 41.3%), together with strong genotype × environment and genotype × race interactions. In total, 275 marker–trait associations were detected for APR and 48 for seedling resistance. These associations were consolidated into 57 genome-wide significant (P < 1.57E–6) or multi-model-supported QTLs across all seven barley chromosomes, including 39 APR and 18 seedling-resistance QTLs. Forty QTLs co-localized with known resistance genes (
Rpt1
,
Rpt2
,
Rpt3
,
Rpt4
,
Rpt6
,
Rpt8
,
Rpt9
, and
SPN1
) or previously reported net blotch QTLs, whereas 17 were potentially novel. Transcriptomic integration identified 87 highly expressed genes within APR QTL regions and 42 within seedling-resistance QTLs. The potentially novel QTLs
Q_NB_1H.6
,
Q_NB_2H.3
, and
Q_NB_3H.1
harbored genes encoding proteins previously associated with pathogen resistance and stress responses. Co-expression analysis revealed stage-specific transcriptional patterns, with APR-associated genes enriched in regulatory functions and seedling-resistance genes enriched in metabolic and structural functions.
The results demonstrate that NFNB resistance is polygenic and developmentally stage-dependent, with partly distinct mechanisms underlying adult plant and seedling resistance. The identified QTLs and prioritized candidate genes provide targets for independent validation, functional characterization, and the development of molecular markers to support breeding for durable NFNB resistance in barley.
Y. Genievskaya, A. Maulenbay, A. Zatybekov et al.· Frontiers in Agronomy· 0 citations
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