Mapping and Comparative Analysis of QTL for Rice Plant Height Based on Different Sample Sizes within a Single Line in RIL Population

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1 Rice Science, 2011, 18(4): Copyright 2011, China National Rice Research Institute Published by Elsevier BV. All rights reserved Mapping and Comparative Analysis of QTL for Rice Plant Height Based on Different Sample Sizes within a Single Line in RIL Population LIANG Yong-shu, GAO Zhi-qiang, SHEN Xi-hong, ZHAN Xiao-deng, ZHANG Ying-xin, WU Wei-ming, CAO Li-yong, CHENG Shi-hua (Chinese National Center for Rice Improvement / State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou , China) Abstract: To clarify the most appropriate sample size for obtaining phenotypic data for a single line, we investigated the main-effect QTL (M-QTL) of a quantitative trait plant height (ph) in a recombinant inbred line (RIL) population of rice (derived from the cross between Xieqingzao B and Zhonghui 9308) using five individual plants in 2006 and Twenty-six ph phenotypic datasets from the completely random combinations of 2, 3, 4, and 5 plants in a single line, and five ph phenotypic datasets from five individual plants were used to detect the QTLs. Fifteen M-QTLs were detected by 1 to 31 datasets. Of these, qph7a was detected repeatedly by all the 31 ph datasets in 2006 and explained 11.67% to 23.93% of phenotypic variation; qph3 was detected repeatedly by all the 31 datasets and explained 5.21% to 7.93% and 11.51% to 24.46% of phenotypic variance in 2006 and 2009, respectively. The results indicate that the M-QTL for a quantitative trait could be detected repeatedly by the phenotypic values from 5 individual plants and 26 sets of completely random combinations of phenotypic data within a single line in an RIL population under different environments. The sample size for a single line of the RIL population did not affect the efficiency for identification of stably expressed M-QTLs. Key words: rice; plant height; QTL mapping; recombination inbred line; simple sequence repeat; sample size There are three indispensable steps for mapping a quantitative trait locus (QTL): construction of molecular genetic maps (Li et al, 1996a; McCouch et al, 1998 and 2002; Temnykh et al, 2001; Lan et al, 2003; Zhao et al, 2008), development of statistical models (Zhu et al, 1988; Lander et al, 1989; Zeng 1994; Wang et al, 1999; Li et al, 2005; Wang et al, 2006; Yang et al, 2009) and accurate measurement of quantitative traits (Shen et al, 1992). In recent years, almost all molecular genetics studies on quantitative traits in rice have focused on developing genetic linkage maps and statistical models for QTL mapping. However, few studies have attempted to identify the appropriate number of individual plants required to obtain phenotypic data for a single line in QTL-mapping using permanent populations (Mao et al, 1999). In some previous studies, only one individual plant for one type of genotype was measured for QTL-mapping in an F 2 or BC 1 F 2 segregating population (Ye et al, 2005; Zhang et al, 2006; Nonoue et al, 2008). However, these kinds of temporary segregating populations are seldom used Received: 3 December 2010; Accepted: 20 April 2011 Corresponding authors: CAO Li-yong (caolycgf@mail.hz.zj.cn); CHENG Shi-hua (shcheng@mail.hz.zj.cn) for QTL-mapping studies because they can not be used simultaneously in multi-environmental experiments. At present, most rice QTL-mapping studies are carried out using permanent populations, such as doubled haploid (DH) and recombinant inbred lines (RIL). In QTL-mapping studies on crop plants, 5 or 10 individual plants in a single line in a permanent population are used to obtain the average value for a particular phenotype (Li et al, 1996b; Lu et al, 1996; Xiao et al, 1996; Mei et al, 2005; Liu et al, 2006; You et al, 2006). Although it is a small sample size, measurements of more plants would significantly increase the workload. Decreases in the measurement efficiency of a quantitative trait would affect research progress for the QTL-mapping program. In particular, some quantitative traits are difficult to measure, such as root surface area, number of root tips, and some important indexes of physiological or biochemical traits (Yan et al, 2004; Wu et al, 2005; Zhu et al, 2005; Tong et al, 2006; Qu et al, 2008). The optimal period for measurement of such quantitative traits could be missed if more individuals had to be measured. Therefore, it is important to clarify the most appropriate sample size for obtaining phenotypic data in a single

