KR101101773B1 - Marker for evaluation of eating quality of Japonica rice and uses thereof - Google Patents

Abstract

The present invention is to evaluate the taste of rice using a primer set for the evaluation of rice comprising a primer set of 13 pairs or 15 pairs of primer set, a kit for the evaluation of rice comprising the primer set, and the primer set. It is about how to.

The method of evaluating the taste of rice based on the molecular marker according to the present invention is not only simple and accurate, but also has the additional advantage of being screened in early generation of breeding.

Rice, Food Flavor, Marker, Regression, Primer Set

Description

Marker for evaluation of eating quality of Japonica rice and uses approximately}

The present invention relates to a method for evaluating the taste of rice using molecular markers, and more particularly, a total of 22 Japonica rice varieties (two varieties from Japan (Koshihikari) using primers based on molecular markers associated with the taste of rice. Hitomimebore), 1 Chinese varieties (Hex 41), and 19 Korean varieties (Gold, Fine, Samgwang, Chucheong, Dongjin, Singeumo, Hwaseong, Hwacheong, Dobong, Samnam, Palgong, Baekjinju 1, Seonong 4, Onnuri, Manmi, Sik, Geumman, Nakdong, and Samdeok)) were used as the independent variables for the amplification of amplified products. It is about developing multiple regression equations.

Rice ( Oryza sativa L.) is a staple of half the world's population. Rice flavors are becoming increasingly important to meet demand. Thus, one of the main objectives of the breeding program is to develop rice varieties with better taste to meet the needs of both the food industry and consumers (Choi (2002) Kor J Crop Sci 47: 15-32). Although Indica rice varieties are popular around the world, consumers in Northeast Asian countries such as Korea, Japan, North China, and Taiwan prefer Japonica rice mainly because of the appropriate elasticity and stickiness.

Rice is a complex trait that involves a number of physicochemical properties, so it is challenging to accurately evaluate food taste through selection in rice breeding programs. Some of the major physicochemical properties that affect the taste are amylose content (AC) (Wada et al. (2006) Breed Sci 56: 253-260), and allahgholipour et al. (2006) Pl Breed 125: 357-362), gel consistency (GC) (Cagampang et al. (1973) J Sci Food Agric 24: 1589-1594), gelatinization temperature (GT) (Little et al. (1958) ) Cereal Chem 35: 111-126), and protein content (Ramesh et al. (2000) Critical Revs Food Sciand Nutri 40: 449-460). Flavor is also associated with rice stickiness, sweetness, shine and taste (Okabe (1979) J Texture Studies 10: 131-152). The taste associated directly with the taste of rice is determined by the aroma, appearance, taste and chew (Huang (1998) Chin J Rice Sci 12: 172-176). In addition to genetic determinants, rice flavor is also greatly influenced by environmental factors, temperature during cultivation and ripening, amount of fertilizer, irrigation control, drying after harvesting, and recipes (Izumi et al. (2007) Sci Reports Faculty of Agri 96: 13-18). In breeding programs, an accurate assessment of taste is crucial in the early generation. Sensory evaluation by a trained panel is the most appropriate assessment method. However, this method requires a large amount of rice per sample and can evaluate only a few samples per day, so sensory tests are more effective when performed in late periods when the strains are almost fixed (Hong & Kuo (2001) Taichung District Agri , Res Report 2001, 70: 9-19). Moreover, the results of sensory tests are sometimes inconsistent for the same sample, probably due to subtle differences in the physical and emotional conditions of the panel members or in sample preparation. Recently, a machine for evaluating the taste of rice was developed and used for lineage selection in breeding programs (Tanaka et al. (1992) Tohuku J Crop Sci 35: 45-46). However, because the ear canal also requires large amounts of rice per sample, the taste test using the machine is usually performed only for advanced breeding generations.

Many genetic studies on taste traits have been conducted. These studies show that the physicochemical properties of rice, such as AC, GT, GC, and gelatinization viscosity, are controlled by one to three major genes with one or more regulators. Starch synthetic genes such as starch branching enzyme (SBE), starch synthase (SS), and granule bound starch synthase (GBSS) are formulated according to variations in the physicochemical properties of starch. Contribute to the sense of taste (Bao et al. (2006) Theor Appl Genet 113: 1185-1196). Food taste, protein content (Takeuchi et al. (2007) Breed Sci 57: 31-242), and wax gene (Wx) and starch synthase II (alk) (Bao et al. (2000) Major genes and / or quantitative trait loci (QTLs) associated with the same taste (Takeuchi et al. (2007) Breed Sci 57: 31-242), such as Theor Appl Genet 100: 280-284), have been reported. Moreover, the interaction between these genes, along with other factors, determines the physicochemical properties of rice and the taste of rice. Collectively, the genetic complexity of the diet, as well as the difficulty of accurate evaluation of the diet in early breeding generation, has forced the development of highly cultivated rice varieties.

Approaches based on DNA markers have been developed to complement physicochemical analyses and sensory tests useful for appetite evaluation. These methods have the added benefit of screening in early breeding generation as well as simplicity and accuracy. Markers based on PCR have been checked for quality evaluation of rice varieties (Garland et al. (2000) Theor Appl Genet 101: 364-371). Recently, the STS (sequence-tagged site) primer developed from random amplified polymorphic DNA (RAPD) analysis can distinguish rice varieties according to the taste (Ohstubo et al. (2003) Nippon Nogeikagaku Kaishi 50: 122-132 ). In particular wax waxes for gelatinization, starch branching enzyme (SBE) for starch viscosity (Han et al. (2004) Pl Sci 166: 357-364), and GT (Bao et al. (2006) Theor Appl Genet 113: Several functional markers have also been developed to distinguish rice physicochemical properties, such as the effects of AC (Ayres et al. (1997) Theor Appl Genet 94: 773-781) and starch synthase IIa (SSIIa) on 1171-1183). (Larkin et al. (2003) Euphytica 131: 243-253). Additional gene-tagged markers have been developed from starch synthetic genes (Bao et al. (2006) 113: 1185-1196). Despite recent advances in the development of markers and the identification of QTLs associated with flavour, a marker-assisted Breeding (MAB) system for better flavour has not been established.

Japanese Patent Laid-Open Publication No. 2006-042608 discloses a DNA marker for the evaluation of rice's taste using primers for genes related to protein and starch quality and quantity, which are the main factors for determining the taste of rice, but the primer set of the present invention is disclosed. Is different.

The present invention has been made in accordance with the above requirements, and the present invention uses a primer based on molecular markers associated with the taste of rice to supplement the physiological and chemical analysis and sensory test useful for the evaluation of the taste of Japonica rice varieties. Nucleotide polymorphism analysis was performed to determine the flavor of japonica rice based on molecular markers. It is to formulate a simple and accurate method of assessment and prediction that can be applied early in the breeding stage.

In order to solve the above problems, the present invention provides a primer set for the evaluation of the rice comprising a specific 13 pairs primer set.

The present invention also provides a primer set for the evaluation of rice comprising a specific set of 15 pairs of oligonucleotide primers.

The present invention also provides a kit for rice taste evaluation comprising the primer set.

In addition, the present invention provides a method for evaluating the taste of rice using the primer set.

