Skip to main content

Selection and Validation of 48 KASP Markers for Variety Identification and Breeding Guidance in Conventional and Hybrid Rice (Oryza sativa L.)



Breeding of conventional and hybrid rice (Oryza sativa L.) have solved hunger problems and increased farmers' income in the world. Molecular markers have been widely used in marker-assisted breeding and identification of larger numbers of different bred varieties in the past decades. The recently developed SNP markers are applied for more stable and detectable compared with other markers. But the cost of genotyping lots SNPs is high. So, it is essential to select less representative SNPs and inexpensive detecting methods to lower the cost and accelerate variety identification and breeding process. KASP (Kompetitive Allele-Specific PCR) is a flexible method to detect the SNPs, and large number of KASP markers have been widely used in variety identification and breeding. However, the ability of less KASP markers on massive variety identification and breeding remains unknown.


Here, 48 KASP markers were selected from 378 markers to classify and analyze 518 varieties including conventional and hybrid rice. Through analyzing the population structure, the 48 markers could almost represent the 378 markers. In terms of variety identification, the 48 KASP markers had a 100% discrimination rate in 53 conventional indica varieties and 193 hybrid varieties, while they could distinguish 89.1% conventional japonica rice from different breeding institutes. Two more markers added would increase the ratio from 68.38 to 77.94%. Additionally, the 48 markers could be used for classification of subpopulations in the bred variety. Also, 8 markers had almost completely different genotypes between japonica and indica, and 3 markers were found to be very important for japonica hybrid rice. In hybrid varieties, the heterozygosity of chromosomes 3, 6 and 11 was relatively higher than others.


Our results showed that 48 KASP markers could be used to identify rice varieties, and the panel we tested could provide a database for breeders to identify new breeding lines. Also, the specific markers we found were useful for marker-assisted breeding in rice, including conventional and hybrid.


Rice is a vital crop that feeds almost half the world’s population. In the past several decades, there has been great progress in rice variety identification and breeding. A few bred varieties, such as the ‘miracle rice’ IR8, solved the lodging problem and increased production (Peng et al. 2010).

With the progress of rice breeding, there are increasingly more bred varieties. Distinguishing a new variety from others is a problem that needs to be resolved quickly. In the past, researchers used the distinctness, uniformity, and stability (DUS) tests to identify these varieties (Aravind et al. 2019). Using this test, researchers need to record a lot of phenotype data from the seedling to maturing stage. Subsequently, the data must be inputted to the server and analyzed. There are several weak points in this approach, such as data instability in different years, time consumption and labor intensity of the process.

To overcome these disadvantages, molecular markers have been explored to assist in identifying varieties in recent years, such as 12 microsatellite markers for authentication in rice (Bonow et al. 2009). Recently, microsatellite markers have been replaced by single nucleotide polymorphism (SNP) markers due to their stability and easy detectability. Many genotyping methods have emerged, such as microarray (Lou et al. 2015; Lu et al. 2017), genotyping by sequencing (GBS) (An et al. 2019; Lin et al. 2020b; Tang et al. 2016), high resolution melting (HRM) (Wang et al. 2013), and kompetitive allele-specific PCR (KASP) (Shen et al. 2021; Steele et al. 2020).

Compared with other genotyping method, KASP was more popular in varietal identification recently. The conventional simple satellite repeat (SSR) or insertion/deletions (InDels) markers had been proved that they could be useful for variety identification and estimation of subpopulation (Rahman et al. 2012; Kumar et al. 2019; Castellana et al. 2020), but the cost of KASP markers which was also used for identification and estimation was lower (Shikari et al. 2021). Additionally, the SSR markers were not evenly distributed in the genome and the genotyping was tedious and time consuming (Semagn et al. 2014). Whole-genome sequencing may be the best way to identify different varieties and have highest accuracy in identification, but the cost of library construction was higher compared with other methods. GBS had a lower cost in library construction, but the distribution of markers are not evenly as other method (Huang and Han 2014). Microarray was a better choice to make the markers distribute evenly, but it was not cheap and not flexible in varietal identification (Semagn et al. 2014). So, the above three methods were not suitable for massive varietal identification perfectly. As previous study showed, KASP was a better choice with more flexibility and higher efficiency (Rasheed et al. 2017). We could add more markers for the polymorphic detection in a specific population according to some newly identified functional genes. Overall, KASP or less representative KASP markers was more suitable for varietal identification compared with other markers in consideration of labor, time, cost, distribution and flexibility.

