Genetic diversity among the Philippine traditional maize (Zea mays L.) populations based on SSR markers.

Genetic diversity among the Philippine traditional maize (Zea mays L.) populations based on SSR markers.



This paper reports the first genetic diversity analysis of Philippine traditional maize populations performed through a cost-effective DNA pooling strategy. The diversity among selected 100 traditional maize populations collected from Luzon, Visayas, and Mindanao was evaluated using twenty simple sequence repeats (SSR) markers at the Institute of Plant Breeding, University of the Philippines Los BaƱos, Laguna, Philippines. A total of 138 bands ranging from two to 12 bands per primer were detected. The average number of polymorphic alleles, polymorphism rate, effective multiplex ratio, marker index, resolving power, and expected heterozygosity are 6.283, 87.17%, 5.798, 4.104, 15.897, and 0.658, respectively. The polymorphism information content (PIC) varied between 0.141 to 0.848, with an average value of 0.620. A dendrogram was constructed with a dissimilarity coefficient ranging from 0.14 to 0.55 and a mean dissimilarity index of 0.425. Cluster analysis revealed 13 groups based on the result of Approximately Unbiased (AU) p-values from 10,000 bootstrap iterations. The cluster analysis enabled the classification of populations with ambiguous places of origin. Analysis of molecular variance (AMOVA) showed higher within-population diversity (70%) than among-population diversity (30%) with PhiPT (pairwise genetic differentiation metric) of 0.298 (P = 0.001). These results revealed the significant diversity of traditional maize populations in the Philippines and the power of SSR markers in diversity and cluster analyses despite the age of this marker technology. These findings will aid plant breeders in developing approaches towards knowledgeable and efficient execution of breeding programs using traditional maize populations.

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Date published: September 2022

Keywords: Traditional maize, Philippine germplasm, cluster analysis, SSR markers, AMOVA, bootstrapping


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