According to the present invention, the taste of Japonica rice can be evaluated simply and accurately at the early stage of the breeding step using the set of the above-described primers. It may also be used in the selection of early breeding lines or individual F ₂ plants in order to promote breeding to improve the taste of rice in Korea or other Japonica rice consumers.

In order to achieve the object of the present invention, the present invention is SEQ ID NO: 3 and 4, SEQ ID NO: 5 and 6, SEQ ID NO: 11 and 12, SEQ ID NO: 17 and 18, SEQ ID NO: 19 and 20, SEQ ID NO: 21 and 22, SEQ ID NO: 13 sets of oligonucleotide primer sets shown as 23 and 24, SEQ ID NOs: 29 and 30, SEQ ID NOs: 31 and 32, SEQ ID NOs: 55 and 56, SEQ ID NOs: 61 and 62, SEQ ID NOs: 63 and 64, and SEQ ID NOs: 69 and 70 It provides a primer set for the evaluation of the rice containing.

The invention also relates to SEQ ID NOs 5 and 6, SEQ ID NOs 9 and 10, SEQ ID NOs 11 and 12, SEQ ID NOs 15 and 16, SEQ ID NOs 19 and 20, SEQ ID NOs 21 and 22, SEQ ID NOs 23 and 24, SEQ ID NOs 31 and Oligonucleotide primer set of 32; 15 pairs of oligonucleotide primers represented by SEQ ID NOs: 37 and 38, SEQ ID NOs: 51 and 52, SEQ ID NOs: 53 and 54, SEQ ID NOs: 55 and 56, SEQ ID NOs: 63 and 64, SEQ ID NOs: 67 and 68, and SEQ ID NOs: 69 and 70 Provided is a primer set for the evaluation of the rice comprising the set.

The oligonucleotide primers are at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22 in the sequence of SEQ ID NO: 3 to SEQ ID NO: 70, depending on the sequence length of each primer. , Oligonucleotides consisting of at least 23, at least 24, at least 25, at least 26, at least 27, at least 28 consecutive nucleotide segments. For example, the oligonucleotide primer of SEQ ID NO: 3 may be an oligonucleotide consisting of segments of at least 16, 17 or more, 18 or more consecutive nucleotides in the sequence of SEQ ID NO: 3. Further, the oligonucleotide primers of SEQ ID NO: 5 are 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 21 or more, 22 or more, 23 or more, 24 in the sequence of SEQ ID NO: 5 It may be an oligonucleotide consisting of fragments of the above consecutive nucleotides.

The rice may be japonica rice, and the primer set for the evaluation of the rice of the present invention may be derived from rice ( Oryza sativa L.).

The primer set of the present invention can be used as an underlying technique for evaluating and predicting the taste of rice based on the marker by using the primer set according to the present invention as described above as the STS, SSR, SNP, dCAPS and indels markers related to the taste. have.

In the present invention, "primer" refers to a single stranded oligonucleotide sequence that is complementary to the nucleic acid strand to be copied and may serve as a starting point for the synthesis of the primer extension product. The length and sequence of the primer should allow to start the synthesis of the extension product. The specific length and sequence of the primers will depend on the primer utilization conditions such as temperature and ionic strength as well as the complexity of the DNA or RNA target required.

As used herein, oligonucleotides used as primers can also include nucleotide analogues, such as phosphorothioate, alkylphosphothioate or peptide nucleic acid. Or an intercalating agent.

In order to achieve another object of the present invention, the present invention

At least one oligonucleotide primer set according to the present invention; And it provides a kit for evaluating the taste of rice, comprising a reagent for performing the amplification reaction. The rice may be Japonica rice.

In the kit of the present invention, the reagent for performing the amplification reaction may include DNA polymerase, dNTPs, buffers and the like. In addition, the kit of the present invention may further include a user manual describing the conditions for performing the optimal reaction.

In order to achieve another object of the present invention, the present invention

Separating genomic DNA from the rice sample;

Amplifying a target sequence by using the isolated genomic DNA as a template and performing an amplification reaction using the oligonucleotide primer set according to claim 1; Determining a base sequence of the amplification product; And it provides a method for assessing the taste of rice, comprising the step of statistically processing the presence or absence of variation between the determined base sequence. The method of the present invention is more specifically

Separating genomic DNA from the rice sample;

Amplifying a target sequence by using the isolated genomic DNA as a template and performing an amplification reaction using an oligonucleotide primer set according to the present invention; Determining a base sequence of the amplification product; Detecting a variation between the base sequences; Measuring the food value of the rice by the cooking and sensory test; And it provides a method for assessing the taste of rice, comprising the step of performing a multiple regression analysis using the mutation and the taste value between the base sequence.

The method includes separating genomic DNA from a rice sample. As a method for separating genomic DNA from the sample, a method known in the art may be used. For example, the CTAB method may be used, or a wizard prep kit (Promega) may be used. Using the isolated genomic DNA as a template, one or more oligonucleotide primer sets according to one embodiment of the present invention may be used as a primer to amplify a target sequence. Methods for amplifying target nucleic acids include polymerase chain reaction (PCR), ligase chain reaction, nucleic acid sequence-based amplification, transcription-based amplification systems, There are any suitable methods for amplifying via strand displacement amplification or Qβ replication or for amplifying nucleic acid molecules known in the art. Among them, PCR is a method of amplifying a target nucleic acid from a primer pair that specifically binds to the target nucleic acid using a polymerase. Such PCR methods are well known in the art, and commercially available kits may be used.

In the method of the present invention, the rice may be Japonica rice.

In the method of the present invention, the amplified target sequence may be labeled with a detectable labeling substance. In one embodiment, the labeling material may be, but is not limited to, a material that emits fluorescence, phosphorescence, or radioactivity. Preferably, the labeling substance is Cy-5 or Cy-3. When amplifying the target sequence, PCR is performed by labeling Cy-5 or Cy-3 at the 5'-end of the primer to allow the target sequence to be labeled with a detectable fluorescent labeling substance. In addition, the label using the radioactive material may add radioactive isotopes such as ³² P or ³⁵ S to the PCR reaction solution when PCR is performed, and the amplification product may be incorporated into the amplification product while the amplification product is synthesized. One or more sets of oligonucleotide primers used to amplify the target sequence are as described above.

The method of the present invention is then determined the base sequence of the amplification product; Detecting a variation between the base sequences; Measuring the food value of the rice by the cooking and sensory test; And performing a multiple regression analysis using the variation and the taste value between the base sequences.

Hereinafter, the present invention will be described in detail by way of examples. However, the following examples are illustrative of the present invention, and the present invention is not limited to the following examples.

