Aralia elata is an Araliaceae woody plant species found in Northeastern Asia. To understand how genetic pools are distributed for A.elata clones, we were to analyze the population structure of A.elata cultivars and identify how these are correlated with thorn-related phenotype which determines the utility of A.elata. We found that the de novo assembled genome of 'Yeongchun' shared major genomic compartments with the public A.elata genome assembled from the wild-type from China. To identify the population structure of the 32 Korean and Japanese cultivars, we identified 44 SSR markers and revealed three main sub-clusters using ΔK analysis with one isolated cultivar. Machine-learning based clustering with thorn-related phenotype correlated moderately with population structure based on SSR analysis suggested multi-layered genetic regulation of thorn-related phenotypes. Thus, we revealed genetic lineage of A.elata and uncovered isolated cultivar which can provide new genetic material for further breeding.
Keywords: Aralia elata; Cultivation; De novo genome assembly; Machine learning; Neighborhood-based algorithm; SSR markers; Thorn.
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