Additionally, it has been widely used for genotyping in crops, especially in rice. KASP was suitable for identifying the varieties in specific subpopulations of rice, such as Basmati and had more accurate identification rate than SSR markers (Steele et al. 2020). In Korea japonica, the number of SSR markers was limited for the construction of genetic maps and quantitative trait locus (QTL) mapping studies because of the polymorphisms. The researchers developed KASP markers from the resequencing data and 205 KASP markers were used for constructing genetic map (Cheon et al. 2018). Furthermore, KASP could be used for rice specific gene genotyping, such as Wx, ALK, and BADH2 which had much influence in the quality of rice, and has been applied to improve rice quality through marker-assisted selection (MAS). The high-throughput of KASP could help accelerating the process of rice breeding lines selection (Addison et al. 2020; Yang et al. 2019a).In indica breeding, the researchers used the resequencing data to generate 1.3 million potential KASP assay designs and 377,178 polymorphic KASP design sites per cross averagely. These KASP markers which could replace SSR makers were useful to whole-genome indica breeding (Steele et al. 2018).In another study, a core SNP arrays consisting of 467 KASP markers were proved to be used in germplasm assessment, genetic diversity and population evaluation in rice (Yang et al. 2019b). From the above study, we could see that KASP was widely used for varietal identification, genetic map construction, MAS, genetic diversity, population evaluation and breeding in rice.

Although large numbers of KASP markers could be applied for genotyping in rice identifying and molecular breeding, the cost will be high if hundreds of thousands of varieties need to be analyzed. So, it is essential to develop less representative KASP markers for the breeders to lower the cost. It is still unknown whether less KASP markers could work in identifying rice varieties and assisting breeding, including conventional and hybrid rice, especially in China. Therefore, we used more than 500 varieties, including conventional and hybrid rice, to validate the 48 KASP markers selected from 378 KASP markers for variety identification and found some markers useful for rice breeding.

To determine the relationship of less KASP markers and variety identification, we herein used 378 markers to test the effect of KASP markers with a population consisting of japonica, indica, and aus. Subsequently, we selected 48 markers to verify the effect using 518 bred varieties, including conventional and hybrid rice, and found that the markers could distinguish 83.40% of the varieties. Particularly, the discrimination rate was 100% in conventional indica and hybrid varieties. Meanwhile, we found some specific markers that played a vital role in subgroup differences and heterosis formation. Therefore, the 48 KASP markers could also be used for marker-assisted breeding.

Main text

KASP Markers Were Developed Using a Diverse Rice Population from Asia

To determine whether the 378 KASP markers (Additional file 1: Table S1) were effective, we used the KASP markers to genotype a diverse rice population. The population consisted of japonica (20), indica (21), and aus (5) subpopulations, and the varieties came from east (33), southeast (8), and south Asia (5) (Additional file 2: Table S2). After filtering with polymorphism, we identified 331 KASP markers that were effective (Additional file 3: Table S3). These markers were well distributed across all 12 chromosomes (Fig. 1). The distance between markers ranged from 0.41 to 4.64 Mb, and the average distance was 1.13 Mb. Among all markers, 98.8% showed missing genotyping less than 16 varieties (Fig. 2a), and the minor allele frequency (MAF) of most markers was more than 0.2 (Fig. 2b). The polymorphism information content (PIC) of 74.6% markers was larger than 0.3 (Fig. 2c). These results provided the basis for subsequent variety identification and finding the specific markers for breeding.

Fig. 1
figure 1

Distribution of filtered 331 markers and selected 48 markers (red) on 12 chromosomes

Fig. 2
figure 2

The data of number of varieties detected (NVD) (a), MAF (b) and PIC (c) of 331markers

Markers Selected and Used for Variety Identification

KASP was an efficient tool to distinguish various varieties depending on the number of markers used; thus, we sorted the markers based on the detection ratio (> 0.8), MAF > 0.2 and PIC > 0.3. As a result, 171 markers remained, from which we selected 48 markers according to the physical position of each marker (Fig. 1, Additional file 4: Table S4). To verify the representativeness of the 48 markers selected, the phylogenetic tree was constructed and the tree both revealed two distinct groups, with subpopulation 1 into one group, and the other was subpopulation 2 (Fig. 3a, b). Secondly, we analyzed the population structure with 331 markers and 48 markers for each of the 46 varieties. The structure analysis was performed by setting the range of K as 1 to 9. The minimum value of CV error in both analyses was when K was 2 (Fig. 3c, e, Additional file 5: Table S5). At last, two groups were separated by principal component analysis corresponding to subpopulation 1 and subpopulation 2 (Fig. 3d, f, Additional file 6: Table S6). The above results showed that 48 markers could almost represent the 331 markers.

Fig. 3
figure 3

Analysis of the 46 various varieties using KASP markers. a, c, d The phylogenetic tree, population structure and PCA of the test population using 331 KASP markers. b, e, f The phylogenetic tree, population structure and PCA of the test population using 48 KASP markers. The brown indicated the japonica varieties while light red and blue represented the indica and aus varieties in a, b. The brown indicated the subpopulation 1 while light red represented subpopulation 2 in d, f

After detection of the 518 varieties (193 hybrid varieties and 325 conventional varieties) (Additional file 7: Table S7), we found that all markers had polymorphisms (Additional file 8: Table S8 and Additional file 9: Table S9). The SNP detection rate was 91.16%. We focused on the resolution of the markers in the panel and found that the markers we selected differed in 83.40% of all varieties (432/518). Then, we separated the whole population into subpopulations, namely japonica (conventional and hybrid) and indica (conventional and hybrid), to observe the duplications of SNP combinations (the same combination among all varieties was defined the duplications). As a result,100% of conventional indica varieties (53/53), 100% of hybrid varieties (193/193), and 68.38% of conventional japonica varieties (186/272) had unique SNP combinations (Table 1).

