Utilization of Elite Korean Japonica Rice Varieties for Association … · variation was observed in heading time, culm length, and amylose and protein content in the 161 japonica

Developing new varieties of rice, the staple for nearly half

of the world’s population, is crucial for enhancing food security

(Awika et al., 2011). As global weather patterns and market

demands change rapidly, rice breeders face an increasing

challenge of accelerating crop improvement to meet the varying

needs in a timely manner (Tester & Langridge, 2010). The use of

DNA markers linked to the genes or quantitative trait loci (QTL)

controlling agriculturally important traits can greatly improve

breeding efficiency by providing the means to precisely design

cross combinations, select breeding lines carrying favorable

alleles, and pyramid desirable alleles of multiple genes/QTL into

the elite varieties (Collard & Mackill, 2008).

Mapping genes or QTL responsible for phenotypic variation is

a prerequisite for marker-assisted selection. Unlike traditional

linkage mapping which normally uses a mapping population

derived from a cross between two parents with contrasting

phenotype (Collard et al., 2005), a collection of preexisting

genetic resources is utilized in association mapping to identify

trait-associated loci based on the historical recombination events

accumulated during evolution/domestication/breeding (Myles et

al., 2009). In spite of several inherent drawbacks (e.g. false

positives/negatives arising from population structure, lack of the

ability to detect causal variants with low allele frequency),

association mapping is being widely utilized in rice thanks to the

abundant genetic and genomic resources (McCouch et al., 2016;

Verdeprado et al., 2018).

As in other crops, association mapping in rice is mostly

conducted using landraces or varieties of various geographical

Utilization of Elite Korean Japonica Rice Varieties for Association Mapping of Heading

Time, Culm Length, and Amylose and Protein Content

Youngjun Mo1, Jong-Min Jeong

1, Bo-Kyeong Kim

2, Soon-Wook Kwon

3, and Ji-Ung Jeung

2,†

ABSTRACT Association mapping is widely used in rice and other crops to identify genes underlying important agronomic

traits. Most association mapping studies use diversity panels comprising accessions with various geographical origins to exploit

their wide genetic variation. While locally adapted breeding lines are rarely used in association mapping owing to limited genetic

diversity, genes/alleles identified from elite germplasm are practically valuable as they can be directly utilized in breeding

programs. In this study, we analyzed genetic diversity of 179 rice varieties (161 japonica and 18 Tongil-type) released in Korea

from 1970 to 2006 using 192 microsatellite markers evenly distributed across the genome. The 161 japonica rice varieties were

genetically very close to each other with limited diversity as they were developed mainly through elite-by-elite crosses to meet the

specific local demands for high quality japonica rice in Korea. Despite the narrow genetic background, abundant phenotypic

variation was observed in heading time, culm length, and amylose and protein content in the 161 japonica rice varieties. Using

these varieties in association mapping, we identified six, seven, ten, and four loci significantly associated with heading time, culm

length, and amylose and protein content, respectively. The sums of allelic effects of these loci showed highly significant positive

correlation with the observed phenotypic values for each trait, indicating that the allelic variation at these loci can be useful when

designing cross combinations and predicting progeny performance in local breeding programs.

Keywords : amylose, association mapping, culm length, heading time, protein, rice

한작지(Korean J. Crop Sci.), 65(1): 1~21(2020)

DOI : https://doi.org/10.7740/kjcs.2020.65.1.001

Original Research Article

ⓒ 본 학회지의 저작권은 한국작물학회지에 있으며, 이의 무단전재나 복제를 금합니다.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

1)Research Scientist, National Institute of Crop Science, Rural Development Administration, Wanju 55365, Republic of Korea2)Senior Scientist, National Institute of Crop Science, Rural Development Administration, Wanju 55365, Republic of Korea3)Associate Professor, Department of Plant Bioscience, College of Natural Resources and Life Science, Pusan National University, Miryang

50463, Republic of Korea

†Corresponding author: Ji-Ung Jeung; (Phone) +82-63-238-5231; (E-mail) [email protected]

<Received 4 January, 2020; Accepted 29 January, 2020>

ISSN 0252-9777(Print)ISSN 2287-8432(Online)

한작지(KOREAN J. CROP SCI.), 65(1), 20202

origins to maximize genotypic and phenotypic diversity. For

example, diverse rice landraces collected in China have been

used successfully in association mapping to identify novel and

previously-known loci associated with major agronomic traits

(Huang et al., 2010, 2012). The genebank core collections are

also popularly used in rice association mapping. The USDA

(United States Department of Agriculture) rice mini-core collection

(n=217) chosen from more than 18,000 accessions have been

used to identify loci associated with yield-related traits, protein

content, biotic and abiotic stress resistance (Li et al., 2011, 2012;

Jia et al., 2012; Bryant et al., 2013; Schläppi et al., 2017).

