Elucidating SNP-based genetic diversity and population structure of advanced breeding lines of bread wheat (Triticum aestivum L.)

Genetic diversity and population structure of Piper nigrum (black pepper) accessions based on next-generation SNP markers

Despite the economic importance of Piper nigrum (black pepper), a highly valued crop worldwide, development and utilization of genomic resources have remained limited, with diversity assessments often relying on only a few samples or DNA markers. Here we employed restriction-site associated DNA sequencing to analyze 175 P. nigrum accessions from eight main black pepper growing regions in Sri Lanka. The sequencing effort resulted in 1,976 million raw reads, averaging 11.3 million reads per accession, revealing 150,356 high-quality single nucleotide polymorphisms (SNPs) distributed across 26 chromosomes. Population structure analysis revealed two subpopulations (K = 2): a dominant group consisting of 152 accessions sourced from both home gardens and large-scale cultivations, and a smaller group comprising 23 accessions exclusively from native collections in home gardens. This clustering was further supported by principal component analysis, with the first two principal components explaining 35.2 and 12.1% of the total variation. Genetic diversity analysis indicated substantial gene flow (Nm = 342.21) and a low fixation index (FST = 0.00073) between the two subpopulations, with no clear genetic differentiation among accessions from different agro-climatic regions. These findings demonstrate that most current black pepper genotypes grown in Sri Lanka share a common genetic background, emphasizing the necessity to broaden the genetic base to enhance resilience to biotic and abiotic stresses. This study represents the first attempt at analyzing black pepper genetic diversity using high-resolution SNP markers, laying the foundation for future genome-wide association studies for SNP-based gene discovery and breeding.

Genome-wide diversity analysis suggests divergence among Upper Guinea and the Dahomey Gap populations of the Sisrè berry (Syn: miracle fruit) plant (Synsepalum dulcificum [Schumach. & Thonn.] Daniell) in West Africa

Although Synsepalum dulcificum is viewed as one of the most economically promising orphan tree crops worldwide, its genetic improvement and sustainable conservation are hindered by a lack of understanding of its evolutionary history and current population structure. Here, we report for the first time the application of genome-wide single nucleotide polymorphism genotyping to a diverse panel of S. dulcificum accessions to depict the genetic diversity and population structure of the species in the Dahomey Gap (DG) and Upper Guinea (UG) regions to infer its evolutionary history. Our findings suggest low overall genetic diversity but strong population divergence within the species. Neighbor-joining analysis detected two genetic groups in the UG and DG regions, while STRUCTURE distinguished three genetic groups, corresponding to the UG, Western DG, and Central DG regions. Application of Monmonier's algorithm revealed the existence of a barrier disrupting connectivity between the UG and DG groups. The Western DG group consistently exhibited the highest levels of nucleotide and haplotype diversities, while that of the Central DG exhibited the lowest. Analyses of Tajima's D, Fu's Fs, and Achaz Y* statistics suggest that while both UG and Central DG groups likely experienced recent expansions, the Western DG group is at equilibrium. These findings suggest a geographical structuring of genetic variation which supports the conclusion of differential evolutionary histories among West African groups of S. dulcificum. These results provide foundational insights to guide informed breeding population development and design sustainable conservation strategies for this species.

Spectral-Based Classification of Genetically Differentiated Groups in Spring Wheat Grown under Contrasting Environments

The global concern about the gap between food production and consumption has intensified the research on the genetics, ecophysiology, and breeding of cereal crops. In this sense, several genetic studies have been conducted to assess the effectiveness and sustainability of collections of germplasm accessions of major crops. In this study, a spectral-based classification approach for the assignment of wheat cultivars to genetically differentiated subpopulations (genetic structure) was carried out using a panel of 316 spring bread cultivars grown in two environments with different water regimes (rainfed and fully irrigated). For that, different machine-learning models were trained with foliar spectral and genetic information to assign the wheat cultivars to subpopulations. The results revealed that, in general, the hyperparameters ReLU (as the activation function), adam (as the optimizer), and a size batch of 10 give neural network models better accuracy. Genetically differentiated groups showed smaller differences in mean wavelengths under rainfed than under full irrigation, which coincided with a reduction in clustering accuracy in neural network models. The comparison of models indicated that the Convolutional Neural Network (CNN) was significantly more accurate in classifying individuals into their respective subpopulations, with 92 and 93% of correct individual assignments in water-limited and fully irrigated environments, respectively, whereas 92% (full irrigation) and 78% (rainfed) of cultivars were correctly assigned to their respective classes by the multilayer perceptron method and partial least squares discriminant analysis, respectively. Notably, CNN did not show significant differences between both environments, which indicates stability in the prediction independent of the different water regimes. It is concluded that foliar spectral variation can be used to accurately infer the belonging of a cultivar to its respective genetically differentiated group, even considering radically different environments, which is highly desirable in the context of crop genetic resources management.