ArticlePloS one2026
Genetic diversity and population structure of early-maturing yellow and orange maize inbred lines for improved breeding applications.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Early-maturing maize is vital for strengthening food security across the drought-prone agro-ecologies of Sub-Saharan Africa (SSA). Advancing genetic gain in these environments requires a clear understanding of genetic diversity and population structure patterns to support parental selection and future genomic-assisted breeding efforts. The study aimed at assessing genetic diversity and population structure of a panel of early-maturing yellow and orange kernel maize inbred lines, classify the inbred lines into putative heterotic groups, and identify a core set of elite, genetically divergent lines suitable for use in hybrid breeding and long-term genetic improvement. A panel of 376 elite early-maturing yellow and orange maize inbred lines from four source populations were genotyped using the DArTag SNP markers. After quality filtering, 1,954 high-quality SNP markers retained exhibited moderate diversity with an average polymorphic information content (PIC) of 0.38 and minor allele frequency (MAF) of 0.28. Linkage disequilibrium analysis showed a mean r2 of 0.046, with rapid LD decay across the genome, indicating substantial historical recombination and suggesting potential utility of the panel for future association studies. The Admixture analysis and discriminant analysis of principal components (DAPC) consistently resolved the panel into two major subpopulations with overlapping membership. Phylogenetic clustering also resolved the panel into two broad molecular clusters corresponding to putative heterotic groups. Analysis of molecular variance (AMOVA) revealed that most genetic variation occurred within groups (86%), with moderate differentiation among groups (14%). A core set representing 20% of the collection was identified using an allelic-richness-based greedy algorithm, offering valuable insights to accelerate breakthrough hybrid development for SSA farming systems. The study provides a comprehensive genomic characterization of early-maturing maize germplasm, providing genomic insights that may support future hybrid development and germplasm management efforts for SSA maize breeding programs.
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