Genetic diversity and population structure among accessions of Perilla frutescens (L.) Britton in East Asia using new developed microsatellite markers

Abstract

SSRs were successfully isolated from the Perilla crop in our current study, and used to analyze Perilla accessions from East Asia. Analyses of the clear genetic diversity and relationship for Perilla crop still remain insufficient. In this study, 40 new simple sequence repeat (SSR) primer sets were developed from RNA sequences using transcriptome analysis. These new SSR markers were applied to analyze the diversity, relationships, and population structure among 35 accessions of the two cultivated types of Perilla crop and their weedy types. A total of 220 alleles were identified at all loci, with an average of 5.5 alleles per locus and a range between 2 and 10 alleles per locus. The MAF (major allele frequency) per locus varied from 0.229 to 0.943, with an average of 0.466. The average polymorphic information content (PIC) value was 0.603, ranging from 0.102 to 0.837. The genetic diversity (GD) ranged from 0.108 to 0.854, with an average of 0.654. Based on population structure analysis, all accessions were divided into three groups: Group I, Group II and the admixed group. This study demonstrated the utility of new SSR analysis for the study of genetic diversity and population structure among 35 Perilla accessions. The GD of each locus for accessions of cultivated var. frutescens, weedy var. frutescens, cultivated var. crispa, and weedy var. crispa were 0.415, 0.606, 0.308, and 0.480, respectively. Both weedy accessions exhibited higher GD and PIC values than their cultivated types in East Asia. The new SSR primers of Perilla species reported in this study may provide potential genetic markers for population genetics to enhance our understanding of the genetic diversity, genetic relationship and population structure of the cultivated and weedy types of P. frutescens in East Asia. In addition, new Perilla SSR primers developed from RNA-seq can be used in the future for cultivar identification, conservation of Perilla germplasm resources, genome mapping and tagging of important genes/QTLs for Perilla breeding programs.

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