Mapping of qTGW1.1, a Quantitative Trait Locus for

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ScienceDirect Rice Science, 2015, 22(1): 9í15

Mapping of qTGW1.1, a Quantitative Trait Locus for 1000-Grain Weight in Rice (Oryza sativa L.) ZHANG Hong-wei, CHEN Yu-yu, CHEN Jun-yu, ZHU Yu-jun, HUANG De-run, FAN Ye-yang, ZHUANG Jie-yun (State Key Laboratory of Rice Biology and Chinese National Center for Rice Improvement, China National Rice Research Institute, Hangzhou 310006, China)

Abstract: 1000-grain weight (TGW) is one of the three component traits of the grain yield in rice (Oryza sativa L). This study was conducted to validate and fine-map qTGW1.1, a minor QTL for TGW which was previously located in a 3.7-Mb region on the long arm of rice chromosome 1. Five sets of near isogenic lines (NILs) were developed from two BC2F4 populations of the indica rice cross Zhenshan 973/Milyang 46. The NIL sets consisted of two homozygous genotypic groups differing in the regions RM11448RM11522, RM11448RM11549, RM1232RM11615, RM11543RM11554 and RM11569RM11621, respectively. Four traits, including TGW, grain length, grain width and heading date, were measured. Phenotypic difference between the two genotypic groups in each NIL population was analyzed using SAS procedure GLM. Significant QTL effects were detected on TGW with the Zhenshan 97 allele increasing grain weight by 0.12 g to 0.14 g and explaining 8.30% to 15.19% of the phenotypic variance. Significant effects were also observed for grain length and width, whereas no significant effect was found for heading date. Based on comparison among the five NILs on the segregating regions and the results of QTL analysis, qTGW1.1 was delimited to a 376.9-kb region flanked by DNA markers Wn28382 and RM11554. Our results indicate that the effects of minor QTLs could be steadily detected in a highly isogenic background and suggest that such QTLs could be utilized in the breeding of high-yielding rice varieties. Key words: 1000-grain weight; minor effect; quantitative trait locus; rice (Oryza sativa L.)

Number of panicles per plant, number of grains per panicle and 1000-grain weight (TGW) are the three component traits determining grain yield in rice (Oryza sativa L.), all of which are controlled by multiple genes termed as quantitative trait loci (QTLs). In the past decade, fine-mapping and cloning of QTLs for agronomically important traits in rice have attracted more and more attentions, in which TGW is the yield trait achieving the greatest progress. To date, seven QTLs for TGW have been cloned, among which GS3, TGW6 and GL3.1/OsPPKL1 are mainly responsible for grain weight and grain length, and GW2, qSW5/GW5, GS5 and GW8 for grain weight and grain width (Qi et al, 2012; Zhang et al, 2012; Huang et al, 2013; Ishimaru

et al, 2013). In the map-based cloning studies, these QTLs were verified to have large additive effects for TGW, ranging from 0.80 g to 5.57 g. In addition, they were consistently detected across different genetic backgrounds and environments, with one QTL explaining 12.1%28.0% of the phenotypic variance in a primary population (Yu et al, 1997; Li et al, 2000; Hua et al, 2002; Xing et al, 2002; Zhang et al, 2012; Ishimaru et al, 2013). Additionally, it is difficult to study minor QTLs since the detection varied greatly depending on the genetic backgrounds and environments. Populations segregating for the target region in an isogenic background are ideal materials to eliminate the background noise. In addition to the classical

Received: 21 August 2014; Accepted: 16 October 2014 Corresponding author: ZHUANG Jie-yun ([email protected]) Copyright © 2015, China National Rice Research Institute. Hosting by Elsevier B.V. This is an reserved. open access article under the CC BY-NC-ND license All rights (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer review under responsibility of China National Rice Research Institute. Peer review under responsibility of China National Rice Research Institute http://dx.doi.org/10.1016/S1672-6308(14)60279-1 http://dx.doi.org/10.1016/j.rsci.2015.05.002

