Planta (2014) 239:1309–1319 DOI 10.1007/s00425-014-2060-z

Original Article

Genetic diversity and association mapping of seed vigor in rice (Oryza sativa L.) Xiaojing Dang · Thu Giang Tran Thi · Guanshan Dong · Hui Wang · Wisdom Mawuli Edzesi · Delin Hong 

Received: 24 November 2013 / Accepted: 6 March 2014 / Published online: 26 March 2014 © Springer-Verlag Berlin Heidelberg 2014

Abstract  Key message  Twenty-seven QTLs were identified for rice seed vigor, in which 16 were novel QTLs. Fifteen elite parental combinations were designed for improving seed vigor in rice. Abstract  Seed vigor is closely related to direct seeding in rice (Oryza sativa L.). Previous quantitative trait locus (QTL) studies for seed vigor were mainly derived from bi-parental segregating populations and no report from natural populations. In this study, association mapping for seed vigor was performed on a selected sample of 540 rice cultivars (419 from China and 121 from Vietnam). Population structure was estimated on the basis of 262 simple sequence repeat (SSR) markers. Seed vigor was evaluated by root length (RL), shoot length (SL) and shoot dry weight in 2011 and 2012. Abundant phenotypic and genetic diversities were found in the studied population. The population was divided into seven subpopulations, and the levels of linkage disequilibrium (LD) ranged from 10 to 80 cM. We identified 27 marker–trait associations involving 18 SSR markers for three traits. According to phenotypic effects Electronic supplementary material The online version of this article (doi:10.1007/s00425-014-2060-z) contains supplementary material, which is available to authorized users. X. Dang · T. G. T. Thi · G. Dong · H. Wang · W. M. Edzesi · D. Hong (*)  State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China e-mail: [email protected] T. G. T. Thi  College of Agronomy, Hue University of Agriculture and Forestry, Hue University, 102 Phung Hung Street, Hue, Vietnam

for alleles of the detected QTLs, elite alleles were mined. These elite alleles could be used to design parental combinations and the expected results would be obtained by pyramiding or substituting the elite alleles per QTL (apart from possible epistatic effects). Our results demonstrate that association mapping can complement and enhance previous QTL information for marker-assisted selection and breeding by design. Keywords Association analysis · Elite allele · Rice · Seed vigor · Simple sequence repeat (SSR) Abbreviations ANOVA Analysis of variance FDR False discovery rate GLM General linear model H2B Heritability in the broad sense FIS  F-statistics; individuals within subpopulations FST  F-statistics; subpopulations within the total population LD Linkage disequilibrium MCMC Markov Chain Monte Carlo PIC Polymorphic information content PVE Proportion of phenotypic variance explained QTL Quantitative trait locus RL Root length SDW Shoot dry weight SL Shoot length SSR Simple sequence repeat

Introduction Rice (Oryza sativa L.) is one of the most important food crops grown worldwide for more than half of the world

13

1310

population (Sasaki and Burr 2000). As the improvement of irrigated condition, implement of efficient herbicide, deployment of new varieties with precocity and high yield, and exaltation of labor cost, direct seeding has become an inevitable trend in rice production. High seed vigor is important for direct seeding because it can not only enhance crop establishment (Yamauchi and Winn 1996) but also increase the plant’s ability to compete against weeds (Dingkuhn et al. 1999; Rao et al. 2007). Seed vigor is a complex trait and root length, shoot length and dry weight of seedling have been identified as good indicators of seed vigor (Regan et al. 1992; Redonˇa and Mackill 1996a). A large number of QTLs associated with rice seed vigor were reported previously using biparental segregating populations. Eleven QTLs controlling root length (RL), locating on the chromosomes of 1, 2, 5, 6, 8 and 12, were detected and the proportion of phenotypic variance explained (PVE) ranged from 5 to 15 % (Redonˇa and Mackill 1996b; Cui et al. 2002; Zhang et al. 2005a). Meanwhile 19 QTLs controlling shoot length (SL), residing on the 8 chromosomes, were identified. Their PVEs ranged from 9.9 to 17 % (Redonˇa and Mackill 1996b; Huang et al. 2004; Zhang et al. 2005a, b; Zhou et al. 2007). In addition, 15 QTLs controlling shoot dry weight (SDW) were detected with PVEs ranging from 3.4 to 15.8 % (Cui et al. 2002; Huang et al. 2004; Zhang et al. 2005a, b; Zhou et al. 2007). Despite success of QTL analysis, traditional bi-parental segregating populations showed several disadvantages, including limited genetic variation and recombination (Cardon and Bell 2001; Gupta et al. 2005; Kassem et al. 2006; Wang et al. 2008; Xu and Crouch 2008). Association mapping identifies QTLs by examining the marker–trait associations, and enables researchers to exploit natural diversity and locate valuable genes in the genome (Zhu et al. 2008), therefore it has become a new method to mine elite alleles in humans and plants (Yu et al. 2005; Zhang et al. 2005c, 2010; Lü et al. 2011; Segura et al. 2012; Zhou and Stephens 2012). In rice, association mapping was used to identify favorable alleles for various traits such as yield (Zhang et al. 2005d; Agrama et al. 2007; Wen et al. 2009; Huang et al. 2010; Ordonez et al. 2010; Vanniarajan et al. 2012); outcrossing ratios (Yan et al. 2009; Huang et al. 2010); quality (Borba et al. 2010; Huang et al. 2010; Jin et al. 2010); resistance (Jia et al. 2012; Cui et al. 2013). However, no report was found on association mapping for seed vigor to our knowledge. In this study, 540 rice cultivars were used to conduct association mapping for seed vigor combining information of 262 simple sequence repeat (SSR) markers. RL, SL and SDW were measured in 2011–2012 to indicate seed vigor. The aims were (1) to evaluate the population structure and genetic diversity in elite germplasm; (2) to detect the extent

13

Planta (2014) 239:1309–1319

of LD between pairs of SSR markers on a whole genome in rice; (3) to detect QTLs controlling seed vigor and mine elite alleles; (4) to explore design of parental combinations for cultivar improvement.

