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Research Article

Evaluation of SSR and SNP Markers for Molecular Breeding in Rice

Zennia Jean Gonzaga1, Kashif Aslam1,2, Endang M. Septiningsih1, Bertrand C.Y. Collard1,*
Plant Breeding and Biotechnology 2015;3(2):139-152.
Published online: June 30, 2015

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*Corresponding author: Bertrand C.Y. Collard, b.collard@irri.org, bcycollard@hotmail.com, Tel: +63-2-580-5600-2478
• Received: May 31, 2015   • Revised: June 17, 2015   • Accepted: June 19, 2015

Copyright © 2015 The Korean Society of Breeding Science

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  • Simple sequence repeats (SSRs) have been the marker of choice for rice molecular breeding due to the high level of polymorphism, technical simplicity and low cost. Recent advances in rice genomics have led to the discovery of abundant single nucleotide polymorphism (SNPs) which have enormous potential for rice molecular breeding. To assess both marker systems for molecular breeding in rice, SSR and SNP markers were evaluated on a set of 23 genotypes representing indica germplasm for their usefulness in molecular research and breeding program. Seven hundred SSR and sequence tagged sites (STS) markers and 384 SNPs were screened for polymorphism. Highly polymorphic markers based on polymorphic information content (PIC) values were identified, which will be useful for molecular breeding. Data was used to identify an “indica genotyping set” based on high level of polymorphism, chromosome position and marker quality which will provide kits of markers for marker assisted selection (MAS). Genetic diversity analysis using SSR data was more consistent with pedigrees compared to analysis with SNP data indicating that more than 384 SNPs are required when elite indica breeding material is used. The results also indicated that there were polymorphic “blind spots” for the fixed SNP set suggesting that SSRs could still be used to complement fixed-SNP genotyping platforms for some molecular breeding applications.
  • Key words: Molecular breeding, Genotyping set, PIC, SSR, STS, SNP
Rice (Oryza sativa L.) is the most important crop in Asia. Many constraints to rice production may be overcome by developing new rice varieties (e.g. breeding for biotic or abiotic stress tolerance). Molecular breeding using DNA markers can greatly increase the precision and efficiency of rice breeding and therefore may provide useful tools for breeders (Jena and Mackill 2008). Marker assisted selection (MAS) is advantageous over conventional phenotypic selection for the following reasons: it may be simpler than phenotypic screening which can save time, resources and effort; selection can be carried out at the seedling stage; and single plants can be accurately selected (Collard and Mackill 2008; Ribaut and Hoisington 1998). Marker-assisted backcrossing (MABC), a specific MAS scheme in plant molecular breeding, has been used to upgrade new varieties for many traits, especially for abiotic stress tolerance in rice (Gregorio et al. 2013; Mackill et al. 2012).
Of the many types of DNA markers, microsatellites or simple sequence repeats (SSRs) are widely used due to their high polymorphism and technically simple method of detection (Wu and Tanksley 1993; Panaud et al. 1996; Akagi et al. 1997; Chen et al. 1997; McCouch et al. 2002). SSRs, like other PCR-based markers, can be run with common lab equipment and are cost effective (for low throughput). In rice, they have been extremely well characterized largely due to the completion of the rice genome reference sequence from Nipponbare (MSU7) and the availability of numerous genomics resources (Schatz et al. 2014). Hence, SSRs have been the marker of choice for rice molecular breeding. The disadvantages of SSRs include the requirement of polyacrylamide gel electrophoresis, limited abundance when saturating genomic regions, constraints in multiplexing, limitations in universal data storage, and automation. Moreover, SSRs may differ in robustness, quality of the amplification products, and amplification of single or multiple loci which suggests that not all SSRs are equal. They may have low information content in the gene of interest (Macaulay et al. 2001).
In recent years, single nucleotide polymorphisms (SNPs) have become widely used DNA markers because they are more efficient, stable and cost effective (for high throughput) (Rafalski 2002; Duran et al. 2009; Edwards and Batley 2010). Moreover, automated, high-throughput processing of large numbers of samples is possible. SNPs are the most abundant variations in the genome that are ideal for high-resolution genotyping, hence, they are appropriate for association mapping, genetic diversity analysis, linkage mapping and MAS (McCouch et al. 2010; Tung et al. 2010). With the development of genotyping by sequencing (GBS), SNPs are expected to remain as the marker of choice in the 21st century (Thomson 2014).
Recently in rice, Narshimulu et al. (2011) provided “ready-to-use” SSR markers for background selection for marker-assisted breeding in rice. A total of 840 hypervariable microsatellite markers (hvRMs) evenly distributed in the genome were selected from 18,828 Class I SSRs (hypervariable markers that are more polymorphic than Class II due to longer repeats of ≥ 20 nucleotides in length as reported by Cho et al. 2000 and Temnykh et al. 2000). A selection of 36 hvRMs were selected from the 840 markers and screened on 24 diverse rice cultivars to estimate the polymorphic information content (PIC). PIC is a measure of polymorphism for a marker locus (Shete et al. 2000). It considers both the number of alleles that are expressed and the relative frequencies of those alleles in order to provide an estimate of the discriminatory power of a locus. The values ranged from 0 (monomorphic) to 1 (highly discriminative) (Anderson et al. 1993). A “MAS kit” of molecular markers was developed using the hvRMs that are tightly-linked to important agronomic traits to effectively speed up the process of crop improvement. Singh et al. (2009) evaluated the relationship between SSR length and level of polymorphism in eight diverse rice genotypes using 201 random SSR loci to find most variable SSR loci for agarose gel electrophoresis.