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Rice Science, Vol. 22, No. 1, 2015

development of near isogenic lines (NILs) through successive backcrossing, the use of residual heterozygotes (RHs) are becoming more and more popular in the establishment of NILs and secondary populations for QTL validation and fine-mapping. Using NILs derived from two RHs identified from a recombinant inbred line (RIL) population of the rice cross Zhenshan 97/Milyang 46, two QTLs for TGW located on the short arm of chromosome 1 were separated (Yu et al, 2008). Using segregating population derived from a RH identified from the D50/HB277 RIL population, GS7 controlling grain size was delimited into a 4.8-kb region on the long arm of chromosome 7 (Shao et al, 2012). Productivity of rice is greatly affected by the growth duration which is largely determined by heading date (HD), and HD itself is the key determinant affecting the seasonal and regional adaption of a rice variety. Understanding the genetic relationship between yield traits and HD is helpful to the utilization of yield-enhancing QTLs in rice breeding. It has been reported for a number of QTLs having large effects for TGW that increased TGW was associated with delayed heading, which is unfavorable in utilizing the enhancing allele for yield improvement. For example, the WY3 allele of GW2 increased grain weight but delayed heading (Song et al, 2007), and the gw9.1 allele from O. rufipogon increased TGW by 1.52.0 g while delayed heading by 4 d (Xie et al,

Fig. 1. Development of rice populations used in this study.

2008). Additionally, such an unfavorable association was not evident for QTLs showing minor effects for HD and yield traits (Guo et al, 2013b). In a previous study (Guo et al, 2013a), two QTLs showing significant effects for TGW and no significant effects for HD were dissected in a 12.0-Mb region on the long arm of rice chromosome 1 using advanced backcross populations derived from an indica rice cross, Zhenshan 97 (ZS97)///ZS97//ZS97/Milyang 46 (MY46). Of them, qTGW1.1 was delimited to a 3.7-Mb region flanked by simple sequence repeat (SSR) markers RM11437 and RM11621, and qTGW1.2 to a 4.5-Mb region flanked by RM11615 and RM11800. In the present study, five sets of NILs with segregating regions overlapped within the qTGW1.1 region were developed and used to narrow down the QTL region and validate its effects on TGW and HD.

MATERIALS AND METHODS Rice materials Five sets of NILs were used in this study. As shown in Fig. 1 and described below, they were derived from two BC2F4 populations of ZS973/MY46, which were segregated in the intervals RM11448RM11615 and RM11448RM11787, respectively. The two BC2F4 populations were produced from two BC2F3 plants, respectively, which were progenies of the same BC2F2

ZHANG Hong-wei, et al. Mapping of qTGW1.1 for 1000-Grain Weight

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Fig. 2. Segregating regions of five sets of near isogenic lines (NILs).

plant (Guo et al, 2013a). The two BC2F4 populations were advanced to higher generations, with marker-based selection performed and pedigree information documented in each generation. Three BC2F7 plants carrying sequential heterozygous segments covering the regions RM11448RM11552, RM11448RM11549 and RM1232RM11615, respectively, were selected. In the resultant BC2F8 populations, homozygous plants with no recombination in the segregating regions were identified. Selfing seeds of these plants resulted in the development of three sets of NILs with sequential segregating regions extending from RM11448 to RM11615. They were named C901, C902 and C903, each consisting of two homozygous genotypic groups differing in the corresponding segregating region (Fig. 2). The number of lines included in each group ranged as 2324 for ZS97 and 2628 for MY46 (Table 1). Similarly, two sets of NILs in BC2F10:11 were developed from the other BC2F4 population which was segregated in RM11448 RM11787. They were named C1001 and C1002, consisting of two homozygous genotypic groups differing in the intervals RM11543RM11554 and

RM11569RM11621, respectively (Fig. 2). Thirty lines were included in each group (Table 1).