2 266 Rice Science, Vol. 18, No. 4, 2011 line to carry out accurate and highly efficient QTLmapping in permanent populations of crops. Rice materials MATERIALS AND METHODS The phenotypic experiment was conducted at the experimental station of the China National Rice Research Institute (CNRRI) in Fuyang City, Zhejiang Province, China ( N). The seeds of a population with 226 RILs derived by single-seed descent from the cross between Xieqingzao B and Zhonghui 9308, and the two parents, were sown on 15 May in 2006 and 2009, respectively. For all single lines, 25-day-old seedlings were transplanted into four rows with six plants per row. The spacings were 20 cm between plants and 23 cm between rows. Phenotyping Plant height (ph), a typical quantitative trait with approximately normal distribution in the RIL population, was selected as the mapping trait (data not shown). The field performance of ph differed significantly between Xieqingzao B (94.6 cm) and Zhonghui 9308 (119.3 cm). Five individuals were selected randomly for sampling, and the phenotypes of ph, denoted as pha, phb, phc, phd and phe, were measured in 2006 and Twenty-six sets of ph phenotypic values were averaged from a series of completely random combinations of 2, 3, 4 and 5 individuals within a single line. A completely random combination of 2 individuals from 5 plants within a single line yielded 10 datasets: phab, phac, phad, phae, phbc, phbd, phbe, phcd, phce and phde. Similarly, phacd, phabe, phabd, phabc, phbcd, phbde, phade, phcde, phace, phbce, phabde, phacde, phabcd, phabce and pbcde denote ph data for a completely random combination of 3 or 4 individuals, respectively. phabcde was averaged from 5 individuals, and the ph phenotypic value from 5 individuals within a single line and 26 sets of completely random combinations of ph phenotypic value were used to detect the QTL for ph. Marker analysis and linkage map construction A total of 198 polymorphic SSR markers distributed on 12 chromosomes ( org/) were used to assay the entire population of 226 RILs from Xieqingzao B Zhonghui PCR amplification was based on the protocol described by McCouch et al (2001). The linkage map was constructed using MAPMAKER/EXP 3.0 software (Lander et al, 1987; Lincoln et al, 1992) with an LOD value 3.0, a recombinant rate 0.4, and the Kosambi map function. This linkage map was consistent with the rice molecular marker linkage map reported in a previous study (McCouch et al, 2002). The genome coverage was equal to cm with an average distance of 8.87 cm between adjacent markers, as described by Shen et al (2008). QTL analysis The main-effect QTL (M-QTL) was analyzed by composite interval mapping (CIM) and multiple interval mapping (MIM) using Windows QTL Cartographer 2.5 software (Zeng et al, 1994; Li et al, 2005; Wang et al, 2006). The CIM was performed using model 6 with a walking speed of 0.5 cm and the inclusion of 10 maximum background marker loci in a stepwise forward and backward regression procedure. The Kosambi function and the likelihood rate thresholds based on 500 permutations at a significance level 0.05 for all ph phenotypic values were used. The threshold of LOD 2.5 was used to declare a significant QTL (Churchill et al, 1994) and to estimate genetic parameters. MIM was used only to verify the presence or absence of main-effect QTLs detected by CIM. This study was aimed to determine the appropriate sample size within a single line to obtain phenotypic data for QTL mapping in a permanent population. The epistatic QTLs and QTL-by-year interactions (Q E) effects were not analyzed. QTLs were named following the nomenclature of McCouch et al (1997), but in alphabetic order for QTLs on the same chromosome. RESULTS M-QTLs detected by 31 sets of phenotypic data of plant height In 2006, nine M-QTLs for ph were detected repeatedly at least by two sets of ph data except for qph2a (RM6427 RM4702) on chromosome 2 and qph6c (RM19417 RM6734) on chromosome 6, which

