Euphytica (2013) 191:9–21 DOI 10.1007/s10681-012-0820-z

Association analysis of physicochemical traits on eating quality in rice (Oryza sativa L.) Wei-Guo Zhao • Jong-Wook Chung • Soon-Wook Kwon • Jeong-Heui Lee • Kyung-Ho Ma • Yong-Jin Park

Received: 8 November 2011 / Accepted: 18 October 2012 / Published online: 30 October 2012 Ó Springer Science+Business Media Dordrecht 2012

Abstract Improvement of rice eating quality is an important objective in current breeding programs. In this study, 130 rice accessions of diverse origin were genotyped using 170 SSR markers to identify marker– trait associations with physicochemical traits on eating quality. Analysis of population structure revealed four subgroups in the population. Linkage disequilibrium Electronic supplementary material The online version of this article (doi:10.1007/s10681-012-0820-z) contains supplementary material, which is available to authorized users. W.-G. Zhao  S.-W. Kwon  Y.-J. Park (&) Department of Plant Resources, College of Industrial Science, Kongju National University, Yesan 340-702, Republic of Korea e-mail: [email protected] W.-G. Zhao Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Jiangsu University of Science and Technology, Zhenjiang 212018, People’s Republic of China J.-W. Chung  K.-H. Ma National Agrobiodiversity Center, National Academy of Agricultural Science, Rural Development Administration, Suwon 441-110, Republic of Korea S.-W. Kwon  Y.-J. Park Legume Bio-Resource Center of Green Manure, Kongju National University, Yesan 340-702, Republic of Korea J.-H. Lee National Institute of Crop Science, Rural Development Administration, Suwon 441-857, Republic of Korea

(LD) patterns and distributions are of fundamental importance for genome-wide mapping associations. The mean r2 value for all intrachromosomal loci pairs was 0.0940. LD between linked markers decreased with distance. Marker–trait associations were investigated using the unified mixed-model approach, considering both population structure (Q) and kinship (K). In total, 101 marker–trait associations (p \ 0.05) were identified using 52 different SSR markers covering 12 chromosomes. The results suggest that association mapping in rice is a viable alternative to quantitative trait loci mapping, and detection of new marker–trait associations associated with rice eating quality will also provide important information for markerassisted breeding and functional analysis of rice grain quality. Keywords Rice  Association mapping (AM)  Linkage disequilibrium (LD)  Population structure  Eating quality

Introduction Rice is the staple food for about half of the world’s population. Improvement of rice quality is among the most important aims in current breeding programs, especially eating and cooking quality because most rice is consumed cooked. Rice quality is a complex trait consisting of many components such as milling, appearance, nutrition, cooking and eating qualities.

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Among these qualitative properties, consumers pay more attention to the fine appearance and high eating quality (Huang et al. 1998; Wan et al. 2004). Rice eating quality has usually been evaluated by three major physicochemical characteristics of the starch as indirect indexes, namely, amylose content (AC) (Juliano 1985), gel consistency (GC) (Cagampang et al. 1973), and gelatinization temperature (GT) (Little et al. 1958). GT is a physical trait responsible for rice cooking time and the capacity to absorb water during the cooking processes, while the GC is for softness, and the AC is for texture and appearance. Hence, regulating AC in rice has been a major concern of rice breeders. To facilitate the development of new varieties with high eating qualities, it is necessary to understand the genetic basis of such traits. So far, most research on cooking and eating quality has focused on AC, GC, and GT, and a few attempts have been made to identify quantitative trait loci (QTLs) associated with eating quality in rice using traditional linkage-mapping methods, of which major QTLs for AC and GC were allelic or tightly linked to the Waxy (Wx) gene on chromosome 6 (Lanceras et al. 2000), and major QTLs for GT were allelic or closely linked to the alk gene on chromosome 6 (He et al. 1999; Lanceras et al. 2000). Furthermore, it was also found that AC, GC, and GT were controlled by the linked genomic region near the Wx locus (Tan et al. 1999). Umemoto et al. (2002) confirmed this and demonstrated that the alk locus encodes the enzyme soluble starch synthase IIa. Bao et al. (2002) also found the effect of the wx region on GC. However, He et al. (1999) and Bao et al. (2002) showed that GC is controlled by two QTLs with minor effects. Li et al. (2004) identified four QTLs for AC, three for GT and five for GC using backcross-inbred lines. The four QTLs for AC were located on chromosomes three, four, five and six. The QTL on chromosome 6 covered the wx gene region and mainly contributed to the variance between japonica and indica varieties (Li et al. 2004). Fan et al. (2005) detected a total of 12 main-effect QTLs for the three traits, with a QTL corresponding to the wx locus showing a major effect on AC and GC, and a QTL corresponding to the alk locus having a major effect on GT. Sun et al. (2006) found seven different QTLs including two for AC, three for GT, two for GC at six chromosomal regions controlling complex traits related to rice grain quality. Wada et al. (2006)