Materials and methods

1. Plant Materials and DNA Extraction

A total of 22 Japonica rice varieties, mostly Korean, were used. 2 Japanese varieties (Koshihikari and Hitomibore), 1 Chinese varieties (Hex 41), and 19 varieties of Korea (Gold, Fine, Samgwang, Chucheong, Dongjin, Singeumo, Hwaseong, Hwacheong, Dobong, Samnam, Palgong, Baekjinju) 1, Seonong 4, Onnuri, Manmi, Symbol, Geumman, Nakdong, and Samdeok) were used. These varieties were selected because they exhibit various taste values in Japonica rice (National Institute of Crop Science, personal opinion). These varieties were cultivated in 2006 by traditional farming methods on experimental farms (Seoul National University, Suwon). Moreover, three control varieties and 32 Japonica rice breeding strains from the National Institute of Crop Science (NICS) harvested at the trial packaging in 2007 were used to identify marker sets developed in the present invention. For physicochemical analysis, rice was dried to 15% moisture content. All rice varieties / lineages were grown in controlled greenhouses until the tilling stage, and tissues for genomic DNA extraction were collected. DNA was extracted by a modified cetyl trimethyl ammonium bromide (CTAB) method based on the protocol of Murray and Thompson (Murray & Thompson (1980) Nucleic Acids Res 8: 4321-4325).

2. Prereported Marker

20 pre-reported STS primer pairs developed from RAPD (Ohstubo et al. (2002) Nippon Nogeikagaku Kaishi 76: 388-397; Ohstubo et al. (2003) Nippon Nogeikagaku Kaishi 50: 122-132; Ohstubo & Nakamura (2007) ) J Agric Food Chem 55: 1501-1509) have been examined in the present invention. To identify the sequence of the amplification products, the amplified bands were cut and purified and sequenced by TA cloning. In homologous search, each cloned sequence was compared against all sequences on a non-redundant database using the BLASTX and BLASTN programs (http://www.ncbi.nlm.nih.gov/BLAST and www.gramene.org). . Six markers corresponding to the previously reported starch synthetic gene (Bao et al. (2006) Theor Appl Genet 113: 1185-1196) were also investigated for their association with the taste of Japonica rice. The sequence and band pattern of the previously reported markers are shown in Table 1 and FIG. 1, respectively.

Table 1.Prereported Markers Used to Evaluate Rice Flavor

PCR primer Primer type chr ^a order Forward (5 '-> 3') Reverse (5 '-> 3') Ohtsubo, etc. * A6 STS 7 CCAGCTGTACGCCTGTACTAC (SEQ ID NO: 1) CCAGCTGTACGTCTTCCCCAGC (SEQ ID NO: 2) A7 STS 12 TGCCTCGCACCAGAAATAG (SEQ ID NO: 3) TGCCTCGCACCATGAG (SEQ ID NO: 4) B1 STS 11 GTTTCGCTCCTACAGTAATTAAGGG (SEQ ID NO: 5) GTTTCGCTCCCATGCAATCT (SEQ ID NO: 6) B43 STS 9 GGCCGGCATGACTCAC (SEQ ID NO: 7) ACTGGCCGGCATCAAGAC (SEQ ID NO: 8) F6 STS 4 ACCACTCCATATATATCATCCAAAG (SEQ ID NO: 9) ACCACTCCATATCACCACAAGG (SEQ ID NO: 10) G4 STS One GAGACCGATATGCGATTC (SEQ ID NO: 11) GTGGTGTTTAGATCCAGAGACTTA (SEQ ID NO: 12) G22 STS 9 CTCACTCAAATTTACAGTGCATTTTCTTG (SEQ ID NO: 13) AGGGCCATGATACAAGACTCTGT (SEQ ID NO: 14) G28 STS One GGCGGTCGTTCTGCGAT (SEQ ID NO: 15) GGAGAATCCCACAGTAAGTTTTTCTTTG (SEQ ID NO: 16) J6 STS 11 GTCGGAGTGGTCAGACCG (SEQ ID NO: 17) GTCGGAGTGGATGGAGTAGC (SEQ ID NO: 18) M2CG STS 8 ACAACGCCTCCGATGA (SEQ ID NO: 19) ACAACGCCTCCGACAACAAGAT (SEQ ID NO: 20) M11 STS 6 GTCCACTGTGACCACAACAT (SEQ ID NO: 21) GTCCACTGTGGGGATTGTTC (SEQ ID NO: 22) P5 STS 10 ACAACGGTCCGTCCTTGCTT (SEQ ID NO: 23) ACAACGGTCCAACAGATACTTTTGA (SEQ ID NO: 24) S13 STS One GTCGTTCCTGTGGTTAGGACAGGGT (SEQ ID NO: 25) GTCGTTCCTGCTGGTGTCTCAGAT (SEQ ID NO: 26) T16 STS 12 GGTGAACGCTGTAGTTGGAATATA (SEQ ID NO: 27) GGTGAACGCTCAGATTTAAATATAAT (SEQ ID NO: 28) WK9 STS 9 CCCGCAGTTAGATGCACCATT (SEQ ID NO: 29) CCGCAGTTAGATCAAGTGGC (SEQ ID NO: 30) E30 STS One TACCTGGTTGATGTATACAGATCTGGTT (SEQ ID NO: 31) ATCCCTCGATCCCTCTAGCATTAT (SEQ ID NO: 32) B7 STS 2 CAGGTGTGGGTTACAAGGATGA (SEQ ID NO: 33) CAGGTGGTTCACGGCCTTT (SEQ ID NO: 34) G49A STS 11 AATCCAGACATGAAATTTATATGCAGATA (SEQ ID NO: 35) AATCCAGACATGTTGTCCTCAATTTTTG (SEQ ID NO: 36) G81 STS 6 TACCTGAACCAGCAAGCATGCGCG (SEQ ID NO: 37) TACCTGAACCAGTATAATCTTTG (SEQ ID NO: 38) P3 STS 5 AACGGGCCAAAAACGGAGGT (SEQ ID NO: 39) AACGGGCCAACGCAG (SEQ ID NO: 40) Bao et al. ** Wx (SNP) dCAPS
/ Acc I 6 CTTTGTCTATCTCAAGACAC (SEQ ID NO: 41) TTTCCAGCCCAACACCTTAC (SEQ ID NO: 42) SS1 (SSR) SSR 6 GATCCGTTTTTGCTGTGCCC (SEQ ID NO: 43) CCTCCTCTCCGCCGATCCTG (SEQ ID NO: 44) SBE1 (SSR) SSR 6 ATTTCTTTGGCCACAGGCGA (SEQ ID NO 45) CCCAGATTCGGAACAAGAAC (SEQ ID NO: 46) SBE1 (STS) STS 6 GAGTTGAGTTGCGTCAGATC (SEQ ID NO: 47)  AATGAGGTTGCTTGCTGCTG (SEQ ID NO 48) SBE3 (SNP) dCAPS
/ Spe I 2 GTCTTGGACTCAGATGCTGGACTC (SEQ ID NO: 49) ATGTATAACTGGCAGTTCGAACGG (SEQ ID NO: 50) SSIIa SNP 6 F7: CTGGATCACTTCAAGCTGTACGAC (SEQ ID NO: 51)
F22: CAAGGAGAGCTGGAGGGGGC (SEQ ID NO: 53) R1: GCCGGCCGTGCAGATCTTAAC (SEQ ID NO: 52)
R21: ACATGCCGCGCACCTGGAAA (SEQ ID NO: 54) chr ^a : chromosome
* 2002, Nippon Nogeikagaku Kaishi 76: 388-397; 2003, Nippon Nogeikagaku Kaishi 50: 122-132; 2007, J Agric Food Chem 55: 1501-1509
** 2006, Theor Appl Genet 113: 1185-1196; 2006, Theor Appl Genet 113: 1171-1183

