Fine-Mapping of qTGW1.2a, a Quantitative Trait Locus for 1000-Grain Weight in Rice

Wang Wenhui, Wang Linlin, Zhu Yujun, Fan Yeyang, Zhuang Jieyun

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Fine-Mapping of, a Quantitative Trait Locus for 1000-Grain Weight in Rice

Wang Wenhui, Wang Linlin, Zhu Yujun, Fan Yeyang, Zhuang Jieyun

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Thousand-grain weight (TGW) is a key component of grain yield in rice. This study was conducted to validate and fine-map, a quantitative trait locus for grain weight and grain size previously located in a 933.6-kb region on the long arm of rice chromosome 1. Firstly, three residual heterozygotes (RHs) were selected from a BC2F11population of therice cross Zhenshan 97 (ZS97)///ZS97//ZS97/Milyang 46. The heterozygous segments in these RHs were arranged successively in physical positions, forming one set of sequential residual heterozygotes (SeqRHs). In each of the populations derived, non-recombinant homozygotes were identified to produce near isogenic lines (NILs) comprising the two homozygous genotypes. The NILs were tested for grain weight, grain length and grain width. QTL analyses for the three traits were performed. Then, the updated QTL location was followed for a new run of SeqRHs identification-NIL development-QTL mapping. Altogether, 11 NIL populations derived from four sets of SeqRHs were developed and used.was finally delimitated into a 77.5-kb region containing 13 annotated genes. In the six populations segregating this QTL, which were in four generations and were tested across four years, the allelic direction ofremained consistent and the genetic effects were stable. For TGW, the additive effects ranged from 0.23 to 0.38 g and the proportions of phenotypic variance explained ranged from 26.15% to 41.65%. These results provide a good foundation for the cloning and functional analysis of.

fine mapping; grain weight; minor effect; quantitative trait locus; rice; grain length; grain width

Rice (L.) is one of the most important food crops in the world, providing staple food for about half of the global population. Grain yield of rice is mainly determined by three key components: number of panicles per plant, number of grains per panicle and grain weight. Grain weight, which is generally measured as 1000-grain weight, is the most heritable yield trait in rice (Huang et al, 2013). This trait is mainly determined by grain size that is mostly decided by grain length, grain width and grain thickness (Fan et al, 2006). With the completion of rice genome sequencing and rapid advancement in marker genotyping technology, fine-mapping and cloning of quantitative trait loci (QTLs) for grain weight and grain size in rice have achieved considerable progresses.

To date, 16 QTLs directly affecting grain weight and grain size in rice have been cloned and all these QTLs are shown to have large phenotypic effects. Ten of them affect grain weight mainly by controlling grain length, including/(Duan et al, 2015; Hu et al, 2015),(Yu et al, 2017),/(Liu et al, 2018; Yu et al, 2018),(Fan et al, 2006),/(Qi et al, 2012; Zhang et al, 2012),//(Hu et al, 2018; Xia et al, 2018; Ying et al, 2018),(Wu et al, 2017),(Ishimaru et al, 2013),(Song et al, 2015) and(Si et al, 2016). Four of the others affect grain weight mainly by controlling grain width, including(Song et al, 2007),//(Duan et al, 2017; Liu et al, 2017),(Li et al, 2011; Xu et al, 2015), and(Wang et al, 2012). The remaining two,/(Wang S K et al, 2015; Wang Y X et al, 2015) and(Zhao et al, 2018),hardly influence grain weight because the effects on grain length and grain width are in opposite directions. These studies have greatly broaden our understanding on the regulation of grain weight and grain size in rice, but the knowledge is rather fragmental and many more QTLs remain to be characterized for constructing a genetic and molecular network regulating these important agronomic traits (Zuo and Li, 2014).

Near isogenic lines (NILs) are ideal materials for the validation and fine-mapping of QTLs, owing to the elimination of genetic background noise (Bai et al, 2012). In addition to the classical approach of developing NILs through consecutive backcrossing, an NIL population could also be constructed from selfing progenies of a residual heterozygote (RH), which is heterozygous in the target region but homozygous in the genetic background (Shao et al, 2010; Zhu et al, 2017; Qi et al, 2018). It has also been shown that QTL dissection and fine-mapping could be facilitated by using sequential residual heterozygotes (SeqRHs), i.e. a series of RHs in which the heterozygous segments are arranged successively in physical positions (Zhu et al, 2017; Dong et al, 2018). In a previous study using SeqRHs selected from a BC2F9population of anrice cross Zhenshan 97 (ZS97)///ZS97//ZS97/Milyang 46 (MY46), three QTLs for grain weight and grain size were separated in a 4.5-Mb region on the long arm of chromosome 1 (Wang L L et al, 2015). One of them,, located within a 933.6-kb region, flanked by simple sequence repeat (SSR) markers RM11730 and RM11762, was targeted for validation and fine-mapping in this study. Through four runs of SeqRHs identification-NIL development-QTL mapping,was delimitated into a 77.5-kb region containing 13 annotated genes.

