High-density single nucleotide polymorphism markers analysis reveals the genetic diversity and population structure in tropical highland maize (Zea mays L.) inbred lines - Worknesh Terefe Gebre; Demissew Abakemal Ababulgu; Tilahun Mekonnen Negassa; Tileye Feyissa Senbeta (PLoS ONE, 2026)
Genetic diversity is critical for crop improvement, germplasm conservation, and sustainable agriculture. It enables breeders to assess genetic relationships among germplasm, select suitable parents, and develop resilient varieties. In this study, a total of 11,203 single nucleotide polymorphism (SNP) markers were used to evaluate the genetic diversity of 93 maize inbred lines adapted to the East African tropical highlands. The results revealed moderate genetic diversity across the panel. Gene diversity, polymorphic information content (PIC), and genetic distance ranged from 0.10 to 0.67, 0.10 to 0.59, and 0.03 to 0.52, with mean values of 0.46, 0.40, and 0.44, respectively. Linkage disequilibrium (LD) analysis identified 36,904 SNP pairs (7.5% of 487,225 comparisons) showing relatively strong LD ( r 2 ≥ 0.20), with an overall mean r 2 of 0.067. Genome-wide LD decayed to r 2 = 0.2 at approximately 93.82 kb, suggesting rapid decay and substantial historical recombination. Analysis of molecular variance (AMOVA) revealed that 95% of the total variation resided within germplasm source groups, whereas 5% was attributed to differences among groups, indicating low to moderate genetic differentiation. Multivariate analyses, including neighbor-joining, principal component analysis, and population structure analysis, consistently grouped the lines into three clusters, which largely corresponded with pedigree information. The observed diversity highlights the presence of valuable alleles that can be harnessed in maize breeding to enhance productivity and resilience in highland environments. Furthermore, the identified SNP markers in this study provide a useful genomic resource for future studies, including marker-trait association studies aimed at identifying genomic regions underlying key agronomic traits and accelerate genetic improvement in challenging environments.