3 LIANG Yong-shu, et al. Mapping QTL for Rice Plant Height 267 Table 1. QTLs associated with plant height (ph) detected on chromosomes 2, 3, 6, 7, 8 and 11 by 31 sets of ph data in the RIL population of rice in 2006 and Year QTL Chr Marker interval Position (cm) LOD Additive effect R 2 (%) Detected by ph dataset 2006 qph2a 2 RM6427 RM qph3 3 RM148 RM qph6a 6 RM5754 RM qph6b 6 RM30 RM , qph6c 6 RM19417 RM qph6d 6 RM587 RM , 11.81, 12.31, qph6e 6 RM7213 RM qph7a 7 RM3670 RM , 64.11, qph8a 8 RM25 RM , qph8b 8 RM8266 RM qph8c 8 RM5556 RM , , qph3 3 RM148 RM qph6f 6 RM162 RM qph7b 7 RM180 RM , 61.81, qph11 11 RM1812 RM , Chr, Chromosome; Position, Genetic distance of QTL away from the left marker; R 2, Ability to explain phenotypic variation. were only detected by the phenotypic data of pha and phe (Table 1, Fig. 1 and Fig. 2). The M-QTL qph3 (RM148 RM85) on chromosome 3 was detected repeatedly by all 31 datasets. Four M-QTLs located on chromosome 6, including qph6d (RM587 RM510), qph6e (RM7213 RM3724), qph6a (RM5754 RM136) and qph6b (RM30 RM3430) were detected repeatedly by 26, 23, 7 and 3 datasets, respectively. Only qph7a (RM3670 RM2) on chromosome 7 was detected repeatedly by all 31 datasets. Three QTLs located on chromosome 8, including qph8a (RM25 RM8266), qph8b (RM8266 RM5556) and qph8c (RM5556 RM310) were detected repeatedly by 13, 16 and 2 datasets, respectively. In 2009, four QTLs were detected repeatedly at least by five sets of ph data except for qph6f (RM162 RM30) on chromosome 6, which was only detected by the phc dataset (Table 1, Fig. 1 and Fig. 2). The M-QTL qph2b (RM106 RM1920) on chromosome 2 was detected repeatedly by 27 datasets. qph3 (RM148 RM85) on chromosome 3 was detected repeatedly by 31 datasets, qph7b (RM180 RM5436) on chromosome 7 was detected repeatedly by 28 datasets, and qph11 (RM1812 RM167) on chromosome 11 was detected repeatedly by 5 datasets. Only qph3 (RM148 RM85) on chromosome 3 was detected repeatedly by all 31 datasets in 2006 and Positions of M-QTLs Nine M-QTLs were identified in 2006 (Table 1, and Fig. 2). The QTL qph3 (RM148 RM85) was Fig. 1. Two QTLs detected by 31 sets of ph data under different environments. mapped at the same distance from its left marker (RM148, cm). qph6a (RM5754 RM136) detected by seven datasets was mapped at the same genetic distance from its left marker (RM5754, cm). qph6b (RM30 RM3430) detected only by three datasets was mapped at two different genetic positions from its left marker (RM30, cm and cm). qph6d (RM587 RM510) detected repeatedly by 26 datasets was mapped in four different genetic positions from its left marker (RM587, cm, cm, cm and cm). qph6e (RM7213 RM3724) detected by 23 datasets was mapped at the

4 268 Rice Science, Vol. 18, No. 4, 2011 Fig. 2. QTLs detected by 31 sets of ph data in the RIL population of rice in 2006 and same genetic distance from its left marker (RM7213, cm). qph7a (RM3670 RM2) detected by all 31 datasets was mapped at three different genetic positions from its left marker (RM3670, cm, cm and cm). qph8a (RM25 RM8266) was mapped at two genetic positions from its left marker (RM25, cm and cm). qph8b (RM8266 RM5556) was mapped at the same genetic position from its left marker (RM8266, cm). qph8c (RM5556 RM310) detected only by two datasets was mapped at the same genetic position from its left marker (RM5556, cm). There were four M-QTLs detected in 2009 (Table 1 and Fig. 2). The QTL qph2b (RM106 RM1920) was