Field trials and trait evaluation The three BC2F8:9 and two BC2F10:11 NIL sets were planted in Hangzhou, Zhejiang, China in 2012 and 2013, respectively (Table 1). Randomized complete block design was used with two replications. In each replication, eight plants per line were planted in one row with spacing of 16.7 cm between plants and 26.7 cm between rows. HD was scored for each plant and averaged for each replication. At maturity, five plants in the middle of each row were bulk-harvested and air-dried. TGW, grain length (GL) and grain width (GW) were measured using SC-A seed counting and grain weighting device (Wanshen Ltd, Hangzhou, China).

DNA marker analysis Total DNA was extracted following the method of Zheng et al (1995). PCR amplification was performed according to Chen et al (1997), and the products were visualized on 6% non-denaturing polyacrylamide gels

Table 1. Rice populations and field experiments. Generation

NIL name

Segregating region

BC2F8:9 C901 RM11448–RM11522 BC2F8:9 C902 RM11448–RM11549 BC2F8:9 C903 RM1232–RM11615 BC2F10:11 C1001 RM11543–RM11554 BC2F10:11 C1002 RM11569–RM11625 NIL, Near isogenic line; ZS97, Zhenshan 97; MY46, Milyang 46.

Sample for two homozygous genotypes 24 lines for ZS97, 26 lines for MY46 23 lines for ZS97, 28 lines for MY46 24 lines for ZS97, 27 lines for MY46 30 lines for ZS97, 30 lines for MY46 30 lines for ZS97, 30 lines for MY46

Growing season May–September, 2012 May–September, 2012 May–September, 2012 April–August, 2013 April–August, 2013

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using silver staining. All the SSR markers were selected from the Gramene database (www.gramene.org). One InDel marker, Wn28382, was amplified using the primers 5ƍ-TGGTGACCCAATTAAACGGTA-3ƍ and 5ƍ-AAATTTTGGGAACATGAAAGGCTC-3ƍ which was designated according to the sequence difference between ZS97 and MY46 detected by whole-genome re-sequencing.

Data analysis Analysis of variance (ANOVA) was performed to test the phenotypic differences between the two genotypic groups in each NIL set using SAS procedure GLM (SAS Institute Inc, 1999) as described previously (Dai et al, 2008). Given the detection of a significant difference (P < 0.05), the same data were used to estimate the genetic effect of the QTL, including additive effect


and the proportion of phenotypic variance explained.

RESULTS Phenotypic variation of five NIL sets In the five NIL populations, little variation was found for HD while the variation was more distinguishable for TGW, GL and GW. When the frequency distribution was plotted using the two genotypic group as two series (Fig. 3), genotype segregation was shown for TGW and either or both the GL and GW in three of the populations, C902, C903 and C1001, in which ZS97 homozygotes tend to cluster in the area of higher values and the MY46 homozygotes to the area of lower values.

Fig. 3. Distributions of 1000-grain weight, grain length and grain width in five near isogenic line populations.


Among the three BC2F8:9 NIL populations which were planted in 2012, tending of the ZS97 homozygotes to higher grain weight and the MY46 homozygotes to lower grain weight was observed in C902 and C903, but this was not observed in C901. These results suggest that allelic difference for TGW was present in the segregating regions of C902 and C903, but it was absent in the segregating region of C901. Similar tendency was observed for GL and GW, except that separation between the two genotypic groups was less obvious for GL in C902. Of the two BC2F10:11 populations planted in 2013, tending of the ZS97 homozygotes to higher values and the MY46 homozygotes to lower values was observed for TGW, GL and GW in C1001, but this was not observed in C1002. These results suggest that allelic difference for TGW was present in the segregating region of C1001, but it was absent in the segregating region of C1002. Together with the results obtained from the three other populations, it could be deduced that a QTL responsible for TGW, GL and GW might be segregated in the segregating regions of C902, C903 and C1001.