3. New Marker  Development

Based on selected sections within or adjacent to the gene associated with the target QTL (Suh et al. (2004) Kor J Breed 36: 31-37) Nucleotide polymorphism analysis was performed on the sequences of the Japonica varieties. For primer preparation, primer3 software (http://frodo.wi.mit.edu/cgi-bin/primer3) was used (Rozen & Skaletsky (2000) Methods Mol Biol 132: 365-286). Purified PCR products from various Japonica varieties were cloned into pGEM-T Easy vectors and subjected to protocols of Sambrook and Russell (Molecular Cloning: A Laboratory Manual (2001) Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY). Escherichia coli coli ) was transformed into strain DH5 a . Plasmids isolated using the DNA-spin ^™ plasmid DNA purification kit (Intron Biotechnology, Korea) were sequenced using ABI 3700 DNA according to the manufacturer's instructions (Applied BioSystems, Inc.). For the identification of SNPs, insertions and deletions, and / or microsatellite repeats among rice varieties, the sequence is determined by the Clustal W program of the EMBL-European Bioinformatics Institute (http://www.ebi.ac.uk/tools). (1994) Nucleic Acids Res 22: 4673-4680), Codoncode Aligner 1.3.4 (CodonCode Corporation, Dedham, Mass.) And BioEdit (http://www.mbio.ncsu.edu/BioEdit/ bioedit.html) was used as a secondary. To detect 1 bp substitutions in specific fragments, dCAPS primers were constructed with the help of dCAPS Finder 2.0 (http://helix.wustl.edu/dcaps). Finally, nine molecular markers were successfully developed based on indels, SNPs, and microsatellite repeats (Table 3).

Table 2. Candidate loci and genes detected by QTL analysis and genome database ^† search

source QTL location chromosome Candidate genes Clone Developed marker Volume, etc. ^* OSR19-RM587 6 Particle-bound starch synthase 1 (GBSS1) AP002542 GBSS1 RM234-RM47 7 Sudang synthase 3 (S3) AP004988 S3cI, S3cII 7 Trehalose phosphatase (Tre) AP004341 Treb RM547-RM72 8 UDP-N-acetylglucosamine pyrokinase (AcPh) AP003875 AcPh RM20b-RM332 11 Glucose Fructose-6-Phosphate Aminotransferase (GPA) AC138454 GPA Calligraphy ^** OSR8-OSR9 2 Aspartate Aminotransferase (AM) AP003991 Ams Wada et al ^*** RM2887 10 Bicyanogenic Beta Glucosidase (CBG) AC074354 CBG Genome DB - One ADP-glucose kinase / depression gene (SH) AP004317 SH51 † http: //www.ncbi.nlm.nih.gov/,http: //rgp.dna.affrc.go.jp/E/IRGSP/index.html, http://rice.plantbiology.msu.edu/ , And http://www.gramene.org/
^* 2007, Kor J Breed Sci 39:94
^** 2004, Kor J Breed 36: 31-37
^*** 2007, Identification of QTLs for eating quality of japonica rice "Koshihikari". The 6 ^th Asian Crop Association Conference and The 2 ^nd International Conference on Rice for the Future: Bangkok, Thailand)

Table 3. Markers developed in the present invention for the evaluation of rice flavor

Marker Name Marker
type chromosome order Amplification
Size (bp) Forward (5 '-> 3') Reverse (5 '-> 3') S3cI Indel 7 CCACTCTCATGTCCTTGAAC (SEQ ID NO: 55) GCCATGACATTTGGACAT (SEQ ID NO: 56) 153/150 S3cII dCAPS /
Taq I 7 TTCCATGATGTGCCACTCTC (SEQ ID NO 57) GGACAAATGTTTTCAGTGAATAAAT (SEQ ID NO: 58) 277/252 + 25 Treb Indel 7 CACTCCAGTTCCTGCTCAAA (SEQ ID NO: 59) CACTCCAGTTCCTGCTCAAA (SEQ ID NO: 60) 167/171 AMs SSR 2 CTTCCAAGGACCCCATCCT (SEQ ID NO: 61) CCCAACATCTCCGTCAGAAT (SEQ ID NO: 62) 187/179 GPA SSR 11 AATACGCGGCCTTCTCCTAT (SEQ ID NO: 63) TTGATCCGAATGGGTCAAAT (SEQ ID NO: 64) 178/149 GBSS1 SSR 6 CAAATAGCCACCCACACCAC (SEQ ID NO: 65) CTTGCAGATGTTCTTCCTGATG (SEQ ID NO: 66) 172/170 AcPh dCAPS /
Mse I 8 AGTTGTGGTTTAAGCATAGG (SEQ ID NO: 67) ATTGTCCTTTTCTTTAAAGTTTATTA (SEQ ID NO: 68) 14 + 10 + 125 + 12 /
14+ 135 +12 CBG SSR 10 AGCTTCCCTAATGGCTTCGT (SEQ ID NO: 69) ATTTGCCAACTTTTGGATGG (SEQ ID NO: 70) 184/151 SH51 dCAPS /
Spe i One ATTCTTGATGAAAATAATTAACTAG (SEQ ID NO: 71) GGTTAACCATCTTATAAAATTTGTC (SEQ ID NO: 72) 475/454 + 21

4. PCR  protocol

PCR amplification of the marker was performed using a PTC-200 Peltier Thermal Cycler (MJ Research, Inc., USA) using PCR reagents (2 μl of 20 ng / μl DNA, 25 mM MgCl ₂ , 1 μl of 2.5 mM dNTPs, Taq polymerase 1). U (Intron Biotechnology, Korea)), 10 × buffer, and 1 μl each of the forward and reverse primers (10 μM). PCR reactions for sequencing were performed with 1 U of ExTaq polymerase (TaKaRa) in a total of 50 μl of reaction solution. All amplification reactions were performed by circulating a total of 35 times for 1 minute at 95 ° C, 30 seconds at 55 ° C, and 1 minute at 72 ° C. For STS markers developed by Ohtsubo et al. (2002, Nippon Nogeikagaku Kaishi 76: 388-397; 2003, Nippon Nogeikagaku Kaishi 50: 122-132; 2007, J Agric Food Chem 55: 1501-1509), the After initial denaturation for 1 minute, 1 minute at 96 ° C, 1 minute at 62 ° C, and 2 minutes at 72 ° C were performed for a total of 40 cycles. All amplification reactions using the primers reported by Bao et al. (1999, Theor Appl Genet 98: 1120-1124) were carried out at the following conditions (5 minutes at 94 ° C., followed by 45 seconds at 94 ° C., 1 minute at 55 ° C., and 72 ° C.). 1 minute was circulated 35 times and last extended 7 minutes at 2 ° C.). Amplified PCR products were electrophoretically separated on 3% agarose gels, stained with ethidium bromide and / or denatured electrophoresed on 8% polyacrylamide gels to ethidium bromide (Model MGV, CBS Scientific Co., CA, USA).