Previous studies in assessing genetic relatedness in maize association mapping panels confirmed that SSR markers with moderate density were more informative than SNPs (Yang et al. 2011). Although, there was a high coefficient of correlation between the genetic similarity of SSR and SNP genotyping data, average PIC values of SSRs in barley were higher compared to SNP markers (Varshney et al. 2008; 2010). High throughput SNP genotyping platforms are becoming more accessible and hence replacing SSR markers in molecular breeding research. Thomson et al. (2012) designed seven GoldenGate VeraCode oligo pool assay (OPA) sets (384 SNPs) for rice using the Illumina BeadXpress platform. The results indicated that 384-plex SNP genotyping using the BeadXpress platform was effective for diversity analysis, QTL mapping, MABC and developing specialized genetic (Thomson et al. 2012).
The aim of this study was to evaluate a large number of SSRs and SNPs using an important set of indica varieties or important donor parents which are highly relevant to current breeding activities at the International Rice Research Institute (IRRI). Our main objective was to identify highly polymorphic SSRs and SNPs that could be used as “genotyping sets” in rice for various molecular breeding applications, as have been reported in other crops such as barley (Macaulay et al. 2001) or cotton (Lacape et al. 2007). In many previous studies, a diverse set of genotypes is often used including indica and japonica subspecies, which may give an exaggerated indication of the potential of markers for breeding applications. Hence our focus was on indica germplasm, which is the predominant sub-species in South and Southeast Asia. Particular emphasis was given to breeding for submergence tolerance by focusing on the SUB1 locus on chromosome 9 (Xu and Mackill 1996). SUB1 is a major QTL controlling submergence tolerance and has been used to develop ‘upgraded’ rice mega-varieties (Iftekharuddaula et al. 2011; Mackill et al. 2012; Neeraja et al. 2007; Septiningsih et al. 2009; 2013; 2015).
Plant material
A total of 23 genotypes including important irrigated and rainfed indica varieties, donor parents and elite breeding lines were assembled, with an emphasis on breeding for flood tolerance at IRRI. Many SUB1 donors (i.e. released SUB1 varieties) and selected recipient parents were specifically included in order to identify polymorphic markers for current research and molecular breeding activities. Parents used in mapping populations for submergence and stagnant flooding tolerance were also included (Table 1). Additional information about these donor parents was described in Collard et al. (2013) and Mackill et al. (1993).
DNA Extraction
DNA was isolated using modified mini prep CTAB method (Zheng et al. 1995). Young leaves of 2 week old plants were sampled and ground using Genogrinder (SPEX SamplePrep, Metuchen, NJ, USA) equipment and liquid nitrogen. Addition of 2% CTAB buffer (100 mM Tris-HCl pH8, 50 mM EDTA pH=8, 500 mM NaCl, 1.25% SDS, 0.38% sodium biosulfate) and incubation in water bath at 65°C for 1 hour followed. Subsequently, the mixture was shaken sporadically, followed by the treatment with chloroform:isoamyl alcohol mixture (24:1) to remove protein. Afterwards, supernatant was precipitated in ice cold isopropanol for 1 hour and pellets were washed with 70% ethanol. RNA was removed using RNase and pellets were dissolved in TE buffer. Finally, the quality of DNA was verified using 1% agarose gel stained with Sybr® safe (Invitrogen, Catalog no. S33102) and DNA was quantified using Nano Drop Spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The DNA samples were normalized to 20 ng/uL and 50 ng/uL for SSR and SNP genotyping, respectively.
SSR genotyping
Seven hundred SSR and STS markers obtained from Genetics and Genomics Laboratory at IRRI were selected across all 12 rice chromosomes. Most SSRs were selected based on previous research identifying these SSRs to have high PIC values, whereas others were based on the availability in the laboratory. Other SSR or STS markers were selected based on the physical position near SUB1 (e.g. ART5, RM3609). PCR amplification was performed in 7 uL volume containing of 20 ng/uL DNA (2uL) template, 10× PCR buffer (0.7 uL), 1 mM dNTPs (0.7 uL), 1.5 mM MgCl2 (0.6 uL), 5 uM of each forward and reverse primer (0.4 uL each) and 1 unit of homemade Taq DNA polymerase (0.9 uL) on thermal cycler (G-Storm, United Kingdom). The temperature profile used for amplification includes: 3 min of initial denaturation at 94°C followed by 35 cycles of denaturation at 94°C for 30 sec, annealing at 54–55°C for 30 sec and extension at 72°C for 30 sec, and the final extension at 72°C for 2 min. A no DNA template control was used to determine if there was contamination within the process. The samples were then mixed with 2 uL of bromophenol blue gel loading dye and 4 uL PCR products were separated using 8% polyacrylamide gel along with a 1 kb ladder (Invitrogen, Cat No.10787026) for 2 hours. Visualization was done using Alpha Imager 1220 (Alpha Innotech, CA, USA) under ultraviolet (UV) light after staining with Sybr® safe (Invitrogen, Catalog no. S33102). The amplicons were scored manually in base pairs using the 1 kb ladder. The protocol was optimized for SSRs that failed once and repeated for 2 or 3 times. After the third attempt, SSRs that still failed to amplify were not used again. Genetic and physical map positions were determined using the Gramene website (www.gramene.org) and from the Nipponbare rice genome reference (IRGSP, 2005). Marker ‘quality’ was assessed and defined as the clarity and reproducibility of the marker.