Similarly, a core collection (n=166) representing the diversity of

over 4,400 world-wide rice varieties maintained by the Korean

RDA (Rural Development Administration) genebank has been

used in association mapping of various agronomic traits (Zhao et

al., 2013; Li et al., 2014; Xu et al., 2016; Bao et al., 2017).

In contrast, elite breeding materials are less frequently used in

association mapping because of the limited genetic variability

(Zhang et al., 2016; Verdeprado et al., 2018). However, association

mapping using elite germplasm adapted to a local environment

has highly practical values by providing information that can be

directly utilized in breeding programs (Fujino et al., 2015; Begum

et al., 2015; Yano et al., 2016). In the present study, we evaluated

genetic diversity of 179 commercial rice varieties (161 japonica

and 18 Tongil-type) released in Korea from 1970 to 2006 and

conducted association mapping of heading time, culm length,

and amylose and protein content using the 161 japonica varieties.

MATERIALS AND METHODS

Plant material

A total of 179 Korean rice varieties used in this study were

bred at the National Institute of Crop Science (NICS), Rural

Development Administration (RDA) of South Korea from 1970

to 2006 and are composed of 161 japonica and 18 Tongil-type

(indica/japonica hybridization) varieties (Supplementary Table

1). Additionally, the rice variety IR24 and its eight near-isogenic

lines (NILs) carrying different bacterial blight resistance genes

were included as indica subspecies checks for genetic diversity

analysis. The International Rice Bacterial Blight (IRBB) NILs in

the IR24 background include IRBB1 (Xa1), IRBB3 (Xa3), IRBB4

(Xa4), IRBB5 (xa5), IRBB7 (Xa7), IRBB8 (xa8), IRBB10 (Xa10),

and IRBB21 (Xa21) (Huang et al., 1997).

Genotyping

DNA was extracted from young seedling leaf tissue of the 188

rice varieties described above using the CTAB method (Murray

& Thompson, 1980) with minor modifications. A total of 192

polymorphic simple sequence repeat (SSR) markers evenly dis-

tributed throughout the genome (11 – 21 markers per chromosome;

Supplementary Table 2) were screened from previously reported

rice SSR markers (McCouch et al., 2002). PCR and gel electro-

phoresis were conducted as described previously (Mo et al., 2013).

Genetic diversity analysis

Summary statistics of the 192 SSRs including the allele

number per locus, gene diversity, and polymorphism information

content (PIC) were calculated using PowerMarker version 3.25

(Liu & Muse, 2005). For phylogeny analysis of the 188 rice

varieties, the Rogers-Tanimoto dissimilarity matrix based on the

192 SSR genotype was generated to construct an unweighted

neighbor-joining tree with 1,000 bootstrap iterations using

DARwin version 6.0.017 (http://darwin.cirad.fr). Principal com-

ponent analysis (PCA) was carried out using NTSYSpc version

2.21o (Exeter Software, Setauket, NY). Population structure was

analyzed using STRUCTURE version 2.3.4 (Pritchard et al.,

2000) with 50,000 burn-in iterations followed by 50,000 Markov-

Chain iterations for each value of the hypothetical subpopulation

numbers (K = 2 to 5).

Phenotyping

Agronomic traits including days to heading, culm length, and

amylose and protein content were evaluated in 161 Korean

japonica rice varieties. The plants were grown in the experimental

field at NICS, RDA, Suwon, South Korea in 2008. A randomized

complete block design (RCBD) with two replications was used,

with an experimental unit of a row including 30 individual plants

for each variety. The plants in a row were spaced by 15 cm and

rows were spaced by 30 cm. The plants were cultivated and

evaluated according to the standard evaluation method for rice

(RDA, 2003). Days to heading was determined as the number of

days from sowing to heading when 40% of the plants in a row

have initiated flowering. Culm length was determined by

measuring the length from the ground to the panicle node of the

longest culm from each plant and the average of 10 random

plants in each row was used to represent an experimental unit.

Upon maturity, the plants were harvested, dehulled and milled to

Association Mapping in Elite Rice Varieties 3

produce white rice and evaluate amylose and protein content.

Amylose content was measured using the Juliano method

(Juliano, 1971). Protein content was determined following the

Kjeldahl method as previously described (AOAC, 1996).

Association analysis

In order to minimize spurious association arising from po-

pulation structure, marker-trait association was analyzed using

the 161 Korean japonica varieties with the 173 polymorphic

SSRs after excluding 18 Tongil-type varieties. Association

analysis was conducted with four different models: the simple

model (single-locus ANOVAs), the Q model considering popula-

tion structure, the K model considering kinship, and the Q + K

model considering both population structure and kinship. The

simple model was implemented using SAS version 9.2 (SAS

institute, Cary, NC, USA). The Q, K, and Q + K models were

carried out using TASSEL version 3.0 with the default para-

meters (Bradbury et al., 2007). As population structure analysis

and PCA identified two potential subpopulations within the 161

japonica varieties, membership coefficients were obtained at K

= 2 using the STRUCTURE analysis as described above and

were used in the Q and Q + K models. Based on quantile-quantile

plots of the observed and expected P-values from the four

models, the K model was selected for all four traits and used to

declare significant maker-trait associations. For the significant

loci identified for each trait, Pearson’s correlation analysis was

conducted between the observed phenotypic values and the sums

of the allelic effects using R version 3.5.1.