Materials and methods Plant materials and field planting A total of 540 rice accessions were used as the plant materials. Of these, 419 were from China, and 121 from Vietnam (Supplementary Table S1). The seeds of all accessions were collected, stored and supplied by State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University. During the past decades, these accessions had been widely used as parents in plant breeding. The 540 accessions were planted from early May to November in 2011 and 2012 at the Nanjing Agricultural University experiment farm, Jiangsu, China. For field experiments, the accessions were grown in randomized complete block design with two replications. The space was 20 cm between rows and 17 cm between each individual with standard agronomic management. Phenotypic data collection The heading date of each accession was recorded, and seeds were harvested in bulk 45–50 days after flowering. Then the seeds were air-dried under natural conditions. For each variety, 100 manually selected plump grains were treated at 50 °C for 5 days to eliminate residual dormancy. The grains were subsequently surface-sterilized with 0.6 % sodium hypochlorite solution for 15 min, rinsed three times with tap water, and pre-germinated by soaking in tap water at 30 °C for 48 h. The pre-germinated seeds were used for testing seed vigor. Thirty pre-germinated seeds were sowed in a Petri dish (diameter 9 cm) with two sheets of filter paper, and 10 ml of tap water was added. The Petri dishes were placed in a GXZ intelligent light incubator (Ningbo Jiangnan Instrument Factory, Ningbo, Zhejiang, China). The culture conditions were dark 20 °C/16 h and illumination 30 °C/8 h. During the cultivation, tap water was added to keep the filter paper moist. Seven days after sowing, the root length and shoot length were measured, respectively. The average of ten root length and shoot length with two repeats were calculated, respectively, accurate to 1 mm. Shoot dry weight of each sample was measured after removing the roots and residual endosperm from the plants, and then drying at 70 °C for 2 days. Shoot dry weight per plant (mg) for each accession was determined for each of the 2 × 10 plants.

Planta (2014) 239:1309–1319

SSR marker genotyping Genomic DNA was extracted from leaf tissue of each selected plant according to the methods described by Murray and Thompson (1980). According to the published rice molecular map and microsatellite database of Temnykh et al. (2000) and McCouch et al. (2002), 262 SSRs scattered on 12 chromosomes were selected. Primers were synthesized by Shanghai Generay Biotech Co. Ltd., Shanghai, China. Each 10 μl PCR reaction consisted of 10 mM tris– HCl (pH 9.0), 50 mM KCl, 0.1 % Triton X-100, 1.5 mM MgCl2, 0.5 nM dNTPs, 0.14 pM forward primers, 0.14 pM reverse primers, 0.5 U of Taq polymerase, and 20 ng of genomic DNA. DNA amplification was performed using a PTC-100™ Peltier Thermal Cycler (MJ Research™ Incorporated, USA) under the following conditions: (1) denaturation at 94 °C for 5 min; (2) 34 cycles of denaturation at 94 °C for 0.5 min, annealing at 55–61 °C for 1 min, and extension at 72 °C for 1 min; (3) final extension at 72 °C for 10 min. The PCR products were run on 8 % polyacrylamide gel at 150 V for 1 h, and visualized using silver staining. Data analysis All the basic statistical analyses were performed using the SAS package (SAS Institute Inc., Cary, NC, USA). Heritability in the broad sense (H2B) was computed on the basis of the natural population through analysis of variance using the formula H2B = σ2g/(σ2g + σ2e /n), where σ2g is genetic variance, σ2e is error variance, and n is number of replicates. The number of subpopulations (K) was determined following the admixture model with correlated alleles using STRUCTURE version 2.2 (Pritchard et al. 2000a), with a K of 2–10. The length of burn-in period equal to 50,000 iterations and a run of 100,000 replications of Markov Chain Monte Carlo (MCMC) after burn in was set. Five independent runs were made for each value of K. The mean log-likelihood value over 5 runs at each K was used. If the mean log-likelihood value increased with the increase in the model parameter K, a suitable value for K could not be determined. In this situation, the optimal K value was determined by the posterior probability [Ln P(D)] and an ad hoc statistic ΔK based on the rate of change in [Ln P(D)] between successive K (Evanno et al. 2005). Based on the correct K, each accession was assigned to a subpopulation for which the membership value (Q value) was >0.5 (Pritchard et al. 2000b; Breseghello and Sorrells 2006), and the population structure matrix (Q) was generated for further analysis. The D′ value (Farnir et al. 2000) was used to measure the level of LD between linked loci. Polymorphic information content (PIC) was used to measure the probability that two randomly chosen alleles from

1311

a population were distinguished. The number of alleles per locus, gene diversity, and PIC value was determined using PowerMarker version 3.25 (Liu and Muse 2005). FIS and FST were estimated based on Wright’s F statistics (Weir and Cockerham 1984; Weir and Hill 2002) using the program Arlequin 3.01 (Excoffier et al. 2005). The software SPAGeDi (Hardy and Vekemans 2002) was used to calculate the pair-wise relatedness coefficients (K, kinship matrix) to estimate the genetic relatedness among individuals with the negative value of kinship set as zero. The associations between traits and markers were calculated using the general linear model (GLM) method as described in TASSEL 2.1 (Yu and Buckler 2006; Bradbury et al. 2007). A false discovery rate (FDR) of 0.05 was used as a threshold for significant association using Benjamini and Hochberg (1995) correction method. Using the association locus identified, the ‘null allele’ (non-amplified allele) was used to determine the phenotypic effects of other alleles (Breseghello and Sorrells 2006). The formula used for calculating phenotypic effect of a single allele was ai = ∑xij/ni − ∑Nk/nk, where ai was the phenotypic effect of the allele of i; xij denoted the phenotypic measurement values of j variety carrying the allele of i; ni represented the number of materials carrying the allele of i; Nk meant the phenotypic value of the variety of k carrying the null allele; and nk represented the number of materials for the null allele. If ai was positive, the allele i had an increasing effect. If ai was negative, the allele i had a decreasing effect.

