New Stably Expressed Loci Responsible for Panicle Angle Trait in Japonica Rice in Four Environments

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1 Rice Science, 013, 0(1): Copyright 01, China National Rice Research Institute Published by Elsevier BV. All rights reserved New Stably Expressed Loci Responsible for Panicle Angle Trait in Japonica Rice in Four Environments NIU Fu-an 1,, LIU Jian 1, GUO Yuan 1, CHEN Lan 1, JIANG Jian-hua 1, HONG De-lin 1 ( 1 State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 10095, China; Shanghai Academy of Agricultural Sciences, Shanghai 01106) Abstract: Panicle angle (PA) of 54 recombinant inbred lines derived from a cross between two japonica varieties Xiushui 79 and C Bao was investigated under four environments, and a genetic linkage map including 111 SSR markers was constructed. Genetic analysis was conducted by mixed major gene plus polygene inheritance models, and QTL identification by QTLNetwork.0 and composite interval mapping approach of WinQTLCart.5 software. Analytic results showed that PA trait was controlled by two major genes plus polygene, mainly by major genes. Eight QTLs for PA were detected by QTLNetwork.0 software, explaining 0.01% to 39.89% of phenotypic variation each locus. Twelve QTLs for PA were detected by WinQTLCart.5 software, explaining.83% to 30.60% of phenotypic variation each locus. Two major QTLs (qpa9. and qpa9.5) distributed in RM3700-RM3600 and RM565-RM410, respectively, and a moderate QTL (qpa9.7) distributed in RM57-OSR8, were simultaneously detected by the two methods in all of the four environments. The negative effect alleles of the three QTLs were contributed by Xiushui 79. In addition, eight pairs of epistatic QTLs with minor effects were also detected. QTL Environment interactions were not significant for additive QTLs and epistatic QTL pairs. Key words: Japonica rice; Panicle angle; Growing environment; Mixed inheritance model; QTL identification; Interaction Rice plays an important role in grain production in China. Each year the planting area of rice is 33 million hectares in China, of which about 8.3 million hectares are planted with japonica rice. Currently, most of the japonica rice cultivars planted in China was conventional cultivar. The proportion of planting area for hybrid japonica rice was less than 5%, far behind that of hybrid indica rice (78%) (Tang et al, 008). Therefore, it is possible and necessary to develop hybrid japonica rice. Utilization of cultivars with erect panicle pose (EP) was one of the most important reasons for conventional cultivars keeping advantages. Previous research figured that cultivars with EP had high yielding potential due to their improved resistance to lodging and superior ecological conditions including canopy illumination, temperature, humidity and concentration of CO (Xu et al, 1990; Chen et al, 1995; Xu et al, 000). According to statistics results, more than 95% of planting area of japonica rice had been planted with EP cultivars in Jiangsu Province (Xu et al, 006). Combination of EP trait and heterosis utilization was a possible way to achieve breakthrough of rice production, and it might be a feasible way for hybrid japonica rice out of the current predicament. Guihuahuang, an EP cultivar, was developed from a Received: 15 May 01; Accepted: August 01 Corresponding author: HONG De-lin (delinhong@njau.cn) Balilla hybrid progeny by Taihu Institute of Agricultural Sciences in Jiangsu Province of China in Thereafter, Jiangsu Province and northern China gradually developed a few japonica varieties with EP. In the 1980s, the high yield EP japonica variety Liaojing 5 was developed by Liaoning Province, which greatly extended the planting area of EP varieties. Panicle Angle (PA) trait, as an important agronomic trait, gradually attracted more and more breeders attention. There have been some studies about PA trait. Zhu and Gu (1979) found that EP trait was controlled by a single recessive nuclear gene using the variety Xiaoli 57 with EP as material, while others reported that EP trait was controlled by a dominant gene and modified by polygene (Xu et al, 1995; Wang et al, 1997). However, our previous research (Liu et al, 005; Chen et al, 006; Wang et al, 009a) showed that PA trait was controlled by two major genes plus polygene. Using F and BC 1 F 1 population derived from the cross of Liaojing 5 and Fengjin, Kong et al (007) further assigned the dominant gene named EP on chromosome 9, between two simple sequence repeat (SSR) markers (RM and RM5833-3). Yan et al (007) reported two QTLs for EP trait using a doubled haploid (DH) population derived from the cross of Wuyunjing 8 and Nongken 57, of which the major QTL, qpe9-1, between sequence tagged site (STS)