SNP genotyping
A 384-plex SNP genotyping assay on the BeadXpress (Illumina®) platform was used. Normalized DNA samples were genotyped using “GoldenGate Genotyping Assay” (VeraCode Manual Protoco; Illumina Part 311275211), following the manufacturer’s instructions. An Allegra 25R (Beckman Coulter, Brea, CA, USA) and GSI thermal cycler (G-storm, Surrey, UK) were used for the plate centrifuge steps and for PCR amplification respectively. RiceOPA2.1 was used for genotyping the 23 rice varieties in which the Illumina OPA ID was GS0011861-OPAa. This OPA is appropriate for indica genotypes and informative for indica/indica populations (Thomson et al. 2012).
Data analysis
The size of SSR marker alleles were estimated and scored based on the 1 kb ladder (10787-018, Life Technologies). SSR data in base pairs was directly inputted in the PowerMarker software (v3.25). The SNP data was analyzed using the Genotyping module (v1.6.3) of the Illumina GenomeStudio (v2010.1) software. For a more accurate allele calling, ALCHEMY program was used because it was created for highly inbred samples (Thomson et al. 2012). The ALCHEMY-Illumina plug-in v1.0 (http://alchemy.sourceforge.net/) was installed into GenomeStudio. The Genome Studio “Reports Wizard” was used to output the data into ALCHEMY format resulting to two data, one with the project name containing sample intensity data and the second as a map file with the SNP map information. The outputs were used as input files for ALCHEMY and all succeeding output re-shaping and manipulations was done in the GALAXY-ALCHEMY bioinformatics workflow environment (http://main.g2.bx.psu.edu/). Allele calls were used for the genetic diversity analysis using Nei1983 genetic distance measure with a neighbor-joining tree with PowerMarker v3.25 software (Liu and Muse 2005; http://statgen.ncsu.edu/powermarker/). Bootstrap NJ phylogenetic trees were produced using TreeView (http://taxonomy.zoology.gla.ac.uk/rod/treeview.html). PIC values were calculated using PowerMarker based on the formula reported by Botstein et al. (1980). The PIC values of 23 genotypes (including FR13A and Super Basmati) were compared with 21 genotypes (excluding FR13A and Super Basmati), however PIC values based on only the 21 indica genotypes were shown in Tables 2 and 3. Polymorphism survey data can be obtained from the corresponding author upon request.
SSR genotyping
Four hundred ninety out of 722 SSRs tested (68%) were polymorphic across the 23 genotypes used. In terms of polymorphism, chromosome 5 had the highest percentage of 80% and chromosome 11 had the lowest percentage of 52.8%. However, chromosome 11 had the highest maximum PIC value of 0.83 and chromosome 12 had the lowest value of 0.563. The average PIC values ranged from 0.331 to 0.461 (Table 2). SSR genotyping set was created based on the reliability of the marker measured visually through the band quality, polymorphism using the generated PIC values and marker position with at least 2 Mb distance between each markers (Table 3). Chromosome 9, the location of SUB1 was examined for tightly linked SSR markers between 5.92–8 Mb. SUB1 marker alleles were recorded for each marker across the 23 genotypes (Table 4).
SNP genotyping
Three hundred sixty five out of 384 SNPs screened (95%) were polymorphic across 23 genotypes which was 27% higher than SSRs. Chromosomes 5, 8 and 9 had 100% polymorphism while chromosome 3 had the lowest polymorphism of 86.4% which was still higher by 33.6% of the lowest value in SSR. However, the maximum PIC value of SSRs (0.563–0.830) was higher compared with SNPs (0.490–0.580) which is due to the multi-allelic nature of SSR polymorphism. The average PIC values of SNP markers range from 0.289 to 0.382 (Table 5). SNPs with high PIC values are indicated in Table 6. There were “blind spots” in SNP coverage where the PIC values of the SNP markers were very low especially on chromosomes 5, 7, 9, 10 and 11 (Fig. 1 and Supplementary Fig. 1).
Molecular analysis
The radial trees generated from SSR and SNP data showed were generally consistent with known pedigrees. Both trees indicated that FR13A and Super Basmati were diverse from the rest of the indica accessions. However, there were differences in the clustering of the 21 indica lines generated from the SSR and SNP analysis. The tree produced from SSR data was more consistent with pedigrees compared to the tree produced from SNP data.
In this study, over 700 SSR markers were selected based on the literature searching for high PIC values and the availability of markers in our labs, and the availability of the 384-plex SNP BeadXpress platform. The plant material used was highly relevant to current breeding activities in which 21 of the 23 genotypes were indica. This was done to avoid the exaggerated PIC values of markers and to provide a more accurate estimate of the usefulness of a marker in actual breeding programs.
The 12 chromosomes in rice have different sizes (Chen et al. 2002), hence different numbers of markers were screened for different chromosomes. Table 2 shows the uneven distribution of polymorphism of SSR markers between chromosomes. Chromosome 5 was the most polymorphic (80%) and chromosome 11 is the least polymorphic (53%). SSRs were identified based on quality of amplification and PIC values, which can be used for MABC, marker-assisted pyramiding and other applications. Specifically for MABC, it is extremely useful to have a set of ‘ready to use’ highly polymorphic SSRs which we refer to as the indica genotyping set (Table 3). The markers comprising the genotyping set were chosen based on the marker quality, polymorphism and position. Some chromosomes had fewer markers due to lower polymorphism levels in the genotypes that we used. For some molecular breeding applications such as MABC, it is important to have evenly spaced markers with a window of approximately 5 Mb for background selection. Also, 5–10 markers per chromosome are generally sufficient to have an efficient recovery of the recipient parent during background selection or to construct a framework map for QTL analysis.