RESULTS

Genetic diversity and population structure of Korean

rice varieties

A total of 828 alleles from the 192 polymorphic SSRs were

identified among the 188 rice varieties (Table 1): 663 alleles

among 161 Korean japonica varieties, 584 alleles among 18

Korean Tongil-type varieties, and 249 alleles among the indica

variety IR24 and its eight IRBB NILs. In spite of the smaller

number of accessions, the Tongil-type varieties had higher

number of polymorphic markers (189 SSRs) than the japonica

varieties (173 SSRs). Also, both gene diversity and PIC values

were higher in the 18 Tongil-type varieties (0.3810 and 0.3410,

respectively) than in the 161 japonica varieties (0.3075 and

0.2703, respectively), indicating that the Korean japonica rice

varieties have narrow genetic diversity.

Phylogenetic analysis using the 192 polymorphic SSRs

clearly differentiated the 188 rice varieties according to their

subspecies designation, i. e., 161 japonica, 18 Tongil-type, and

nine indica (IR24 and eight IRBBs) varieties (Fig. 1A). Most

Tongil-type varieties were genetically close to indica except for

Nogyang which was closer to japonica and Nongan which was

intermediate between indica and japonica. Similar pattern was

observed in PCA, in which all the japonica varieties except for

Sangnambat formed a very tight cluster that is differentiated

from the Tongil-type and indica varieties by the first and the

second principal components explaining 59.3% and 9.2% of the

variance, respectively (Fig. 1B). Both phylogenetic analysis

(Fig. 1A) and PCA (Fig. 1B) demonstrated that Korean japonica

rice varieties are genetically very close to each other and thus

have limited genetic diversity.

Table 1. Summary statistics of 192 SSR markers.

GroupNo. of

varieties

No. of

alleles

No. of alleles

per locusGene diversity PICa

No. of

monomorphic

markers

japonica 161 663 3.45 0.3075 0.2703 19

Tongil 18 584 3.04 0.3810 0.3410 3

IRBBb 9 249 1.30 0.0805 0.0701 142

All 188 828 4.31 0.4390 0.3963 0

aPolymorphism information content.bThe indica cultivar IR24 and its near-isogenic lines carrying different bacterial blight resistant genes (see the materials and

methods section).


In the structure analysis conducted at K = 3 (i.e., three

hypothetical subpopulations) among the 188 rice varieties, the

161 japonica varieties were further divided into two potential

subpopulations (Fig. 2). In order to examine the subpopulation

structure within the japonica population, an additional PCA was

conducted among the 161 Korean japonica varieties using the

173 polymorphic SSRs after excluding the 18 Tongil-type and

nine indica varieties (Fig. 1C). The first principal component

explained 68.4% of the variance but did not classify the 161

japonica varieties. Although the second principal component

divided the 161 japonica varieties into two potential sub-

populations, it explained only 3.5% of the variance, indicating

that the Korean japonica varieties used in this study likely

belong to a single population.

Fig. 1. Genetic diversity of 188 rice varieties. (A) Unweighted neighbor-joining tree of 188 rice varieties using 192 polymorphic

SSR markers. Blue and purple lines indicate 161 japonica and 18 Tongil-type Korean varieties, respectively. Red lines

indicate the indica cultivar IR24 and its eight near-isogenic lines. Numbers in blue indicate bootstrap values (%) higher

than 60%. (B) Principal component analysis of 188 varieties using 192 polymorphic SSR markers. (C) Principal

component analysis of 161 Korean japonica varieties using 173 polymorphic SSR markers.

Fig. 2. Population structure analysis of 188 rice varieties using

192 polymorphic SSR markers (K = 2 to 5). Different

colors indicate membership probability of each variety

at a given number of subpopulations.


Phenotypic analysis

Despite the narrow genetic diversity, abundant phenotypic

variation was observed in days to heading (CV 8.5%), culm

length (8.1%), amylose content (20.0%) and protein content

(10.2%) among the 161 Korean japonica rice varieties (Table 2,

Fig. 3). The average days to heading was 107.6 days ranging

from 74 to 122 days. The majority of the varieties (53%)

belonged to the mid-late maturity group with >110 days to

heading. The average culm length was 81.8 cm ranging from

54.8 cm to 95.8 cm and the majority (52%) belonged to the

semi-dwarf group with the culm length of 75 cm – 85 cm. The

average amylose content was 17.6% ranging from 6.3% to

25.9%. Amylose content of the 147 varieties categorized as the

non-glutinous type ranged from 13.5% to 25.9%, while that of

the 14 varieties categorized as the glutinous type ranged from

6.3% to 10.5%. The average protein content was 7.2% ranging

from 5.7% to 9.2%.