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mapped four QTLs for AC. Amarawathi et al. (2008) mapped QTLs related to AC, GC and GT on seven different chromosomes. Lestari et al. (2009) reported 18 DNA markers associated with eating quality of temperate japonica rice. Although traditional QTL mapping will continue to be an important tool in gene tagging of crops, overall, it is very costly (Hansen et al. 2001; Stella and Boettcher 2004; Gupta et al. 2005; Stich et al. 2006) and has low resolution for simultaneous evaluation of only a few alleles over a long research timescale (Cardon and Bell 2001; Flint-Garcia et al. 2003; Stich et al. 2005; Roy et al. 2006). These limitations, however, can be reduced with the use of ‘‘association mapping’’ (Ross-Ibarra et al. 2007). This methodology has become popular in human genetics as association mapping (AM) or linkage disequilibrium (LD) mapping, and has led to many successes. The basic objective of AM studies is to detect correlations between genotypes and phenotypes in a sample of individuals on the basis of LD (Zondervan and Cardon 2004). In plant genetics, AM offers the important advantage of sampling unrelated individuals in a population, as compared to other experimental designs that require sampling within families (Risch 2000), producing a broader genetic variation in a more representative genetic background. Of particular interest to rice breeders is the possibility of using existing germplasm resources for gene and allele discovery on the basis of AM strategies (Farnir et al. 2000). Moreover, no need exists to develop expensive and tedious biparental populations, which makes the AM approach time-saving and cost-effective. In plant, the model species Arabidopsis provided some of the first examples of AM in plants through identification of previously cloned flowering time genes (Aranzana et al. 2005), and now the AM has been applied successfully to many plant species, such as maize (Remington et al. 2001), rice (Agrama et al. 2007), wheat (Breseghello and Sorrells 2006; Tommasini et al. 2007), barley (Kraakman et al. 2006), oilseed rape (Hasan et al. 2008), Brassica rapa (Zhao et al. 2005), soybean (Jun et al. 2008) and G. hirsutum (Abdurakhmonov et al. 2008). Therefore, the objective of the present study was to use a large collection of rice accessions to determine the utility of population structure analysis and LD, and identify associated markers with physicochemical traits involved in rice eating quality using the AM approach.

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Materials and methods Plant material A core set of 166 rice accessions was previously developed from 4,406 worldwide varieties collected from the National GeneBank of the Rural Development Administration (RDA-GeneBank), Republic of Korea, using PowerCore program (Kim et al. 2007). Based on the available SSR genotyping data after removing those accessions with[25 missing data, 130 rice accessions from 28 different countries were finally selected for the marker–trait association studies (Supplemental Table 1). Plant young leaves were sampled and stored at -80 °C until genomic DNA extraction using CTAB method. Evaluation of physicochemical traits for eating quality AC was estimated by the relative absorbency of starch-iodine color in a digested solution of 100-mesh rice flour according to the method of Juliano (1971) and Cho et al. (2008). The pasting properties of rice flour were determined on a Rapid Visco Analyzer (RVA; NewPort Sci. Co., Australia) in accordance with the procedures described by Bao and Xia (1999) and Bao et al. (2008). Among those paste viscosity parameters, seven parameters, including peak viscosity (PKV), trough viscosity (TV), breakdown viscosity (BD), final viscosity (FV), setback viscosity (SBV), peak time (PKT), and pasting temperature (PT), were used for marker-trait association analysis (Table 1). Amylopectin chain length distribution (ACLD) in rice endosperm starch was analyzed by high-performance anion exchange chromatography-pulsed amperometric detection (HPAEC-PAD) (Dionex, Sunnyvale, CA) (Nagamine and Komae 1996; Nakamura et al. 1997; Kang et al. 2003). Data were obtained by calculating the sum of the peak areas corresponding to fractions: fa [degree of polymerization (DP) B 12], fb1 (12 \ DP B 24), fb2 (24 \ DP B 36) and fb3 (DP \ 36). All analysis was conducted in duplicate. SSR genotyping In total, 170 SSR markers located on the 12 chromosomes were selected for population structure analysis

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and AM (Supplemental Table 2). All SSR markers were obtained from GRAMENE (http://www.gramene. org/). A three-primer system (Schuelke 2000) was used that included a universal M13 oligonucleotide (TGTAAAACGACGGCCAGT) labeled with one of three fluorescent dyes (6-FAM, NED, or HEX) that allowed PCR products to be triplexed during electrophoresis, a special forward primer composed of the concatenation of the M13 oligonucleotide, and the normal reverse primer for SSR PCR amplification. Information on primer sequences and PCR amplification conditions for each set of primers is available at http://www.gramene.org/. SSR alleles were resolved on an ABI Prism 3100 DNA sequencer (Applied Biosystems, Foster City, CA, USA) using GENESCAN 3.7 software, and sized precisely using GeneScan 500 ROX (6-carbon-X-rhodamine) molecular size standards (35–500 bp) with GENOTYPER 3.7 software (Applied Biosystems). Allelic diversity and population structural analysis The number of alleles, gene diversity (GD), and polymorphism information content (PIC) per locus were calculated with the PowerMarker 3.25 program (Liu and Muse 2005). A tree using the unweighted pair group method with arithmetic mean (UPGMA) based on the genetic distance matrix was constructed using the MEGA 4.0 program (Tamura et al. 2007). The AM population was analyzed for possible population structure with the model-based Structure 2.2.3 program (Pritchard et al. 2000; Falush et al. 2003) for the 130 rice accessions using a burn-in of 50,000, run length of 100,000, and a model allowing for admixture and correlated allele frequencies. Five runs of Structure were performed by setting the number of populations (K) from two to nine, and an average likelihood value, L(K), across all runs was calculated for each K. The model choice criterion to detect the most probable value of K was DK, an ad hoc quantity related to the second-order change of the log probability of data with respect to the number of clusters inferred by Structure (Evanno et al. 2005). The highest DK of the data was observed for K = 4 clusters of plants. Therefore, the Q matrix as the average of five runs for K = 4 was calculated. The correlation of alleles within subpopulations, FST, was calculated using analysis of molecular variance (AMOVA) with the program Arlequin 3.11 (Excoffier et al. 2005).

