bioRxivGeneticsPreprint
Genetic Diversity and Population Structure of Maize Doubled Haploid Lines from Drought and Low Nitrogen Tolerant Populations
Georgina Lala EhembaiD, Beatrice Elohor IfieiD, Biswanath DasiD, Pearl AbuiD, Emmanuel Amponsah AdjeiiD, Mathieu A. T. AyenaniD, Ana Luísa Garcia‐OliveiraiD, Priscilla Francisco RibeiroiD, Manilal William, Pangirayi Bernard TongoonaiD, Eric Yirenkyi DanquahiD
Abstract
Understanding the genetic diversity and population structure of breeding materials is essential for developing stress-resilient cultivars. In tropical maize, where drought and low soil nitrogen (low N) severely limit productivity, continuous development of tolerant varieties remains a priority. This study assessed the genetic diversity and population structure of 250 doubled haploid lines (DHLs) derived from five drought- and low N-tolerant tropical populations. Genotyping was performed using mid-density DArTseq markers, yielding 3,305 high-quality SNPs for analysis. Results revealed a moderate level of diversity among the DHLs, with an average genetic distance of 0.39, a polymorphism information content (PIC) of 0.33, and a minor allele frequency (MAF) of 0.29. These values reflect substantial allelic variation, important for identifying complementary parental combinations in hybrid development. Discriminant analysis of principal components (DAPC) grouped the DHLs into five distinct clusters, largely corresponding to their source populations, although some admixture was observed. This indicates that while the genetic backgrounds of the source populations were mostly retained, recombination introduced useful variation. Overall, the clear population structure and high diversity observed among these DHLs provide a strong genetic foundation for future maize improvement. These lines represent valuable resources for heterotic group formation, hybrid development, and recurrent selection schemes aimed at enhancing drought and low nitrogen tolerance in tropical maize.
- organism: plant (other)
- assay: whole-genome sequencing
- research focus: clustering
- approach: statistical model
- Citations
- 0
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