2 Rice Science, Vol. 0, No. 1, 013 marker H90 and SSR marker RM565, was further cloned (Wang et al, 009b; Zhou et al, 009). Piao et al (009) identified and cloned a mutation designated ep3 on chromosome by an erect panicle mutant from a glutinous japonica cultivar. It is obvious that there are still different views about genetic basis of PA trait. Besides, previous studies were mostly designed under only one environment. There is no report about QTL identification performed for PA trait under different environments. In the present study, genetic analysis by mixed major gene plus polygene inheritance models and QTL identification by QTLNetwork.0 and WinQTLCart.5 softwares were performed using a recombinant inbred line (RIL) population composed of 54 lines under four environments. The aim was to excavate new stably expressed loci responsible for PA trait in japonica rice, so as to provide genetic information for hybrid japonica rice breeding. MATERIALS AND METHODS Experimental population, field experiments and phenotypic measurements Japonica variety Xiushui 79 (P 1 ), japonica restorer line C Bao (P ), a population of 54 recombinant inbred lines (RILs) and F 1 generation derived from the cross of Xiushui 79 and C Bao were used. In 008, F 10:11 generation of the RIL population was obtained. Xiushui 79 with EP, bred by Jiaxing Institute of Agricultural Sciences, Zhejiang Province, China, has 95 cm plant height, high yield and poor quality. C Bao with drooping panicle (DP), bred by Anhui Academy of Agricultural Sciences, Anhui Province, China, has 100 cm plant height and good quality. Both Xiushui 79 and C Bao were sowed in middle-may in Nanjing, China, and headed in middle-august. In 008, RILs, F 1 generation and the two parents were grown in paddy fields at Jiangpu Experiment Station, Nanjing Agricultural University (E1 environment) and Foundation Seed Production Farm in Sihong County in Jiangsu Province (E environment), respectively. Under the two environments, each line was planted in two rows. Each row had eight single-seedling hills in a density of 17 cm 0 cm. The parents and RILs were planted in two replications with complete random block design. Regular field management was carried out. Twenty-five days after full heading stage, ten representative plants of the parents and five representative plants of each line of the RILs in the middle of each plot were sampled, and the PA value of each plant was measured by protractor. Panicle angle was defined as the angle between the line connection of neck-panicle node with panicle tip and the elongation line of stem (Fig. 1). In 009, 54 lines of the RILs and the two parents harvested last year were grown in paddy fields at Jiangpu Experiment Station, Nanjing Agricultural University (E3 environment) and Foundation Seed Production Farm in Sihong County in Jiangsu Province (E4 environment), respectively. Seeds were sown in the seedling nursery on May 1 and transplanted to paddy fields on June 1 in E3 environment. In E4 environment seeds were sown in the seedling nursery on May 19 and transplanted to paddy fields on June 19. Methods of planting and management were conducted as 008. The mean values of each line over two replications in the same environment were used for following statistical analysis. Genetic segregation analysis of Panicle angle in japonica rice Frequency distribution of PA in P 1, P, F 1 and RIL population was first made. Then the phenotypic frequency distribution of P 1, P and RIL population was fitted with the 38 genetic models in the 7 categories, and the maximum likelihood value of each genetic model was determined by using the computer software provided by Professor ZHANG Yuan-ming of Nanjing Agricultural University, China. Akaike s information criterion (AIC) value was calculated based on the maximum likelihood value. The AIC value expressed the fitness degree between the estimated distributions of observation probability and the real distribution. The genetic model with the smallest AIC value was considered the most possible genetic model. Several candidate models could be Fig. 1. Measurement of panicle angle.

