utilization of elite korean japonica rice varieties for association … · variation was observed...
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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).
한작지(KOREAN J. CROP SCI.), 65(1), 20204
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.
Association Mapping in Elite Rice Varieties 5
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.
한작지(KOREAN J. CROP SCI.), 65(1), 20206
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.
Association Mapping in Elite Rice Varieties 7
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.
한작지(KOREAN J. CROP SCI.), 65(1), 20208
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.
Association Mapping in Elite Rice Varieties 9
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).
REFERENCES
AOAC. 1996. Official methods of analysis of AOAC International.
Gaithersburg, MD: AOAC International.
Awika, J. M., V. Piironen, and S. Bean. 2011. Advances in cereal
science: implications to food processing and health promotion.
Washington D.C.: American Chemical Society.
Bao, J., X. Zhou, F. Xu, Q. He, and Y. Park. 2017. Genome-wide
association study of the resistant starch content in rice grains.
Starch 69 : 1600343.
Begum, H., J. E. Spindel, A. Lalusin, T. Borromeo, G. Gregorio,
J. Hernandez, P. Virk, B. Collard, and S. R. McCouch. 2015.
Genome-wide association mapping for yield and other agro-
nomic traits in an elite breeding population of tropical rice
(Oryza sativa). PLoS One 10 : e0119873.
Bradbury, P. J., Z. Zhang, D. E. Kroon, T. M. Casstevens, Y.
Ramdoss, and E. S. Buckler. 2007. TASSEL: software for
association mapping of complex traits in diverse samples.
Bioinformatics 23 : 2633-2635.
Bryant, R. J., A. K. Jackson, K. M. Yeater, W. G. Yan, A. M.
McClung, and R. G. Fjellstrom. 2013. Genetic variation and
association mapping of protein concentration in brown rice
using a diverse rice germplasm collection. Cereal Chemistry
Journal 90 : 445-452.
Chung, G. S. and M. H. Heu. 1991. Improvement of Tongil-type
rice cultivars from indica/japonica hybridization in Korea. In:
Bajaj YPS (ed.) Rice. Heidelberg, Germany: Springer-Verlag
Berlin, pp. 105-112.
Collard, B. C. Y., M. Z. Z. Jahufer, J. B. Brouwer, and E. C. K.
Pang. 2005. An introduction to markers, quantitative trait loci
(QTL) mapping and marker-assisted selection for crop improve-
ment: the basic concepts. Euphytica 142 : 169-196.
Collard, B. C. and D. J. Mackill. 2008. Marker-assisted selection:
an approach for precision plant breeding in the twenty-first
century. Philosophical Transactions of the Royal Society B:
Biological Sciences 363 : 557-572.
Fujino, K., M. Obara, T. Shimizu, K. O. Koyanagi, and T.
Ikegaya. 2015. Genome-wide association mapping focusing
on a rice population derived from rice breeding programs in a
region. Breeding Science 65 : 403-410.
Huang, N., E. R. Angeles, J. Domingo, G. Magpantay, S. Singh,
G. Zhang, N. Kumaravadivel, J. Bennett, and G. S. Khush.
한작지(KOREAN J. CROP SCI.), 65(1), 202010
1997. Pyramiding of bacterial blight resistance genes in rice:
marker-assisted selection using RFLP and PCR. Theoretical
and Applied Genetics 95 : 313-320.
Huang, X., X. Wei, T. Sang, Q. Zhao, Q. Feng, Y. Zhao, C. Li, C.
Zhu, T. Lu, Z. Zhang, M. Li, D. Fan, Y. Guo, A. Wang, L.
Wang, L. Deng, W. Li, Y. Lu, Q. Weng, K. Liu, T. Huang, T.
Zhou, Y. Jing, W. Li, Z. Lin, E. S. Buckler, Q. Qian, Q. Zhang,
J. Li, and B. Han. 2010. Genome-wide association studies of
14 agronomic traits in rice landraces. Nature Genetics 42 :
961-967.
Huang, X., Y. Zhao, X. Wei, C. Li, A. Wang, Q. Zhao, W. Li, Y.
Guo, L. Deng, C. Zhu, D. Fan, Y. Lu, Q. Weng, K. Liu, T.
Zhou, Y. Jing, L. Si, G. Dong, T. Huang, T. Lu, Q. Feng, Q.
Qian, J. Li, and B. Han. 2012. Genome-wide association study
of flowering time and grain yield traits in a worldwide
collection of rice germplasm. Nature Genetics 44 : 32-39.
Jia, L., W. Yan, C. Zhu, H. A. Agrama, A. Jackson, K. Yeater, X.
Li, B. Huang, B. Hu, A. McClung, and D. Wu. 2012. Allelic
analysis of sheath blight resistance with association mapping
in rice. PLoS One 7 : e32703.
Juliano, B. O. 1971. A simplified assay for milled rice amylose.
Cereal Science Today 16 : 334-360.
Kwon, S. J., S. N. Ahn, H. C. Hong, Y. K. Kim, H. G. Hwang, H.