5 LIANG Yong-shu, et al. Mapping QTL for Rice Plant Height 269 mapped at two genetic positions from its left marker (RM106, cm and cm). qph3 (RM148 RM85) was mapped at the same genetic position from its left marker (RM148, cm). qph7b (RM180 RM5436) detected by 28 datasets was mapped at three different genetic positions from its left marker (RM180, cm, cm, and cm). qph11 (RM1812 RM167) was mapped at two different genetic positions from its left marker (RM1812, cm and cm). qph3 (RM148 RM85) detected by all 31 datasets in 2006 and 2009 was mapped at the same genetic position from its left marker (RM148, cm). Genetic parameters for M-QTLs The LOD values and the percentage of phenotypic variance (R 2 ) of genetic parameters for M-QTLs detected by different ph phenotypic datasets were not fully consistent, but the additive effect had the same direction in 2006 and 2009 (Table 1). In 2006, for qph3 (RM148 RM85), the LOD value ranged from 3.44 to 4.96, the additive effect value ranged from 4.22 to 3.47, and it explained 5.21% to 7.93% phenotypic variation; whereas in 2009, for qph3, the LOD value was from 5.38 to 9.77, the additive effect value from 6.18 to 4.57, and it explained 11.51% to 24.46% phenotypic variation. The M-QTLs detected repeatedly by all 31 ph datasets would show higher genetic effect than those detected by partial ph datasets under different environments. DISCUSSION An essential step for QTL mapping is to obtain accurate phenotypic values of a quantitative trait. Previous QTL-mappings were based largely on mean phenotypic values from 5 to 10 individual plants within a single line in the RIL or DH population of crops. However, research progress in QTL-mapping was sometimes affected by the efficiency of measuring particular quantitative traits. Few studies had focused on the effect of sample size within a single line on the results of QTL-mapping (Mao et al, 1999; Mei et al, 2005; Liu et al, 2006; You et al, 2006). In the present study, ph values of five randomly selected individual plants within a single line in an RIL rice population were measured in 2006 and The experimental field had a consistent rate of fertilizer application, and the experimental materials were randomly planted in the field. This strategy can minimize errors due to environmental variations and improve the accuracy of ph phenotypic data. Twenty-six ph datasets were averaged from a series of random combinations of 2, 3, 4 and 5 individual plants within a single line. A total of 31 ph datasets were used for QTL mapping. These phenotypic data are sufficient to determine the effect of sample size within a single line on the accuracy of QTL mapping. Thus, we can determine the most appropriate sample size for obtaining the phenotypic value of a quantitative trait within a single line in QTL-mapping. The results of this study will provide some guidelines on experimental design of mapping QTLs for complex traits using permanent populations in crop species. Chromosome distribution of M-QTLs for plant height in rice RIL To date, a total of QTLs underlying plant height of rice have been identified. These QTLs are distributed on the entire genome of rice ( www. gramene.org/). In the present study, 11 M-QTLs distributed on chromosomes 2, 3, 6, 7 and 8 were detected repeatedly by 31 ph datasets in 2006, and four M-QTLs located on chromosomes 3, 6, 7 and 11 were detected repeatedly from 1 to 31 datasets in The distribution of the M-QTLs on chromosomes showed slight differences across two environments. However, several M-QTLs were detected repeatedly by all the 31 datasets under both environments. This was particularly evident for the chromosomal regions between RM148 RM85 on chromosome 3 and RM3670 RM2 on chromosome 7, where QTLs for plant height were detected repeatedly by all the 31 datasets. These QTLs have also been reported in previous studies (Mei et al, 2003; Zhao et al, 2005; Lu et al, 1997), which confirmed that the M-QTL (chromosomal region) can be detected using different genetic populations under different experimental environments and not affected by sample size within a single line. Therefore, our experiment design was