3 NIU Fu-an et al. New Stably Expressed Loci Responsible for PA in Japonica Rice in Four Environments selected simultaneously when the differences of AIC value were not significant. The best genetic model was obtained based on likelihood ratio test and a group of goodness-of-fit test with parameters U 1, U, U 3, nw and D n. Finally, the related genetic parameters, including gene effects, as well as the genetic variances of major genes and polygene, were estimated according to the best genetic model (Gai et al, 003). Genetic linkage map construction and QTL identification On the basis of the initial genetic linkage map constructed by our laboratory (Guo et al, 010), 54 pairs of new SSR primers and 8 pairs of STS primers were screened for polymorphism between parents using total DNA as template. The 54 RILs were genotyped using the polymorphic markers selected. Under the condition that LOD = 3.0 and r max = 0.37, a new genetic linkage map was constructed using software MapMaker/EXP 3.0. QTL identification was carried out by QTLNetwork.0 software (Yang and Zhu, 005) and Composite Interval Mapping (CIM) approach in WinQTLCart.5 software. The computer program QTLNetwork.0 was used to detect additive QTLs, additive additive QTL pairs and QTL Environment (Q E) interactions using mixed linear model. A threshold probability of P = was used. A putative QTL was determined between markers when the probability was lower than the threshold. For CIM, a -cm window was used for the genome scanning. The threshold value was determined by permutation 1000 times to ensure the probability of committing type I error less than 5% (Churchill and Doerge, 1994). A putative QTL was declared when the LOD score was larger than the threshold. The interval, 1 unit below the LOD peak, was designated as confidence interval. All the QTLs nomenclature above followed the propositions of the Rice Genetics Cooperative (McCouch, 008). RESULTS AND ANALYSIS Frequency distributions and genetic analysis of Panicle angle in P 1, P, F 1 and RIL population As shown in Fig. 1, the PA value was (4.1 ± 4.3) in P 1 and (94.1 ± 4.8) in P under E1 environment, which showed that there were significant differences between the two parents. The average value of PA in F 1 was (53.0 ±.0), which was between the parents and close to the lower parent Xiushui 79, indicating that EP was incomplete dominant trait. The PA values of RILs showed continuous distribution and displayed multi-peaks, showing the existence of major genes. Fig.. Frequency distribution, fitted mixed distribution and its component distributions of panicle angle in RILs derived from Xiushui 79/C Bao in japonica rice under four environments.

4 Rice Science, Vol. 0, No. 1, 013 Distributions of the PA values for the other three environments were similar to E1 environment (Fig. ). Based on the 38 genetic models in seven classes and frequency distributions of the PA values in Fig. 1, the maximum likelihood value and AIC value of various inheritance models were calculated. The AIC value of D-0 model was the smallest in E1 environment. But the AIC values of E-1-0, E-1-6, E--0 and E-1-1 models were close to that of D-0 model. Thereby, all the five models were regarded as candidate models in E1 environment. Similarly, D-0, E-1-6, E-1-0, E--0 and E-1-1 in E environment, E-1-7, D-1, E--7, E--5 and E--0 in E3 environment and E-1-7, E-1-5, E-1-4, E--7 and E--0 in E4 environment were regarded as candidate models. The maximum likelihood values and AIC values of candidate models were listed in Table 1. Among the 15 statistics for goodness-of-fit test, the number of significant statistics (α = 0.05) were five and two for D-0 and E-1-0 models in E1 environment, respectively. No significant statistics were found for E-1-6, E--0 and E-1-1 models, therefore, the three models appeared better suitability than D-0 and E-1-0 models. The coordination between theoretical and observed distributions of E--0 model was better than E-1-6 and E-1-1 models because E--0 model possessed more statistics with larger probability than the other models. Therefore, E--0 was determined as the best-fitted inheritance model in E1 environment, which implied that PA trait followed the mixed inheritance of two major genes with additive-epistatic effects plus polygene with