Genetic variation between individuals within a population or between populations of crop species because of either or both genetic and environmental influences can be easily assessed using various molecular markers. Among various molecular markers, SSR markers in particular have many advantages compared with other marker systems as follows: the first advantage is high reproducibility, which is most important in genetic analysis; the second advantage of the SSR marker system is the polymorphic genetic information contents, and the hypervariable nature of SSRs produces very high allelic variations even among very closely related varieties; and the third advantage relates to the codominant nature of SSR polymorphisms, as the codominant nature of SSRs is suitable for genetic analysis in segregation of F2 populations or pedigree analysis in hybrids (Park et al. 2009; Pejic et al. 1998; Sa et al. 2018).
In plants, the presence of SSRs was first demonstrated by the hybridization of poly (G-T) and poly (A-G) oligonucleotide probes on phage libraries of tropical tree genomes (Condit and Hubbell 1991). A search of published DNA sequences revealed that SSRs are also abundant in diverse plant genomes (Morgante and Olivieri 1993; Wang et al. 1994). Therefore, SSRs have become the preferred molecular marker system for the analysis of plant genetics and ecology. However, SSR markers can only be applied to crop species with large amounts of EST or cDNA sequences that are freely accessible to the public. Recently, the transcriptome sequencing approach RNAseq has become a powerful tool for novel gene discovery and the development of molecular markers for nonmodel crop, such as Perilla crop. In the case of the Perilla crop, in a previous study, we sequenced and assembled one cultivated type (PF98095) of P. frutescens var. frutescens using transcriptome sequencing by RNA-seq (Tong et al. 2015). This information will help develop SSR primers for Perilla crop. As explained in the Introduction, Sa et al. (2018) detected a total of 15,991 SSR loci based on the SSR flanking sequences via de novo assembly of the PF98095 RNA-seq data (Tong et al. 2015), which were classified based on the number of repeating units. Dinucleotide SSRs had the highest number of SSRs (9,910), followed by trinucleotide SSRs (5,652) and tetranucleotide SSRs (429) (Sa et al. 2018). In the Perilla crop, analysis of the distribution of different repeat motifs showed that dinucleotide repeats were the most abundant (62.0%) type, followed by trinucleotide repeats (35.3%), with the two together comprising 97.3% of the eSSR repeats. This finding is important for developing a large number of effective eSSR primers in Perilla crop, as the markers associated with smaller SSR motif lengths (di- and tri-) are more variable than other motif lengths (Ellegren 2004). Our results also showed that dinucleotide repeats were more abundant than other repeat units in the Perilla crop. While an almost equal distribution of di- and trinucleotide repeats was reported in ramie (Liu et al. 2013), a high abundance of trinucleotide repeats was reported in flax (Cloutier 2009), jute (Saha et al. 2017), and kenaf ESTs (Zhang et al. 2015).
In this study, a total of 130 alleles with 39 SSRs were detected segregating in the 9 Perilla accessions, which yielded an average of 3.3 alleles per locus (Supplement Tables 1, 2). Depending on the number of repeat units in the 39 SSR primer sets, the dinucleotide SSRs such as KNUPF87, KNUPF97, KNUPF103, KNUPF105, KNUPF110, and KNUPF116 showed comparatively more allelic bands and genetic diversity values than the tri- and tetranucleotide SSRs in Perilla accessions. In our study, although we analyzed only nine Perilla accessions, the allele number, GD, PIC, and MAF values for 39 new Perilla EST-SSR markers were found to determine the unique genetic profiles of individual genotypes of the Perilla crop. Therefore, these new SSR primer sets may be useful in the future for studying GD for Perilla germplasm resources by association mapping and designating important genes/QTLs for future genetic and breeding programs in Perilla crop.
In addition, analysis of genetic diversity and relationships and association mapping of plant breeding materials will help develop new molecular markers associated with target morphological traits or useful varieties and lines in Perilla crop breeding programs. In our study, we developed an F2 population from a cross between PF13-110 (female parent, cultivated var. frutescens) and PF13-160 (male parent, weedy var. crispa) to develop molecular markers for traits showing morphological differences between two cultivated types of var. frutescens and var. crispa through 40 individuals of the F2 population of the Perilla crop (Fig. 1, Tables 1, 2). According to our results, the 40 individuals of the F2 population showed discontinuous variations within the range of morphological characteristics of their parental lines, as shown in Tables 2 and 5. That is, some morphological traits investigated in our study showed a Mendelian segregation ratio of 1:2:1 (or AA:AB:BB), while other traits showed different separation patterns in 40 individuals of the F2 population. For example, in the case of QL1, of the 40 lines of the F2 population, 12 lines were large, 14 lines were medium, and 14 lines were small; whereas in the case of QL2, 28 lines were green, 6 lines were mixed green/purple, and 6 lines were purple. However, unfortunately, as mentioned in the Introduction, the F2 population lines used in this study did not represent the entire population, so it was not possible to predict the exact segregation ratio of the F2 population.