Table 1 Summary of the detection rate, heterozygous rate, and identification rate using 48 and 50 markers

Analysis of 48 Markers in Conventional Varieties

In conventional varieties, the SNP detection rate was 92.69% (Table 1). There were 29.78% duplications in conventional japonica and 0% in conventional indica or hybrid varieties, indicating that the japonica varieties had more similarities than others, and we should add more markers to assess them. To decrease the ratio of varieties that could not be distinguished, we added two markers (M54, M234) according to their physical position. After that, the identifying ratio increased from 68.38 to 77.94% (Table 1). It was demonstrated that more markers could be added as expected and a higher ratio would be obtained. Additionally, after analyzing the conventional japonica varieties that could not be discriminated from each other using the 48 KASP markers, we found that almost all the varieties came from the same breeding institutes, and the varieties may share the same parents. After deleting the possible redundant sib lines from the same breeding institutes (Additional file 10: Table S10), the identifying rate in conventional japonica of 48 KASP markers could be 89.1% (106/119). The above results showed that the 48 or 50 markers could be very useful for identification of conventional varieties.

To verify the 48 markers could be used for population analysis and breeding of rice conventional varieties, we analyzed the population structure and constructed the phylogenetic tree. The K which had minimum CV error showed that the population could divided into two subspecies, subpopulation 1 and subpopulation 2 (Fig. 4a, Additional file 11: Table S11). Also, the result of phylogenetic tree was the same as that of population structure analysis (Fig. 4b). At last, the result of PCA again verified the above two results (Fig. 4c, Additional file 12: Table S12). From the results, we approved that the 48 markers were suitable for rice conventional varieties analysis, and the breeders could use the 48 KASP markers and the panel to identify the breeding lines.

Fig. 4
figure 4

Analysis of the 48 KASP markers in conventional varieties. ac The phylogenetic tree, population structure and PCA of the conventional varieties using 48 KASP markers. d The difference of reference allele frequency between japonica and indica. The blue star indicated the sites which showed almost completely different alleles. The brown indicated the japonica varieties while light red represented the indica varieties in b. The blue indicated that the varieties with the classification of the phylogenetic tree different from the original annotation coming from the breeding institutes in b. The brown indicated the subpopulation 1 while light red represented subpopulation 2 in c

To detect the genetic differences between subgroups and identify if some KASP markers could be used for breeding, we summarized the ratio of reference allele frequency and found that eight markers (M21, M314, M224, M95, M98, M273, M342, M283) in conventional rice were almost completely different between indica (< 10%) and japonica rice (> 90%) (Fig. 4d). To find the highest percentage of reference allele frequencies of markers between japonica and indica, we conculcated the percentage of each marker and average percentage of each chromosome (Additional file 13: Table S13). The result showed that almost 75.00% (36/48) markers had more than 95% reference allele frequency and only two markers (M345, M349) had less than 80% reference allele frequency in japonica. Conversely, 33.33% (16/48) markers had more than 30% reference allele frequency and no markers exceeded half of reference allele frequency in indica (Additional file 13: Table S13). Also, we found that the chromosome 3 was the least and 10 was the highest in average percentages of reference allele frequency (Fig. 4d). From the above result, we could see the indica subpopulation had more diversity and these KASP markers could be used for assisting breeders to determinate the subgroup of varieties.

Analysis of 48 Markers in Hybrid Varieties

In comparison, in hybrid varieties, the SNP detection rate was 91.98%, and the heterozygous rate was 41.73% (Table 1). We used 48 markers to assess the difference between them and found that each hybrid variety had unique combinations. This showed that hybrid varieties had more polymorphisms than conventional varieties, especially in japonica hybrids. Additionally, we found that the heterozygous rate of each variety in japonica hybrids was 35.42–67.39%, and the average rate was 51.84%, while it was 12.77–60.42% and 40.62% in indica hybrids (Table 1). This indicated that japonica hybrids may require a higher heterozygous rate and could show stronger heterosis.

To study the ability of the 48 KASP markers for characteristic analysis in hybrid varieties, we analyzed the population structure. As results, the ΔK value was much higher for the model parameter K = 2 than other values of K (Additional file 14: Table S14). The 193 accessions could be divided into two subpopulations. The result of phylogenetic tree construction and PCA showed the same with that of population structure analysis (Additional file 15: Table S15). The result showed that the 48 KASP markers could be used for classification of subpopulations in hybrid varieties.

To observe the heterozygous rate of each chromosome, we summarized the heterozygous rate. In all 12 chromosomes, 3, 6 and 11 had more heterozygosity than the others (Fig. 5d). This indicated that the 3 chromosomes may have more genes that contributed to heterosis-related traits, such as yield and heading date. Through a literature review, we observed that some genes were located on the 3 chromosomes, such as GS3, Ha3a, MOC1 and IPA1 (Jiao et al. 2010; Lin et al. 2020a; Mao et al. 2010; Tamaki et al. 2007).