Maker-trait association analysis

To minimize false positives arising from the population

structure, association analysis was conducted using only the 161

Korean japonica varieties after excluding the 18 Tongil-type

varieties. The quantile-quantile plots of observed and expected

P-values from the four models (simple, Q, K, Q + K) indicated

Table 2. Descriptive statistics of four agronomic traits in 161 Korean japonica varieties.

Trait Mean ± SD Min Max CV (%) Kurtosis Skewness

Days to heading 107.6 ± 9.10 74.0 122.0 8.5 -0.12 -0.81

Culm length (cm) 81.8 ± 6.63 54.8 95.8 8.1 0.48 -0.33

Amylose content (%) 17.6 ± 3.52 6.3 25.9 20.0 4.65 -2.21

Protein content (%) 7.2 ± 0.74 5.7 9.2 10.2 -0.32 0.53

Fig. 3. Frequency distribution of four agronomic traits in 161 Korean japonica rice varieties. (A) Days to heading. (B) Culm

length. (C) Amylose content. (D) Protein content.


that for all four traits (days to heading, culm length, and amylose

and protein content), adding the kinship (K) matrix in the model

controls false positives effectively (Fig. 4). However, adding the

population structure (Q) matrix in the model had little effect

(Fig. 4), which was consistent with the PCA results (Fig. 1B, C)

indicating that no significant population structure exists within

the 161 japonica rice varieties. Therefore, the K model was

selected to declare significant marker-trait associations.

At the threshold of P < 0.01, six, seven, and ten significant loci

were identified for days to heading, culm length, and amylose

content, respectively (Table 3). As only one locus was detected

for protein content at P < 0.01, we used a lower threshold of P <

0.02 to declare four significant loci (Table 3). Phenotypic

variance explained by the significant loci (R2) for each trait was

4.9% (RM01300) – 15.0% (RM05717) for days to heading, 6.2%

(RM01300) – 16.1% (RM00152) for culm length, 4.6% (RM0

3571) – 18.3% (RM00206) for amylose content, and 5.2% (RM

05963) – 14.7% (RM05717) for protein content. Four loci were

significantly associated with two or more traits – RM05963 and

RM05717 for days to heading and protein content, RM01300 for

days to heading and culm length, and RM01376 for days to

heading, and amylose and protein content (Table 3).

We next tested the additive effects of the significant loci by

analyzing correlation between the observed phenotypic values

and the sums of the allelic effects of the significant loci for each

trait. All four traits showed highly significant (P < 0.0001)

positive correlations (Fig. 5) – days to heading (r = 0.6119), culm

length (r = 0.5765), amylose content (r = 0.6301), and protein

content (r = 0.4954).

DISCUSSION

Modern rice breeding programs in Korea have been focusing

on two major goals – 1) developing high-yielding Tongil-type

varieties to achieve self-sufficiency of rice before the 1980s, and

2) developing high-quality japonica varieties to meet the

Fig. 4. Quantile-quantile plots comparing different association analysis models for four agronomic traits. (A) Days to heading. (B)

Culm length. (C) Amylose content. (D) Protein content. P-values from the simple, Q, K, and Q + K models were used.


changing market demands for rice with good appearance and

high eating quality since the 1980s. Tongil-type rice varieties

were developed through the hybridization between the two

different subspecies, japonica an indica, which broadened

genetic background of the Korean rice breeding population

(Chung & Heu, 1991). However, in order to maintain the

marketable grain characteristics (i.e., translucent and short-grain

japonica rice with relatively low amylose and protein content for

palatability preferred by the Korean consumers), crosses

between elite varieties with similar genetic background have

been mainly used in the high-quality japonica rice breeding

programs since the 1980s. This resulted in the narrow genetic

Table 3. Markers significantly associated with four agronomic traits.

Trait Markera Chr. Mbb Pc R2Neighboring gene

Name Mbb Refd

Days to

heading

RM05631 2 28.3 0.0014 0.066 DTH2 30.1 (1)

RM05963 6 8.8 0.0017 0.083 Hd1 9.3 (2)

RM01243 7 3.6 0.0069 0.078

RM01376 8 3.2 0.0026 0.077 Hd18 2.4 (3)

RM05717 8 27.3 0.0009 0.150

RM01300 2 26.0 0.0091 0.049

Culm

length

RM06324 1 2.4 0.0018 0.126 SUI1 1.0 (4)