In our study, we also performed a correlation analysis for nine leaf- and plant-related traits in 40 individuals of the F2 population (Table 6). The results showed that there were highly statistically significant positive correlations of QL2 with QL3 (0.682**) and QL6 (0.429**), of QL3 with QL6 (0.390*) and QL7 (0.462**), of QL5 with QL8 (0.489**) and QL9 (0.313*) at P < 0.01 or P < 0.05. Among the morphological traits examined in our study, leaf and plant color-related traits, such as QL2, QL3, QL6, and QL7, and leaf shape-related traits, such as QL5 and QL8, were closely related to each other. In addition, to better understand the genetic relationships among 40 individuals of the F2 population, we analyzed the phylogenetic relationships of the 40 individuals of the F2 population and their two parental lines using 37 SSR markers. In the phylogenetic tree analysis, the 40 individuals of the F2 population and two parental lines were clustered into two major groups. Except for exceptional individuals, Group I mainly contained many individuals with similar characteristics to Parent B, while Group II contained many individuals with similar characteristics to Parent A (Table 2, Fig. 2). Therefore, these separated individuals and leaf- and plant-related traits of the F2 population are considered to be useful genetic materials for finding molecular markers associated with the leaf and plant morphological characteristics of Perilla crop through association mapping analysis.
Association mapping has been proposed as a selection method to identify loci involved in the inheritance of complex traits (Risch and Merikangas 1996). In this study, we developed an F2 population for association mapping analysis to find molecular markers associated with leaf- and plant-related traits of Perilla crop. Additionally, in our study, we used SSR markers for association mapping analysis because SSR markers are highly polymorphic and generally codominant (Park et al. 2009; Powell et al. 1996; Sa et al. 2018, 2019). Therefore, the SSR marker is considered to be a useful analysis method for identifying separation patterns of allelic bands in the F2 population.
To select the SSR markers associated with leaf- and plant-related traits of the F2 population, we analyzed marker-trait associations (SMTAs) between 37 SSR markers and 9 leaf- and plant-related characteristics in 40 individuals of the F2 population using TASSEL software. From the results, we identified 12 and 11 SSR markers associated with leaf-related traits and plant-related traits, respectively (Table 7). In the case of leaf-related traits, 12 SSR markers, namely, KWPF25, KNUPF11, KNUPF21, KNUPF49, KNUPF60, KNUPF74, KNUPF81, KNUPF89, KNUPF103, KNUPF108, KNUPF109, and GBPEM111, were associated with QL2, QL3, QL5, and QL8. In addition, in the case of plant-related traits, 11 SSR markers, namely, KNUPF5, KNUPF11, KNUPF15, KNUPF21, KNUPF29, KNUPF39, KNUPF54, KNUPF61, KNUPF82, KNUPF88, and GBPEM111, were associated with QL4, QL6, QL7, and QL9 (Table 7). Our results showed that with the exception of QL1, most leaf- and plant-related traits were associated with 1 to 7 SSR markers, depending on the leaf- and plant-related characteristics. Additionally, some SSR markers (KNUPF11, KNUPF21, GBPEM111) were associated with leaf- or plant-related characteristics, such as QL3, QL4, QL6, QL7, and QL9. For example, KNUPE11 was found to be associated with QL3, QL6, and QL7 traits; GBPEM111 was associated with QL8 and QL9 traits; and KNUPE21 was associated with QL4 and QL8 traits (Table 7).
In a previous study by Lim et al. (2000), bulk segregant analysis (BSA) was performed to identify SSR markers linked to leaf- and seed-related traits in Perilla crop. They reported that four SSR markers (KNUPF15, KNUPF21, KNUPF29, and KNUPF60) were associated with the color of the leaf surface, four SSR markers (KNUPF11, KNUPF15, KNUPF21, and KNUPF60) were associated with the color of the leaf reverse side, and three SSR markers (KNUPF25, KNUPF61, and GBPEM111) were associated with the depth of leaf teeth. In addition, five SSR markers (KNUPF11, KNUPF12, KNUPF16, KNUPF29, and KNUPF42) were associated with seed-related traits. According to our results, among the SSR markers associated with the leaf- and plant-related traits, eight SSR markers (KNUPE11, KNUPF15, KNUPF21, KWPE25, KNUPF29, KNUPE60, KNUPE61, GBPEM111) appeared to overlap with leaf-related traits previously reported by Lim et al. (2000). Therefore, these SSR markers are thought to be useful molecular markers for selecting leaf- and plant-related traits in Perilla crop. Additionally, these SSR markers are thought to be useful molecular markers for distinguishing between two cultivated types of Perilla crop. However, in Perilla crop, it is difficult to compare genetic characteristics because genomic information on SSR markers associated with leaf- and plant-related characteristics is still lacking.
In the future, if studies such as genome analysis for each chromosome are actively conducted in Perilla crop, it will be possible to analyze genomic information of specific Perilla SSR markers related to leaf- and plant-related characteristics. As explained in the Introduction, many taxonomic studies have been conducted to distinguish between two cultivated and weedy types of Perilla crop using morphological characteristics and molecular markers, but they failed to distinguish the two cultivated types of Perilla crop because of the existence of intermediate types, such as weedy plants. Therefore, the SSR markers associated with leaf- and plant-related characteristics selected in this study are expected to provide useful information for distinguishing the two cultivated types of Perilla crop and their weedy types in the future. In addition, the new Perilla SSR primers described in this study could be helpful in identifying genetic diversity and genetic mapping, designating important genes/QTLs for Perilla crop breeding programs, and allowing Perilla breeders to improve leaf and plant quality through MAS breeding programs.