Results Phenotypic evaluations Mean value, coefficient of variation, kurtosis, and skewness for each trait measured in 540 accessions were calculated (Table  1). Continuous distributions were observed in all three seed vigor traits (RL, SL, and SDW), and the phenotypic data of all the three traits followed a normal distribution based on the values of skewness and kurtosis statistics. A two-way analysis of variance (ANOVA) showed that differences among cultivars for each trait were highly significant (P < 0.01), indicating a large amount of genetic variation existed in the population. The H2B, averaged across 2 years, of RL, SL, and SDW, was 85, 82 and 81 %. Analysis of linear correlations showed that RL was correlated negatively with SL (r = −0.25, n = 540, P = 2.35E − 9), and high positive correlation was found between SL and SDW (r = 0.50, n = 540, P = 3.96E − 35). There existed variances between 2011 and 2012 for the three traits studied, and the extent of variance varied with trait. The means of RL over the 540 accessions were 5.2

13

1312

Planta (2014) 239:1309–1319

Table 1  Phenotypic characteristics for seed vigor in 540 rice accessions Traits

Years

Mean ± SD

Maximum

Minimum

CV (%)

Kurtosis

Skewness

H2B(%)

Root length (cm)

2011 2012 2011 2012 2011

5.24 ± 0.94 4.82 ± 0.69 4.27 ± 0.88 4.45 ± 0.84 2.19 ± 0.43

7.85 7.19 7.25 7.76 3.57

2.76 2.60 2.26 2.57 1.23

17.92 14.29 20.68 18.92 19.59

0.45 −0.16 0.22 0.35 0.13

84.62 85.67 76.76 87.46 76.17

2012

2.15 ± 0.46

3.30

1.10

21.45

0.34 0.40 −0.19 0.19 −0.45

0.15

84.98

Shoot length (cm) Shoot dry weight (mg)

−0.61

SD standard deviation, CV coefficient of variation, H2B heritability in the broad sense

and 4.8 cm, with 84.62 and 85.67 % of H2B in 2011 and 2012, respectively (Table 1). Shoot length had means of 4.3 and 4.6 cm, with 76.76 and 87.46 % of H2B in 2011 and 2012, respectively (Table 1). The means of SDW over the 540 accessions were 2.19 and 2.15 mg, with 76.17 and 84.98 % of H2B in 2011 and 2012, respectively (Table 1). The correlation coefficient between 2011 and 2012 for RL, SL, and SDW was 0.52, 0.62 and 0.90, respectively. Molecular markers and genetic structure Marker analysis of the 540 accessions using 262 molecular markers resulted in the detection of a total of 2,754 alleles. Numbers of alleles ranged from 2 (at locus RM437_Chr5) to 25 (RM7545_Chr10) with an average of 10.5 alleles per locus (Supplementary Table S2). The genetic diversity averaged 0.7331 ranging from 0.0829 (RM7163_Chr11) to 0.9420 (RM7545_Chr10) (Supplementary Table S2). The PIC had a mean of 0.7053 ranging from 0.0794 (RM7163_Chr11) to 0.9389 (RM7545_Chr10) with a major distribution between 0.5225 and 0.9021 (Supplementary Table S2). Two hundred and twenty-six markers (86 %) were highly informative (PIC >0.5), 30 (12 %) moderately informative (0.5 > PIC > 0.25) and 6 (2 %) slightly informative (PIC <0.25). The distribution of FST, indicating population genetic differentiation, and FIS, an indicator of inbreeding depression or fixation index, was presented across the entire genome in Fig. 1. Sixty-two loci had FST, in the range of 0.50–0.81, suggesting that these markers contributed the majority of the divergence in the population. Besides, 189 loci had FST, in the range 0.20–0.49 and the rest were below 0.20. For FIS, there were 247 loci above 0.95, indicating these loci are fixed in the population (Fig. 1). A comprehensive view of rice genetic diversity could be appreciated after taking into consideration the collective distribution of FST, FIS and selection sweep. Here FST, distribution for Chr10 was the lowest of all chromosomes and in the range of 0.20–0.57, indicating that the markers