We emphasized the SUB1 region in this study; hence more markers were screened within and near this locus on chromosome 9. Table 4 shows the markers tightly-linked to SUB1 and compares the allele sizes for comparison between SUB1 and non-SUB1 genotypes. Markers with the highest PIC values will be useful to select markers for tracking SUB1 in indica breeding populations. Polymorphism information is also currently being used to introgress SUB1 into new varieties (Collard et al. 2013; Gregorio et al. 2013).
SNP markers have the potential for providing the highest map resolution (Nasu et al. 2002). SNPs are rapidly replacing SSRs in plant breeding and genetics research because of their abundance and many SNP platforms are high throughput (i.e. many samples per assay) which increases efficiency and cost effectiveness (Rafalski 2002; Duran et al. 2009; Edwards and Batley 2010). An earlier SNP platform assayed 1536 markers but a recent study compared the cost effectiveness of using 96 or 384 SNPs subsets compared to the original one for diversity studies. Results indicated that the 384 SNP markers gave the optimal balance between power and economy for germplasm characterization in barley (Moragues et al. 2010). SNP genotyping (384-plex) using the BeadXpress was used in this experiment due to lower costs per sample (Lin et al. 2009). Table 6 shows the most polymorphic SNP markers based on the PIC values. These SNP markers could also be used in lower-plex SNP genotyping platforms (e.g. 24 or 96 SNPs) such as the Fluidigm EP1™ System (Thomson 2014).
SNP platforms such as the BeadXpress platform usually contain a fixed set of SNP markers; hence there may be a need for complementation with other marker systems. Our results indicate that SSR markers can be used to “fill in the gaps” and complement SNP marker genotyping for some molecular breeding applications. Additionally, as shown in Fig. 1 and Supplementary Fig. 1, the 384-plex SNP set has “blind spots” in which markers have very low PIC values; SSRs can specifically be used in these regions for QTL mapping or background selection. For example, on chromosomes 5, 7, 9, 10 and 11, where the SSR markers had higher PIC values compared to SNPs (Fig. 1).
Genetic diversity analysis was also conducted to compare two marker systems. Figures 2 and 3 show radial trees using SSR and SNP markers, respectively. From this analysis, Super Basmati and FR13A were distant to the rest of the lines. However, based on pedigree information, the tree formed using SSR data was more consistent with pedigrees as shown in Table 1. This was probably the result of more SSRs being tested compared to SNPs, and suggests that more markers are needed for genetic diversity analysis to complement with the fixed 384 SNPs when elite indica breeding germplasm is used. Breeding material is more genetically similar compared to diversity panels. The requirement to use more SNP markers compared to SSRs for genetic diversity analysis has been previously reported in maize (Hamblin et al. 2007; Yang et al. 2011)
In conclusion, SSRs and SNPs are complementary marker systems that can be used for molecular breeding in rice. SSRs are needed to “fill in the gaps” for fixed SNP arrays like BeadXpress platform, containing the same set of SNP markers for a more efficient and effective process. It is hoped that the genotyping sets identified in this paper will be useful for other rice researchers to efficiently identify polymorphic SSRs or SNPs especially for activities involving indica germplasm.
PBB-03-139-suppl.pdf
The authors gratefully acknowledge funding from the Bill and Melinda Gates Foundation (BMGF) for the Stress Tolerant Rice for Africa and South Asia (STRASA) program. We also thank Dr. Tobias Kretzschmar and Dr. J. Damien Platten (IRRI) for critically reviewing the manuscript.
Fig. 1
Graphs of PIC values (y axis) plotted for SSR and SNP markers based on chromosome position (x axis).
pbb-03-139f1.jpg
Fig. 2
Radial tree using unrooted neighbor-joining method of SSR data using Nei’s similarity coefficient.
pbb-03-139f2.jpg
Fig. 3
Radial tree view of unrooted neighbor-joining tree of SNP data using Nei’s similarity coefficient.
pbb-03-139f3.jpg
Table 1
Rice genotypes used for marker evaluation.
Table 1
Name Alias Parentage Designation GID Information
Swarna-Sub1 Swarna*4/IR49830-7-1-2-3 IR05F102 1847271 New variety with submergence tolerance in India
Samba Mahsuri-Sub1 SAMBHA MAHSURI*3/IR 49830-7-1-2-3 IR07F101 2159598 New variety with submergence tolerance in India
BR11-Sub1 BRRI dhan 52 (Bangladesh) BR 11*3/IR 40931-33-1-3-2 IR07F290 2295328 New variety with submergence tolerance in Bangladesh
CR1009-Sub1 CR 1009*3/IR 49830-7-1-2-3 IR07F291 2403712 New variety with submergence tolerance in India
IR09F434 IRRI 123*2/IRRI 149 IR09F434 2847875 Sub1 near isogenic line (NIL) in popular Philippine variety PSBRc82
Ciherang-Sub1 Inpari 30 (Indonesia) CIHERANG*2/IRRI 149 IR09F436 2847870 New variety with submergence tolerance in Indonesia
PSBRc18-Sub1 PSB RC 18 (IR 51672-62-2-1-1-2-3)*2/IRRI 149 IR09F437 2853229 Sub1 near isogenic line (NIL) in popular Philippine variety awaiting to be released
IR64-Sub1 IRRI 149 or NSICRc194 or “Submarino” (Philippines) IR 40931-33-1-3-2/3*IR 64 IR07F102 2159583 New variety with submergence tolerance in the Philippines
Sabitri IR 1561-228-1/IR 1737//CR 94-13 IRTP 8487 2268099 Popular variety in Nepal
IR6 SIAM 29 (ACC 42)/DEE GEO WOO GEN IRTP 25398 2440173 Popular variety in Pakistan
Super basmati BAS 320/IR 661 IRTP 20918 2274228 Popular basmati variety in Pakistan
IR09F185 BR 11/IR 49830-7-1-2-3//IR04N106 IR 85288-SUB 38-1-1 2707581 Stagnant flooding donor. Parent of RIL population