5. Rice Varieties Food  Evaluation of traits

For the taste and physicochemical analysis, rice was inverted to 91% whitening. The taste was measured according to the Operating Instructions (TRCM Co.) using a rice taste measuring system (Toyo Seasoning, Model MA-90). Protein content (PC) was calculated by multiplying total nitrogen by 5.95 after determining the nitrogen content of rice by the Micro-Kjeldahl method (Official Methods of Analysis. 16 ^th ed. AOAC: Gaithersburg, MD, 1995, 30 pp.). The amylose content (AC) of the milled rice was determined by the relative starch-iodine color of the suspension of 100-mesh rice flour according to the methods of Perez and Juliano (Reinke et al. (1991) Intl Rice Res Newsletter 16: 10-11). It was determined by absorbance. RVA gelatinization was determined by a rapid visco analyzer (RVA) according to the operating instructions (NewPort Sci. Co., Australia). The characteristics of the rice starch dough are divided into six parameters: peak viscosity (PV), hot paste viscosity (HPV), cool paste viscosity (CPV), and drop viscosity (BDV = PV-HPV: breakdown). viscosity), plaque viscosity (SBV = CPV-PV: setback viscosity), and cohesive viscosity (CTV = CPV-HPV: consistency viscosity) (Bao & Xia (1999) Theor Appl Genet 98: 1120-11247) .

6. Sensory test of taste by judges

Milled rice was cooked according to the protocol of the National Institute of Crop Science (Rural Development Administration, Korea). The dry-milled pure rice (300 g) was washed four times, infiltrated with distilled water for 30 minutes, and the water was filtered for 10 minutes. Rice cooked in an electric rice cooker with a volume ratio of 1: 1.25. After the completion of auto cooking, the rice was placed in a pot for 30 minutes. The rice was spread in a dish and left for about 10 minutes at room temperature until the rice cooled to 35-37 ° C. The sensory evaluation of rice taste by 11 well trained examiners was performed five times. The overall taste is assessed according to appearance (gloss), aroma, taste, tackiness, chewability, and taste (total score / combination), from +3 to -3 compared to the control sample Chuchu (score = 0). Scored. The dietary value of each cultivar by sensory evaluation was the mean scored by 11 panelists.

7. Statistical analysis

The collected data was analyzed and used for analysis of variance using SAS software version 8 (2001, SAS User's Guide. Release 8.2. Statistical Analysis Systems Institute: Cary, NC). The Duncan method was used to assess the mean difference between the traits. In addition, regression and correlation analysis were performed to determine the softness relationship between the rice traits of rice. Multiple regression analysis was also performed to determine the soft relationship between the taste scores scored by taste analyzer and sensory tests, and the taste was evaluated by molecular markers. STS marker results were recorded as 1 (free) and 0 (no). Similarly, two other alleles resulting from each simple sequence repeat (SSR) and SNP marker were also converted to binary numbers of 1 or 0. Using dietary values as dependent variables and binary data from molecular markers as independent or regressor variables, an optimal model equation for rice taste prediction was obtained. The most accurate prediction gave a significantly higher coefficient of determination (R ² ) consisting of the lowest standard deviation and a highly significant regression coefficient for each marker in the model.

Using binary matrix, unweighted pair-group method (UPGMA) using NTSYS program (Exeter Software, Setauket, NY) (Rohlf, 1993, FJ NTSYS-PC numerical taxonomy and multivariate analysis system.Version 1.8 , Exeter Publ.Setauket: New York).

< Example  One. Of marker  Development and Evaluation>

20 pre-reported STS primer pairs (Ohstubo et al. (2002) Nippon Nogeikagaku Kaishi 76: 388-397; Ohstubo et al. (2003) Nippon Nogeikagaku Kaishi 50: 122-132; Ohstubo & Nakamura (2007) J Agric Food Chem 55: 1501-1509). The DNA sequences of the PCR amplification products generated using the BLASTX and BLASTN programs (http://www.ncbi.nlm.nih.gov/BLAST and www.gramene.org) Comparison was made for all sequences. The size of the amplification product was 450-1700 bp. However, genes with known functions associated with vaginal traits were not searched for sequences derived from STS primers. Thus, marker primers were used directly in the present invention (Table 1). Most of the markers developed by Bao et al. (2006, Theor Appl Genet 113: 1185-1196; 2006, Theor Appl Genet 113: 1171-1183) showed no polymorphism between the 22 varieties and only SSIIa was used.

Based on the QTL analysis of rice taste (Suh et al. (2004) Kor J Breed 36: 31-37), the following seven candidate genes could be selected under the QTL interval: sucrose synthase 3 : S3, clone AP004988), trehalose phosphatase: Tre, clone AP004341, granule bound starch synthase 1 (GBSS1 / wax gene, clone AP002542), UDP-N acetylglucosamine fatigue Phosphatase (UDP-N-acetylglucosamine pyrophosphorylase: AcPh, clone AP003875), glucosamine-fructose-6-phosphate aminotransferase (GPA, clone AC138454), aspartate aminotransferase : AM, clone AP003991), and noncyanogenic beta glucosidase (non-cyanogenic-β-glucosidase: CBG, clone AC074354) (Table 2). Additionally, ADP, a gene located on chromosome 1, is involved in starch biosynthesis by searching the genome DB (http://www.ncbi.nlm.nih.gov/) (Hirose & Terao (2004) Planta 220: 9-16). Glucose kinase (ADP-glucose pyrophosphorylase (SH)) was also selected for marker development. By comparing the sequence of each gene between the varieties of Japonica, a total of nine DNA markers were developed (Table 3). Examples of PCR amplification products showing differences between varieties of Japonica are shown in FIG. 1.

< Example  2. Genotyping and Cluster Analysis of Rice Varieties>

A total of 30 markers were used for genotyping of 22 varieties of Japonica rice. 21 of the 30 markers were previously developed and 9 markers were developed in the present invention. Cluster analysis was performed based on the similarity coefficient matrix calculated from the molecular markers to draw the branching map (FIG. 2). Chochu and Hwachung showed the highest genetic similarity (0.96), while Koshihikari and Seonong 4 showed the lowest genetic similarity (0.41). Using the 0.64 minimum baseline for genetic similarity between all varieties, three clusters were formed, containing 12, 7, and 3 varieties, respectively. Koshihikari and Hitomibore formed independent subgroups, supporting the fact that Hitomibore was bred using Koshihikari as a parent. Similarly, other varieties that share lineage formed a subgroup.

< Example  3. Food  Traits>

The taste values according to Toyo seasoning (P), the taste level according to sensory evaluation (ST), and the physicochemical properties of 22 Japonica rice varieties are summarized in Table 4. All the traits except for SBV showed significant differences among the varieties. P and ST showed a wide range of variation as expected. Four varieties (Koshihikari, a la carte, Samgwang, and Gold only) showed good taste for both P and ST. Only eight varieties showed better ST than those used as controls in sensory evaluation. The amylose content (AC) was similar between the remaining varieties except for the two varieties (Baekjinju 1 and Seonong 4) that were developed as extremely low AC varieties.

Correlation analysis between vaginal traits showed that P and ST were significantly correlated (r = 0.854 ^** ) as expected (Table 5). However, the fact that P and ST are not significantly associated with other traits indicates that the taste is a complex trait involving many factors. AC was significantly associated with RVA luxury.