13

on this chromosome contributed less effect to population genetic differentiation because of selection sweep. In contrary, FIS distribution across Chr9 was the highest, implying fixation of alleles and inbreeding (Fig. 1). STRUCTURE analysis with 262 SSR markers showed that the log-likelihood increased with the elevation of model parameter K, so the statistic ΔK was used to determine a suitable value for K. Here, the ΔK value was much higher for the model parameter K = 7 than for other values of K. Population structure data based on the Q matrix for each accession are summarized in Supplementary Table S1, and the 540 accessions could be divided into seven subpopulations, viz. from POP1 to POP7 (Fig. 2). Linkage disequilibrium and association mapping Figure 3 showed the distribution of linkage disequilibrium on 12 chromosomes based on 262 SSRs. Out of 34,191 pairs, 10,723 pairs showed LD, which includes both interand intra-chromosomal combinations (non-white small grid above the diagonal line in Fig. 3). SSR pairs with LD supported by P < 0.05 occupied 20.2 % in total population (non-white small grid below the diagonal line in Fig. 3). Among the 7 subpopulations, the lowest percentage of significant pair-wise loci in LD was found in POP5 (1.4 %), and the highest one was found in POP4 (8.9 %) (Table 2). POP2 had the lowest average of D′ among the 7 subpopulations, suggesting that accessions in this subpopulation might be subjected to intensively artificial selection. Regression analysis between the D′ value and genetic distance of syntenic marker pairs revealed that the seven subpopulation genomes fitted the equation y = b lnx + c. The minimum distance of LD decay for POP1–POP7 was 60.2, 13.0, 85.4, 70.8, 29.8, 72.9, and 61.8 cM, respectively. It could be seen that POP3 had the lowest decay velocity, while POP2 demonstrated the fastest decay velocity among the seven subpopulations. GLM analysis of marker–trait association revealed seven markers associated with RL (Table 3), locating on

Planta (2014) 239:1309–1319

1313

Fig. 1  Distribution pattern of FST (population genetic differentiation) and FIS (fixation index) across the length of the rice genetic map. Red line represents FIS; blue line represents FST. Digits in X-axis represents SSR markers, which are consistent with code of Supplemental Table S2; Digits in Y-axis represents values of FST and FIS in each locus

Fig. 2  Posterior probabilities of each rice variety belonging to seven subpopulations calculated by STRUCRURE software. Each accession is represented by a vertical bar. The colored subsections within each vertical bar indicate membership coefficient (Q) of the accession to

different clusters. Identified subpopulations are POP1 (red color), POP2 (green color), POP3 (navy blue color), POP4 (yellow color), POP5 (purple color), POP6 (light blue color), POP7 (brown color)

chromosomes 1, 2, 5, 6, and 10. The range of PVE was from 15.5 to 27.1 %. RM3850_Chr2, residing on 156.3 cM, explained maximum phenotypic variations for RL, viz. 27.1 % in 2011 and 25.3 % in 2012 (Table 3). There were 12 markers associated with SL distributed on eight chromosomes (Table 3), of which RM573_Chr2 had the highest PVE of 58.8 % in 2011 and 55.6 % in 2012. There were eight markers associated with SDW distributed on chromosomes 1, 3, 5, 6, 9 and 12 (Table 3). More than 35 % markers located on chromosome 6, and the corresponding PVE ranged from 22.6 to 26.9 %. RM276 explained maximum phenotypic variation, viz. 26.9 % in 2011 and

25.3 % in 2012. Nine markers were co-associated with two traits: RM84, RM3453 and RM5389 with both RL and SL, RM276 with both RL and SDW, and RM8095, RM480, RM528, RM201 and RM20 with both SL and SDW (Table 3). Mining elite alleles In this study, alleles with positive effects are elite alleles for all the three traits measured. A summary of elite alleles and their typical carrier materials are shown in Supplementary Table S3. As for the RL QTLs associated with marker

13

1314

Planta (2014) 239:1309–1319

Fig. 3  Linkage disequilibrium (LD) patterns among 540 accessions genotyped with 262 molecular markers. Each pixel above the diagonal indicates the standardized disequilibrium coefficients (D′) size of the corresponding marker pair as shown in the color code at the upper right; while each pixel below the diagonal indicates the P value size of the testing LD of the corresponding marker pairs as shown in the color code at the lower right: white P > 0.05, blue 0.05 > P > 0.01, green 0.01 > P > 0.001 and red P < 0.001

Table 2  Comparison of D′ values for pair-wise SSR loci in each subpopulation

a

 LD means linkage disequilibrium b

 Ratio between the number of significant LD locus pairs and total number of LD locus pairs c

  D′ means standardized disequilibrium coefficients

No. of LDa locus pairs

Ratiob (%)

POP1 POP2 POP3 POP4 POP5 POP6

1,860 1,899 1,801 3,051 495 555

POP7

1,062

Cluster

Means of D′

0–0.2

0.2–0.4

0.4–0.6

0.6–0.8

0.8–1.0

5.4 5.6 5.3 8.9 1.4 1.6

120 435 321 298 101 111

429 596 165 777 156 212

479 505 483 808 96 84

610 212 615 828 52 45

222 100 217 340 90 103

0.550 0.388 0.560 0.519 0.441 0.425

3.1

170

255

277

175

185

0.514

RM3850, there were 14 alleles, including six positive and eight negative. Of the 6 alleles, the 115-bp allele showed the largest phenotypic effect (0.88 cm), and was supposed as an elite allele. It could be found in cultivars Zhongjing 131, Zhongjing 212, and Xiangjing 9407. Similarly, elite alleles of RM84 (115 bp), RM480 (135 bp), and RM480 (105 bp) had positive effect on RL, SL and SDW, respectively, and were stored in cultivars Xiushui 79, Yuedao 108, and Yuedao 119, respectively.