IR67440-NDR-5-1-1-1-1 CNM 539/IR 53479-B-45-3-2-3 84299 Stagnant flooding donor. Parent of RIL population
FR13A DHALPUTTIA 32293 Donor of Sub1 and highly flood tolerant landrace
IR40931 BKNFR 76106-16-0-1/IR 19661-131-1-2 IR40931-33-1-3-2 71615 Sub1 donor parent.
IR49830 “Popoul” (Myanmar) IR 4568-86-1-3-2/IR 26702-111-1//IR 20992-7-2-2-2-2-3/IR 21567-9-2-2-2-1 IR49830-7-1-2-3 88474 Sub1 and stagnant flooding donor parent/variety
IR42 IR 1561-228-1-2/IR 1737//CR 94-13 IR 2071-586-5-6 13988 IRRI variety released for irrigated and rainfed areas. Susceptible check for submergence tolerance screening.
IRBB66 IRBB 7/IR BB 60 (IR 72920-1-44-4) 1847199 Widely-used bacterial blight gene donor (Xa4, xa5, xa7, xa13 and Xa21) in IR24 background
OR 142-99 Santepheap 3 (Cambodia) PANKAIJ/SIGADIS IRTP 13636 415260 Indian breeding line released as a variety in Cambodia
IRRI 119 PSBRc68 or ‘Sacobia’ (Philippines); Shwe Pyi Tan (Myanmar) IR 43581-57-3-3-6/IR 26940-20-3-3-3-1//KHAO DAWK MALI 105 PSB RC 68 2266161 IRRI variety released with Sub1 for rainfed areas in the Philippines with stagnant flooding tolerance
IRRI 154 NSICRc222 or ‘Tubigan 18’ (Philippines) IR 73012-137-2-2-2/PSB RC 10 (IR 50404-57-2-2-3) IR04A412 1253989 IRRI variety released for irrigated and favourable rainfed areas in the Philippines
FL478 IR 66946-3R-178-1-1 IR 29/POKKALI B IR 66946-3R-178-1-1 1192884 Widely used salinity tolerant donor line
IRRI 148 NSICRc192 or ‘Sahod Ulan 1’ (Philippines) IR 55419-4*2/WAY RAREM IR74371-54-1-1 1161411 Drought released in tolerant the Philippines IRRI variety
Table 2
Polymorphic SSR markers and PIC values per chromosome.
Table 2
Chr No. SSRs % monomorphic % polymorphic PIC Min PIC Max PIC Ave
1 107 35.2 64.8 0.087 0.720 0.338
2 64 25.4 74.6 0.087 0.691 0.333
3 60 27.1 72.9 0.087 0.648 0.331
4 61 35.0 65.0 0.087 0.748 0.332
5 56 20.0 80.0 0.087 0.735 0.340
6 53 24.0 76.0 0.087 0.791 0.386
7 38 26.3 73.7 0.087 0.813 0.402
8 52 25.0 75.0 0.087 0.711 0.396
9 102 24.7 75.3 0.087 0.772 0.400
10 33 37.5 62.5 0.087 0.703 0.414
11 42 47.2 52.8 0.087 0.830 0.461
12 54 37.3 62.7 0.087 0.563 0.352
Table 3
Indica SSR genotyping set based on reliability, polymorphism and marker position.
Table 3
SSR Name Chr. Physical (Mb) PIC Values
RM495 1 0.2 0.4393
RM1 1 4.6 0.2608
RM243 1 7.9 0.3975
RM582 1 9.1 0.4470
RM449 1 15.1 0.1575
RM24 1 18.9 0.5334
RM237 1 26.8 0.4345
RM84 1 29.7 0.3554
RM486 1 34.9 0.3249
RM14 1 41.3 0.5885
RM154 2 1 0.6913
RM279 2 2.8 0.5468
RM71 2 8.7 0.5623
RM324 2 11.3 0.4394
RM300 2 13.1 0.4925
RM341 2 19.3 0.5192
RM263 2 25.8 0.5109
RM1342 2 28.1 0.5396
RM6 2 29.5 0.3457
RM208 2 35.1 0.5743
RM231 3 2.4 0.5736
RM218 3 8.4 0.6479
RM3297 3 13.2 0.3131
RM15187 3 16.6 0.4551
RM411 3 21.4 0.3604
RM426 3 27.5 0.3850
RM186 3 28.8 0.5736
RM571 3 33.1 0.2970
RM570 3 35.5 0.4647
RM85 3 36.3 0.5038
RM335 4 0.6 0.6550
RM261 4 6.5 0.4065
RM307 4 13.1 0.5623
RM16945 4 20.5 0.2373
RM3839 4 23.9 0.2970
RM252 4 25.1 0.5509
RM3820 4 27.6 0.4898
RM303 4 28.5 0.2922
RM5473 4 31.4 0.7481
RM280 4 34.9 0.4638
RM13 5 2 0.5396
RM592 5 2.7 0.7349
RM17954 5 3.6 0.5425
RM18188 5 9.1 0.6657
RM1115 5 14.7 0.5109
RM5454 5 17.8 0.6221
RM163 5 19.1 0.5509
RM3575 5 21.3 0.4160
RM274 5 26.8 0.0866
RM31 5 28.6 0.4551
RM435 6 0.5 0.3967
RM586 6 1.4 0.6272
RM204 6 3.1 0.7913
RM3408 6 4.5 0.3249
RM276 6 6.2 0.3604
RM549 6 6.9 0.3604
RM136 6 8.7 0.2149
RM3827 6 22.2 0.5179
RM30 6 27.2 0.5921
RM400 6 28.4 0.6172
RM51 7 0.2 0.4368
RM481 7 2.8 0.8129
RM21077 7 4 0.0866
RM214 7 12.7 0.3967
RM500 7 15.9 0.3698
RM2 7 16 0.3457
RM320 7 18.6 0.6175
RM336 7 21.8 0.7137
RM18 7 25.6 0.4252
RM248 7 29.3 0.6398
RM6925 8 0.6 0.6608
RM38 8 2.1 0.4303
RM25 8 4.3 0.4160
RM72 8 6.7 0.6494
RM3395 8 10.2 0.7069
RM22837 8 12.3 0.7109
RM404 8 15.4 0.6024
RM223 8 20.6 0.4368
RM52 8 24.7 0.5109
RM5545 8 28.2 0.5425
RM23679 9 0.8 0.4783
RM23793 9 4.3 0.5603
RM5515 9 7.1 0.4925
RM23958 9 7.9 0.4394
RM3855 9 9.3 0.4160
RM24087 9 10.8 0.5109
RM105 9 12.5 0.5267
RM24260 9 14.1 0.3554
RM410 9 17.6 0.4818
RM242 9 18.8 0.4783
RM108 9 19.3 0.3457
RM205 9 22.7 0.3850
RM24888 10 0.5 0.3554
RM222 10 2.6 0.5015
RM216 10 5.3 0.4842
RM25436 10 14.9 0.5578
RM25459 10 15.2 0.5780
RM258 10 18 0.5880
RM228 10 22.2 0.6801
RM286 11 0.3 0.5877
RM167 11 4 0.2149
RM202 11 9 0.3967
RM26664 11 15.3 0.8295
RM209 11 17.8 0.6982
RM26834 11 18.6 0.5736
RM21 11 19.1 0.6769
RM206 11 22 0.6641
RM224 11 27.2 0.3554
RM27389 11 28.4 0.6690
RM3472 12 3.5 0.5578
RM27809 12 7.4 0.3744
RM27933 12 10.4 0.3975
RM28102 12 16 0.4303
RM1261 12 17.5 0.5629
RM415 12 19.5 0.3026
RM17 12 26.9 0.4813
RM28825 12 27.5 0.2373
Table 4
SSR markers tightly linked to SUB1.
Table 4
Genotype Chromosome 9a,b
Marker RM8206 ART5 RM464 RM8300 RM6920 RM5515 RM5526 RM219 SC30 RM23958
Physical position (Mb) 5.92 6.30 6.58 6.60 7.01 7.15 7.31 7.89 8.00 8.00
PIC Value 0.157 0.484 0.425 0.325 0.537 0.493 0.374 0.527 0.568 0.439
Swarna-Sub1 152 200 262 200 300 n/a 172 202 178 98
Samba Mahsuri-Sub1 152 200 262 200 300 124 170 206 178 80
CR1009-Sub1 152 200 262 200 300 124 172 208 184 98
BR11-Sub1 152 200 262 200 250 126 172 204 178 98
PSBRc82-Sub1 152 200 262 200 300 126 172 204 170 80
Ciherang-Sub1 152 200 262 200 300 126 170 206 170 80
PSBRc18-Sub1 154 200 262 200 250 126 170 206 190 98
IR64-Sub1 152 200 262 200 300 126 170 206 178 80
Sabitri 152 210 262 198 300 124 170 206 170 80
IR6 152 210 300 200 250 124 172 208 178 80
Super basmati 158 210 258 200 300 128 172 206 190 82
IR09F185 152 200 262 200 300 126 172 206 178 80
IR67440-NDR-5-1-1-1-1 152 210 262 198 250 124 170 206 170 80
FR13Ab 152 200 262 200 300 126 172 202 178 82
IR40931 152 200 262 200 250 126 172 202 178 82
IR49830 152 200 262 200 300 124 170 206 170 80
IR42 152 210 262 198 290 124 170 206 170 80
IRRBB66 152 210 300 200 290 124 170 206 178 80
OR 142-99 152 210 270 198 250 124 172 208 178 98
IRRI 119 152 n/a n/a 200 290 126 172 206 178 80
IRRI 154 152 210 270 198 290 128 170 206 190 80
FL478 152 210 262 198 300 124 170 206 184 80
IR74371 154 n/a 300 200 300 124 172 204 178 78