Fig. 5
figure 5

Analysis of the 48 KASP markers in hybrid varieties. ac The phylogenetic tree, population structure and PCA of the hybrid varieties using 48 KASP markers. d The heterozygous rate of each chromosome. e The heterozygous rate of japonica hybrid variety. The blue star indicated three sites with higher heterozygous rate. The brown indicated the japonica varieties while light red represented the indica varieties in b. The blue indicated that the varieties with the classification of the phylogenetic tree different from the original annotation coming from the breeding institutes in b. The brown indicated the subpopulation 1while light red represented subpopulation 2 in c

To determine the difference between japonica and indica hybrid varieties, we calculated the heterozygous rate of each site and found that two markers (M21, M283) had a higher percentage in japonica than indica hybrid varieties (Table 2). Conversely, four markers (M26, M178, M265, M111) predominated in the indica hybrid (Table 2). Thus, the KASP markers could be used for detecting which subpopulation the variety belonged to, and the markers would help rice breeders to identify the pedigree earlier. More importantly, we found that three markers (M224, M283, M314) which had higher heterozygous rate (> 0.7) in japonica hybrids shared the same positions as the markers which had almost completely different genotypes between japonica and indica conventional varieties (Fig. 5e). This indicated that the japonica hybrids rice showed heterosis need these differentiated sites between japonica and indica more. Therefore, the identified markers should be helpful for rice breeders to select japonica hybrids in rice breeding.

Table 2 Heterozygous rate of markers showed differences between japonica and indica


KASP Was a Better Selection for Identifying Varieties

Compared with SSR markers, the KASP had advantages with a higher efficiency and labor-saving (Rasheed et al. 2017). The SNPs produced by GBS were mostly used for population structure analysis (Pereira-Dias et al. 2019). Whole-genome sequencing was frequently used for scanning the polymorphism of varieties and the polymorphisms were used for GWAS in rice (Chen et al. 2014; Crowell et al. 2016; Yano et al. 2016). KASP markers had been used for varietal identification (Swisher Grimm and Porter 2020; Wang et al. 2021; Steele et al. 2020). In our study, we selected and validated less KASP markers (about one eighth of all 378 markers) for identifying varieties, and the cost decreased much compared with the 378 markers correspondingly. So, the selected KASP markers by us and more added markers may be the best choice for identifying varieties including conventional and hybrid rice, especially in China.

The Lower Identifying Rate of Conventional Japonica May be due to the Narrow Genetic Base

In previous study, the researchers found that the temperate japonica had lower average gene diversity and PIC value compared with other subpopulations (Garris et al. 2005). Also, the japonica subspecies experienced a more severe bottleneck than the indica subspecies (Gao and Innan 2008). For the analysis of the chloroplast genome, a strong positive selection may occur in japonica (Cheng et al. 2019). These researches all showed that the japonica subspecies especially the temperate japonica had lower genetic diversity. So, the 48 KASP markers could not identify japonica varieties as precisely as indica varieties. Additionally, the conventional japonica bred varieties almost came from Jiangsu province, China. The local climate and the soil which differed from other regions limited the emergence of diversity in variety. But for indica, more genetic diversity was found than japonica from our results and previous study (Xu et al. 2012). The more diversity may be related with diverse environments experienced by indica (Wang et al. 2018). The higher genetic diversity caused more combinations and identification more precisely in indica.

More KASP Markers Could be Added According to the Identification of Functional Alleles

In our study, more than 15% of varieties could not be separated from the others. So, we added more KASP markers to obtain a finer resolution in conventional japonica. The addition of two markers demonstrated that it could increase the identification ratio in our results. In a previous study, KASP markers were used for functional genes, such as Wx and ALK. With the progress of modern biological science, more genes will be identified by genome-wide association study (GWAS) (Tang et al. 2019), bi-parental QTL (Wei et al. 2010), or map-based cloning (Xu et al. 2019); thus, more functional alleles will be discovered among different subpopulations, and functional alleles could be used for KASP marker development. More KASP markers combined with the phenotype of varieties will distinguish more accurately, especially in japonica. In the subpopulation of Korean japonica, 400 markers were suitable for genetic analysis (Cheon et al. 2018). Thus, the identifying rate would meet the requirements of researchers with adding more KASP markers.

Specific Markers Could be Used for Molecular Identification and Breeding

With the shortage of a labor force and the development of technology, variety identification will enter the era of molecular identification (Pourabed et al. 2015). In our results, the markers with different variations between japonica and indica may be used for variety identification and then combined with the DUS test to make variety identification more accurately. The heterozygosity of different chromosomes identified in hybrids will provide a reference for cross breeding and suggestions for breeders to select parents. In japonica hybrid rice, three important markers could be used as breeding target markers to improve the efficiency of breeding.