13

Frequency of D′c value (P < 0.05)

Design for novel parental combination Based on the number of positive alleles that could be pyramided into an individual plant and the expected phenotypic effects, the five best cross combinations for improving RL, SL and SDW, respectively, were proposed (Table 4). The elite alleles carried by the parents in excellent crosses were listed in Supplementary Table S4. Some cultivars were found repeatedly in the supposed parental combinations

3.2 66.6 60.6 34.8 130.6 33.5 33.6 100.8 81.2 3.2

3 5 6 6 6

9

12

RM201

RM20

RM528

RM480 RM276 RM314

RM3766

RM201 12 RM20 RM7102 12 1 RM8095

970,538

20,174,289

6,933,576 27,313,250 6,230,045 4,751,360 26,554,756 970,958

20,174,430

6,933,717 27,313,474 6,230,185 4,976,641 26,554,987

13,211,536 11,239,473

970,958

25,472,820 20,174,430

26,554,987

35,732,554 27,940,996 30,570,344 27,313,474

35,732,554 35,425,926 20,944,466 6,230,185 18,688,012 4,864,535 4,886,065 11,239,473

4,864,535 4,886,965

End position (bp)a

0.182

0.100

0.245 0.103 0.269 0.237 0.236

0.111 0.139

0.235

0.186 0.186

0.242

0.389 0.588 0.384 0.243

0.222 0.271 0.156 0.221 0.238 0.215 0.300 0.233

0.161 0.250

0.0024

0.0047

0.0055 0.0044 0.0095 0.0067 0.0069

0.0056 0.0058

0.007

0.0055 0.0055

0.0021

0.0012 0.0013 0.0042 0.002

0.0027 0.0007 0.0016 0.0079 0.0014 0.0013 0.0022 0.0016

0.0043 0.0035

0.411 0.556 0.278 0.228

0.197 0.253 0.180 0.196 0.22 0.201 0.437 0.137

0.225 0.107 0.253 0.228 0.226 0.0026 0.167

0.0079 0.094

0.0105 0.0053 0.0211 0.0158 0.0184

0.0116 0.155 0.0132 0.124

0.0134 0.277

0.0089 0.184 0.0098 0.184

0.0063 0.271

0.0018 0.0036 0.008 0.0054

0.0111 0.0028 0.0083 0.0194 0.0056 0.0027 0.0071 0.0045

0.0166 0.157 0.0138 0.155

PVE

PVE

P value FDR

2012

2011

0.0034

0.0055

0.009 0.0089 0.0104 0.0084 0.0087

0.002 0.0078

0.0025

0.0058 0.0058

0.0029

0.0002 0.0031 0.0036 0.0018

0.0046 0.0011 0.0063 0.0047 0.0023 0.0012 0.0014 0.0086

0.0093 0.0013

P value

0.0038

0.0065

0.0159 0.0136 0.0182 0.0091 0.0114

0.0090 0.0088

0.0098

0.0172 0.0180

0.0115

0.0032 0.0123 0.0139 0.0074

0.0125 0.0031 0.0188 0.0156 0.0094 0.0057 0.0066 0.023

0.0219 0.0063

FDR

532,909

3,885,926

17,525,817

6,927,624

1,595,325

27,489,485

25,775,868

29,028,429

27,342,124

22,556,049

21,701,719

11,077,466

40,567,354

5,941,581

4,424,392

34,937,981

37,713,609

6,072,325 6,072,325

34,470,620

4,753,787 4,753,787

Zhang et al. (2005a)

Ishimaru et al. (2001)

Mei et al. (2005)

Xiao et al. (1996)

Moncada et al. (2001)

Redonˇa and Mackill (1996b) Yan et al. (1998)

Marri et al. (2005)

Cui et al. (2002)

Cui et al. (2002) Cui et al. (2002)

Start positiona End positiona References

QTL reported in the previous studies

 The estimated map position (cM) and physical position (bp) was inferred the Gremene (http://www.gramene.org/markers) and NCBI (http://blast.ncbinlm.nih.gov/Blast.cgi)

a

SSR marker in boldface represents the co-associated marker for RL and SL; SSR marker with underline represents the co-associated marker for RL and SDW; SSR marker in italic represents the co-associated marker for SL and SDW

Shoot dry weight

970,538 13,211,325 11,239,014

93.9 81.2

26,554,756 25,472,688 20,174,289

100.8

6

7 9

RM528 RM234

RM480

35,732,311 27,940,796 30,569,880 27,313,250

35,732,311 35,425,642 20,944,257 6,230,045 18,442,240 4,864,000 4,885,912 11,239,014

142.4 118.1 137.9 130.6

142.4 156.3 96.9 33.5 69.6 18.8 25.4 60.6

RM5389 1 RM3850 2 RM305 5 RM276 6 RM269 10 Shoot length RM84 1 RM3453 1 1 RM8095

4,864,000 4,885,912

1 2 3 5

18.8 25.4

1 1

RM84 RM3453

Root length

Start position (bp)a

RM5389 RM573 RM5475

Chromosome Position (cM)a

SSR marker

Traits

Table 3  Marker–trait associations with P value <0.05, their equivalent false discovery rate probability (FDR), proportion of phenotypic variance explained (PVE), marker position on chromosome derived from 262 markers and 540 rice accessions

Planta (2014) 239:1309–1319 1315

13

1316

Planta (2014) 239:1309–1319

Table 4  Parental combinations and numbers of elite alleles after combinations predicted from association mapping of seed vigor traits Traits

Parental combinations

Root length

Zhongjing 131 × Xishihuang Zhongjing 131 × 24248 Zhongjing131 × Nannongjing002 Xishihuang × 24248 Zhongjing 131 × Yandao 6 Shoot length Maozitou × Yuedao 61 Yangzhongdao × Yuedao 61 Maozitou × Yuedao 108 Maozitou × Yuedao 43 Yangzhongdao × Yuedao108 Shoot dry weight Yangdao 6 × Yuedao 119 Yuedao 61 × Yuedao 86 Yuedao 61 × Yuedao 119 Yuedao 86 × Yuedao 109 Yuedao 86 × Yuedao 119

No. of elite alleles predicted 5 6 5 6 6 12 12 12 11 12 6 8 7 7 6

(e.g., Zhongjing 131 emerged four times and Yuedao 61 twice in the combinations for RL and SDW, respectively), indicating these varieties possess unique elite alleles.