RM00152 8 0.7 2.6 × 10-5 0.161

RM05494 10 22.3 0.0065 0.117

RM05704 11 5.5 0.0039 0.152

RM00144 11 28.2 0.0034 0.105

RM08215 12 1.6 0.0080 0.077

RM01300 12 26.0 0.0022 0.062

Amylose

content

RM01054 5 29.1 0.0004 0.103

RM03805 6 2.9 0.0015 0.128 Wx 1.8 (5)

RM00180 7 5.7 0.0032 0.104

RM01376 8 3.2 0.0084 0.061

RM03571 8 26.1 0.0073 0.046

RM00257 9 17.7 0.0083 0.102

RM06704 10 17.7 0.0030 0.105

RM00206 11 22.0 0.0008 0.183

RM03472 12 3.5 0.0097 0.060

RM01986 12 21.2 0.0086 0.123

Protein

content

RM05963 6 8.8 0.0175 0.052

RM08263 7 4.7 0.0136 0.055

RM01376 8 3.2 0.0138 0.055

RM05717 8 27.3 0.0011 0.147aUnderlined markers are associated with two or more traits. When two or more adjacent markers are significantly associated

with a trait, only one marker with the highest R2 was retained in the table to represent the corresponding locus.bOs-Nipponbare-Reference-IRGSP 1.0.cP-values from the K model. P < 0.01 was used as the threshold for days to heading, culm length, and amylose content, whereas

P < 0.02 was used for protein content.d(1) Wu et al., 2013; (2) Yano et al., 2000; (3) Shibaya et al., 2016; (4) Zhu et al., 2011; (5) Wang et al., 1995.


diversity among the Korean japonica rice varieties. By

genotyping 179 rice varieties (161 japonica and 18 Tongil-type)

released in Korea from 1970 to 2006 with 192 polymorphic

SSRs evenly distributed throughout the genome, our study

molecularly demonstrated that Korean japonica rice varieties

have very narrow genetic diversity. Despite the greater number

of accessions, the PIC and gene diversity values from the

japonica varieties were smaller than those from the Tongil-type

varieties (Table 1), and both phylogenetic analysis and PCA

showed that Korean japonica rice varieties are genetically very

close to each other (Fig. 1). Our results are consistent with the

previous works showing the limited genetic background of

Korean japonica rice varieties, many of which share common

elite japonica parents in the pedigree (Kwon et al., 1999; Song et

al., 2002).

Elite breeding lines developed for a specific target region have

limited genotypic and phenotypic variation compared to diversity

panels such as world-wide landraces/varieties collections, there-

fore are rarely used in association mapping. While association

mapping with a diversity panel can be powerful for identifying

new genes/loci controlling important agronomic traits, accessions

with diverse geographical origins often have strong population

structure resulting in the high rate of spurious associations

(Myles et al., 2009; Zhang et al., 2016). This is especially the

case for traits strongly correlated with population structure such

as heading time, and it is difficult to solve this issue using

statistical models (Huang et al., 2010, 2012). Also, the effects of

alleles identified from a diversity panel should be validated in

the genetic background of local varieties prior to be utilized in

breeding programs (Verdeprado et al., 2018). In contrast, local

breeding lines such as the Korean elite japonica population used

in this study have much less complex population structure, and

loci/alleles identified from these populations can be directly

utilized for breeding. To exploit such advantages, association

Fig. 5. Correlation analysis between the sum of allelic effects and the observed phenotypic values for each trait. (A) Days to

heading. (B) Culm length. (C) Amylose content. (D) Protein content. The allelic effects of the significant markers

according to the K-model (Table 3) were used to calculate the sum of allelic effects for each trait. For culm length,

two extreme outliers were excluded from the plot to better represent the correlation pattern.


mapping studies in elite rice panels have been recently con-

ducted using Japanese japonica varieties (Yano et al., 2016),

local varieties in Hokkaido region of Japan (Fujino et al., 2015;

Shinada et al., 2015), and advanced indica breeding lines from

the International Rice Research Institute (Begum et al., 2015),

identifying agronomically important loci and alleles that can be

directly applied in breeding programs.

As the Korean japonica rice varieties used in this study

possessed abundant phenotypic variation (CV 8.1% – 20.0%;

Table 2, Fig. 3) in spite of the limited genetic diversity, we were

able to successfully identify loci significantly associated with

days to heading, culm length, and amylose and protein content

(Table 3). Some of the identified loci corresponded to the

previously cloned genes such as DTH2 (RM05631; Wu et al.,

2013), Hd1 (RM05963; Yano et al., 2000), and Hd18 (RM0

1376; Shibaya et al., 2016) for days to heading, SUI1 (RM0

6324; Zhu et al., 2011) for culm length, and Wx (RM03805;

Wang et al., 1995) for amylose content, while others were

previously uncharacterized loci (Table 3). The additive allelic

effects of these loci for each trait showed highly significant

positive correlation with the observed phenotypic values (Fig.