aMarker allele sizes (bp).

bSUB1 marker alleles from FR13A are shaded in grey.

Table 5
Percentage of polymorphic SNP markers and average PIC value per chromosome.
Table 5
Chr. Total % monomorphic % polymorphic PIC Min PIC Max PIC Ave
1 45 8.9 91.1 0.045 0.531 0.314
2 37 8.1 91.9 0.130 0.574 0.381
3 44 13.6 86.4 0.045 0.574 0.310
4 31 3.2 96.8 0.087 0.588 0.342
5 33 0.0 100.0 0.087 0.490 0.289
6 38 2.6 97.4 0.045 0.501 0.323
7 28 3.6 96.4 0.087 0.527 0.319
8 27 0.0 100.0 0.124 0.580 0.377
9 24 0.0 100.0 0.087 0.530 0.320
10 19 5.3 94.7 0.087 0.517 0.382
11 31 0.0 100.0 0.045 0.537 0.343
12 26 3.9 96.2 0.087 0.501 0.331
Table 6
Set of genotyping SNPs based on PIC value.
Table 6
Chr SNP Position (Mb) PIC Values
1 id1003559 4.3 0.5308
1 id1004817 6.1 0.4292
1 ud1000727 16.1 0.5267
1 id1010609 19.1 0.5174
1 id1023158 38.2 0.5290
1 id1028304 44.7 0.5015
2 id2008501 22 0.5578
2 id2008866 23.2 0.4728
2 id2010969 26.1 0.4898
2 id2011561 27.2 0.4842
2 id2014684 34 0.5743
2 id2015767 35.8 0.5743
3 id3000362 0.7 0.5743
3 id3005216 10.1 0.4842
3 id3008333 17.3 0.4336
3 id3011383 27.8 0.5229
3 id3013669 29.9 0.4842
3 id3015399 32.9 0.3750
4 ud4000438 5.8 0.5771
4 id4003973 13.5 0.5629
4 id4007105 21.8 0.5880
4 id4007444 22.8 0.5724
4 id4009293 28.6 0.5880
4 id4010924 32.3 0.5015
5 id5000015 0 0.4898
5 id5001470 2.5 0.4842
5 id5001756 3 0.4336
5 id5006311 15.7 0.4551
5 id5006821 17.1 0.3604
5 id5010661 23.7 0.3658
6 fd17 6.8 0.5015
6 id6004481 7 0.3604
6 id6005350 8.2 0.4460
6 id6011018 21.8 0.4904
6 id6015793 28.5 0.4303
6 id6016490 30.1 0.4292
7 id7000461 2.7 0.4783
7 id7001478 8.2 0.4904
7 id7002051 12.2 0.4160
7 id7002105 13.4 0.5267
7 id7002427 15.9 0.4065
7 id7002859 19.2 0.3604
8 id8000699 2.2 0.4368
8 id8003681 12.5 0.5552
8 ud8001072 16.6 0.5798
8 id8004838 18.4 0.5743
8 id8006727 23.6 0.5290
8 id8007472 27.4 0.4160
9 id9000661 2.3 0.3967
9 id9000881 4.2 0.4160
9 id9003188 12.5 0.4470
9 id9003720 14.4 0.3845
9 id9004168 15.5 0.2970
9 id9006988 20.1 0.5298
10 id10000174 0.9 0.4470
10 id10001250 4 0.5174
10 id10001624 5.2 0.4792
10 wd10001251 6.3 0.4898
10 id10005538 18.9 0.4792
10 id10006726 21.9 0.4292
11 id11003924 10.8 0.5371
11 id11004398 15.1 0.4160
11 id11004812 16.6 0.4898
11 id11005065 17.3 0.4728
11 id11005646 18.4 0.4551
11 id11008862 24.9 0.4908
12 id12000252 0.7 0.5015
12 id12001996 4.4 0.4677
12 id12003019 7.6 0.3026
12 id12004491 12.5 0.4393
12 id12005205 14.6 0.3967
12 id12005501 15.7 0.4842
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Evaluation of SSR and SNP Markers for Molecular Breeding in Rice
Plant Breed. Biotech.. 2015;3(2):139-152.   Published online June 30, 2015
DOI: https://doi.org/10.9787/PBB.2015.3.2.139
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Evaluation of SSR and SNP Markers for Molecular Breeding in Rice
Plant Breed. Biotech.. 2015;3(2):139-152.   Published online June 30, 2015
DOI: https://doi.org/10.9787/PBB.2015.3.2.139
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Evaluation of SSR and SNP Markers for Molecular Breeding in Rice
Image Image Image
Fig. 1 Graphs of PIC values (y axis) plotted for SSR and SNP markers based on chromosome position (x axis).
Fig. 2 Radial tree using unrooted neighbor-joining method of SSR data using Nei’s similarity coefficient.
Fig. 3 Radial tree view of unrooted neighbor-joining tree of SNP data using Nei’s similarity coefficient.

Fig. 1

Fig. 2

Fig. 3

Evaluation of SSR and SNP Markers for Molecular Breeding in Rice

Rice genotypes used for marker evaluation.

Name Alias Parentage Designation GID Information
Swarna-Sub1 Swarna*4/IR49830-7-1-2-3 IR05F102 1847271 New variety with submergence tolerance in India
Samba Mahsuri-Sub1 SAMBHA MAHSURI*3/IR 49830-7-1-2-3 IR07F101 2159598 New variety with submergence tolerance in India
BR11-Sub1 BRRI dhan 52 (Bangladesh) BR 11*3/IR 40931-33-1-3-2 IR07F290 2295328 New variety with submergence tolerance in Bangladesh
CR1009-Sub1 CR 1009*3/IR 49830-7-1-2-3 IR07F291 2403712 New variety with submergence tolerance in India
IR09F434 IRRI 123*2/IRRI 149 IR09F434 2847875 Sub1 near isogenic line (NIL) in popular Philippine variety PSBRc82
Ciherang-Sub1 Inpari 30 (Indonesia) CIHERANG*2/IRRI 149 IR09F436 2847870 New variety with submergence tolerance in Indonesia
PSBRc18-Sub1 PSB RC 18 (IR 51672-62-2-1-1-2-3)*2/IRRI 149 IR09F437 2853229 Sub1 near isogenic line (NIL) in popular Philippine variety awaiting to be released
IR64-Sub1 IRRI 149 or NSICRc194 or “Submarino” (Philippines) IR 40931-33-1-3-2/3*IR 64 IR07F102 2159583 New variety with submergence tolerance in the Philippines
Sabitri IR 1561-228-1/IR 1737//CR 94-13 IRTP 8487 2268099 Popular variety in Nepal
IR6 SIAM 29 (ACC 42)/DEE GEO WOO GEN IRTP 25398 2440173 Popular variety in Pakistan
Super basmati BAS 320/IR 661 IRTP 20918 2274228 Popular basmati variety in Pakistan
IR09F185 BR 11/IR 49830-7-1-2-3//IR04N106 IR 85288-SUB 38-1-1 2707581 Stagnant flooding donor. Parent of RIL population
IR67440-NDR-5-1-1-1-1 CNM 539/IR 53479-B-45-3-2-3 84299 Stagnant flooding donor. Parent of RIL population
FR13A DHALPUTTIA 32293 Donor of Sub1 and highly flood tolerant landrace
IR40931 BKNFR 76106-16-0-1/IR 19661-131-1-2 IR40931-33-1-3-2 71615 Sub1 donor parent.
IR49830 “Popoul” (Myanmar) IR 4568-86-1-3-2/IR 26702-111-1//IR 20992-7-2-2-2-2-3/IR 21567-9-2-2-2-1 IR49830-7-1-2-3 88474 Sub1 and stagnant flooding donor parent/variety
IR42 IR 1561-228-1-2/IR 1737//CR 94-13 IR 2071-586-5-6 13988 IRRI variety released for irrigated and rainfed areas. Susceptible check for submergence tolerance screening.
IRBB66 IRBB 7/IR BB 60 (IR 72920-1-44-4) 1847199 Widely-used bacterial blight gene donor (Xa4, xa5, xa7, xa13 and Xa21) in IR24 background
OR 142-99 Santepheap 3 (Cambodia) PANKAIJ/SIGADIS IRTP 13636 415260 Indian breeding line released as a variety in Cambodia
IRRI 119 PSBRc68 or ‘Sacobia’ (Philippines); Shwe Pyi Tan (Myanmar) IR 43581-57-3-3-6/IR 26940-20-3-3-3-1//KHAO DAWK MALI 105 PSB RC 68 2266161 IRRI variety released with Sub1 for rainfed areas in the Philippines with stagnant flooding tolerance
IRRI 154 NSICRc222 or ‘Tubigan 18’ (Philippines) IR 73012-137-2-2-2/PSB RC 10 (IR 50404-57-2-2-3) IR04A412 1253989 IRRI variety released for irrigated and favourable rainfed areas in the Philippines
FL478 IR 66946-3R-178-1-1 IR 29/POKKALI B IR 66946-3R-178-1-1 1192884 Widely used salinity tolerant donor line
IRRI 148 NSICRc192 or ‘Sahod Ulan 1’ (Philippines) IR 55419-4*2/WAY RAREM IR74371-54-1-1 1161411 Drought released in tolerant the Philippines IRRI variety