In summary, we selected 48 markers from 378 KASP markers to distinguish different varieties, including conventional and hybrid varieties, and the resolution was about 85%. Through the analysis of the detection rate and heterozygous rate of each variety, we found that more markers could benefit the detection rate. The 48 KASP markers could differ from each other in indica and hybrid japonica. In conventional japonica, the 48 KASP markers could almost identify the varieties from different breeding institutes. The KASP markers in three chromosomes (3, 6 and 11) may have contributed to heterosis according to the heterozygous rate. For KASP marker analysis, three markers were vitally important for japonica hybrids.

Overall, we found that less KASP markers could be effective in identifying varieties and assisting rice breeding. The breeders could use the 48 KASP markers to genotype the varieties and add the data to the panel. According to the SNP combinations, the breeders could observe the SNP combination was the same with the panel or not.

Materials and Methods

Plant materials

The seeds of 46 and 518 accessions were supplied by Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, China. The name of all accessions was listed in the Additional file tables. Genomic DNA was extracted from 2-weeks leaf tissues of the rice accessions using the DNeasy Plant Mini Kit (QIAGEN, Hilden, Germany).

Primer Selection

The 378 KASP markers were selected from the LGC Genomics database. All primers including the name, position and sequence were listed in the Additional file 1: Table S1. After detection the panel with 378 KASP markers, we selected the makers with the detection ratio (> 0.8), MAF (> 0.2), PIC (> 0.3) and physical position. At last, we selected 48 markers that were almost evenly distributed on 12 chromosomes for analysis the 518 varieties. The location of markers was visualized using MG2C tools (Chao et al. 2021).

KASP Marker Assay

KASP amplifications and allelic discriminations were performed using a Nexar system (LGC Douglas Scientific, Alexandria, USA) in the Jiangsu Academy of Agricultural Sciences. KASP assays were performed using 0.07 µL of 2 × Master Mix and 2.5 µL of 2 × KASP assay mix (LGC Genomics, London, UK) with 2.43 ng genomic DNA in a final reaction volume of 5 µL in a 384-well Array Tape. NTCs (Non-template controls) were included in each run. KASP amplification was performed using the following thermal cycling profile: 94 °C, 15 min; The second step: 94 °C, 20 s, 61 °C, 1 min, 10 cycles in total; Step 3: 94 °C, 20 s, 55 °C, 1 min, 26 cycles in total 94 °C, 15 min; The second step: 94 °C, 20 s, 61 °C, 1 min, 10 cycles in total; Step 3: 94 °C, 20 s, 55 °C, 1 min, 26 cycles in total. The fluorescence measurement was taken for KASP genotyping after PCR amplification. Genotypes of each sample were called using Intellics software (LGC Douglas Scientific, Alexandria, USA). Markers showing clear allelic discrimination were regarded as polymorphic.

Population Structure Analysis

After integrating all the sites information, we convert the data form into HapMap format. The Admixture software was used for analyze the K. Neighbor-Joining method was used to build phylogenetic tree using Tassel 5 (Glaubitz et al. 2014). The iTOL website ( was for phylogenetic tree figure. The PCA was analyzed using the TASSEL 5, and the results were visualized using R (

PIC, MAF, Detection Rate, Heterozygous Rate and Identifying Rate Calculation

PIC_Calc software was used to calculate the PIC according to the following formula:

$$PIC = 1 - \sum\limits_{j = 1}^{n} {P_{ij}^{2} }$$

The number of detected SNPs and heterozygous sites in the population was observed using TASSEL5. MAF wea calculated using TASSEL5. The detection rate was calculated according to the following formula: Detection rate = detected site (the sites with well-defined genotypes)/all sites.

In hybrid, the heterozygosity of different individuals and sites was summarized using the Tassel 5 and calculated using the following formula: Heterozygous rate = heterozygous sites/all detected sites. The SNP combination was counted of each variety. Secondly, the varieties which shared with the same combination were counted. At last, the ratio of the varieties which has unique combination to the all varieties was the identifying rate.

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article and its additional files.



Kompetitive allele-specific PCR


Distinctness, uniformity, and stability


Single nucleotide polymorphism


Genotyping by sequencing


High resolution melting


Simple satellite repeat




Quantitative trait loci


Marker-assisted selection


Polymorphism information content


Minor allele frequency


Genome-wide association study


  • Addison CK, Angira B, Kongchum M, Harrell DL, Baisakh N, Linscombe SD, Famoso AN (2020) Characterization of haplotype diversity in the BADH2 aroma gene and development of a KASP SNP assay for predicting aroma in U.S. rice. Rice (N Y) 13:47

    Article  Google Scholar 

  • An H, Liu K, Wang B, Tian Y, Ge Y, Zhang Y, Tang W, Chen G, Yu J, Wu W, Liu X, Zhao Y, Li J, Zhang H, Yang B, Jiang X, Peng C, Zhou C, Zhu C, Wang C, Wan J (2019) Genome-wide association study identifies QTLs conferring salt tolerance in rice. Plant Breed 139:73–82