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-0.362**

-0.338**

-0.171

-0.025

-0.025

-0.195*

0.086 -0.175

0.102

0.166

0.152

PKV

TV

BD

FV

SBV

PKT

PT fa (%)

fb1 (%)

fb2 (%)

fb3 (%)

-0.148

-0.177*

0.074

-0.103 -0.006

0.639**

0.176*

0.176*

0.519**

0.912**

PKV

-0.163

-0.200*

0.119

0.095 -0.045

0.751**

0.476**

0.476**

0.123

TV

-0.017

-0.010

-0.068

-0.447** 0.078

-0.019

-0.567**

-0.567**

BD

-0.214*

-0.328**

0.353**

0.466** -0.248**

0.581**

1.000**

FV

-0.214*

-0.328**

0.353**

0.466** 0.248**

0.581**

SBV

-0.107

-0.169

0.141

0.418** -0.086

PKT

0.051

0.024

-0.141

0.135

PT

-0.155

-0.018

-0.919**

faa (%)

-0.222*

-0.372**

fb1 (%)

0.865**

fb2 (%)

a

Grouping of degree of polymerization (DP) numbers followed that of Hanashiro et al. (1996)

** Correlation is significant at the 1 % probability level (two-tailed)

* Correlation is significant at the 5 % probability level (two-tailed)

AC amylase content, PKV peak viscosity, TV trough viscosity, BD breakdown viscosity, FV final viscosity, SBV setback viscosity, PKT peak time, PT pasting temperature, fa degree of polymerization (DP) B12, fb1 12 \ DP B 24, fb2 24 \ DP B 36, fb3 DP \ 36, SD standard deviation

AC (%)

Traits

Table 1 Correlation coefficients among traits in 130 rice accessions

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Linkage disequilibrium decay LD values (r2) between SSR loci on the same chromosome were calculated using the TASSEL program 2.2.3 (http://www.maizegenetics.net), with the rapid permutation test in 10,000 shuffles (Churchill and Doerge 1994). For multiple alleles, a weighted average of r2 (squared allele frequency correlation) between each locus pair was calculated (Farnir et al. 2000), except for 7 microsatellite loci (not mapped or unknown genetic distance). The extent of LD was estimated separately for loci on the same chromosome. Only alleles with a frequency equal to or greater than 0.05 were considered for LD calculation (Thornsberry et al. 2001). The pairs of loci were considered to have a significant LD if p \ 0.01. Significance thresholds corrected for multiple testing within chromosomes were approximately proportional to the reciprocal of the number of markers tested in each chromosome. The estimated genetic distance (cM) between loci was inferred from http://www.gramene.org. Association mapping The hypothesis of the association of SSR markers with eating quality parameters in the presence of population structure was tested using a mixed linear model (MLM), as described by Yu et al. (2006), in the program TASSEL 2.0.1 (http://www.maizegenetics. net/). This recently developed unified mixed-model method simultaneously takes into account multiple levels of both gross population structure (Q) and finerscale relative kinship (K). The population structure matrix (Q) was identified by running the Structure program at K = 4. The relative kinship matrix (K matrix) was obtained using the program SPAGeDi

(Hardy and Vekemans 2002). Output from SPAGeDi was formatted to a text file readable in TASSEL. All SSR markers were used for marker–trait associations. Only markers with an allele frequency of 5 % or more were included in the association analysis. To account for type I error bias, p values were adjusted for multiple tests using the procedure proposed by Whitt and Buckler (2003) based on permuted p-values of random markers. The p-value determines whether a trait was associated with a marker, and the r2 marker evaluates the magnitude of the QTL effects. MapChart 2.2 was used to draw the map (Voorrips 2002).

Results Phenotypic traits The results of determining physicochemical traits related to eating quality in 130 rice accessions of diverse origin were shown in Table 2. The majority of the traits among accessions exhibited a wide range of variation as expected, such as PKV, TV, and BD. It indicates that the presence of abundant diversity in pasting properties that may allow the breeders to make effective selections for improving the cooking quality according to consumers’ choice. The simple paired t test (at p = 0.05) was carried out to identify varieties that showed significant differences (similar) from the checked accessions. Significant variation among the rice accessions for AC and pasting properties were observed. The correlation coefficients among physicochemical traits are shown in Table 1. AC was negatively correlated with PKV, TV and PKT. SBV and FV showed significant correlations with all traits

Table 2 Descriptive statistics of the traits for eating quality in 130 rice accessions Parameter

AC (%)

PKV

TV

BD

FV

SBV

PKT

PT

faa (%)

fb1 (%)

fb2 (%)

fb3 (%)

Minimum

18.1

57.4

13.5

19.3

-65.3

-65.3

2.8

68.0

26.8

52.6

6.9

0.5

Maximum

20.9

332.8

248.4

131.2

125.0

125.0

6.7

86.1

37.2

61.0

10.8

2.4

111.8

190.3

190.3

Range

2.8

275.3

234.9

3.9

18.1

10.4

8.5

3.9

1.8

Mean

19.12

204.60

139.76

64.84

37.56

37.56

6.01

75.07

32.04

57.51

9.12

1.33

0.52

49.50

42.65

20.44

37.78

37.78

0.74

4.98

2.58

2.73

0.75

0.36

SD

AC amylase content, PKV peak viscosity, TV trough viscosity, BD breakdown viscosity, FV final viscosity, SBV setback viscosity, PKT peak time, PT pasting temperature, fa degree of polymerization (DP) B12, fb1 12 \ DP B 24, fb2 24 \ DP B 36, fb3 DP \ 36, SD standard deviation a

Grouping of degree of polymerization (DP) numbers followed that of Hanashiro et al. (1996)

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except AC. PKV and PT showed no significant correlation with ACLD. Genetic diversity and population structure A total of 170 SSR markers produced 1,763 alleles among the 130 accessions assayed (Supplemental Table 2). The average number of alleles per locus was 10.4, ranging from 2 to 40. Average heterogeneities for all markers were 0.033. Average genetic diversity over all SSR loci was 0.69, with a mean PIC value of 0.65. AM requires population structure to be taken into account to avoid false-positive associations (Yu et al. 2006). Analysis of genetic distance and population structure confirmed significant population Fig. 1 UPGMA dendrogram based on a genetic distance matrix between 130 rice accessions. The colors (S1–S4) correspond to those of the model-based populations. The accession names correspond to those listed on Supplemental Table 1. (Color figure online)

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structure in these rice accessions. Estimated likelihood values for a given K in five independent runs yielded consistent results, but the distribution of L(K) did not show a clear mode for the true K. To overcome difficulties in finding the real value of K, another ad hoc quantity (DK) was used (Evanno et al. 2005). In this study, the highest value of DK for the 130 rice accessions was K = 4. Analysis of these data identified the major substructure groups when the number of populations was set at four, with the highest value of DK (Supplemental Fig. 1) consistent with clustering based on genetic distance (Fig. 1). We therefore used the respective Q matrix outputs of the four subpopulations (S1–S4) for the structure-based association analysis.

