Assessment of the Genetic Structure and Diversity of Soybean (Glycine max L.) Germplasm Using Diversity Array Technology and Single Nucleotide Polymorphism Markers

Knowledge of the genetic structure and diversity of germplasm collections is crucial for sustainable genetic improvement through hybridization programs and rapid adaptation to changing breeding objectives. The objective of this study was to determine the genetic diversity and population structure of 281 International Institute of Tropical Agriculture (IITA) soybean accessions using diversity array technology (DArT) and single nucleotide polymorphism (SNP) markers for the efficient utilization of these accessions. From the results, the SNP and DArT markers were well distributed across the 20 soybean chromosomes. The cluster and principal component analyses revealed the genetic diversity among the 281 accessions by grouping them into two stratifications, a grouping that was also evident from the population structure analysis, which divided the 281 accessions into two distinct groups. The analysis of molecular variance revealed that 97% and 98% of the genetic variances using SNP and DArT markers, respectively, were within the population. Genetic diversity indices such as Shannon’s diversity index, diversity and unbiased diversity revealed the diversity among the different populations of the soybean accessions. The SNP and DArT markers used provided similar information on the structure, diversity and polymorphism of the accessions, which indicates the applicability of the DArT marker in genetic diversity studies. Our study provides information about the genetic structure and diversity of the IITA soybean accessions that will allow for the efficient utilization of these accessions in soybean improvement programs, especially in Africa.

Genetic Diversity, Population Structure Analysis Using Ultra-High Throughput Diversity array Technology (DArTseq) in Different Origin Sesame (Sesamum indicum L)

BackgroundSesame is an important oil crop widely cultivated in Africa and Asia continent. Characterization of genetic diversity and population structure of sesame genotypes in these continents can be used to designing breeding methods. In the present study, 300 sesame genotypes comprising 209 local, and 75 exotic collection, and 16 released varieties provided from the Ethiopian Biodiversity Institute and research centers were used in the present study.ResultsThe panel was genotyped using two ultra-high-throughput diversity array technology (DArT) markers (silicoDArT and SNP). Both markers were used to identify the genetic diversity and population structure of sesame germplasm. A total of 6115 silicoDArT and 6474 SNP markers were reported, of which 5002 silicoDArT and 4638 SNP markers were screening with quality control parameters. The average polymorphic information content values of silicoDArT and SNP markers were 0.07 and 0.08, respectively. For further analysis, the allele frequency for each SNP site was calculated and purified with MAF < 0.01 and left 2997 high-quality SNPs evenly distributed across the whole genome that could be used for subsequent analysis. All genotypes used in this study were descended from eight 8 geographical origins. The genetic diversity analysis showed that the average nucleotide diversity of the panel was 0.14. Considering the genotypes based on their geographical origin, Africa collections (0.21) as a whole without Ethiopian collection was more diverse than Asia and when further portioned Africa, North Africa (0.23) collection was more diverse than others, but at the continent level, Asia (0.17) was more diverse than Africa (0.14). The genetic distance among the sesame populations was ranged from 0.015 to 0.394, with an average of 0.165. The sesame populations was clustered into four groups. The structure analysis divided the panel into four subgroups and 21 genotypes were clustered as an admixture. These indicates genotypes from the same origin didn’t classify properly on the premise of the country of origin. ConclusionsThe genetic diversity and population structure revealed in this study should guide the future research work to design association studies and the systematic utilization of the genetic variation characterizing the sesame panel.

Genetic structure and diversity of upland rice germplasm using diversity array technology (DArT)-based single nucleotide polymorphism (SNP) markers

Investigating genetic structure and diversity is crucial for rice improvement strategies, including mapping quantitative trait loci with the potential for improved productivity and adaptation to the upland ecology. The present study elucidated the population structure and genetic diversity of 176 rice germplasm adapted to the upland ecology using 7063 genome-wide single nucleotide polymorphism (SNP) markers from the Diversity Array Technology (DArT)-based sequencing platform (DArTseq). The SNPs were reasonably distributed across the rice genome, ranging from 432 SNPs on chromosome 9 to 989 SNPs on chromosome 1. The minimum minor allele frequency was 0.05, while the average polymorphism information content and heterozygosity were 0.25 and 0.03, respectively. The model-based (Bayesian) population structure analysis identified two major groups for the studied rice germplasm. Analysis of molecular variance revealed that all (100%) of the genetic variance was attributable to differences within the population, and none was attributable to the population structure. The estimates of genetic differentiation (PhiPT = 0.001; P = 0.197) further showed a negligible difference among the population structures. The results indicated a high genetic exchange or gene flow (number of migrants, Nm = 622.65) and a substantial level of diversity (number of private alleles, Pa = 1.52 number of different alleles, Na = 2.67; Shannon's information index, I = 0.084; and percentage of polymorphic loci, %PPL = 55.9%) within the population, representing a valuable resource for rice improvement. The findings in this study provide a critical analysis of the genetic diversity of upland rice germplasm that would be useful for rice yield improvement. We suggested further breeding and genetic analyses.

How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data

The standard approach to genetic mapping was supplemented by machine learning (ML) to establish the location of the rye gene associated with epicuticular wax formation (glaucous phenotype). Over 180 plants of the biparental F2 population were genotyped with the DArTseq (sequencing-based diversity array technology). A maximum likelihood (MLH) algorithm (JoinMap 5.0) and three ML algorithms: logistic regression (LR), random forest and extreme gradient boosted trees (XGBoost), were used to select markers closely linked to the gene encoding wax layer. The allele conditioning the nonglaucous appearance of plants, derived from the cultivar Karlikovaja Zelenostebelnaja, was mapped at the chromosome 2R, which is the first report on this localization. The DNA sequence of DArT-Silico 3585843, closely linked to wax segregation detected by using ML methods, was indicated as one of the candidates controlling the studied trait. The putative gene encodes the ABCG11 transporter.