Discussion Based on Bayesian clustering method, the population consisted of 540 accessions was clustered into seven subpopulations, i.e., POP1–POP7, using Structure software (Fig. 2). A large proportion of the accessions with the same geographical origin were classed into the same cluster. For example, the accessions from Vietnam were basically classed into POP6, and the accessions from Northeastern China mostly in POP1. This suggested that the result of grouping by structure analysis was essentially consistent with the geographic region. Genetic relatedness analysis based on 262 SSR markers showed that more than 75 % of the kinship coefficient values were <0.05, 6.01 % had a range of 0.05–0.10, and the remaining 10.8 % showed various degrees of genetic relatedness (Fig. 4), indicating that there was no or weak relatedness between pair-wise accessions used in the present study. Linkage disequilibrium is the basis of association analysis (Flint-Garcia et al. 2003). Compared to other populations, the levels of LD for POP2 and POP5 in this study (13.0 and 29.8 cM, respectively) were similar to those reported by Vanniarajan et al. (2012), in which LD extended from 10 to 30 cM. However, the LD decay distance in the other five subpopulations in this study ranged

13

Fig. 4  Distribution of pair-wise kinship coefficients among 540 rice accessions kinship coefficients between accessions were calculated using 262 SSR markers

from 60 to 80 cM, larger than 50 cM, which was the largest LD decay compared to previous studies detected by either SSR markers or DNA sequence (Olsen et al. 2006; Mather et al. 2007; Jin et al. 2010). Therefore, the level of LD varied among different genomic regions, rice accessions, and markers (Agrama et al. 2007; Mather et al. 2007; Rakshit et al. 2007; Agrama and Eizenga 2008). Using the whole-genome marker resources for rice on the Gramene website (http://www.gramene.org/), we compared the chromosome regions of SSR markers associated with seed vigor with previous studies. The positions of 11 SSR loci here were close to those reported in previous studies, including three QTL regions for RL (Cui et al. 2002), seven QTL regions for SL (Redonˇa and Mackill 1996b; Xiao et al. 1996; Yan et al. 1998; Ishimaru et al. 2001; Moncada et al. 2001; Marri et al. 2005; Mei et al. 2005) and one QTL region for SDW (Zhang et al. 2005a) (Table 3). Except for the 11 SSR loci mentioned above, the remaining 16 SSR loci (4 for RL, 5 for SL and 7 for SDW) were novel marker loci found in this study. The chromosome position of the novel marker loci was listed in Table 3. For RL trait, heritability in the broad sense averaged across 2 years was 85 %, which was considerable high. Thus, expected results of improving RL could be obtained by marker-assisted selection. Among the seven SSR associated markers detected for RL, RM3850_Chr 2 had the largest PVE (27.1 % in 2011 and 25.3 % in 2012). RM3850115bp had the largest phenotypic effect value (0.88 cm) among the three elite alleles found at this marker locus (Supplementary Table S3). This elite allele was carried by six accessions, in which Zhongjing 131 was the typical carrier material. RL could be improved greatly by the crosses described in Table 4. Heritability in the broad sense averaged across 2 years for SL trait was 82 %, which was also high. Among the

Planta (2014) 239:1309–1319

12 SSR associated markers detected for SL, RM573_Chr 2 had the largest PVE (58.8 % in 2011 and 55.6 % in 2012). Four elite alleles, i.e., RM573-100bp, RM573-190, RM573-195, and RM573-215bp, were found at this marker locus (Supplementary Table S3). We expected SL could be improved by the crosses listed in Table 4. For SDW trait, heritability in the broad sense averaged across 2 years was 80 %. Among the eight SSR associated markers detected for SDW, RM276_Chr 6 had the largest PVE (26.9 % in 2011 and 25.3 % in 2012). RM276-105bp had the largest phenotypic effect value (0.90 mg) among the five elite alleles found at this marker locus (Supplementary Table S3). This elite allele was carried by 20 accessions, in which Yangdao 6 was the typical carrier material. The crosses that could be used to improve SDW are showed in Table 4. Of course, if the target trait was needed to improve further, we might pyramid the best elite alleles into one cultivar as far as possible by multi-round crossing. For example, there were a total of 53 positive alleles detected for SL, 12 best elite alleles could be pyramided or substituted by the combination of cultivars Maozitou, Yuedao 43, Yuedao 61, Yangzhongdao, and Yuedao 108. Correlations between measured traits were observed. SL was significantly positive correlated with SDW, whereas significantly negative correlated with RL. Meanwhile, we identified five SSR markers co-associated with SL and SDW, in which alleles RM8095-115bp, RM8095-140bp, RM480-80bp, RM480-135bp, RM480-150bp, RM480205bp, RM528-175bp, RM528-200bp, RM528-205bp, RM201-130bp, RM201-160bp, and RM20-165bp increased phenotypic effect values of SL and SDW simultaneously. We also detected three SSR markers co-associated with RL and SL, in which alleles RM84-100bp, RM84-110bp, RM84-125bp, RM3453-140bp, RM3453-150bp, RM3453170bp, RM3453-290bp, RM5389-90bp, RM5389-120bp, RM5389-140bp, and RM5389-145bp increased SL but decreased RL. These co-associated alleles have the right sign in respect to trait correlations. These data elucidated the genetic basis of the trait correlation. We speculate if shoot grows rapidly, it will consume more nutrition in endosperm, thereby resulting in less supply for root growth, and vice versa. Seven loci significantly associated with RL, and in total 26 elite alleles were found. Among them, 6 % of the elite alleles were carried by varieties collected from Northeastern China, 60 % by varieties from Middle China, and 32 % from Vietnam. Similarly, some unique elite alleles in various accessions for SL and SDW were identified, respectively (Supplementary Table S3). These results suggest that during the process of rice evolution from South to North, some alleles had disappeared in the process of natural or artificial selection, and others retained in modern cultivars.