5), indicating that these loci can provide useful information for

designing cross combinations and predicting progeny perfor-

mance in the local breeding programs.

However, owing to the limited number of SSR markers used

in this study, we were not able to precisely designate candidate

genes underlying the identified loci and analyze allelic/haplotypic

diversity of the potential causal genes. Another limitation of this

study is that the phenotypic data was available from a single

location for only one year, which precluded the opportunity to

evaluate environmental variation and reduce experimental

errors. To complement the present work, we have initiated

genotyping-by-sequencing experiments of the rice varieties used

in this study and the additional ones released in Korea since 2007

to increase the marker density and improve the mapping

resolution. Phenotypic evaluations are also being conducted on

additional traits including yield components, grain quality, biotic

and abiotic stress resistance in three different locations (i.e.,

Suwon, Wanju and Miryang in South Korea) for multiple years

(Lee et al., 2019). This is expected to improve our understanding

of the genetic basis underlying variation in agronomically

important traits in locally adapted elite rice germplasm and

provide molecular tools to enhance the efficiency of crop

improvement.

ACKNOWLEDGEMENTS

This work was supported by the National Institute of Crop

Science, Rural Development Administration, Republic of Korea

(Project ID: PJ01357205).

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Supplementary Table 1. Korean rice varieties (179 in total) used in this study.

Year released Variety name Subspecies

1970 Chucheong japonica

1975 Nagdong japonica

1979 Hangangchal Tongil-type

1979 Taebaeg Tongil-type

1980 Daecheong japonica

1981 Dongjin japonica

1981 Nampung Tongil-type

1982 Odae japonica

1982 Samgang Tongil-type

1982 Shinseonchal japonica

1982 Sobaeg japonica

1985 Hwaseong japonica

1985 Jangseong Tongil-type

1985 Unbong japonica

1985 Yongmoon Tongil-type

1986 Palgong japonica

1986 Yongju Tongil-type

1988 Donghae japonica

1988 Geumo japonica

1988 Hwajin japonica

1988 Sangnambat japonica

1988 Tamjin japonica

1989 Cheongmyeong japonica

1989 Gyehwa japonica

1989 Jangan japonica

1989 Jinmi japonica

1989 Namweon japonica

1989 Obong japonica

1990 Ilpum japonica

1990 Jinbuchal japonica

1990 Seoan japonica

1991 Anjung japonica

1991 Hwayeong japonica

1991 Jinbu japonica

1991 Jinbuol japonica

1991 Mangeum japonica

1991 Sangju japonica

1991 Shinunbong japonica

1991 Yeongnam japonica

1992 Daeya japonica

1992 Dunnae japonica

1992 Gancheog japonica

1992 Hwaseonchal japonica

1992 Joryeong japonica


Supplementary Table 1. Korean rice varieties (179 in total) used in this study (Continued).


