GENETIC CHARACTERIZATION OF SELECTED Moringa oleifera PROVENANCES FROM THE KENYAN COASTAL REGION

Abstract

Globally, moringa oleifera is a major economic crop. Traditional medicine has used it to treat diabetes, inflammation, cancer, bacterial, viral, and fungal infections, joint pain, and cardiovascular health. Moringa has been used for food, cattle feed, and water purification. Cross pollinated moringa has great species variation. Rapid growth and adaptation to varied climates have led to widespread production of M. oleifera in Coastal and Eastern Kenya. Identifying useful genotypes and improving drumstick tree cultivars requires studying its genetic diversity. Genetic diversity patterns must be understood to improve breeding strategies. Marker-assisted selection for desirable cultivars will boost breeding efficiency. A variety of molecular markers, including AFLP, SSR, RAPD, ISSR, and RFLP, have been used to characterize the Moringa plant. This study used SNPs, a cutting-edge molecular marker. This study meticulously characterized 164 genotypes of Moringa oleifera sourced from 17 provenances in Coastal Kenya through the application of genome sequencing, employing the genotyping by sequencing (GBS) technique. The analysis also discerned polymorphisms (SNP Calling) within the chosen genotypes and conducted investigations into population genetics. DNA was extracted from homogenized and crushed Moringa leaves. After reducing genomic complexity, library preparation includes barcoding, electrophoresis, pooling, and sequencing library preparation, followed by sequencing. Sequencing and SNP mining used Illumina Hiseq 2500 next-generation sequencing. The process of binning was implemented to eliminate the noise. In silico assembly of sequence contigs was undertaken. BLAST facilitated the alignment of the sequences with the Moringa reference genome. The analysis of data was conducted utilizing the DArT R and KDCompute platforms. The genetic characterization was accomplished through the implementation of cluster analysis, principal coordinate analysis (PCoA), three-dimensional plotting, and the construction of a phylogenetic tree. The computation of molecular variance analysis (AMOVA) and principal component analysis was conducted utilizing the genetic distance matrix. The SNP calling process was executed using the SNP caller algorithm provided by Illumina within the CASAVA software framework. A total of 20,921 SNPs were identified, exhibiting an average call rate of 0.82. The mean polymorphism content (PIC) for the SNPs was calculated to be 0.24, while the reproducibility rate was determined to be 0.98. The cluster analysis clearly indicated that the genotypes were organized into four distinct clades. The similarity coefficient derived from hierarchical clustering suggested that the genotypes exhibited reduced variability. A 3D plot was generated interactively utilizing DArT R, specifically through the Adegenet package. A phenetic tree was constructed employing a neighbor-joining methodology utilizing DArT R. In the analysis of population genetics, the F statistic (Fst) was calculated using the StAMPP package in DArT R. The results indicated that Gede and Samburu displayed the lowest heterozygosity/correlation, recorded at 0.0003, while Pwani University and Samburu demonstrated the highest gene correlation, reaching a value of 0.37. The Euclidean metric served as a means of quantifying distance, revealing that the mean distance among the populations was 33.024. The analysis of molecular variance (AMOVA) indicated a modest variation of 2.55% within the population and a slightly higher variation of 2.73% among the populations. The notable resemblance among the genotypes may be ascribed to the Moringa plants across different provenances sharing a common lineage. Considering the prevalence of SNPs and their role as a source of allele variations, this research may facilitate the identification of associations between gene allelic forms and phenotypes, thereby allowing for the connection of alleles to advantageous traits such as rapid growth and increased seed production.