Table 4. Means and ranges of food parameters of 22 Japonica rice varieties

Parameter ^a Mean + standard deviation Coefficient of variation (%) range Polarization Kurtosis P 74.46 + 8.11 ** ^b 10.899 49.83-84.0 -1.675 2.997 ST 0.47 + 0.47 * -215.044 -1.24-0.39 -0.934 -0.013 AC (%) 17.46 + 3.09 ** 17.866 8.75-19.93 -1.934 3.162 PC (%) 6.85 + 0.62 ** 8.918 5.91-7.93 0.335 -0.933 CPV 232.61 + 42.08 ** 18.091 93.53-284.39 -2.05 5.263 BDV 89.19 + 21.16 ** 23.729 39.83-123.08 -0.524 0.034 PV 242.37 + 28.52 ** 11.769 181.89-298.95 0.053 0.68 SBV -9.75 + 31.08 ^ns -318.713 -94.75-25.0 -1.495 2.113 HPV 153.17 + 30.99 ** 20.235 65.20-208.8 -0.768 1.961 CTV 79.44 + 18.03 ** 22.703 28.33-98.92 -1.476 2.227 ^a P, taste; ST, food taste from sensory evaluation; AC, amylose content; PC, protein content; CPV, cold dough viscosity; BDV, drop viscosity; PV, peak viscosity; HPV, whole dough viscosity; SBV, placenta viscosity; CTV, cohesive viscosity; M, taste value evaluated from equation based on marker data
^b ns, not significant at 5% level; ** and *, significant at 1% and 5% levels, respectively.

Table 5. Correlation Matrix of Sourcing Parameters

Parameters AC P ST PC CPV BDV PV HPV SBV CTV P 0.184 ^ns ST 0.056 ^ns 0.854 ^** PC -0.476 ^* -0.431 ^* -0.241 ^ns CPV 0.735 ^** -0.144 ^ns -0.121 ^ns -0.165 ^ns BDV -0.461 ^* 0.116 ^ns 0.230 ^ns 0.398 ^ns -0.442 ^* PV 0.224 ^ns -0.142 ^ns 0.019 ^ns 0.178 ^ns 0.674 ^** 0.249 ^ns HPV 0.522 ^* -0.209 ^ns -0.139 ^ns -0.108 ^ns 0.922 ^** -0.454 ^* 0.750 ^** SBV 0.789 ^** -0.066 ^ns -0.182 ^ns -0.387 ^ns 0.735 ^** -0.827 ^** -0.005 ^ns 0.561 ^** CTV 0.819 ^** 0.022 ^ns -0.044 ^ns -0.200 ^ns 0.748 ^** 0.252 ^ns 0.284 ^ns 0.434 ^* 0.752 ^** ns , not significant at 5% level; * And **, significant at 5% and 1% levels, respectively.

Example 4 Regression Analysis Between Marker Genotypes and Dietary Traits of Varieties

Association analysis was performed between marker genotypes based on 30 primer sets and taste values by Toyo taster (P) and sensory testing (Table 6). General linear model regression analysis showed that individual molecular markers could not distinguish the flavor of varieties. Therefore, multiple regression analysis of the entire marker set was performed. By using only significant markers as an independent variable, the model equations were able to predict flavours with highly significant resolution (R ² = 0.99) (Table 6). This indicates that the taste values of 22 varieties evaluated by Toyo appetizers and / or sensory tests can be evaluated at high resolution by these regression equations based on the marker genotype. The set of markers for the equation consisted of 13 markers for the taste of taste by Toyo-flavor, 15 markers for the taste of taste by sensory test, and 9 markers shared in both equations. Three newly developed markers (GPA, S3cI, CBG) were included in these nine markers. The other three markers (AMs, SSIIa, and AcPh) were included in 13 markers for the tasteful taste by the Toyo taster, or 15 markers for the tasteful taste by the sensory test.

Table 6. Model equations for the evaluation of the taste of rice containing significant coefficients of each marker t-value by multiple regression analysis

PCR
primer Food taste by Toyo seasoning Food taste by sensory test Parameter evaluation   t-value R ² Parameter evaluation   t-value R ² G4 -16.97 + 1.194 -14.22 ** 0.087 -1.20 + 0.055 -21.77 ** 0.212 M11 -1.94 + 0.597 -3.25 ** 0.096 -0.14 + 0.029 -5.03 ** 0.010 E30 26.55 + 0.826 32.12 ** 0.104 0.86 + 0.044 19.62 ** 0.059 M2CG -2.40 + 0.555 -4.33 ** 0.060 -0.38 + 0.025 -15.21 ** 0.041 GPA -21.14 + 1.105 -19.12 ** 0.129 -0.82 + 0.048 -17.28 ** 0.021 S3cI -1.62 + 0.620 -2.60 * 0.017 -0.38 + 0.029 -13.41 ** 0.005 P5 19.01 + 1.316 14.44 ** 0.307 1.09 + 0.041 26.81 ** 0.288 B1 6.42 + 0.773 8.30 ** 0.047 0.41 + 0.032 12.90 ** 0.015 CBG 13.45 + 1.110 12.12 ** 0.087 0.68 + 0.066 10.27 ** 0.228 J6 3.87 + 0.743 5.21 ** 0.083 WK9 2.62 + 0.594 4.42 ** 0.003 A7 -12.33 + 1.269 -9.72 ** 0.031 AMs -8.72 + 1.564 -5.58 ** 0.021 G81 0.27 + 0.026 10.41 ** 0.021 F6 0.32 + 0.030 10.36 ** 0.033 SSIIa -0.27 + 0.033 -8.06 ** 0.001 G28 0.33 + 0.038 8.81 ** 0.005 AcPh -0.48 + 0.042 -11.44 ** 0.060 Intercept 76.66 + 2.709 28.29 ** -0.54 + 0.113 -4.74 ** gun 0.99 0.99 equation Y = 76.66-16.97 (G4)-
1.94 (M11) + 26.55 (E30)-
2.40 (M2CG)-21.14 (GPA)-
1.62 (S3cI) + 19.01 (P5)
+ 6.42 (B1) + 13.45 (CBG) +
3.87 (J6) + 2.62 (WK9) -12.33 (A7)
8.72 (Ams) Y = -0.54-1.20 (G4)-0.14 (M11)
+ 0.86 (E30)-0.38 (M2CG)-
0.82 (GPA)-0.38 (S3cI) +
1.09 (P5) + 0.41 (B1) + 0.68 (CBG)
+ 0.27 (G81) + 0.32 (F6)-
0.27 (SSIIa) + 0.33 (G28)-
0.48 (AcPh) ** and *, significant at 1% and 5% levels, respectively.

< Example  5. Validity of Regression Equations Using Breeding Lines>

To assess the validity of the regression equation, 13 markers were used to determine the genotypes of 32 breeding lines and three Japonica control varieties, and the taste values were evaluated by Toyo cooker. The taste evaluation was calculated by a regression equation based on the marker genotype. There is a highly significant correlation (R ² = 0.715 **) between the taste evaluation by the regression equation and the taste value according to the Toyo seasoning machine (R ² = 0.715 **), which is a model equation developed in the present invention of any breeding line of Japonica rice. It indicates that the taste can be predicted.