    Article  Google Scholar 

  • Aravind K, Banumathy S, Vanniarajan C, Arunachalam P, Ilamaran M, Kalpana K (2019) DUS characterization and genetic variability studies of rice mutants. Electron J Plant Breed 10:451

    Article  Google Scholar 

  • Bonow S, Von Pinho EVR, Vieira MGC, Vosman B (2009) Microsatellite markers in and around rice genes: applications in variety identification and DUS testing. Crop Sci 49:880–886

    Article  CAS  Google Scholar 

  • Castellana S, Ranzino L, Beritognolo I et al (2020) Genetic characterization and molecular fingerprint of traditional Umbrian tomato (Solanum lycopersicum L.) landraces through SSR markers and application for varietal identification. Genet Resour Crop Evol 67:1807–1820

    Article  CAS  Google Scholar 

  • Chao J, Li Z, Sun Y, Aluko OO, Wu X, Wang Q, Liu G (2021) MG2C: a user-friendly online tool for drawing genetic maps. Mol Horticult 1:1–4

    Google Scholar 

  • Chen W, Gao Y, Xie W et al (2014) Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism. Nat Genet 46:714–721

    Article  CAS  PubMed  Google Scholar 

  • Cheng L, Nam J, Chu SH, Rungnapa P, Min MH, Cao Y, Yoo JM, Kang JS, Kim KW, Park YJ (2019) Signatures of differential selection in chloroplast genome between japonica and indica. Rice (N Y) 12:65

    Article  Google Scholar 

  • Cheon K-S, Baek J, Cho Y-i, Jeong Y-M, Lee Y-Y, Oh J, Won YJ, Kang D-Y, Oh H, Kim SL, Choi I, Yoon IS, Kim K-H, Han J-H, Ji H (2018) Single nucleotide polymorphism (SNP) discovery and kompetitive allele-specific PCR (KASP) marker development with Korean Japonica rice varieties. Plant Breed Biotechnol 6:391–403

    Article  Google Scholar 

  • Crowell S, Korniliev P, Falcao A et al (2016) Genome-wide association and high-resolution phenotyping link Oryza sativa panicle traits to numerous trait-specific QTL clusters. Nat Commun 7:10527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gao LZ, Innan H (2008) Nonindependent domestication of the two rice subspecies, Oryza sativa ssp. indica and ssp. japonica, demonstrated by multilocus microsatellites. Genetics 179:965–976

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Garris AJ, Tai TH, Coburn J, Kresovich S, McCouch S (2005) Genetic structure and diversity in Oryza sativa L. Genetics 169:1631–1638

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES (2014) TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLOS ONE 9:e90346

    Article  PubMed  PubMed Central  Google Scholar 

  • Huang X, Han B (2014) Natural variations and genome-wide association studies in crop plants. Annu Rev Plant Biol 65:531–551

    Article  CAS  PubMed  Google Scholar 

  • Jiao Y, Wang Y, Xue D, Wang J, Yan M, Liu G, Dong G, Zeng D, Lu Z, Zhu X, Qian Q, Li J (2010) Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat Genet 42:541–544

    Article  CAS  PubMed  Google Scholar 

  • Kumar M, Rakesh Sharma V, Kumar V et al (2019) Genetic diversity and population structure analysis of Indian garlic (Allium sativum L.) collection using SSR markers. Physiol Mol Biol Plants 25:377–386

    Article  CAS  PubMed  Google Scholar 

  • Lin Q, Zhang Z, Wu F, Feng M, Sun Y, Chen W, Cheng Z, Zhang X, Ren Y, Lei C, Zhu S, Wang J, Zhao Z, Guo X, Wang H, Wan J (2020a) The APC/C(TE) E3 ubiquitin ligase complex mediates the antagonistic regulation of root growth and tillering by ABA and GA. Plant Cell 32:1973–1987

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lin T, Zhou C, Chen G, Yu J, Wu W, Ge Y, Liu X, Li J, Jiang X, Tang W, Tian Y, Zhao Z, Zhu C, Wang C, Wan J (2020b) Heterosis-associated genes confer high yield in super hybrid rice. TAG Theor Appl Genet 133:3287–3297

    Article  CAS  PubMed  Google Scholar 

  • Lou Q, Chen L, Mei H, Wei H, Feng F, Wang P, Xia H, Li T, Luo L (2015) Quantitative trait locus mapping of deep rooting by linkage and association analysis in rice. J Exp Bot 66:4749–4757

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lu Q, Niu X, Zhang M, Wang C, Xu Q, Feng Y, Yang Y, Wang S, Yuan X, Yu H, Wang Y, Chen X, Liang X, Wei X (2017) Genome-wide association study of seed dormancy and the genomic consequences of improvement footprints in rice (Oryza sativa L.). Front Plant Sci 8:2213

    Article  PubMed  Google Scholar 

  • Mao H, Sun S, Yao J, Wang C, Yu S, Xu C, Li X, Zhang Q (2010) Linking differential domain functions of the GS3 protein to natural variation of grain size in rice. Proc Natl Acad Sci USA 107:19579–19584

