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

Effect of Chromosomal Localization of NGS-Based Markers on Their Applicability for Analyzing Genetic Variation and Population Structure of Hexaploid Triticale

This study aimed to determine whether using DNA-based markers assigned to individual chromosomes would detect the genetic structures of 446 winter triticale forms originating from two breeding companies more effectively than using the entire pool of markers. After filtering for quality control parameters, 6380 codominant single nucleotide polymorphisms (SNPs) markers and 17,490 dominant diversity array technology (silicoDArT) markers were considered for analysis. The mean polymorphic information content (PIC) values varied depending on the chromosomes and ranged from 0.30 (2R) to 0.43 (7A) for the SNPs and from 0.28 (2A) to 0.35 (6R) for the silicoDArTs. The highest correlation of genetic distance (GD) matrices based on SNP markers was observed among the 5B-5R (0.642), 5B-7B (0.626), and 5A-5R (0.605) chromosomes. When silicoDArTs were used for the analysis, the strongest correlations were found between 5B-5R (0.732) and 2B-5B (0.718). A Bayesian analysis showed that SNPs (total marker pool) allowed for the identification of a more complex structure (K = 4, ΔK = 2460.2) than the analysis based on silicoDArTs (K = 2, ΔK = 128). Triticale lines formed into groups, ranging from two (most of the chromosomes) to four (7A) groups depending on the analyzed chromosome when SNP markers were used for analysis. Linkage disequilibrium (LD) varied among individual chromosomes, ranging from 0.031 for 1A to 0.228 for 7R.

Revealing the Genetic Diversity and Population Structure of Garlic Resource Cultivars and Screening of Core Cultivars Based on Specific Length Amplified Fragment Sequencing (SLAF-Seq)

Garlic is an important vegetable and condiment that has good medical and health care effects. At present, the origin of Chinese garlic and its association with other types of quality are limited to the molecular marker level, and there are few reports at the genome level. Therefore, this study is based on the specific length amplified fragment sequencing (SLAF-seq) of 102 copies of garlic germplasm resources, the group structure, and further screening of the core germplasm. SLAF-seq of 102 garlic cultivars yielded 1949.85 Mb of clean data and 526,432,275 SNPs. Through principal component analysis, evolutionary tree, population structure, and genetic relationship analysis, all garlic cultivars were divided into 3 groups. Among them, Group 1 contains 45 Chinese cultivars and 1 Egyptian cultivar, which are distributed mainly in the coastal and central areas of China. Group 2 contains 36 Chinese cultivars and 1 U.S. cultivar, which are distributed mainly in Northwest China. Group 3 contains 19 Chinese cultivars, which are distributed mainly in Xinjiang, China. The genetic diversity results indicate that the fixation index (Fst) values of Group 1 and Group 2 are lower than those of Group 1 and Group 3 and that the diversity of nucleotides (π) of Group 3 is greater than those of Group 2 and Group 1. Finally, the 30 parts of the cultivars were used as the core germplasms, and there was no difference between the two cultivars in terms of core quality. In summary, this study provides tags for the determination of garlic molecular markers and genotypes and provides a theoretical basis for subsequent resource protection and utilization, genetic positioning of important agronomic traits, and molecular marking agglomeration breeding.

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.