Euphytica (2013) 191:9–21 Table 3 Analysis of molecular variance (AMOVA) for the modelbased four clusters of rice accessions

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Source of variation

d.f.

Variance components

Percentage of variation

p value

3

2131.04

12.82

40.67

\0.001

Among accessions

110

3936.04

17.08

54.21

\0.001

Within accessions Total

114 227

184 6251.08

1.61 31.51

5.12

\0.001

Among populations

The distribution of molecular genetic variation among and within the four clusters of accessions was estimated by AMOVA, which revealed that 40.67 % of total variation was among clusters, and 54.21 % of variation was within clusters (Table 3). Calculation of Wright’s F statistic for all SSR loci revealed FIS = 0.91, suggesting there was deviation from the Hardy–Weinberg expectation for molecular variation within the clusters. FIT was 0.95, signifying nonequilibrium conditions across clusters and a deficiency in heterozygous SSR loci. The overall FST value was 0.407; indicating moderate differentiation among the four groups (Table 4).

– –

Discussion

Table 4 Model-based population pair-wise FST between four clusters S4

Population

S1

S1 S2

– 0.498

S3

0.478

S4

0.303

0.429

0.396

–

–

Overall

–

–

–

–

0.407

0.317

Association mapping

Overall

The squared allele frequency correlations (r2) were obtained by analysis of 851 intra-chromosomal loci pairs using the 163 selected SSR markers. The r2 values ranged from 0.0071 to 0.7746 for all intrachromosomal loci pairs, with an average of 0.0940. The r2 between markers on the same chromosome was mostly less than 0.10. Of the 851 assessed loci pairs, only 270 had r2 of more than 0.10 (31.7 %). The distribution of data points in the plot of LD (r2) decay against distance (cM) within the 12 chromosomes showed that LD was not a simple monotonic function of the distance between markers. However, r2 decreased as genetic distance between loci pairs

S3

increased, indicating that the probability of LD is low between distant locus pairs. The highest scores for the frequency of loci pairs in LD and the highest mean r2 were reported for loci pairs that mapped within \20 cM of each other, suggesting it should be possible to achieve resolution down to the 20 cM level, with r2 [ 0.10 at p \ 0.01.

Marker–trait associations (p \ 0.05) for all the traits were evaluated. A total of 101 marker–trait associations with eating quality traits were identified using 52 different SSR markers on the 12 chromosomes and explained more than 10 % of the total variation (Supplemental Table 3; Fig. 2). The 101 marker–trait associations for the physicochemical traits ranged from one to eighteen associations, including: SBV associated with eighteen markers, FV with sixteen markers, PKT with fourteen markers, TV with thirteen markers, fa3 with twelve markers, fa2 with nine markers, BD with eight markers, PKV with six markers, AC with four markers, fa with one marker, and the remaining two traits (PT and fa1) had no marker-trait association. We also found that some markers associated simultaneously with two or more traits (Supplemental Table 3). Two markers were associated with five traits: RM154 on chromosome 2 was associated with AC, PKT, SBV, TV and fa, and RM3525 also on chromosome 3 was associated with FV, PKV, PKT, TV and fa3, while five markers (RM1509, RM1153, RM171, RM3785 and RM5515) were associated with four traits (Supplemental Table 3; Fig. 2).

Level of LD among intra-chromosomal SSR loci

S2

Sum of squares

–

For AMOVA-based estimates, p \ 0.05 for 1,000 permutations for all population comparisons

The studies on allelic diversity have been proved to be fruitful in understanding the genetic basis of complex traits. The allelic richness of 10.4 observed in our study was the same with the previous reported by

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Fig. 2 The positions of markers used and marker–trait associations on 12 chromosomes except not mapped and located markers. Genetic distances are indicated in cM on the left of the map and the corresponding trait-marker names are indicated on

the right. AC amylase content, PKV peak viscosity, TV trough viscosity, BD breakdown viscosity, FV final viscosity, SBV setback viscosity, PKT peak time, fa degree of polymerization (DP) B12, fb2 24 \ DP B 36, fb3 DP \ 36

Garris et al. (2003) (mean 11.8) using 169 SSRs and 234 rice accessions, indicating higher levels of allele diversit y. After comparing allelic richness with the respective index of genetic diversity, we found that allelic richness was significantly associated with genetic diversity index, the correlation coefficients (r) between allelic richness and GD index and PIC were 0.757 and 0.789, respectively. Grain quality in rice has received increasing attention in recent years. The application of mapping for eating quality may greatly facilitate improvement of grain quality in rice. Diverse populations are required for accurate association mapping based on LD. The Structure program implements a model-based clustering method for inferring population structure using genotype data consisting of unlinked markers (Pritchard et al. 2000). The model does not assume a

particular mutation process and, in most cases, the estimated ‘log probability of data’ does not provide an accurate estimation of the number of clusters, K (Evanno et al. 2005). In this study, the distribution of L(K) did not show a clear mode for the true K, but DK did show a clear peak at the true value of K (Evanno et al. 2005). Using the structure with K = 4, the rice accessions were significantly differentiated into four (S1–S4) subgroups. The relatively small value of alpha (a = 0.035) indicates that most accessions in each subgroup originated from one primary ancestor, with a few admixed individuals. The analysis revealed that some accessions with partial ancestry probably had a complex breeding history involving intercrossing and introgression among germplasms from diverse backgrounds, overlaid with strong selection pressure for agronomic and quality characteristics (Mather et al.