additive-epistatic effects (Table ). The best-fitted inheritance model was also E--0 in E, E3 and E4 environments, after goodness-of-fit test by the same method as in E1 environment (Data not shown). Fitted mixed distribution and its component distributions of PA trait under the four environments were shown in Fig.. Estimates of genetic parameters under the best-fitted inheritance model were listed in Table 3. It could be seen from the first-order parameters that d a > d b in all the four environments and there were no significant differences among the four environments, showing that additive effects of the two major genes were similar to each other in magnitude. The value of additive-by-additive interaction was less than zero (i < 0) in all the four environments, indicating that interaction of the two major genes could decrease the PA value. It could be observed from the second-order parameters that the major gene heritability in the four environments were 77.47%, 73.59%, 7.15% and 65.7%, respectively, and polygene heritability in the Table 1. Maximum likelihood value and Akaike s information criterion (AIC) value of candidate models calculated with Iterated Error Correction Model (IECM) method for panicle angle trait. Environment Model Max likelihood value AIC value E1 D E E E E E D E E E E E3 E D E E E E4 E E E E E four environments were 1%, 5.18%,.93% and 9.39%, respectively. Therefore, PA trait was mainly controlled by major genes in the cross of Xiushui 79 and C Bao because the heritability of major genes was far larger than that of polygene in all of the four environments. Genetic linkage map construction and QTL identification for panicle angle in RIL population Significant differences between the two parents in panicle angle were found in all of the four environments. Phenotypic values of panicle angle in RIL population showed a continuous distribution. The PA means over all RIL lines was larger than that of the lower value parent and less than but close to the higher value parent. Multiple peaks in the distribution curves and transgressive segregation of the trait at two directions in the RIL population was observed, indicating the existence of major QTL with large effects. Twenty of the 6 pairs of SSR primers used in this study showed polymorphism between the two parents and the polymorphic rate was 7.6%. By adding the 0

5 NIU Fu-an et al. New Stably Expressed Loci Responsible for PA in Japonica Rice in Four Environments Table. Tests for goodness-of-fit of candidate models for panicle angle trait in E1 environment. Model Population Parameter for goodness-of-fit U 1 U U 3 D-0 P ( ) ( ) (0.9469) P ( ) ( ) ( ) RIL (<0.0001) (<0.0001) (<0.0001) E-1-0 P ( ) ( ) ( ) P ( ) ( ) ( ) RIL (0.0507).14 (0.1367).468 ( ) E-1-6 P ( ) ( ) (0.9508) P 0.01 ( ) (0.9498) ( ) RIL 1.08 (0.98) ( ) (0.3377) E--0 P ( ) ( ) (0.9730) P (0.9093) 0.03 ( ) 0.09 ( ) RIL ( ) ( ) ( ) E-1-1 P ( ) ( ) ( ) P 0.01 ( ) ( ) ( ) RIL 1.08 (0.984) ( ) (0.3370) nw D n Table 3. Estimates of genetic parameters for panicle angle trait under the four environments. 1st order parameter Estimate nd order parameter Estimate E1 E E3 E4 E1 E E3 E4 m σ p d a σ mg d b σ pg i h mg (%) h pg (%) σ p, Phenotypic variance; σ mg, Major gene variance; σ pg, Polygene variance; h mg (%), Major gene heritability; h pg (%), Polygene heritability. SSR markers to the original genetic linkage map (Guo et al, 010), a new linkage map with 19 linkage groups, including in total 111 SSR markers, was constructed for the RIL population. The entire map length was 130. cm, and the average interval between adjacent markers was 11.9 cm. Eight additive QTLs of panicle angle, i.e., qpa4.1, qpa4., qpa5, qpa6.1, qpa9., qpa9.5, qpa9.7 and qpa11, were detected by using QTLNetwork.0 software. They were located at chromosome 4, 5, 6, 9 and 11, and each locus accounted for 0.01% to 39.89% of phenotypic variation (Table 4). The negative effect alleles of all the QTLs came from Xiushui 79 except qpa11. qpa9., qpa9.5 and qpa9.7 were major QTLs with large effects, accounting for 38.65%, 39.89% and 0.4% of the phenotypic variations, respectively. qpa9. was located between RM3700 and RM3600, with a distance of 4.0 cm from the nearest marker RM3700. qpa9.5 was located between RM565 and RM410, with a distance of 4.0 cm from the