C. Choi, and H. P. Moon. 1999. Genetic diversity of Korean
japonica rice cultivars. Korean Journal of Breeding Science
31 : 268-275 (in Korean with English abstract).
Lee, C. M., H. S. Park, M. K. Baek, J. P. Suh, C. S. Kim, K. M.
Lee, S. G. Park, and Y. C. Cho. 2019. Characterization of
grain-related traits of 300 Korean rice varieties. Presented at
the 2019 Korean Society of Breeding Science (KSBS) &
Society for the Advancement of Breeding Research in Asia
and Oceania (SABRAO) international conference on plant
breeding for sustainable development, Gwangju, Republic of
Korea, 2-5 July 2019.
Li, G., Y. Na, S. Kwon, and Y. Park. 2014. Association analysis of
seed longevity in rice under conventional and high-temperature
germination conditions. Plant Systematics and Evolution 300
: 389-402.
Li, X., W. Yan, H. Agrama, L. Jia, A. Jackson, K. Moldenhauer,
K. Yeater, A. McClung, and D. Wu. 2012. Unraveling the
complex trait of harvest index with association mapping in
rice (Oryza sativa L.). PLoS One 7 : e29350.
Li, X., W. Yan, H. Agrama, L. Jia, X. Shen, A. Jackson, K.
Moldenhauer, K. Yeater, A. McClung, and D. Wu. 2011.
Mapping QTLs for improving grain yield using the USDA rice
mini-core collection. Planta 234 : 347-361.
Liu, K. and S. V. Muse. 2005. PowerMarker : an integrated
analysis environment for genetic marker analysis. Bioinformatics
21 : 2128-2129.
McCouch, S. R., L. Teytelman, Y. Xu, K. B. Lobos, K. Clare, M.
Walton, B. Fu, R. Maghirang, Z. Li, Y. Xing, Q. Zhang, I.
Kono, M. Yano, R. Fjellstrom, G. DeClerck, D. Schneider, S.
Cartinhour, D. Ware, and L. Stein. 2002. Development and
mapping of 2240 new SSR markers for rice (Oryza sativa L.).
DNA Research 9 : 199-207.
McCouch, S. R., M. H. Wright, C. Tung, L. G. Maron, K. L.
McNally, M. Fitzgerald, N. Singh, G. DeClerck, F. Agosto-
Perez, P. Korniliev, A. J. Greenberg, M. E. B. Naredo, S. M. Q.
Mercado, S. E. Harrington, Y. Shi, D. A. Branchini, P. R.
Kuser-Falcão, H. Leung, K. Ebana, M. Yano, G. Eizenga, A.
McClung, and J. Mezey. 2016. Open access resources for
genome-wide association mapping in rice. Nature Communi-
cations 7 : 10532.
Mo, Y., J. Jeung, Y. Shin, C. Park, K. Kang, and B. Kim. 2013.
Agronomic and genetic analysis of Suweon 542, a rice floury
mutant line suitable for dry milling. Rice 6 : 37.
Murray, M. G. and W. F. Thompson. 1980. Rapid isolation of
high molecular weight plant DNA. Nucleic Acids Research
8 : 4321-4326.
Myles, S., J. Peiffer, P. J. Brown, E. S. Ersoz, Z. Zhang, D. E.
Costich, and E. S. Buckler. 2009. Association mapping :
critical considerations shift from genotyping to experimental
design. Plant Cell 21 : 2194-2202.
Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of
population structure using multilocus genotype data. Genetics
155 : 945-959.
RDA. 2003. Manual for standard evaluation method in agricultural
experiment and research. Suwon, Korea : Rural Development
Administration.
Shibaya, T., K. Hori, E. Ogiso-Tanaka, U. Yamanouchi, K. Shu,
N. Kitazawa, A. Shomura, T. Ando, K. Ebana, J. Wu, T.
Yamazaki, and M. Yano. 2016. Hd18, encoding histone
acetylase related to Arabidopsis FLOWERING LOCUS D, is
involved in the control of flowering time in rice. Plant Cell and
Physiology 57 : 1828-1838.
Schläppi, M. R., A. K. Jackson, G. C. Eizenga, A. Wang, C. Chu,
Y. Shi, N. Shimoyama, and D. L. Boykin. 2017. Assessment
of five chilling tolerance traits and GWAS mapping in rice
using the USDA mini-core collection. Frontiers in Plant Science
8 : 957.
Shinada, H., T. Yamamoto, H. Sato, E. Yamamoto, K. Hori, J.
Yonemaru, T. Sato, and K. Fujino. 2015. Quantitative trait loci
for rice blast resistance detected in a local rice breeding
population by genome-wide association mapping. Breeding
Science 65 : 388-395.
Song, M., J. Lee, Y. Cho, Y. Jeon, S. Lee, J. Ku, S. Choi, and H.
Hwang. 2002. Narrow genetic background of Korean rice
germplasm as revealed by DNA fingerprinting with SSR
markers and their pedigree information. Korean Journal of
Genetics 24 : 397-403.