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2004). Model-based analysis of population structure, such as those conducted here, may be helpful in providing information that could be incorporated into association mapping analysis. The ability to detect significant associations between molecular polymorphism(s) and particular phenotypes, as well as the resolving power of LD mapping techniques, depends on knowledge of the LD extent in species genomes and the rate of decay of LD with physical distance (Pritchard et al. 2000). D0 and r2 are most commonly used measures of LD (Gaut and Long 2003; Gupta et al. 2005), but the r2 has more reliable sampling properties than D0 in cases with lowallele frequencies, especially for self-pollinated species such as rice (Abdurakhmonov and Abdukarimov 2008). In large sets of rice accessions with diverse origins such as those here examined, linkage disequilibrium as r2, which is also an indication of marker– trait correlations, is the most appropriate LD quantification measure for association mapping (Gupta et al. 2005). In our study, the intra-chromosomal LD decay was up to 10 cM for all of the 130 accessions. Extensive LDs have also been reported in other selfing species. Malysheva-Otto et al. (2006), for instance, reported that intra-chromosomal LD extended up to 50 cM (r2 [ 0.05) in 953 barley accessions. Although this level of LD persistence is considered to be high, long-distance LD with up to 50–100 cM (r2 [ 0.2) has also been reported in some local populations of Arabidopsis accessions (Nordborg et al. 2002). Additionally, long-distance LD of up to 100 cM (r2 [ 0.1) was detected in a population of European two-row spring barley (Kraakman et al. 2004). Many studies have examined the LD in rice (Garris et al. 2003; Rakshit et al. 2007; Mather et al. 2007; Agrama et al. 2007; Agrama and Eizenga 2008); although the amount of LD will vary across the genome due to such factors as recombination rates, mutation, population structure, relatedness, outcrossing, and selective pressure (Gupta et al. 2005; Abdurakhmonov and Abdukarimov 2008). There is evidence that LD is remarkably different in different rice species; therefore, our results could have important implications for association testing in rice. Structured population and relatedness characteristics in rice accessions suggest that population structure and kinship in conducting population-based association mapping in rice germplasms should be considered; in particular, with our material, where predefined

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groups represented an unbalanced number of accessions (Pritchard et al. 2000; Yu et al. 2006). Association mapping without consideration of population structure would give a high rate of false positive type-I errors (Mather et al. 2004). A unified mixed-model approach to account for multiple levels of relatedness simultaneously, as detected by genetic markers, has resulted in improved control of both type-I and type-II error rates (Yu et al. 2006). Hence, we applied the mixed linear model (MLM) approach of Yu et al. (2006), considering both population structure (Q) and kinship (K) to eliminate possible spurious associations. This approach successfully identified a number of SSR markers that are significantly associated with physiological traits controlling rice eating quality. In this study, twelve parameters representing eating quality of rice were used to analyze the marker-trait associations using 170 SSR markers. Some markers associated simultaneously with two or more traits, which might be the genetic reason for correlation among traits as well as pleiotropic effects of the gene(s) (Koyama et al. 2001). There have been several other reports on QTL analysis for rice eating quality, revealing that some rice physicochemical properties such as AC, GT and GC are controlled by one to three major genes. The enzymes involved in starch biosynthesis, such as starch branching enzyme (SBE), starch synthase (SS), and granule bound starch synthase (GBSS) contribute greatly to the variation of starch physicochemical properties and thus eating quality and used for ideotype breeding for eating quality by marker-assisted selection (Bao et al. 2006; Jin et al. 2010; Jantaboona et al. 2011). In the present study, the eating quality physicochemical properties were not significantly associated with these genes, only SS associated with BD. Bao et al. (2008) also found that the starch properties were not associated with the starch branching enzyme 1 (SBE 1) gene alleles. On chromosome 6, we found three markers (RM276, RM5815 and SS) associated with AC, BD and BD, respectively. Major genes, such as Wx (waxy gene) and alk (starch synthase II) (Lanceras et al. 2000) associated with eating quality (Bao et al. 2006, 2008) and AC, GC, and GT were controlled by the linked genomic region near the Wx locus (Tan et al. 1999), RM5815 and RM276 located on the proximal regions of Wx and alk, respectively, which may primarily be a linkage effect (Larkin et al. 2003). Yuan et al. (2010) identified 28 QTLs for grain quality for the 14 traits

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using inter mapping, on chr. 6 a few QTLS shared the similar genomic region, such as RM144 on chr. 11 associated with fb2, while they found it was associated with spr11.2. Furthermore, it was also found that although it is difficult to directly compare the chromosomal location of marker-trait associations detected in this study with the previous reported QTLs because different materials and mapping molecular markers were used, most marker-trait associations were in regions where QTLs associated with the given trait had previously been identified and some located in similar or proximal regions related with starch synthesis (http://www.gramene.org/). However, the new markers related with eating quality will facilitate the understanding of QTLs and marker-assisted selection (MAS). In conclusion, whole-genome association studies have the advantage of enabling the entire genome rather than specific genes to be assessed for traitassociated variants. Application of association mapping to plant breeding is a promising means of overcoming the limitations of conventional linkage mapping. The results of our study demonstrate the significant potential of LD-based association mapping of physicochemical traits related to eating quality in rice accessions with SSR markers. This type of mapping could be useful alternative to linkage mapping for detection of marker–phenotype associations toward implementation of marker-assisted selection and the findings from the present study should provide important information for a functional analysis of rice eating quality and also be useful for MAS in rice breeding program aimed at developing new varieties with a high level of eating quality. Acknowledgments This study was supported by a grant from the BioGreen 21 Program (No. PJ009099), Rural Development Administration, Republic of Korea.