6 270 Rice Science, Vol. 18, No. 4, 2011 suitable for fulfilling the aim of the present study. Stability of QTL expression under different environments The same M-QTL for a quantitative trait can be detected under different environments (Cao et al, 2001; Guo et al, 2003). By comparison of the QTLs detected under different environments, stably expressed M-QTLs can be detected repeatedly, and such QTLs are not affected by environmental factors (Li et al, 2002; Li et al, 2003; Yuan et al, 2003; Ye et al, 2006). In the present study, only 1 of the 15 M-QTLs could be stably expressed and detected repeatedly by all the 31 ph datasets under the two environments, whereas the other 14 M-QTLs could not be detected under the two experiments because of their unstable expression. For example, five M-QTLs for ph on chromosome 6 could be detected by 1 to 26 datasets in However, only one M-QTL on chromosome 6 could be detected by one dataset in The results revealed that the stable expression of QTLs for ph could be detected repeatedly by all ph datasets under two environments, and was not affected by the sample size within a single line in an RIL population. In this study, M-QTLs for plant height with high heritability were detected by all the 31 datasets. However, QTLs with a low phenotypic contribution rate were not detected repeatedly by all phenotypic data under different environments. To date, 15 M-QTLs for traits with high heritability have been successfully cloned, including Hd6 gene (Takahashi et al, 2001) and GS3 gene (Fan et al, 2006; Jiang et al, 2008). These M-QTLs were related to important traits and explained more than 40% of phenotypic variance. Consequently, we selected a quantitative trait of plant height with high heritability as mapping traits. Comparison of genetic parameters for M-QTLs of plant height in RIL population In crop breeding research, genetic parameters for QTLs are used to describe gene action related to a quantitative trait (Kong et al, 2006). The same QTL for a quantitative trait with inconsistent genetic parameters can be detected under different environments (Li et al, 2003; Zhao et al, 2005; You et al, 2006). Until now, there have been no reports on the best sample size for a single line to obtain accurate phenotypic values for a particular quantitative trait. Some interesting results were obtained in our study. The M-QTLs with relatively high LOD values, additive effect values and phenotypic contribution rates were rather stable and were detected repeatedly by all the 31 ph datasets under different environments. For example, qph3 was detected repeatedly by all the 31 datasets with LOD values ranging from 5.38 to 9.77 and 3.44 to 4.96, additive effects values from to and to -3.47, and explained 5.21% to 7.93% and 11.51% to 24.46% of phenotypic variance in 2006 and 2009, respectively. These values were greater than their corresponding values of QTLs detected by partial datasets under different environments. Therefore, an interesting conclusion could be deduced from this study that M-QTLs with relatively high genetic parameter values could be detected repeatedly from phenotypic values of five individual plants and 26 datasets of completely random combinations of phenotypic data in an RIL population. In addition, the sample size of individuals within a single line in an RIL population did not affect the efficiency for identification of stably expressed M-QTLs. These results will provide a strategy of highly efficient sampling for identification of M-QTL of complex quantitative traits in the permanent population of crops. ACKNOWLEDGEMENTS This work was supported by the grants from the Chinese Natural Science Foundation (Grant No ), the National Program on Super Rice Breeding, the Ministry of Agriculture (Grant No ); National High Technology Research and Development Program of China (Grant No. 2006AA10Z1E8); the Provincial Program of 8812, Zhejiang Province, China (Grant No ). REFERENCES Cao G Q, Zhu J, He C X Environment interaction for developmental impacts of epistasis and QTL behavior of plant height in rice (Oryza sativa L.). Theor Appl Genet, 103: Churchill G A, Doerge R W Empirical threshold values for

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