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Peng S, Huang J, Cassman KG, Laza RC, Visperas RM, Khush GS (2010) The importance of maintenance breeding: a case study of the first miracle rice variety-IR8. Field Crop Res 119:342–347

    Article  Google Scholar 

  • Pereira-Dias L, Vilanova S, Fita A et al (2019) Genetic diversity, population structure, and relationships in a collection of pepper (Capsicum spp) landraces from the Spanish centre of diversity revealed by genotyping-by-sequencing (GBS). Horticult Res 6:54

    Article  Google Scholar 

  • Pourabed E, Jazayeri Noushabadi MR, Jamali SH, Moheb Alipour N, Zareyan A, Sadeghi L (2015) Identification and DUS testing of rice varieties through microsatellite markers. Int J Plant Genom 2015:965073

    Google Scholar 

  • Rahman MM, Rasaul MG, Hossain MA et al (2012) Molecular characterization and genetic diversity analysis of rice (Oryza sativa L.) using SSR markers. J Crop Improv 26:244–257

    Article  CAS  Google Scholar 

  • Rasheed A, Hao Y, Xia X, Khan A, Xu Y, Varshney RK, He Z (2017) Crop breeding chips and genotyping platforms: progress, challenges, and perspectives. Mol Plant 10:1047–1064

    Article  CAS  PubMed  Google Scholar 

  • Semagn K, Babu R, Hearne S, Olsen M (2014) Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Mol Breed 33:1–14

    Article  CAS  Google Scholar 

  • Shen Y, Wang J, Shaw RK, Yu H, Sheng X, Zhao Z, Li S, Gu H (2021) Development of GBTS and KASP panels for genetic diversity, population structure, and fingerprinting of a large collection of broccoli (Brassica oleracea L. var. italica) in China. Front Plant Sci 12:655254

    Article  PubMed  PubMed Central  Google Scholar 

  • Shikari AB, Najeeb S, Khan G et al (2021) KASPTM based markers reveal a population sub-structure in temperate rice (Oryza sativa L.) germplasm and local landraces grown in the Kashmir valley, north-western Himalayas. Genet Resour Crop Evol 68:821–834

    Article  CAS  Google Scholar 

  • Steele KA, Quinton-Tulloch MJ, Amgai RB, Dhakal R, Khatiwada SP, Vyas D, Heine M, Witcombe JR (2018) Accelerating public sector rice breeding with high-density KASP markers derived from whole genome sequencing of indica rice. Mol Breed New Strateg Plant Improv 38:38

    Google Scholar 

  • Steele K, Tulloch MQ, Burns M, Nader W (2020) Developing KASP markers for identification of basmati rice varieties. Food Anal Methods 14:663–673

    Article  Google Scholar 

  • Swisher Grimm KD, Porter LD (2020) Development and validation of KASP markers for the identification of Pea seedborne mosaic virus pathotype P1 resistance in Pisum sativum. Plant Dis 104:1824–1830

    Article  PubMed  Google Scholar 

  • Tamaki S, Matsuo S, Wong HL, Yokoi S, Shimamoto K (2007) Hd3a protein is a mobile flowering signal in rice. Science 316:1033–1036

    Article  CAS  PubMed  Google Scholar 

  • Tang W, Wu T, Ye J, Sun J, Jiang Y, Yu J, Tang J, Chen G, Wang C, Wan J (2016) SNP-based analysis of genetic diversity reveals important alleles associated with seed size in rice. BMC Plant Biol 16:1

    Google Scholar 

  • Tang W, Ye J, Yao X, Zhao P, Xuan W, Tian Y, Zhang Y, Xu S, An H, Chen G, Yu J, Wu W, Ge Y, Liu X, Li J, Zhang H, Zhao Y, Yang B, Jiang X, Peng C, Zhou C, Terzaghi W, Wang C, Wan J (2019) Genome-wide associated study identifies NAC42-activated nitrate transporter conferring high nitrogen use efficiency in rice. Nat Commun 10:5279

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang C, Ye J, Tang W, Liu Z, Zhu C, Wang M, Wan J (2013) Loop nucleotide polymorphism in a putative miRNA precursor associated with seed length in rice (Oryza sativa L.). Int J Biol Sci 9:578–586

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang WS, Mauleon R, Hu ZQ, Chebotarov D, Tai SS, Wu ZC, Li M, Zheng TQ, Fuentes RR, Zhang F, Mansueto L, Copetti D, Sanciangco M, Palis KC, Xu JL, Sun C, Fu BY, Zhang HL, Gao YM, Zhao XQ, Shen F, Cui X, Yu H, Li ZC, Chen ML, Detras J, Zhou YL, Zhang XY, Zhao Y, Kudrna D, Wang CC, Li R, Jia B, Lu JY, He XC, Dong ZT, Xu JB, Li YH, Wang M, Shi JX, Li J, Zhang DB, Lee S, Hu WS, Poliakov A, Dubchak I, Ulat VJ, Borja FN, Mendoza JR, Ali J, Li J, Gao Q, Niu YC, Yue Z, Naredo MEB, Talag J, Wang XQ, Li JJ, Fang XD, Yin Y, Glaszmann JC, Zhang JW, Li JY, Hamilton RS, Wing RA, Ruan J, Zhang GY, Wei CC, Alexandrov N, McNally KL, Li ZK, Leung H (2018) Genomic variation in 3010 diverse accessions of Asian cultivated rice. Nature 557:43–49