1317

Meanwhile, new alleles could be developed to accommodate the new environments, such as longer day time and relative lower temperature in rice growing season. In conclusion, we found significant associations between seed vigor and SSR markers in elite rice germplasm. This study demonstrated that association mapping in elite germplasm can enhance the information from QTL studies toward the implementation of marker-assisted selection and breeding by design. Acknowledgments The authors are grateful to Dr. Linglong Liu (National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University) for critical review of the manuscript. Funding support was provided by a grant from the China national “863” program (2010AA101301), a grant from key program of Scientific Base Platform of Chinese Government (505005) and a grant from doctoral found of Educational Ministry (B0201100690). Conflict of interest No conflict of interest among authors and in the research work.

References Agrama HA, Eizenga GC (2008) Molecular diversity and genomewide linkage disequilibrium patterns in a worldwide collection of Oryza sativa and its wild relatives. Euphytica 160:339–355 Agrama HA, Eizenga GC, Yan W (2007) Association mapping of yield and its components in rice cultivars. Mol Breed 19:341–356 Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57:289–300 Borba TCO, Brondanil RPV, Breseghello F, Coelho ASG, Mendonça JA, Range PHN, Brondani C (2010) Association mapping for yield and grain quality traits in rice (Oryza sativa L.). Gen Mol Biol 33:515–524 Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 2:2633–2635 Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177 Cardon L, Bell J (2001) Association study designs for complex diseases. Nat Rev Genet 2:91–99 Cui KH, Peng SB, Xing YZ, Xu CG, Yu SB, Zhang Q (2002) Molecular dissection of seedling-vigor and associated physiological traits in rice. Theor Appl Genet 105:745–753 Cui D, Xu CY, Tang CF, Yang CG, Yu TQ, A XX, Cao GL, Xu FR, Zhang JG, Han LZ (2013) Genetic structure and association mapping of cold tolerance in improved japonica rice germplasm at the booting stage. Euphytica 193:369–382 Dingkuhn M, Johnson DE, Sow A, Audebert AY (1999) Relationship between upland rice canopy characteristics and weed competitiveness. Field Crop Res 61:71–95 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620 Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: an integrated software package for population genetics data analysis. Evol Bioinform Online 1:47–50 Farnir F, Coppieters W, Arranz JJ, Berzi P, Cambisano N, Grisart B, Karim L, Marcq F, Moreau L, Mni M, Nezer C, Simon P,

13

1318 Vanmanshoven P, Wagenaar D, Georges M (2000) Extensive genome-wide linkage disequilibrium in cattle. Genome Res 10:220–227 Flint-Garcia S, Thornsberry J, Buckler ES (2003) Structure of linkage disequilibrium in plants. Annu Rev Plant Biol 54:357–374 Gupta P, Rustgi S, Kulwal P (2005) Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol 57:461–485 Hardy O, Vekemans X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620 Huang Z, Yu T, Su L, Yu SB, Zhang ZH, Zhu YG (2004) Identification of chromosome regions associated with seedling vigor in rice. Acta Genetica Sinica 31:596–603 Huang XH, Wei XH, Sang T (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–969 Ishimaru K, Yano M, Aoki N, Ono K, Hirose T, Lin SY, Monna L, Sasaki T, Ohsugi R (2001) Toward the mapping of physiological and agronomic characters on a rice function map: QTL analysis and comparison between QTLs and expressed sequence tags. Theor Appl Genet 102:793–800 Jia LM, Yan WG, Zhu CS, Agrama HA, Jackson A, Yeater K, Li XB, Huang BH, Hu BL, McClung A, Wu DX (2012) Allelic analysis of sheath blight resistance with association mapping in rice. PLoS One 7:e32703 Jin L, Lu Y, Xiao P, Sun M, Corke H, Bao JS (2010) Genetic diversity and population structure of a diverse set of rice germplasm for association mapping. Theor Appl Genet 121:475–487 Kassem MA, Shultz J, Meksem K, Cho Y, Wood AJ, Iqbal MJ, Lightfoot DA (2006) An updated ‘Essex’ by ‘Forrest’ linkage map and Wrst composite interval map of QTL underlying six soybean traits. Theor Appl Genet 113:1015–1026 Liu K, Muse SV (2005) PowerMarker: integrated analysis environment for genetic marker data. Bioinformatics 21:2128–2129 Lü HY, Liu XF, Wei SP, Zhang YM (2011) Epistatic association mapping in homozygous crop cultivars. PLoS One 6:e17773 Marri PR, Sarla N, Reddy LV, Siddiq EA (2005) Identification and mapping of yield and yield related QTLs from an Indian accession of Oryza rufipogon. BMC Genet 6:1–14 Mather K, Caicedo A, Polato N, Olsen K, McCouch S, Purugganan MD (2007) The extent of linkage disequilibrium in rice (Oryza sativa L.). Genetics 177:2223–2232 McCouch SR, Teytelman L, Xu YB, Lobos KB, Clare K, Walton M, Fu BY, Maghirang R, Li ZK, Xing YZ, Zhang QF, Kono I, Yano M, Jellstorm RF, DeClerck G, Schneider D, Cartinhour S, Ware D, Stein L (2002) Development and mapping of 2240 new SSR markers for rice (Oryza sativa L.). DNA Res 9:199–207 Mei HW, Li ZK, Shu QY, Guo LB, Wang YP, Yu XQ, Ying CS, Luo LJ (2005) Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two backcross populations. Theor Appl Genet 110:649–659 Moncada P, Martinez CP, Borrero J, Chatel M, Gauch H, Guimaraes E, Tohme J, McCouch SR (2001) Quantitative trait loci for yield and yield components in an Oryza sativa  ×  Oryza rufipogon BC2F2 population evaluated in an upland environment. Theor Appl Genet 102:41–42 Murray MG, Thompson WF (1980) Rapid isolation of high-molecular-weight-plant DNA. Nucleic Acids Res 8:4321–4325 Olsen KM, Caicedo AL, Polato N, McClung A, McCouch S, Purugganan MD (2006) Selection under domestication: evidence for a sweep in the rice waxy genomic region. Genetics 173:975–983 Ordonez SA Jr, Silva J, Oard JH (2010) Association mapping of grain quality and flowering time in elite japonica rice germplasm. J Cereal Sci 51:337–343