1993 Daerip1 japonica

1993 Hwajung japonica

1993 Hwanam japonica

1993 Hyangmi1 Tongil-type

1993 Nongan Tongil-type

1993 Sambaeg japonica

1993 Sangsan japonica

1994 Daean japonica

1994 Geumnam japonica

1994 Juan japonica

1994 Unjang japonica

1994 Yangjo japonica

1995 Ansan japonica

1995 Dasan Tongil-type

1995 Geumo1 japonica

1995 Hwasin japonica

1995 Hyangnam japonica

1995 Ilmi japonica

1995 Junghwa japonica

1995 Naepung japonica

1995 Namcheon Tongil-type

1995 Samcheon japonica

1996 Daejin japonica

1996 Daesan japonica

1996 Dongan japonica

1996 Hwasam japonica

1996 Seojin japonica

1997 Aranghyangchal japonica

1997 Gru japonica

1997 Heugjinju japonica

1997 Heugnam japonica

1997 Hwadong japonica

1997 Hwamyeong japonica

1997 Namgang japonica

1997 Nampyeong japonica

1997 Sangjuchal japonica

1997 Yeonghae japonica

1998 Anda Tongil-type

1998 Dongjinchal japonica

1998 Hoan japonica

1998 Hwabong japonica

1998 Inweol japonica

1998 Kwangan japonica

1998 Manan japonica




1998 Mihyang japonica

1998 Nongho japonica

1998 Sangmi japonica

1998 Sura japonica

1998 Undu japonica

1998 Weonhwang japonica

1999 Areum Tongil-type

1999 Jinpum japonica

1999 Jungan japonica

1999 Moonjang japonica

1999 Seolhyangchal japonica

1999 Shindongjin japonica

1999 Sobi japonica

1999 Sujin japonica

2000 Goami japonica

2000 Haepyeong japonica

2000 Heughyang japonica

2000 Hojin japonica

2000 Hwaan japonica

2000 Jeogjinju japonica

2000 Jinbong japonica

2000 Junam japonica

2000 Jungsan japonica

2000 Manpung japonica

2000 Sampyeong japonica

2000 Taebong japonica

2001 Dongjin1 japonica

2001 Jongnam japonica

2001 Manchu japonica

2001 Manweol japonica

2001 Saegyehwa japonica

2001 Saesangju japonica

2001 Seogjeong japonica

2001 Yeongan japonica

2002 Daepyeong japonica

2002 Geuman japonica

2002 Hanareum Tongil-type

2002 Manho japonica

2002 Manmi japonica

2002 Namil japonica

2002 Samdeog japonica

2002 Seogan japonica

2002 Taeseong japonica

2003 Heugkwang japonica




2003 Hopyeong japonica

2003 Hwarang japonica

2003 Joan japonica

2003 Pyeongan japonica

2003 Samkwang japonica

2003 Sangok japonica

2003 Seopyeong japonica

2004 Boseogchal japonica

2004 Cheongho japonica

2004 Gopum japonica

2004 Goun japonica

2004 Haepyeongchal japonica

2004 Hanmaeum japonica

2004 Josaengheugchal japonica

2004 Pungmi japonica

2004 Unkwang japonica

2005 Baegjinju1 japonica

2005 Dongjin2 japonica

2005 Geumo3 japonica

2005 Hanam japonica

2005 Hwasin1 japonica

2005 Juan1 japonica

2005 Manna japonica

2005 Odae1 japonica

2005 Onnuri japonica

2005 Pungmi1 japonica

2005 Seoan1 japonica

2005 Sinunbong1 japonica

2006 Cheonga japonica

2006 Cheongdam japonica

2006 Dami japonica

2006 Dasan1 Tongil-type

2006 Donghaejinmi japonica

2006 Gangbaeg japonica

2006 Haechanmulgyeol japonica

2006 Hangangchal1 Tongil-type

2006 Hongjinju japonica

2006 Hopum japonica

2006 Hwanggeumbora japonica

2006 Hwanggeumnuri japonica

2006 Junamjosaeng japonica

2006 Keunseom Tongil-type

2006 Malgeumi japonica

2006 Nogyang Tongil-type




2006 Nunbora japonica

2006 Sandeuljinmi japonica

2006 Sinmyeongheugchal japonica


Supplementary Table 2. SSR markers (192 in total) used in this study.

Marker Chromosome Position (IRGSP 1.0) No. of alleles in 188 varieties

RM03252 1 299,681 4

RM06324 1 2,374,623 6

RM01167 1 4,236,966 2

RM00001 1 4,633,595 4

RM00522 1 5,242,654 2

RM00259 1 7,443,444 2

RM00600 1 9,461,346 6

RM03412 1 11,566,482 5

RM00449 1 15,305,619 2

RM05638 1 21,262,702 6

RM06716 1 23,447,704 6

RM01349 1 25,398,082 5

RM03440 1 27,518,587 3

RM03336 1 28,942,156 3

RM01268 1 31,737,739 3

RM01003 1 33,803,800 3

RM03825 1 36,798,053 4

RM03602 1 39,335,497 6

RM05362 1 41,414,916 3

RM06407 1 42,703,925 4

RM06321 1 43,251,019 4

RM00154 2 1,083,895 5

RM06067 2 3,772,408 3

RM06230 2 5,201,945 2

RM00492 2 7,285,639 3

RM05699 2 8,981,409 7

RM01234 2 11,336,378 4

RM01211 2 18,450,427 4

RM00341 2 19,336,148 3

RM03787 2 20,043,111 3

RM01303 2 20,966,754 3

RM05789 2 22,384,523 3

RM06318 2 24,420,604 5

RM03508 2 27,076,961 4

RM05631 2 28,267,608 3

RM06424 2 29,620,007 2

RM03302 2 32,853,489 2

RM01092 2 33,847,788 4

RM03850 2 35,425,798 4

RM00569 3 1,888,272 6

RM00081B 3 1,925,952 4

RM00489 3 4,313,795 2

RM05477 3 6,532,845 6

RM00218 3 8,385,483 5


Supplementary Table 2. SSR markers (192 in total) used in this study (Continued).