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang Y, Lv H, Xiang X et al (2021) Construction of a SNP fingerprinting database and population genetic analysis of cigar tobacco Germplasm resources in China. Front Plant Sci 12:618133

    Article  PubMed  PubMed Central  Google Scholar 

  • Wei X, Xu J, Guo H, Jiang L, Chen S, Yu C, Zhou Z, Hu P, Zhai H, Wan J (2010) DTH8 suppresses flowering in rice, influencing plant height and yield potential simultaneously. Plant Physiol 153:1747–1758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Q, Chen H, Wang CH, Yu HY, Yuan XP, Wang YP, Feng Y, Tang SX, Wei XH (2012) Genetic diversity and structure of new inbred rice cultivars in China. J Integr Agric 11:1567–1573

    Article  CAS  Google Scholar 

  • Xu X, Ren Y, Wang C, Zhang H, Wang F, Chen J, Liu X, Zheng T, Cai M, Zeng Z, Zhou L, Zhu S, Tang W, Wang J, Guo X, Jiang L, Chen S, Wan J (2019) OsVIN2 encodes a vacuolar acid invertase that affects grain size by altering sugar metabolism in rice. Plant Cell Rep 38:1273–1290

    Article  CAS  PubMed  Google Scholar 

  • Yang G, Chen S, Chen L, Gao W, Huang Y, Huang C, Zhou D, Wang J, Liu Y, Huang M, Xiao W, Wang H, Guo T, Chen Z (2019) Development and utilization of functional KASP markers to improve rice eating and cooking quality through MAS breeding. Euphytica 215:1–12

    Article  CAS  Google Scholar 

  • Yang G, Chen S, Chen L, Sun K, Huang C, Zhou D, Huang Y, Wang J, Liu Y, Wang H, Chen Z, Guo T (2019b) Development of a core SNP arrays based on the KASP method for molecular breeding of rice. Rice (N Y) 12:21

    Article  Google Scholar 

  • Yano K, Yamamoto E, Aya K et al (2016) Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet 48(8):927–934

    Article  CAS  PubMed  Google Scholar 

Download references


The authors will thank Professor Han Zhao (Jiangsu Academy of Agricultural Sciences) for his great help in the experimental design and manuscript preparation.


National Natural Science Foundation of China (31971871), Natural Science Foundation of Jiangsu Province, China (BK20210153), Jiangsu Independent Innovation Project, China (CX(20)3142), Open Project Fund of Jiangsu Key Laboratory of Crop Genetics and Physiology (YCSL202112), and Hunan Provincial Natural Science Foundation of China (2019JJ50714) supported this study. The funding agencies had no role in the study design, data collection and analysis, decision to publish or manuscript preparation.

Author information

Authors and Affiliations



YZ, XF and HL directed the project. WT, JL and YW performed the experiments. WT analysis the data. HA, HC, GP, SZ, BG and KY participated in the experiments. YZ revised the manuscript. WT wrote the manuscript and finalized the manuscript. All the coauthors approved the manuscripts.

Corresponding authors

Correspondence to Huayong Li, Xianwen Fang or Yunhui Zhang.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1

The list of 378 KASP markers.

Additional file 2: Table S2

The list of 46 varieties.

Additional file 3: Table S3

The 331 KASP genotype of 46 varieties.

Additional file 4: Table S4

The list of 50 KASP markers.

Additional file 5: Table S5

The result of CV error produced by Admixture.

Additional file 6: Table S6

The result of PCA of 46 varieties using 331 markers and 48 markers.

Additional file 7: Table S7

The list of 518 varieties.

Additional file 8: Table S8

The 48 KASP genotype of 325 conventional varieties.

Additional file 9: Table S9

The 48 KASP genotype of 193 hybrid varieties.

Additional file 10: Table S10

The list of conventional japonica after deleting the possible redundant sib lines from the same breeding institutes.

Additional file 11: Table S11

The result of CV error produced by Admixture.

Additional file 12: Table S12

The result of PCA of 325 conventional varieties using 48 KASP markers.

Additional file 13: Table S13

The reference allele frequency.

Additional file 14: Table S14

The result of Loglikelihood (LK) produced by Admixture.

Additional file 15: Table S15

The result of PCA of 193 hybrid varieties using 48 KASP markers.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tang, W., Lin, J., Wang, Y. et al. Selection and Validation of 48 KASP Markers for Variety Identification and Breeding Guidance in Conventional and Hybrid Rice (Oryza sativa L.). Rice 15, 48 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • KASP
  • Rice
  • Variety identification
  • Marker-assisted breeding