13

Planta (2014) 239:1309–1319 Pritchard JK, Stephens M, Donnelly P (2000a) Inference of population structure using multilocus genotype data. Genetics 155:945–959 Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000b) Association mapping in structured populations. Am J Hum Genet 67:170–181 Rakshit S, Rakshit A, Matsumura H, Takahashi Y, Hasegawa Y, Ito A, Ishii T, Miyashita NT, 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 114:731–743 Rao AN, Johnson DE, Sivaprasad B, Ladha JK, Mortimer AM (2007) Weed management in direct-seeded rice. Adv Agron 93:153–255 Redonˇa ED, Mackill DJ (1996a) Genetic variation for seedling vigor traits in rice. Crop Sci 36:285–290 Redonˇa ED, Mackill DJ (1996b) Mapping quantitative trait loci for seedling-vigor in rice using RFLPs. Theor Appl Genet 92:395–402 Regan KL, Siddique KHM, Turner NC, Whan BR (1992) Potential for increasing early vigor and total biomass in spring wheat. II. Characteristics associated with early vigor. Aust J Agric Res 43:541–553 Sasaki T, Burr B (2000) International rice genome sequencing project: the effort to completely sequence the rice genome. Curr Opin Plant Biol 3:138–141 Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, Nordborg M (2012) An efficient multi-locus mixed model approach for genome-wide association studies in structured populations. Nat Genet 44:825–830 Temnykh S, Park WD, Ayres N, Cartinhour S, Hauck N, Lipovich L, Cho YG, Ishii T, McCouch SR (2000) Mapping and genome organization of microsatellite sequence in rice (Oryza sativa L.). Theor Appl Genet 100:697–712 Vanniarajan C, Vinod KK, Pereira A (2012) Molecular evaluation of genetic diversity and association studies in rice (Oryza sativa L.). J Genet 91:1–11 Wang J, McClean P, Lee R, Goos R, Helms T (2008) Association mapping of iron deficiency chlorosis loci soybean (Glycine max L. Merr.) advanced breeding lines. Theor Appl Genet 116:777–787 Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370 Weir BS, Hill WG (2002) Estimating F-statistics. Annu Rev Genet 36:721–750 Wen WW, Mei HW, Feng FJ, Yu SB, Huang ZC, Wu JH, Chen L, Xu XY, Luo LJ (2009) Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice (Oryza sativa L.). Theor Appl Genet 119:459–470 Xiao JH, Li JM, Yuan LP, Tanksley SR (1996) Identification of QTLs affecting traits of agronomic importance in a recombinant inbred population derived from a subspecific rice cross. Theor Appl Genet 92:230–244 Xu Y, Crouch J (2008) Marker-assisted selection in plant breeding: from publications to practice. Crop Sci 48:391–407 Yamauchi M, Winn T (1996) Rice seed vigor and seedling establishment in anaerobic soil. Crop Sci 36:680–686 Yan JQ, Zhu J, He CX, Benmoussa M, Wu P (1998) Molecular dissection of developmental behavior of plant height in rice (Oryza sativa L.). Genetics 150:1257–1265 Yan WG, Li Y, Agrama HA, Luo DG, Gao FY, Lu XJ, Ren GJ (2009) Association mapping of stigma and spikelet characteristics in rice (Oryza sativa L.). Mol Breed 24:277–292 Yu JM, Buckler ES (2006) Genetic association mapping and genome organization of maize. Biotechnology 17:155–160 Yu JM, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S,

Planta (2014) 239:1309–1319 Buckler ES (2005) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208 Zhang ZH, Qu XS, Wan S, Chen LH, Zhu YG (2005a) Comparison of QTL controlling seedling vigour under different temperature conditions using recombinant inbred lines in rice (Oryza sativa). Ann Bot 95:423–429 Zhang ZH, Yu SB, Yu T, Huang Z, Zhu YG (2005b) Mapping quantitative trait loci (QTLs) for seedling-vigor using recombinant inbred lines of rice (Oryza sativa L.). Field Crop Res 91:161–170 Zhang YM, Mao YC, Xie CQ, Smith H, Luo L, Xu SZ (2005c) Mapping QTL using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.). Genetics 169:2267–2275

1319 Zhang N, Xu Y, Akash M, McCouch S, Oard JH (2005d) Identification of candidate markers associated with agronomic traits in rice using discriminant analysis. Theor Appl Genet 110:721–729 Zhang ZW, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu JM, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360 Zhou X, Stephens M (2012) Genome-wide efficient mixed model analysis for association studies. Nat Genet 44:821–824 Zhou L, Wang JK, Yi Q, Wang YZ, Zhu YG, Zhang ZH (2007) Quantitative trait loci for seedling vigor in rice under field conditions. Field Crop Res 100:294–301 Zhu C, Gore M, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20

13