RM00007 3 9,808,540 4

RM00282 3 12,387,497 2

RM06080 3 13,913,689 2

RM01164 3 14,840,558 3

RM07134 3 21,967,525 14

RM03513 3 25,068,806 6

RM03436 3 27,371,471 3

RM01350 3 28,632,514 8

RM03856 3 28,731,521 5

RM03525 3 30,344,279 14

RM08269 3 31,278,128 3

RM08203 3 31,338,238 4

RM03684 3 34,562,204 3

RM03585 3 36,080,616 2

RM00551 4 168,620 7

RM05633 4 13,059,370 4

RM00471 4 18,809,396 4

RM01155 4 20,328,759 3

RM03839 4 23,870,755 3

RM00241 4 26,823,436 5

RM00451 4 28,352,161 2

RM03217 4 30,083,469 3

RM05709 4 31,841,549 10

RM01113 4 34,052,017 2

RM00559 4 35,117,645 2

RM05693 5 441,872 4

RM05361 5 502,594 5

RM00592 5 2,774,918 12

RM00437 5 3,854,243 3

RM03328 5 5,244,969 3

RM00289 5 7,787,118 4

RM03838 5 16,475,417 5

RM00146 5 18,029,848 2

RM00440 5 19,891,890 3

RM05558 5 21,168,727 3

RM03870 5 22,879,699 4

RM00538 5 26,012,913 4

RM00031 5 28,590,085 7

RM01054 5 29,144,035 3

RM00133 6 226,944 4

RM03353 6 435,582 3

RM03805 6 2,853,068 7

RM00253 6 5,425,498 6

RM00276 6 6,230,046 3




RM00557 6 7,177,167 2

RM05963 6 8,814,621 3

RM07311 6 11,045,702 2

RM01161 6 13,752,128 3

RM03498 6 20,980,907 6

RM03628 6 23,737,032 4

RM06071 6 25,019,609 2

RM06274 6 25,019,609 2

RM03430 6 27,432,606 3

RM05604 6 29,047,077 3

RM05753 6 30,966,850 6

RM01093 7 668,161 3

RM00481 7 2,874,465 9

RM01243 7 3,553,941 5

RM08263 7 4,654,194 3

RM00180 7 5,734,573 7

RM01377 7 12,782,829 5

RM06449 7 15,409,111 3

RM01135 7 16,931,300 2

RM05793 7 17,488,937 3

RM03743 7 19,342,334 3

RM03799 7 21,630,136 4

RM05508 7 23,557,850 7

RM00234 7 25,471,987 4

RM00118 7 26,635,903 2

RM03555 7 27,889,886 5

RM00172 7 29,560,592 2

RM00408 8 119,935 2

RM00152 8 677,616 5

RM03309 8 1,193,615 4

RM01376 8 3,162,526 3

RM03572 8 3,921,984 3

RM00547 8 5,586,058 10

RM00072 8 6,757,363 7

RM03395 8 10,288,469 3

RM00339 8 17,812,339 3

RM08264 8 19,703,065 4

RM00284 8 21,012,219 4

RM03262 8 22,248,334 4

RM06976 8 23,425,537 2

RM00149 8 24,591,236 5

RM03571 8 26,117,972 4

RM05717 8 27,315,755 8

RM03840 8 27,793,649 6




RM05545 8 28,141,927 3

RM23654 9 151,453 4

RM00316 9 1,074,933 5

RM05688 9 1,715,785 7

RM00444 9 5,925,291 7

RM00464 9 6,575,147 5

RM00219 9 7,887,585 6

RM03855 9 9,368,791 6

RM00296 9 10,784,114 2

RM00524 9 12,924,219 3

RM00566 9 14,704,798 5

RM00257 9 17,719,660 8

RM06570 9 18,576,133 3

RM05519 9 19,226,760 2

RM01553 9 21,003,444 6

RM00205 9 22,720,646 2

RM07492 10 39,037 5

RM06370 10 329,556 4

RM00216 10 5,102,302 5

RM00311 10 9,487,243 7

RM05689 10 13,223,351 6

RM06144 10 15,343,741 2

RM01375 10 16,386,764 7

RM06704 10 17,676,151 5

RM03773 10 19,636,981 8

RM06691 10 19,974,642 8

RM05494 10 22,270,057 8

RM00590 10 22,784,993 5

RM00286 11 383,839 6

RM00332 11 2,840,211 4

RM05599 11 3,824,361 3

RM05704 11 5,476,884 9

RM03133 11 6,182,769 4

RM00479 11 7,692,442 2

RM00536 11 8,968,470 3

RM03428 11 13,445,211 3

RM06272 11 16,488,311 3

RM00287 11 16,730,846 6

RM05349 11 19,148,807 4

RM00206 11 21,979,485 8

RM06499 11 23,580,588 5

RM07277 11 24,183,559 2

RM01233 11 26,498,854 5

RM00144 11 28,246,930 6




RM05766 11 28,313,604 3

RM27404 12 204,669 2

RM08215 12 1,585,781 4

RM03747 12 2,304,368 4

RM06296 12 3,200,576 2

RM03472 12 3,520,117 4

RM00101 12 8,826,829 10

RM01337 12 11,933,319 4

RM00277 12 18,290,458 3

RM01986 12 21,213,063 8

RM06869 12 22,219,621 2

RM03726 12 23,241,704 4

RM01103 12 23,539,495 4

RM01300 12 25,965,369 3

RM00017 12 26,954,668 3

RM01226 12 27,310,436 3

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Utilization of Elite Korean Japonica Rice Varieties for Association … · variation was observed in heading time, culm length, and amylose and protein content in the 161 japonica