Validation and mapping of qTGW1.1 Results of the two-way ANOVA for TGW were shown in Table 2. Significant genotypic variation was detected in C902, C903 and C1001, whereas it was not detected in C901 and C1002. The enhancing allele

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was all derived from ZS97 in C902, C903 and C1001, with the additive effects estimated as 0.12 g, 0.14 g and 0.13 g, and the contributions to the phenotypic variance as 10.55%, 8.30% and 15.19%, respectively. QTL qTGW1.1 has previously shown to have a consistent effect in Hangzhou and Lingshui (Guo et al, 2003a). The NIL populations used in the present study were all tested in Hangzhou but three were grown in 2012 and the remaining two in 2013. In the three populations showing significant QTL effects, including C902 and C903 tested in 2012 and C1001 tested in 2013, the allelic direction remained the same and the magnitude was similar across the three populations. It is assumable that the same QTL was responsible for the segregations of TGW in C902, C903 and C1001. This QTL was located within the region flanked by Wn28382 and RM11554, which was totally segregated in C903 and consisted of a segregating region and a cross-over region in C902 and C1001, respectively (Fig. 2). This is a region corresponding to a 376.9-kb area of the Nipponbare genome (www.gramene.org). As expected, this region was homozygous in C901 and C1002 where no QTL for TGW, GL and GW was detected.

Effects of qTGW1.1 on grain shape and heading date Mapping results for grain shape were similar to those for TGW (Table 2). In C901 and C1002 which showed

Table 2. QTL analysis for 1000-grain weight, grain length, grain width and heading date using five near isogenic line (NIL) populations. Values of the two homozygous genotypic groups (Mean ± SD) P A R2 (%) Zhenshan 97 Milyang 46 TGW (g) C901 27.08 ± 0.26 27.10 ± 0.33 0.8877 C902 27.49 ± 0.21 27.24 ± 0.34 0.0033 -0.12 10.55 C903 27.60 ± 0.34 27.32 ± 0.35 0.0065 -0.14 8.30 C1001 24.68 ± 0.37 24.43 ± 0.28 0.0043 -0.13 15.19 C1002 24.59 ± 0.71 24.76 ± 0.61 0.3269 GL (mm) C901 8.658 ± 0.036 8.640 ± 0.035 0.0653 C902 8.888 ± 0.052 8.888 ± 0.063 0.9646 C903 8.819 ± 0.038 8.754 ± 0.045 < 0.0001 -0.032 23.93 C1001 7.951 ± 0.064 7.915 ± 0.057 0.0297 -0.018 8.57 C1002 7.904 ± 0.114 7.929 ± 0.100 0.3882 GW (mm) C901 3.002 ± 0.017 2.998 ± 0.017 0.5798 C902 2.980 ± 0.017 2.960 ± 0.018 < 0.0001 -0.011 17.30 C903 2.978 ± 0.019 2.961 ± 0.020 0.0018 -0.009 11.73 C1001 2.913 ± 0.024 2.906 ± 0.019 0.2231 C1002 2.921 ± 0.032 2.918 ± 0.029 0.8199 HD (d) C901 62.7 ± 0.8 62.4 ± 0.8 0.2234 C902 58.8 ± 0.6 59.0 ± 0.6 0.3662 C903 63.0 ± 1.1 63.4 ± 0.8 0.1270 C1001 74.5 ± 0.6 74.5 ± 0.7 0.9086 C1002 72.3 ± 0.9 72.1 ± 0.7 0.3852 TGW, 1000-grain weight; GL, Grain length; GW, Grain width; HD, Heading date; A, Genetic effect when a Zhenshan 97 allele is replaced by a Milyang 46 allele; R2, Proportion of phenotypic variance explained by the QTL effect. Trait

NIL name

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no significant QTL effect for TGW, no significant genotypic variation was detected for GL and GW. Of the three populations showing significant QTL effects for TGW, significant genotypic variation was detected for both the GL and GW in C903, for GL in C1001, and for GW in C902. In all cases, the enhancing alleles were derived from ZS97. For GL, the additive effects were estimated as 0.032 mm and 0.018 mm, explaining 23.93% and 8.57% of the phenotypic variance in C903 and C1001, respectively. For GW, the additive effects were estimated as 0.011 mm and 0.009 mm, explaining 17.30% and 11.73% of the phenotypic variance in C902 and C903, respectively. These results indicate that the effect of qTGW1.1 on TGW was attributed to the joint effect on GL and GW. No significant effects were detected for HD in the five NIL populations, suggesting that no QTL responsible for HD was segregated in the segregating regions of these populations. In other words, qTGW1.1 segregated in C902, C903 and C1001 was independent from HD.

