Tree Genetics and Molecular Breeding 2025, Vol.15, No.2, 80-88 http://genbreedpublisher.com/index.php/tgmb 81 2.2 Applications in genetic studies Molecular markers can assess the genetic diversity, population structure and genetic relationship among different Punica granatum varieties. SSR markers were used to analyze the genetic conditions of Punica granatum germplasm resources. The results showed that there were significant differences among these resources, which was beneficial for researchers to find core populations suitable for breeding (Mahajan et al., 2018; Zarei and Sahraroo, 2018; Liu et al., 2020; Patil et al., 2020; Parashuram et al., 2022). Sinjare and Jubrael (2020) demonstrated that AFLP markers were used to study the genetic diversity of Punica granatum and discovered many genetic variations. Jeong et al. (2018) found that molecular markers could detect mutations related to anthocyanin synthase, a gene that has a significant impact on the color of Punica granatum peels. 2.3 Advantages and limitations SSR has a high polymorphism and can simultaneously display the information of two alleles, making it suitable for detailed genetic analysis and breeding research (Mahajan et al., 2018; Zarei and Sahraroo, 2018; Parashuram et al., 2022). Sinjare and Jubrael (2020) hold that AFLP can generate many markers with high polymorphism and is suitable for evaluating genetic diversity. However, in the same year, that is, in 2020, Liu et al. and Patil et al. proposed that before conducting SSR analysis, it is necessary to know a specific DNA sequence used for designing primers, and this process is time-consuming and costly. Sinjare and Jubrael (2020) also demonstrated that AFLP has relatively high technical requirements and requires the use of more complex instruments during analysis. Yan et al. (2019) found that chloroplast DNA sequences can be used to study kinship, but their sequence changes are not significant and are not sufficient for studying genetic diversity (Table 1). Table 1 Summary of the complete chloroplast genome characteristics of five species in Lythraceae (Adopted from Yan et al., 2019) Species Punica granatum Lagerstromeia indica Sonneratia alba Trapa maximowicizz Heimia myrtifolia Genome size 158 638 152 025 153 061 155 577 159 219 LSC size 89 021 84 046 87 226 88 528 88 571 SSC size 18 684 16 914 18 032 18 272 18 821 IR size 25 467 25 623 23 902 24 389 25 914 Number of genes 113 113 107 110 112 Protein-coding genes 79 (6) 79 (7) 79 (6) 77 (5) 78 (7) tRNA genes 30 (7) 30 (7) 24 (5) 29 (9) 30 (6) rRNA genes 4 (4) 4 (4) 4 (4) 4 (4) 4 (4) Number of genes duplicated in IR 17 18 15 18 17 GC content 36.92 37.59 37.29 36.4 36.95 GenBank accession MK603511 NC_030484 NC_039975 NC_037023 MG921615 3 Genetic Mapping and QTL Analysis 3.1 Construction of genetic maps High-density genetic maps (HDGMs) maps are like a genetic map and are useful for identifying the locations of genes related to important traits. Wang et al. (2018) established a genetic map containing 3 630 SNP markers using the SAF-Seq technique in the study of peanuts. These markers were distributed across 20 linkage groups, covering a genetic distance of 2 098.14 cM, with an average distance of only 0.58 cM between every two markers. Kulkarni et al. (2020) covered 294.2 cM with 126 SSR markers in the study of rice to analyze the traits related to yield. Guo et al. (2020) established a high-density genetic map containing 10 739 loci through the RIL population of ‘Tainong 18 × Linmai 6’. 3.2 Identification of quantitative trait loci (QTL) QTL mapping can identify the gene regions related to certain traits. Wang et al. (2018) used QTL plotting to identify 62 QTLs related to 14 yield-related traits, which were distributed on 12 chromosomes. They also found that the traits of peanut seeds and pods occurred in the same region. Kulkarni et al. (2020) identified 22 QTLs related to traits such as total rice yield, panicle weight, and plant height, and some of them also showed obvious
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