Polymorphic SSR markers and PIC values per chromosome.

Chr No. SSRs % monomorphic % polymorphic PIC Min PIC Max PIC Ave
1 107 35.2 64.8 0.087 0.720 0.338
2 64 25.4 74.6 0.087 0.691 0.333
3 60 27.1 72.9 0.087 0.648 0.331
4 61 35.0 65.0 0.087 0.748 0.332
5 56 20.0 80.0 0.087 0.735 0.340
6 53 24.0 76.0 0.087 0.791 0.386
7 38 26.3 73.7 0.087 0.813 0.402
8 52 25.0 75.0 0.087 0.711 0.396
9 102 24.7 75.3 0.087 0.772 0.400
10 33 37.5 62.5 0.087 0.703 0.414
11 42 47.2 52.8 0.087 0.830 0.461
12 54 37.3 62.7 0.087 0.563 0.352

Indica SSR genotyping set based on reliability, polymorphism and marker position.

SSR Name Chr. Physical (Mb) PIC Values
RM495 1 0.2 0.4393
RM1 1 4.6 0.2608
RM243 1 7.9 0.3975
RM582 1 9.1 0.4470
RM449 1 15.1 0.1575
RM24 1 18.9 0.5334
RM237 1 26.8 0.4345
RM84 1 29.7 0.3554
RM486 1 34.9 0.3249
RM14 1 41.3 0.5885
RM154 2 1 0.6913
RM279 2 2.8 0.5468
RM71 2 8.7 0.5623
RM324 2 11.3 0.4394
RM300 2 13.1 0.4925
RM341 2 19.3 0.5192
RM263 2 25.8 0.5109
RM1342 2 28.1 0.5396
RM6 2 29.5 0.3457
RM208 2 35.1 0.5743
RM231 3 2.4 0.5736
RM218 3 8.4 0.6479
RM3297 3 13.2 0.3131
RM15187 3 16.6 0.4551
RM411 3 21.4 0.3604
RM426 3 27.5 0.3850
RM186 3 28.8 0.5736
RM571 3 33.1 0.2970
RM570 3 35.5 0.4647
RM85 3 36.3 0.5038
RM335 4 0.6 0.6550
RM261 4 6.5 0.4065
RM307 4 13.1 0.5623
RM16945 4 20.5 0.2373
RM3839 4 23.9 0.2970
RM252 4 25.1 0.5509
RM3820 4 27.6 0.4898
RM303 4 28.5 0.2922
RM5473 4 31.4 0.7481
RM280 4 34.9 0.4638
RM13 5 2 0.5396
RM592 5 2.7 0.7349
RM17954 5 3.6 0.5425
RM18188 5 9.1 0.6657
RM1115 5 14.7 0.5109
RM5454 5 17.8 0.6221
RM163 5 19.1 0.5509
RM3575 5 21.3 0.4160
RM274 5 26.8 0.0866
RM31 5 28.6 0.4551
RM435 6 0.5 0.3967
RM586 6 1.4 0.6272
RM204 6 3.1 0.7913
RM3408 6 4.5 0.3249
RM276 6 6.2 0.3604
RM549 6 6.9 0.3604
RM136 6 8.7 0.2149
RM3827 6 22.2 0.5179
RM30 6 27.2 0.5921
RM400 6 28.4 0.6172
RM51 7 0.2 0.4368
RM481 7 2.8 0.8129
RM21077 7 4 0.0866
RM214 7 12.7 0.3967
RM500 7 15.9 0.3698
RM2 7 16 0.3457
RM320 7 18.6 0.6175
RM336 7 21.8 0.7137
RM18 7 25.6 0.4252
RM248 7 29.3 0.6398
RM6925 8 0.6 0.6608
RM38 8 2.1 0.4303
RM25 8 4.3 0.4160
RM72 8 6.7 0.6494
RM3395 8 10.2 0.7069
RM22837 8 12.3 0.7109
RM404 8 15.4 0.6024
RM223 8 20.6 0.4368
RM52 8 24.7 0.5109
RM5545 8 28.2 0.5425
RM23679 9 0.8 0.4783
RM23793 9 4.3 0.5603
RM5515 9 7.1 0.4925
RM23958 9 7.9 0.4394
RM3855 9 9.3 0.4160
RM24087 9 10.8 0.5109
RM105 9 12.5 0.5267
RM24260 9 14.1 0.3554
RM410 9 17.6 0.4818
RM242 9 18.8 0.4783
RM108 9 19.3 0.3457
RM205 9 22.7 0.3850
RM24888 10 0.5 0.3554
RM222 10 2.6 0.5015
RM216 10 5.3 0.4842
RM25436 10 14.9 0.5578
RM25459 10 15.2 0.5780
RM258 10 18 0.5880
RM228 10 22.2 0.6801
RM286 11 0.3 0.5877
RM167 11 4 0.2149
RM202 11 9 0.3967
RM26664 11 15.3 0.8295
RM209 11 17.8 0.6982
RM26834 11 18.6 0.5736
RM21 11 19.1 0.6769
RM206 11 22 0.6641
RM224 11 27.2 0.3554
RM27389 11 28.4 0.6690
RM3472 12 3.5 0.5578
RM27809 12 7.4 0.3744
RM27933 12 10.4 0.3975
RM28102 12 16 0.4303
RM1261 12 17.5 0.5629
RM415 12 19.5 0.3026
RM17 12 26.9 0.4813
RM28825 12 27.5 0.2373

SSR markers tightly linked to SUB1.