DISCUSSION Generally, QTLs showing large additive effects in primary populations and steadily detected in different genetic backgrounds and environments are of high priority for fine-mapping and cloning. In recent years, a number of major QTLs for yield traits including grain weight (Qi et al, 2012; Zhang et al, 2012; Huang et al, 2013; Ishimaru et al, 2013) and grain number (Bai et al, 2012; Yan et al, 2013) have been cloned. In the same time, identification of minor QTLs which are easily affected by genetic background and environmental noise remains to be a great challenge. As compared with other yield traits, TGW has performed more stably across different environments and usually a larger number of QTLs were detected in a primary population, thus this trait could be a potential candidate for fine-mapping QTLs of minor effects. This is supported by the present study and the previous one (Guo et al, 2013a) in which a minor QTL for TGW was detected across different generations, locations and years. Using a RIL population of ZS97/MY46, Zhuang et al (2002) detected mutiple epistatic QTLs for TGW on the long arm of rice chromosome 1, of which none showed significant main effect. Targeting at this region, NIL populations were developed and used by Guo et al (2013a). Two QTLs for TGW showing small effects and linked in repulsion phase were separated.

One of them, qTGW1.1, was located in a 3.7-Mb region flanked by RM11437 and RM11621, displaying significant effects in Lingshui, Hainan (18q31c N, 110q00c E) and Hangzhou, Zhejiang (30q04c N, 119q54c E), with the enhancing allele remaining to be derived from ZS97. In the present study, the effect of this QTL was validated in two more years in Hangzhou, and the QTL region was narrowed down to a 376.9-kb region flanked by Wn28382 and RM11554. Our results provide evidence that minor QTLs could be readily identified and validated using NILs of high homogeneity. NIL-F2 populations have been widely used in QTL fine-mapping and cloning. In general, two strategies are used for developing NIL-F2 populations (Bai et al, 2012). In the classic strategy, about two to four times of backcrossing with considerable sample size and/or intensive selection are needed (Bai et al, 2012; Li et al, 2014), which is time and labor consuming. Moreover, the genetic backgrounds of the NILs may still slightly differ from the recurrent parent, and the difference will remain unchanged once it was fixed in the homozygous state. In the new strategy using RH-derived NIL, a RH could be identified from an advanced population through marker assay and selfed to produce an NIL-F2 population. This strategy is time and labor saving, and at the meantime, the homogeneity of the genetic background will automatically increase with the generation advancement. In this study, each NIL population was derived either from a single BC2F7 or BC2F9 plant, thus the two genotypic groups are expected to be at least 99.5% homozygous in the genetic background. In a following step of fine-mapping, a segregating population is to be derived from a RH in higher generation and the homogeneity will be further improved. It has been shown that accumulation of small effects on TGW has contributed greatly to the increase of grain yield in rice (Gu, 2010; Gong et al, 2012), suggesting that the utilization of minor QTLs for TGW is of great importance for rice breeding. Using NILs developed from a cross between maintainer line ZS97 and restorer line MY46 of a three-line hybrid rice, Shanyou 10, four QTLs having small effects for TGW were separated in a 12.0-Mb region of chromosome 1, including qTGW1.1 reported here and qTGW1.2a, qTGW1.2b and qTGW1.2c reported by Wang et al (2014). The effects of the QTLs were validated in different years and environments, indicating that minor QTLs for TGW could have


stable effects and pyramiding of such QTLs with marker assisted selection could be applicable in the improvement of rice yield.

ACKNOWLEDGEMENTS This work was supported by the National Science Foundation of China (Grant No. 31221004), and a research grant of the China National Rice Research Institute (Grant No. 2012RG002-3).

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