References Abdurakhmonov IY, Abdukarimov A (2008) Application of association mapping to understanding the genetic diversity of plant germplasm resources. Int J Plant Genomics 2008:574927. doi:10.1155/2008/574927 Abdurakhmonov IY, Kohel RJ, Yu JZ, Pepper AE, Abdullaev AA, Kushanov FN, Salakhutdinov IB, Buriev ZT, Saha S, Scheffler BE, Jenkins JN, Abdukarimov A (2008) Molecular diversity and association mapping of fiber quality traits

123

Euphytica (2013) 191:9–21 in exotic G. hirsutum L. germplasm. Genomics 92(6): 478–487. doi:10.1016/j.ygeno.2008.07.013 Agrama HA, Eizenga GC (2008) Molecular diversity and genome-wide linkage disequilibrium patterns in a worldwide collection of Oryza sativa and its wild relatives. Euphytica 160(3):339–355. doi:10.1007/s10681-007-9535-y Agrama HA, Eizenga GC, Yan W (2007) Association mapping of yield and its components in rice cultivars. Mol Breeding 19(4):341–356. doi:10.1007/s11032-006-9066-6 Amarawathi Y, Singh R, Singh A, Singh V, Mohapatra T, Sharma T, Singh N (2008) Mapping of quantitative trait loci for basmati quality traits in rice (Oryza sativa L.). Mol Breeding 21(1):49–65. doi:10.1007/s11032-007-9108-8 Aranzana MJ, Kim S, Zhao K, Bakker E, Horton M, Jakob K, Lister C, Molitor J, Shindo C, Tang C, Toomajian C, Traw B, Zheng H, Bergelson J, Dean C, Marjoram P, Nordborg M (2005) Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes. PLoS Genet 1(5):e60. doi: 10.1371/journal.pgen.0010060 Bao JS, Xia YW (1999) Genetic control of paste viscosity characteristics in indica rice (Oryza sativa L.). Theor Appl Genet 98(6):1120–1124. doi:10.1007/s001220051175 Bao JS, Sun M, Corke H (2002) Analysis of the genetic behavior of some starch properties in indica rice (Oryza sativa L.): thermal properties, gel texture, swelling volume. Theor Appl Genet 104(2):408–413. doi:10.1007/s001220100688 Bao JS, Corke H, Sun M (2006) Microsatellites, single nucleotide polymorphisms and a sequence tagged site in starchsynthesizing genes in relation to starch physicochemical properties in nonwaxy rice (Oryza sativa L.). Theor Appl Genet 113(7):1185–1196. doi:10.1007/s00122-006-0394-z Bao JS, Jin L, Xiao P, Shen SQ, Sun M, Corke H (2008) Starch physicochemical properties and their associations with microsatellite alleles of starch-synthesizing genes in a rice RIL population. J Agric Food Chem 56(5):1589–1594. doi: 10.1021/jf073128? Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172(2):1165–1177. doi:10.1534/ genetics.105.044586 Cagampang GB, Perez CM, Juliano BO (1973) A gel consistency test for eating quality of rice. J Sci Food Agr 24(12):1589–1594. doi:10.1002/jsfa.2740241214 Cardon LR, Bell JI (2001) Association study designs for complex diseases. Nat Rev Genet 2(2):91–99. doi:10.1038/ 35052543 Cho YH, Lee SY, Kim SM, Yu JW, Lee JR, Hong HC, Kim JB, Ma KH, Kwon TR, Kang HK, Lee GA, Gwag JG, Kim TS, Park YJ (2008) Amylose, tocopherol, free sugar and fatty acid content in selected mutant lines of Oryza sativa cv. Shindongjin. J Crop Sci Biotechnol 11(3):83–90 Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping. Genetics 138(3):963–971 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14(8):2611–2620. doi:10.1111/ j.1365-294X.2005.02553.x Excoffier L, Laval G, Schneider S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform Online 1:47–50

Euphytica (2013) 191:9–21 Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164(4):1567– 1587 Fan CC, Yu XQ, Xing YZ, Xu CG, Luo LJ, Zhang QF (2005) The main effects, epistatic effects and environmental interactions of QTLs on the cooking and eating quality of rice in a doubled-haploid line population. Theor Appl Genet 110(8):1445–1452. doi:10.1007/s00122-005-1975-y Farnir F, Coppieters W, Arranz JJ, Berzi P, Cambisano N, Grisart B, Karim L, Marcq F, Moreau L, Mni M, Nezer C, Simon P, Vanmanshoven P, Wagenaar D, Georges M (2000) Extensive genome-wide linkage disequilibrium in cattle. Genome Res 10(2):220–227. doi:10.1101/gr.10.2.220 Flint-Garcia SA, Thornsberry JM, Buckler ES IV (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54(1):357–374. doi:10.1146/annurev.arplant.54. 031902.134907 Garris AJ, McCouch SR, Kresovich S (2003) Population structure and its effect on haplotype diversity and linkage disequilibrium surrounding the xa5 locus of rice (Oryza sativa L.). Genetics 165(2):759–769 Gaut BS, Long AD (2003) The lowdown on linkage disequilibrium. Plant Cell 15(7):1502–1506. doi:10.1105/tpc. 150730 Gupta PK, Rustgi S, Kulwal PL (2005) Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol 57(4):461–485. doi: 10.1007/s11103-005-0257-z Hanashiro I, Abe J, Hizukuri S (1996) A periodic distribution of the chain length of amylopectin as revealed by high performance anion-exchange chromatography. Carbohydr Res 283:151–159 Hansen M, Kraft T, Ganestam S, Sa¨ll T, Nilsson NO (2001) Linkage disequilibrium mapping of the bolting gene in sea beet using AFLP markers. Genet Res 77(1):61–66. doi: 10.1017/S0016672300004857 Hardy OJ, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2(4):618–620. doi:10.1046/j.1471-8286.2002.00305.x Hasan M, Friedt W, Pons-Ku¨hnemann J, Freitag NM, Link K, Snowdon RJ (2008) Association of gene-linked SSR markers to seed glucosinolate content in oilseed rape (Brassica napus ssp. napus). Theor Appl Genet 116(8): 1035–1049. doi:10.1007/s00122-008-0733-3 He P, Li SG, Qian Q, Ma YQ, Li JZ, Wang WM, Chen Y, Zhu LH (1999) Genetic analysis of rice grain quality. Theor Appl Genet 98(3):502–508. doi:10.1007/s001220051098 Huang FS, Sun ZX, Hu PS, Tang SQ (1998) Present situations and prospects for the research on rice grain quality forming. Chin J Rice Sci 12(3):172–176 Jantaboona J, Siangliwa M, Im-markb S, Jamboonsria W, Vanavichitc A, Toojindaa T (2011) Ideotype breeding for submergence tolerance and cooking quality by markerassisted selection in rice. Field Crop Res 123:206–213. doi: 10.1016/j.fcr.2011.05.001 Jin L, Lu Y, Shao YF, Zhang G, Xiao P, Shen SQ, Corke H, Bao JS (2010) Molecular marker assisted selection for improvement of the eating, cooking and sensory quality of