Genotype Chromosome 9a,b
Marker RM8206 ART5 RM464 RM8300 RM6920 RM5515 RM5526 RM219 SC30 RM23958
Physical position (Mb) 5.92 6.30 6.58 6.60 7.01 7.15 7.31 7.89 8.00 8.00
PIC Value 0.157 0.484 0.425 0.325 0.537 0.493 0.374 0.527 0.568 0.439
Swarna-Sub1 152 200 262 200 300 n/a 172 202 178 98
Samba Mahsuri-Sub1 152 200 262 200 300 124 170 206 178 80
CR1009-Sub1 152 200 262 200 300 124 172 208 184 98
BR11-Sub1 152 200 262 200 250 126 172 204 178 98
PSBRc82-Sub1 152 200 262 200 300 126 172 204 170 80
Ciherang-Sub1 152 200 262 200 300 126 170 206 170 80
PSBRc18-Sub1 154 200 262 200 250 126 170 206 190 98
IR64-Sub1 152 200 262 200 300 126 170 206 178 80
Sabitri 152 210 262 198 300 124 170 206 170 80
IR6 152 210 300 200 250 124 172 208 178 80
Super basmati 158 210 258 200 300 128 172 206 190 82
IR09F185 152 200 262 200 300 126 172 206 178 80
IR67440-NDR-5-1-1-1-1 152 210 262 198 250 124 170 206 170 80
FR13Ab 152 200 262 200 300 126 172 202 178 82
IR40931 152 200 262 200 250 126 172 202 178 82
IR49830 152 200 262 200 300 124 170 206 170 80
IR42 152 210 262 198 290 124 170 206 170 80
IRRBB66 152 210 300 200 290 124 170 206 178 80
OR 142-99 152 210 270 198 250 124 172 208 178 98
IRRI 119 152 n/a n/a 200 290 126 172 206 178 80
IRRI 154 152 210 270 198 290 128 170 206 190 80
FL478 152 210 262 198 300 124 170 206 184 80
IR74371 154 n/a 300 200 300 124 172 204 178 78

aMarker allele sizes (bp).

bSUB1 marker alleles from FR13A are shaded in grey.

Percentage of polymorphic SNP markers and average PIC value per chromosome.

Chr. Total % monomorphic % polymorphic PIC Min PIC Max PIC Ave
1 45 8.9 91.1 0.045 0.531 0.314
2 37 8.1 91.9 0.130 0.574 0.381
3 44 13.6 86.4 0.045 0.574 0.310
4 31 3.2 96.8 0.087 0.588 0.342
5 33 0.0 100.0 0.087 0.490 0.289
6 38 2.6 97.4 0.045 0.501 0.323
7 28 3.6 96.4 0.087 0.527 0.319
8 27 0.0 100.0 0.124 0.580 0.377
9 24 0.0 100.0 0.087 0.530 0.320
10 19 5.3 94.7 0.087 0.517 0.382
11 31 0.0 100.0 0.045 0.537 0.343
12 26 3.9 96.2 0.087 0.501 0.331

Set of genotyping SNPs based on PIC value.

Chr SNP Position (Mb) PIC Values
1 id1003559 4.3 0.5308
1 id1004817 6.1 0.4292
1 ud1000727 16.1 0.5267
1 id1010609 19.1 0.5174
1 id1023158 38.2 0.5290
1 id1028304 44.7 0.5015
2 id2008501 22 0.5578
2 id2008866 23.2 0.4728
2 id2010969 26.1 0.4898
2 id2011561 27.2 0.4842
2 id2014684 34 0.5743
2 id2015767 35.8 0.5743
3 id3000362 0.7 0.5743
3 id3005216 10.1 0.4842
3 id3008333 17.3 0.4336
3 id3011383 27.8 0.5229
3 id3013669 29.9 0.4842
3 id3015399 32.9 0.3750
4 ud4000438 5.8 0.5771
4 id4003973 13.5 0.5629
4 id4007105 21.8 0.5880
4 id4007444 22.8 0.5724
4 id4009293 28.6 0.5880
4 id4010924 32.3 0.5015
5 id5000015 0 0.4898
5 id5001470 2.5 0.4842
5 id5001756 3 0.4336
5 id5006311 15.7 0.4551
5 id5006821 17.1 0.3604
5 id5010661 23.7 0.3658
6 fd17 6.8 0.5015
6 id6004481 7 0.3604
6 id6005350 8.2 0.4460
6 id6011018 21.8 0.4904
6 id6015793 28.5 0.4303
6 id6016490 30.1 0.4292
7 id7000461 2.7 0.4783
7 id7001478 8.2 0.4904
7 id7002051 12.2 0.4160
7 id7002105 13.4 0.5267
7 id7002427 15.9 0.4065
7 id7002859 19.2 0.3604
8 id8000699 2.2 0.4368
8 id8003681 12.5 0.5552
8 ud8001072 16.6 0.5798
8 id8004838 18.4 0.5743
8 id8006727 23.6 0.5290
8 id8007472 27.4 0.4160
9 id9000661 2.3 0.3967
9 id9000881 4.2 0.4160
9 id9003188 12.5 0.4470
9 id9003720 14.4 0.3845
9 id9004168 15.5 0.2970
9 id9006988 20.1 0.5298
10 id10000174 0.9 0.4470
10 id10001250 4 0.5174
10 id10001624 5.2 0.4792
10 wd10001251 6.3 0.4898
10 id10005538 18.9 0.4792
10 id10006726 21.9 0.4292
11 id11003924 10.8 0.5371
11 id11004398 15.1 0.4160
11 id11004812 16.6 0.4898
11 id11005065 17.3 0.4728
11 id11005646 18.4 0.4551
11 id11008862 24.9 0.4908
12 id12000252 0.7 0.5015
12 id12001996 4.4 0.4677
12 id12003019 7.6 0.3026
12 id12004491 12.5 0.4393
12 id12005205 14.6 0.3967
12 id12005501 15.7 0.4842
Table 1 Rice genotypes used for marker evaluation.
Table 2 Polymorphic SSR markers and PIC values per chromosome.
Table 3 Indica SSR genotyping set based on reliability, polymorphism and marker position.
Table 4 SSR markers tightly linked to SUB1.

Marker allele sizes (bp).

SUB1 marker alleles from FR13A are shaded in grey.

Table 5 Percentage of polymorphic SNP markers and average PIC value per chromosome.
Table 6 Set of genotyping SNPs based on PIC value.

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

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