1 shows examples of PCR amplification products of several markers. A shows Tre's polymorphic indel. M, 100 bp DNA marker; 1, Koshikari; 2, three light; 3, a la carte; 4, Shin Geum Oh; 5, dobong; 6, three months; 7, armholes; 8, Hitomibore; 9, hex 41; 10, Samdeok; a shows the insertion of 'CTTT'. B shows the polymorphic SNP for S3. 1, Koshikari; 2, three light; 3, gem 4, Shin Geum Oh; 5, dobong; 6, three months; 7, hex 41; b shows the point mutation from 'A' to the 'G' allele. C shows the variation of '(CTT) _n ' repeat sequence in CBG. 1, Koshikari; 2, high quality; 3, three light; 4, a la carte; 5, referral; 6, Dongjin; 7, Shin Geum Oh; 8, Mars; 9, Hwacheong; 10, dobong; 11, three sons; 12, armholes; 13, hitomimebore; 14, white pearl 1; 15, weston 4; 16, full. c is the '(CTT) ₁₉ ' allele and the rest is the '(CTT) ₈ ' allele. D is 16 STS markers developed by Ohtsubo (2002, Nippon Nogeikagaku Kaishi 76: 388-397; 2003, Nippon Nogeikagaku Kaishi 50: 122-132; 2007, J Agric Food Chem 55: 1501-1509), in Koshihikari DNA banding patterns generated. (M, Marker 1, B7; 2, A7; 3, G81; 4, B43; 5, E30; 6, F6; 7, G4; 8, G22; 9, G28; 10, J6; 11, M11; 12, P5; 13, M2CG; 14, S13; 15, T16; 16, WK9).

Figure 2 is a branching map of 22 japonica varieties based on the polymorphism of the molecular marker was prepared using UPGMA according to the similarity coefficient. The varieties were clustered into three groups (I, II, and III).

FIG. 3 shows the correlation between the taste values of 32 breeding strains and 3 control varieties measured by Toyo cooker and the taste values evaluated by the regression equation.

<110> SNU R & DB FOUNDATION <120> Marker for evaluation of eating quality of Japonica rice and uses          the <130> PN09006 <160> 72 <170> KopatentIn 1.71 <210> 1 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> A6-f <400> 1 ccagctgtac gcctgtacta c 21 <210> 2 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> A6-r <400> 2 ccagctgtac gtcttcccca gc 22 <210> 3 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> A7-f <400> 3 tgcctcgcac cagaaatag 19 <210> 4 <211> 16 <212> DNA <213> Artificial Sequence <220> <223> A7-r <400> 4 tgcctcgcac catgag 16 <210> 5 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> B1-f <400> 5 gtttcgctcc tacagtaatt aaggg 25 <210> 6 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> B1-r <400> 6 gtttcgctcc catgcaatct 20 <210> 7 <211> 16 <212> DNA <213> Artificial Sequence <220> <223> B43-f <400> 7 ggccggcatg actcac 16 <210> 8 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> B43-r <400> 8 actggccggc atcaagac 18 <210> 9 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> F6-f <400> 9 accactccat atatatcatc caaag 25 <210> 10 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> F6-r <400> 10 accactccat atcaccacaa gg 22 <210> 11 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> G4-f <400> 11 gagaccgata tgcgattc 18 <210> 12 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> G4-r <400> 12 gtggtgttta gatccagaga ctta 24 <210> 13 <211> 29 <212> DNA <213> Artificial Sequence <220> <223> G22-f <400> 13 ctcactcaaa tttacagtgc attttcttg 29 <210> 14 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> G22-r <400> 14 agggccatga tacaagactc tgt 23 <210> 15 <211> 17 <212> DNA <213> Artificial Sequence <220> <223> G28-f <400> 15 ggcggtcgtt ctgcgat 17 <210> 16 <211> 28 <212> DNA <213> Artificial Sequence <220> <223> G28-r <400> 16 ggagaatccc acagtaagtt tttctttg 28 <210> 17 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> J6-f <400> 17 gtcggagtgg tcagaccg 18 <210> 18 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> J6-r <400> 18 gtcggagtgg atggagtagc 20 <210> 19 <211> 16 <212> DNA <213> Artificial Sequence <220> <223> M2CG-f <400> 19 acaacgcctc cgatga 16 <210> 20 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> M2CG-r <400> 20 acaacgcctc cgacaacaag at 22 <210> 21 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> M11-f <400> 21 gtccactgtg accacaacat 20 <210> 22 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> M11-r <400> 22 gtccactgtg gggattgttc 20 <210> 23 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> P5-f <400> 23 acaacggtcc gtccttgctt 20 <210> 24 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> P5-r <400> 24 acaacggtcc aacagatact tttga 25 <210> 25 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> S13-f <400> 25 gtcgttcctg tggttaggac agggt 25 <210> 26 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> S13-r <400> 26 gtcgttcctg ctggtgtctc agat 24 <210> 27 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> T16-f <400> 27 ggtgaacgct gtagttggaa tata 24 <210> 28 <211> 26 <212> DNA <213> Artificial Sequence <220> <223> T16-r <400> 28 ggtgaacgct cagatttaaa tataat 26 <210> 29 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> WK9-f <400> 29 cccgcagtta gatgcaccat t 21 <210> 30 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> WK9-r <400> 30 ccgcagttag atcaagtggc 20 <210> 31 <211> 28 <212> DNA <213> Artificial Sequence <220> <223> E30-f <400> 31 tacctggttg atgtatacag atctggtt 28 <210> 32 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> E30-r <400> 32 atccctcgat ccctctagca ttat 24 <210> 33 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> B7-f <400> 33 caggtgtggg ttacaaggat ga 22 <210> 34 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> B7-r <400> 34 caggtggttc acggccttt 19 <210> 35 <211> 29 <212> DNA <213> Artificial Sequence <220> <223> G49A-f <400> 35 aatccagaca tgaaatttat atgcagata 29 <210> 36 <211> 28 <212> DNA <213> Artificial Sequence <220> <223> G49A-r <400> 36 aatccagaca tgttgtcctc aatttttg 28 <210> 37 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> G81-f <400> 37 tacctgaacc agcaagcatg cgcg 24 <210> 38 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> G81-r <400> 38 tacctgaacc agtataatct ttg 23 <210> 39 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> P3-f <400> 39 aacgggccaa aaacggaggt 20 <210> 40 <211> 15 <212> DNA <213> Artificial Sequence <220> <223> P3-r <400> 40 aacgggccaa cgcag 15 <210> 41 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Wx-f <400> 41 ctttgtctat ctcaagacac 20 <210> 42 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> Wx-r <400> 42 tttccagccc aacaccttac 20 <210> 43 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SS1-f <400> 43 gatccgtttt tgctgtgccc 20 <210> 44 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SS1-r <400> 44 cctcctctcc gccgatcctg 20 <210> 45 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SBE1-f1 <400> 45 atttctttgg ccacaggcga 20 <210> 46 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SBE1-r1 <400> 46 cccagattcg gaacaagaac 20 <210> 47 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SBE1-f2 <400> 47 gagttgagtt gcgtcagatc 20 <210> 48 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SBE1-r2 <400> 48 aatgaggttg cttgctgctg 20 <210> 49 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> SBE3-f <400> 49 gtcttggact cagatgctgg actc 24 <210> 50 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> SBE3-r <400> 50 atgtataact ggcagttcga acgg 24 <210> 51 <211> 24 <212> DNA <213> Artificial Sequence <220> <223> SSIIa-f7 <400> 51 ctggatcact tcaagctgta cgac 24 <210> 52 <211> 21 <212> DNA <213> Artificial Sequence <220> <223> SSIIa-r1 <400> 52 gccggccgtg cagatcttaa c 21 <210> 53 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SSIIa-f22 <400> 53 caaggagagc tggagggggc 20 <210> 54 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> SSIIa-r21 <400> 54 acatgccgcg cacctggaaa 20 <210> 55 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> S3cI-f <400> 55 ccactctcat gtccttgaac 20 <210> 56 <211> 18 <212> DNA <213> Artificial Sequence <220> <223> S3cI-r <400> 56 gccatgacat ttggacat 18 <210> 57 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> S3cII-f <400> 57 ttccatgatg tgccactctc 20 <210> 58 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> S3cII-r <400> 58 ggacaaatgt tttcagtgaa taaat 25 <210> 59 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> TreB-f <400> 59 cactccagtt cctgctcaaa 20 <210> 60 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> TreB-r <400> 60 cactccagtt cctgctcaaa 20 <210> 61 <211> 19 <212> DNA <213> Artificial Sequence <220> <223> AMs-f <400> 61 cttccaagga ccccatcct 19 <210> 62 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> AMs-r <400> 62 cccaacatct ccgtcagaat 20 <210> 63 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> GPA-f <400> 63 aatacgcggc cttctcctat 20 <210> 64 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> GPA-r <400> 64 ttgatccgaa tgggtcaaat 20 <210> 65 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> GBSS1-f <400> 65 caaatagcca cccacaccac 20 <210> 66 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> GBSS1-r <400> 66 cttgcagatg ttcttcctga tg 22 <210> 67 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> AcPh-f <400> 67 agttgtggtt taagcatagg 20 <210> 68 <211> 26 <212> DNA <213> Artificial Sequence <220> <223> AcPh-r <400> 68 attgtccttt tctttaaagt ttatta 26 <210> 69 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> CBG-f <400> 69 agcttcccta atggcttcgt 20 <210> 70 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> CBG-r <400> 70 atttgccaac ttttggatgg 20 <210> 71 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> SH51-f <400> 71 attcttgatg aaaataatta actag 25 <210> 72 <211> 25 <212> DNA <213> Artificial Sequence <220> <223> SH51-r <400> 72 ggttaaccat cttataaaat ttgtc 25  