19 rice (Oryza sativa L.). J Cereal Sci 51:159–164. doi: 10.1016/j.jcs.2009.11.007 Juliano BO (1971) A simplified assay for milled-rice amylose. Cereal Sci Today 16:334–338 Juliano BO (1985) Criteria and test for rice grain quality. In: Juliano BO (ed) Rice chemistry and technology. American Association of Cereal Chemists, Saint Paul, pp 443–513 Jun TH, Van K, Kim MY, Lee SH, Walker DR (2008) Association analysis using SSR markers to find QTL for seed protein content in soybean. Euphytica 162(2):179–191. doi:10.1007/s10681-007-9491-6 Kang HJ, Hwang IK, Kim KS, Choi HC (2003) Comparative structure and physicochemical properties of Ilpumbyeo, a high-quality japonica rice, and its mutant, Suweon 464. J Agric Food Chem 51(22):6598–6603. doi:10.1021/ jf0344946 Kim KW, Chung HK, Cho GT, Ma KH, Chandrabalan D, Gwag JG, Kim TS, Cho EG, Park YJ (2007) PowerCore: a program applying the advanced M strategy with a heuristic search for establishing core sets. Bioinformatics 23(16):2155–2162. doi:10.1093/bioinformatics/btm313 Koyama ML, Levesley A, Koebner RMD, Flowers TJ, Yeo AR (2001) Quantitative trait loci for component physiological traits determining salt tolerance in rice. Plant Physiol 125(1):406–422. doi:10.1104/pp.125.1.406 Kraakman ATW, Niks RE, Van den Berg PMMM, Stam P, Van Eeuwijk FA (2004) Linkage disequilibrium mapping of yield and yield stability in modern spring barley cultivars. Genetics 168(1):435–446. doi:10.1534/genetics.104.026831 Kraakman ATW, Martı´nez F, Mussiraliev B, van Eeuwijk FA, Niks RE (2006) Linkage disequilibrium mapping of morphological, resistance, and other agronomically relevant traits in modern spring barley cultivars. Mol Breeding 17(1):41–58. doi:10.1007/s11032-005-1119-8 Lanceras JC, Huang ZL, Naivikul O, Vanavichit A, Ruanjaichon V, Tragoonrung S (2000) Mapping of genes for cooking and eating qualities in Thai jasmine rice (KDML105). DNA Res 7(2):93–101. doi:10.1093/dnares/7.2.93 Larkin PD, McClung AM, Ayres NM, Park WD (2003) The effect of the Waxy locus (granule bound starch synthase) on pasting curve characteristics in specialty rices (Oryza sativa L.). Euphytica 131(2):243–253. doi:10.1023/a:1023 962406605 Lestari PJ, Ham TH, Lee HH, Woo MO, Jiang WZ, Chu SH, Kwon SW, Ma KH, Lee JH, Cho YC, Koh HJ (2009) PCR marker-based evaluation of the eating quality of japonica rice (Oryza sativa L.). J Agric Food Chem 57(7):2754– 2762. doi:10.1021/jf803804k Li J, Xiao JH, Grandillo S, Jiang LY, Wan YZ, Deng QY, Yuan LP, McCouch SR (2004) QTL detection for rice grain quality traits using an interspecific backcross population derived from cultivated Asian (O. sativa L.) and African (O. glaberrima S.) rice. Genome 47(4):697–704. doi: 10.1139/g04-029 Little RR, Hilder GB, Dawson EH (1958) Differential effect of dilute alkali on 25 varieties of milled white rice. Cereal Chem 35:111–126 Liu KJ, Muse SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21(9):2128–2129. doi:10.1093/bioinformatics/bti282