Tester, M. and P. Langridge. 2010. Breeding technologies to
increase crop production in a changing world. Science 327 :
818-822.
Verdeprado, H., T. Kretzschmar, H. Begum, C. Raghavan, P.
Association Mapping in Elite Rice Varieties 11
Joyce, P. Lakshmanan, J. N. Cobb, and B. C. Y. Collard. 2018.
Association mapping in rice : basic concepts and perspectives
for molecular breeding. Plant Production Science 21 : 159-
176.
Wang, Z., F. Zheng, G. Shen, J. Gao, D. P. Snustad, M. Li, J.
Zhang, and M. Hong. 1995. The amylose content in rice
endosperm is related to the post‐transcriptional regulation of
the waxy gene. Plant Journal 7 : 613-622.
Wu, W., X. Zheng, G. Lu, Z. Zhong, H. Gao, L. Chen, C. Wu, H.
Wang, Q. Wang, K. Zhou, J. Wang, F. Wu, X. Zhang, X. Guo,
Z. Cheng, C. Lei, Q. Lin, L. Jiang, H. Wang, S. Ge, and J. Wan.
2013 Association of functional nucleotide polymorphisms at
DTH2 with the northward expansion of rice cultivation in
Asia. Proceedings of the National Academy of Sciences of the
United States of America 110 : 2775-2780.
Xu, F., J. Bao, T. Kim, and Y. Park. 2016. Genome-wide association
mapping of polyphenol contents and antioxidant capacity in
whole-grain rice. Journal of Agricultural and Food Chemistry
64 : 4695-4703.
Yano, K., E. Yamamoto, K. Aya, H. Takeuchi, P. Lo, L. Hu, M.
Yamasaki, S. Yoshida, H. Kitano, K. Hirano, and M. Matsuoka.
2016. Genome-wide association study using whole-genome
sequencing rapidly identifies new genes influencing agronomic
traits in rice. Nature Genetics 48 : 927-934.
Yano, M., Y. Katayose, M. Ashikari, U. Yamanouchi, L. Monna,
T. Fuse, T. Baba, K. Yamamoto, Y. Umehara, Y. Nagamura,
and T. Sasaki. 2000. Hd1, a major photoperiod sensitivity
quantitative trait locus in rice, is closely related to the
Arabidopsis flowering time gene CONSTANS. Plant Cell 12 :
2473-2483.
Zhang, P., K. Zhong, M. Q. Shahid, and H. Tong. 2016. Association
analysis in rice : from application to utilization. Frontiers in
Plant Science 7 :1202.
Zhao, W., J. Chung, S. Kwon, J. Lee, K. Ma, and Y. Park. 2013.
Association analysis of physicochemical traits on eating
quality in rice (Oryza sativa L.). Euphytica 191 : 9-21.
Zhu, L., J. Hu, K. Zhu, Y. Fang, Z. Gao, Y. He, G. Zhang, L. Guo,
D. Zeng, G. Dong, M. Yan, J. Liu, and Q. Qian. 2011. Identifi-
cation and characterization of SHORTENED UPPERMOST
INTERNODE 1, a gene negatively regulating uppermost
internode elongation in rice. Plant Molecular Biology 77 :
475.
한작지(KOREAN J. CROP SCI.), 65(1), 202012
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
Association Mapping in Elite Rice Varieties 13
Supplementary Table 1. Korean rice varieties (179 in total) used in this study (Continued).
Year released Variety name Subspecies
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
한작지(KOREAN J. CROP SCI.), 65(1), 202014
Supplementary Table 1. Korean rice varieties (179 in total) used in this study (Continued).
Year released Variety name Subspecies
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
Association Mapping in Elite Rice Varieties 15
Supplementary Table 1. Korean rice varieties (179 in total) used in this study (Continued).
Year released Variety name Subspecies
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
한작지(KOREAN J. CROP SCI.), 65(1), 202016
Supplementary Table 1. Korean rice varieties (179 in total) used in this study (Continued).
Year released Variety name Subspecies
2006 Nunbora japonica
2006 Sandeuljinmi japonica
2006 Sinmyeongheugchal japonica
Association Mapping in Elite Rice Varieties 17
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
한작지(KOREAN J. CROP SCI.), 65(1), 202018
Supplementary Table 2. SSR markers (192 in total) used in this study (Continued).
Marker Chromosome Position (IRGSP 1.0) No. of alleles in 188 varieties
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
Association Mapping in Elite Rice Varieties 19
Supplementary Table 2. SSR markers (192 in total) used in this study (Continued).
Marker Chromosome Position (IRGSP 1.0) No. of alleles in 188 varieties
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
한작지(KOREAN J. CROP SCI.), 65(1), 202020
Supplementary Table 2. SSR markers (192 in total) used in this study (Continued).
Marker Chromosome Position (IRGSP 1.0) No. of alleles in 188 varieties
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
Association Mapping in Elite Rice Varieties 21
Supplementary Table 2. SSR markers (192 in total) used in this study (Continued).
Marker Chromosome Position (IRGSP 1.0) No. of alleles in 188 varieties
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