Claims (7)

delete delete delete Oligonucleotide primer sets of SEQ ID NOs: 3 and 4; Oligonucleotide primer sets of SEQ ID NOs: 5 and 6; Oligonucleotide primer sets of SEQ ID NOs: 11 and 12; Oligonucleotide primer sets of SEQ ID NOs: 17 and 18; Oligonucleotide primer sets of SEQ ID NOs: 19 and 20; Oligonucleotide primer sets of SEQ ID NOs: 21 and 22; Oligonucleotide primer sets of SEQ ID NOs: 23 and 24; Oligonucleotide primer sets of SEQ ID NOs: 29 and 30; Oligonucleotide primer sets of SEQ ID NOs: 31 and 32; Oligonucleotide primer sets of SEQ ID NOs: 55 and 56; Oligonucleotide primer sets of SEQ ID NOs: 61 and 62; Oligonucleotide primer sets of SEQ ID NOs: 63 and 64; And oligonucleotide primer sets of SEQ ID NOs: 69 and 70 or Oligonucleotide primer sets of SEQ ID NOs: 5 and 6; Oligonucleotide primer sets of SEQ ID NOs: 9 and 10; Oligonucleotide primer sets of SEQ ID NOs: 11 and 12; Oligonucleotide primer sets of SEQ ID NOs: 15 and 16; Oligonucleotide primer sets of SEQ ID NOs: 19 and 20; Oligonucleotide primer sets of SEQ ID NOs: 21 and 22; Oligonucleotide primer sets of SEQ ID NOs: 23 and 24; Oligonucleotide primer sets of SEQ ID NOs: 31 and 32; Oligonucleotide primer sets of SEQ ID NOs: 37 and 38; Oligonucleotide primer sets of SEQ ID NOs: 51 and 52; Oligonucleotide primer sets of SEQ ID NOs: 53 and 54; Oligonucleotide primer sets of SEQ ID NOs: 55 and 56; Oligonucleotide primer sets of SEQ ID NOs: 63 and 64; Oligonucleotide primer sets of SEQ ID NOs: 67 and 68; And oligonucleotide primer sets of SEQ ID NOs: 69 and 70; And a reagent for conducting an amplification reaction. The kit as claimed in claim 4, wherein the rice is japonica rice. Separating genomic DNA from the rice sample; Using the isolated genomic DNA as a template, Oligonucleotide primer sets of SEQ ID NOs: 3 and 4; Oligonucleotide primer sets of SEQ ID NOs: 5 and 6; Oligonucleotide primer sets of SEQ ID NOs: 11 and 12; Oligonucleotide primer sets of SEQ ID NOs: 17 and 18; Oligonucleotide primer sets of SEQ ID NOs: 19 and 20; Oligonucleotide primer sets of SEQ ID NOs: 21 and 22; Oligonucleotide primer sets of SEQ ID NOs: 23 and 24; Oligonucleotide primer sets of SEQ ID NOs: 29 and 30; Oligonucleotide primer sets of SEQ ID NOs: 31 and 32; Oligonucleotide primer sets of SEQ ID NOs: 55 and 56; Oligonucleotide primer sets of SEQ ID NOs: 61 and 62; Oligonucleotide primer sets of SEQ ID NOs: 63 and 64; And oligonucleotide primer sets of SEQ ID NOs: 69 and 70 or Oligonucleotide primer sets of SEQ ID NOs: 5 and 6; Oligonucleotide primer sets of SEQ ID NOs: 9 and 10; Oligonucleotide primer sets of SEQ ID NOs: 11 and 12; Oligonucleotide primer sets of SEQ ID NOs: 15 and 16; Oligonucleotide primer sets of SEQ ID NOs: 19 and 20; Oligonucleotide primer sets of SEQ ID NOs: 21 and 22; Oligonucleotide primer sets of SEQ ID NOs: 23 and 24; Oligonucleotide primer sets of SEQ ID NOs: 31 and 32; Oligonucleotide primer sets of SEQ ID NOs: 37 and 38; Oligonucleotide primer sets of SEQ ID NOs: 51 and 52; Oligonucleotide primer sets of SEQ ID NOs: 53 and 54; Oligonucleotide primer sets of SEQ ID NOs: 55 and 56; Oligonucleotide primer sets of SEQ ID NOs: 63 and 64; Oligonucleotide primer sets of SEQ ID NOs: 67 and 68; And amplifying the target sequence by performing an amplification reaction using the oligonucleotide primer sets of SEQ ID NOs: 69 and 70; Determining a base sequence of the amplification product; And Statistical processing of the presence or absence of variation between the determined base sequence, Rice's taste evaluation method. 7. The method of claim 6, wherein the rice is Japonica rice.

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