nearest marker RM410. qpa9.7 was located between RM57 and OSR8, with a distance of 0.5 cm from the nearest marker OSR8. Eight additive additive QTL pairs were also detected, but their effects were all small, only accounting for 0.36% to 1.71% of the phenotypic variations. QTL Environment interactions were not significant for additive QTLs or additive additive QTL pairs (Table 5). LOD threshold in E1, E, E3 and E4 environments were 3.,.8,.9 and.8, respectively. Totally 1 additive QTLs of panicle angle in the RIL population were detected, located at chromosome 4, 5, 6, 9 and 11. Each locus accounted for.83% to 30.60% of the phenotypic variations (Table 6). There were seven QTLs distributed on chromosome 9, of which qpa9.1, qpa9., qpa9.5 and qpa9.7 were all detected across the four environments, indicating that chromosome 9 played an important role for PA trait and the QTLs mentioned above were stably expressed loci across

6 Rice Science, Vol. 0, No. 1, 013 Table 4. Additive QTLs identified for panicle angle trait by using QTLNetwork.0 software. QTL Marker interval Distance (cm) a P value Additive effect (A) H (A) (%) qpa4.1 RM551* RM < qpa4. RM388* RM < qpa5 RM1* RM < qpa6.1 RM839* RM < qpa9. RM3700* RM < qpa9.5 RM565 RM410* 4.0 < qpa9.7 RM57 OSR8* 0.5 < qpa11 RM710* RM87.0 < * indicates the nearest marker from putative QTL; a indicates the distance between the putative QTL and the nearest marker; + and - represent positive allele came from C Bao and Xiushui 79, respectively. Table 5. Epistatic QTLs identified for panicle angle trait using QTLNetwork.0 software. QTL-i Interval-i QTL-j Interval-j P value Epistasis (AA) H (AA) (%) qpa4.3 RM388 RM303 qpa9. RM3700 RM3600 < qpa5 RM1 RM118 qpa11 RM710 RM qpa9.7 RM57 OSR8 qpa11 RM710 RM qpa1 RM6696 RM348 qpa7.3 RM11 RM346 < qpa RM37 RM547 qpa6. RM16 RM5753 < qpa3 RM3766 RM5639 qpa10 RM171 RM1108 < qpa7.1 RM863 RM8 qpa7. RM180 RM14 < qpa9. RM3700 RM3600 qpa9.4 RM6570 RM565 < and - represent positive allele came from C Bao and Xiushui 79, respectively. diverse environments. The phenotypic variations explained by qpa9. were more than 0% under two environments, while phenotypic variation explained by qpa9.5 were more than 0% under three environments. In addition, the chromosome locations of the qpa9. and qpa9.5 were consistent with the results detected by QTLNetwork.0 software. So qpa9. and qpa9.5 could be considered as two stably expressed loci responsible for PA trait in different environments. qpa9.1 and qpa9. might be the same locus because they were both quite near RM3700, and qpa9.1 was not detected by QTLNetwork.0 software. qpa9.7 was located between RM57 and OSR8, accounting for more than 10% of the phenotypic variations in two environments, which was consistent with that detected by QTLNetwork.0 software. qpa4.1 and qpa6.1 were detected in three environments, but the percentages of the phenotypic variations explained by the two loci were small. Elite alleles of all loci came from Xiushui 79 except the locus qpa11, which came from C Bao. The chromosome locations of the QTLs detected were shown in Fig. 3. DISCUSSION In the present study, we found that panicle angle trait was controlled by two major genes plus polygene, with major genes dominant, in the cross of Xiushui 79 and C Bao in japonica rice in four environments. The result was consistent with those of our previous studies (Liu et al, 005; Chen et al, 006). qpa9. and qpa9.5, which were detected in all of the four environments using both of the two statistic models (mixed linear model and multiple regression model), were two stably expressed major loci, explained 38.65% and 39.89% of the phenotypic variations, respectively. qpa9.5 might be the same locus as qpe9- detected by Yan et al (007) because confidence intervals, genetic distances and percentages of the phenotypic variations explained by the two QTLs were all coincide. qpa9. detected in this study, located between SSR markers RM3700 and RM3600, was a new locus by map comparative analysis with qpe9-1 detected by Yan et al (007), which was located between markers H58 and H59. Of the other three new loci we found, qpa9.7 was detected by the two statistic models in all of the four environments, and qpa4.1 as well as qpa6.1 was detected by the two statistic models under three environments.