123

20 Malysheva-Otto LV, Ganal MW, Roder MS (2006) Analysis of molecular diversity, population structure and linkage disequilibrium in a worldwide survey of cultivated barley germplasm (Hordeum vulgare L.). BMC Genet 7(1):6. doi: 10.1186/1471-2156-7-6 Mather DE, Hyes PM, Chalmers KJ, Eglinton J, Matus I, Richardson K, Von Zitzewitz J, Marquez-Cedillo L, Hearnden P, Pal N (2004) Use of SSR marker data to study linkage disequilibrium and population structure in Hordeum vulgare: prospects for association mapping in barley. In: International barley genetics symposium. Brno, Czech Republic, pp 302–307 Mather KA, Caicedo AL, Polato NR, Olsen KM, McCouch S, Purugganan MD (2007) The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177(4):2223–2232. doi:10.1534/genetics.107.079616 Nagamine T, Komae K (1996) Improvement of a method for chain-length distribution analysis of wheat amylopectin. J Chromatogr A 732:255–259 Nakamura Y, Kubo A, Shimamune T, Matsuda T, Harada K, Satoh H (1997) Correlation between activities of starch debranching enzyme and a-polyglucan structure in endosperms of sugary-1 mutants of rice. Plant J 12(1):143–153. doi:10.1046/j.1365-313X.1997.12010143.x Nordborg M, Borevitz JO, Bergelson J, Berry CC, Chory J, Hagenblad J, Kreitman M, Maloof JN, Noyes T, Oefner PJ, Stahl EA, Weigel D (2002) The extent of linkage disequilibrium in Arabidopsis thaliana. Nat Genet 30(2):190–193. doi:10.1038/ng813 Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155(2):945–959 Rakshit S, Rakshit A, Matsumura H, Takahashi Y, Hasegawa Y, Ito A, Ishii T, Miyashita N, Terauchi R (2007) Large-scale DNA polymorphism study of Oryza sativa and O. rufipogon reveals the origin and divergence of Asian rice. Theor Appl Genet 114(4):731–743. doi:10.1007/s00122-0060473-1 Remington DL, Thornsberry JM, Matsuoka Y, Wilson LM, Whitt SR, Doebley J, Kresovich S, Goodman MM, Buckler ES (2001) Structure of linkage disequilibrium and phenotypic associations in the maize genome. PNAS USA 98(20):11479–11484. doi:10.1073/pnas.201394398 Risch NJ (2000) Searching for genetic determinants in the new millennium. Nature 405(6788):847–856. doi:10.1038/ 35015718 Ross-Ibarra J, Morrell PL, Gaut BS (2007) Plant domestication, a unique opportunity to identify the genetic basis of adaptation. PNAS USA 104(Suppl 1):8641–8648. doi: 10.1073/pnas.0700643104 Roy JK, Bandopadhyay R, Rustgi S, Balyan HS, Gupta PK (2006) Association analysis of agronomically important traits using SSR, SAMPL and AFLP markers in bread wheat. Curr Sci 90(5):683–689 Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18(2):233–234. doi:10.1038/72708 Stella A, Boettcher PJ (2004) Optimal designs for linkage disequilibrium mapping and candidate gene association tests in livestock populations. Genetics 166(1):341–350. doi: 10.1534/genetics.166.1.341

123

Euphytica (2013) 191:9–21 Stich B, Melchinger AE, Frisch M, Maurer HP, Heckenberger M, Reif JC (2005) Linkage disequilibrium in European elite maize germplasm investigated with SSRs. Theor Appl Genet 111(4):723–730. doi:10.1007/s00122-005-2057-x Stich B, Maurer HP, Melchinger AE, Frisch M, Heckenberger M, van der Voort JR, Peleman J, Sørensen AP, Reif JC (2006) Comparison of linkage disequilibrium in elite European maize inbred lines using AFLP and SSR markers. Mol Breeding 17(3):217–226. doi:10.1007/s11032005-5296-2 Sun SY, Hao W, Lin HX (2006) Identification of QTLs for cooking and eating quality of rice grain. Rice Sci 13(3): 161–169 Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol 24(8):1596–1599. doi: 10.1093/molbev/msm092 Tan YF, Li JX, Yu SB, Xing YZ, Xu CG, Zhang QF (1999) The three important traits for cooking and eating quality of rice grains are controlled by a single locus in an elite rice hybrid, Shanyou 63. Theor Appl Genet 99(3):642–648. doi: 10.1007/s001220051279 Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet 28(3):286–289. doi:10.1038/90135 Tommasini L, Schnurbusch T, Fossati D, Mascher F, Keller B (2007) Association mapping of Stagonospora nodorum blotch resistance in modern European winter wheat varieties. Theor Appl Genet 115(5):697–708. doi:10.1007/ s00122-007-0601-6 Umemoto T, Yano M, Satoh H, Shomura A, Nakamura Y (2002) Mapping of a gene responsible for the difference in amylopectin structure between japonica-type and indica-type rice varieties. Theor Appl Genet 104(1):1–8. doi: 10.1007/s001220200000 Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93(1):77–78. doi:10.1093/jhered/93.1.77 Wada T, Uchimura Y, Ogata T, Tsubone M, Matsue Y (2006) Mapping of QTLs for physicochemical properties in japonica rice. Breeding Sci 56(3):253–260. doi: 10.1270/jsbbs.56.253 Wan XY, Wan JM, Su CC, Wang CM, Shen WB, Li JM, Wang HL, Jiang L, Liu SJ, Chen LM, Yasui H, Yoshimura A (2004) QTL detection for eating quality of cooked rice in a population of chromosome segment substitution lines. Theor Appl Genet 110(1):71–79. doi:10.1007/s00122-0041744-3 Whitt SR, Buckler ES (2003) Using natural allelic diversity to evaluate gene function. In: Grotewald E (ed) Plant functional genomics: methods and protocols. Humana Press, New York, pp 123–140 Yu JM, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixedmodel method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38(2):203–208. doi:10.1038/ng1702 Yuan PR, Kim HJ, Chen QH, Ju HG, Ji SD, Ahn SN (2010) Mapping QTLs for grain quality using an introgression line

Euphytica (2013) 191:9–21 population from a cross between Oryza sativa and O. rufipogon. J Crop Sci Biotechnol 13:205–212. doi:10.1007/ s12892-010-0094-8 Zhao JJ, Wang XW, Deng B, Lou P, Wu J, Sun RF, Xu ZY, Vromans J, Koornneef M, Bonnema G (2005) Genetic relationships within Brassica rapa as inferred from AFLP

21 fingerprints. Theor Appl Genet 110(7):1301–1314. doi: 10.1007/s00122-005-1967-y Zondervan KT, Cardon LR (2004) The complex interplay among factors that influence allelic association. Nat Rev Genet 5(2):89–100. doi:10.1038/nrg1270

123