7 NIU Fu-an et al. New Stably Expressed Loci Responsible for PA in Japonica Rice in Four Environments Table 6. QTLs identified for panicle angle trait using Composite Interval Mapping approach under four environments. QTL Environment Marker interval Distance (cm) LOD value Additive effect Variance explained (%) qpa4.1 E1 RM551* RM E RM551* RM E3 RM551* RM qpa4. E3 RM388* RM qpa5 E4 RM1 RM118* qpa6.1 E1 RM839 RM5314* E3 RM839 RM5314* E4 RM839 RM5314* qpa9.1 E1 RM6839 RM3700* E RM6839 RM3700* E3 RM6839 RM3700* E4 RM6839 RM3700* qpa9. E1 RM3700* RM E RM3700* RM E3 RM3700* RM E4 RM3700* RM qpa9.3 E1 RM3600* RM E RM3600* RM qpa9.4 E1 RM6570 RM565* E RM6570 RM565* qpa9.5 E1 RM565 RM410* E RM565 RM410* E3 RM565 RM410* E4 RM565 RM410* qpa9.6 E RM410 RM57* E4 RM410 RM57* qpa9.7 E1 RM57* OSR E RM57* OSR E3 RM57* OSR E4 RM57* OSR qpa11 E4 RM710* RM * indicates the nearest marker from putative QTL; + and - represent positive allele came from C Bao and Xiushui 79, respectively. It was beneficial to get accurate and reliable result with a variety of methods for QTL identification because any kind of method was not perfect and had certain limitations (Hui et al, 1997). The merits of QTL mapping using QTLNetwork.0 computer software are that it can detect interactions of QTL QTL (epistasis) and QTL Environment, as well as less false positive QTLs. However, the demerit of QTL mapping using QTLNetwork.0 software is that some QTLs may not be detected by the mixed linear model. Su et al (010a, b) put forward that, when QTL identification was performed, it was better to scan the whole genome for QTL using the complex model included in QTLNetwork.0 at first, and then to verify the result of QTL mapping using other models. The QTLs detected by two or more methods (models) were highly reliable. And it is necessary to verify the results further if a QTL was detected by only one method. In the present study, eight additive QTLs of panicle angle detected by both QTLNetwork.0 software (mixed linear model) and WinQTL Cartographer.5 software (multiple regression model) were reliable. The loci qpa9., qpa9.5 and qpa9.7, detected in all of the four environments by both of the methods, could be used for fine mapping and cloning further. No significant interactions between QTLs and environments were detected, indicating that panicle

8 Rice Science, Vol. 0, No. 1, 013 Fig. 3. Locations of QTLs for panicle angle in the RIL population. angle trait was less affected by the environment, which was beneficial for introduction and utilization of erect panicle cultivars among different regions. Effects of the eight pairs of epistatic QTLs were all small. This would increase the efficiency of molecular marker assisted breeding. So far, EP japonica cultivars are mostly derived from the Italian variety Ballila. Two thirds of the cultivars in northern China are derived from Ballila, and related with cultivar Liaojing 5 by pedigree analysis directly or indirectly. So EP cultivars were susceptible to some specific diseases and pests due to their narrow genetic basis. Discovering new EP genes and their carrier materials could provide genetic information and breeding materials for japonica rice breeding (Chen et al, 1995; Zhang et al, 00). Xiushui 79 was derived from the variety Nongken 58 by pedigree analysis. It was calculated that the EP gene of Xiushui 79 was from the variation of Nongken 58 (Cheng et al, 007; Wang et al, 007). The new EP gene might provide an approach for solving the foregoing problem. ACKNOWLEDGEMENTS This study was supported by the Program of National High Technology Research and Development, Ministry of Science and Technology, China (Grant No. 010AA101301), the Program of Introducing International Advanced Agricultural Science and Technology in China (Grant No. 006-G8[4]-31-1) and the Program of Science-Technology Basis and Conditional Platform in China (Grant No ). REFERENCES Chen W F, Xu Z J, Zhang L B, Zhou H F, Yang S R Effects of different panicle type on canopy properties light distribution and dry matter production of rice population. Acta Agron Sin, 1(1): Chen X G, Liu J B, Hong D L Genetic analysis on panicle angle and number of spikelets per panicle by using six generations of three crosses derived from erect curve panicles in japonica rice (Oryza sativa L.). Acta Agron Sin, 3(): (in Chinese with English abstract) Cheng B S, Wan Z B, Hong D L Establishment of SSR fingerprint map and analysis of genetic similarity among 35 varieties in japonica rice (Oryza sativa L.). J Nanjing Agri Univ, 30(3): 1 8. (in Chinese with English abstract) Churchill G A, Doerge R W Empirical threshold values for quantitative trait mapping. Genetics, 138(3): Gai J Y, Zhang Y M, Wang J K Genetic System of Quantitative Traits in Plants. Beijing: Science Press: 8 94; 4 60; (in Chinese)

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