Materials and methods

Rice materials

A total of 11 NIL populations were used for QTL mapping (Table 1). These populations were divided into four sets, each derived from one series of SeqRHs and tested in the same year. The first set comprised three populations in the BC2F12generation and was tested in 2014; the second comprised two populations in BC2F14and was tested in 2016; the third comprised three populations in BC2F16and was tested in 2017; and the fourth comprised three populations in BC2F17and was tested in 2018. All of them were originated from a RH plant of the ZS973/MY46 BC2F9population reported by Wang L L et al (2015). Development of these populations were as follows.

The BC2F9plant was selfed and advanced to BC2F11(Supplemental Fig. 1). Three BC2F11plants with sequential heterozygous segments extending from RM212 to Wn33304, the segregating region ofpreviously detected (Wang L L et al, 2015), were selected. In the resultant BC2F12populations, non-recombinant homozygotes (plants having the same homozygous genotype throughout the segregating region) were identified and selfed to produce homozygous lines. Three NIL populations, namely W14-1, W14-2 and W14-3, were developed and used for QTL analysis. This procedure was repeated for three more runs, leading to the developments of other three sets of NIL populations that were in the generations of BC2F14, BC2F16and BC2F17, respectively (Table 1).

Table 1. Eleven rice populations used for QTL analysis in this study.

NILZS97and NILMY46are near isogenic lines (NILs) having Zhenshan 97 and Milyang 46 homozygous genotypes in the segregating regions, respectively.

Field experiments

All the populations were grown in the paddy fields at the China National Rice Research Institute, Hangzhou, China. The experiments followed a randomized complete block design with two replications. In each replication, one line was planted in a single row of ten plants. The planting density was 16.7 cm between plants and 26.7 cm between rows. Field management followed the normal agricultural practice. At maturity, five of the middle eight plants in each row were bulk-harvested and sun-dried. Approximately 600 fully filled grains were selected and evaluated according to Zhang et al (2016). Three traits were measured, including 1000-grain weight (TGW, g), grain length (GL, mm) and grain width (GW, mm).

DNA marker analysis

Total DNA was extracted from young leaves following the method of Zheng et al (1995). PCR amplification was performed according to Chen et al (1997), and the products were separated on 6% non-denaturing polyacrylamide gels and visualized using silver staining. Fifteen polymorphic markers located in the target region were used, including three SSR markers selected from the Gramene database (http://www.gramene.org) and 12 InDel markers (Supplemental Table 1) developed according to the sequence difference between ZS97 and MY46 detected by the whole-genome re-sequencing.

Data analysis

In each population, trait values of each line were averaged over the two replications and used for plotting frequency distribution and computing basic descriptive statistics (i.e. mean value, standard deviation, the minimum and maximum values, skewness and kurtosis). Frequency distribution was plotted as a histogram of the number of rice lines in each group classified based on the genotype and trait value. The original data of each replication was applied for QTL mapping. To determine whether a QTL was segregated in a population, phenotypic differences between the two homozygous genotypic groups were tested using two-way analysis of variance (ANOVA). The SAS procedure GLM (SAS Institute, 1999) was employed for the analysis as described previously (Dai et al, 2008). Given the detection of a significant difference (< 0.05), the additive effect and the proportion of phenotypic variance explained by the QTL were estimated.

Results

Phenotypic variations of TGW, GL and GW in the 11 rice populations

Descriptive statistics of TGW, GL and GW in each of the NIL populations tested are presented in Table 2. In all the populations, the three traits were continuously distributed with low skewness and kurtosis, showing a typical pattern of quantitative variation.

When the frequency distribution of TGW was plotted using the two genotypic groups as two series (Fig. 1), differentiation between the ZS97 and MY46 homozygous genotypes was evident in six populations. Among the three populations in BC2F12, difference on the frequency distribution was observed in two populations, W14-1 and W14-2. In both cases, the MY46 homozygous lines were clustered towards to the high-value area and the ZS97 homozygous lines to the low-value area. This phenomenon was found in four more populations, including W16-1 in BC2F14, W17-1 and W17-2 in BC2F16, and W18-2 in BC2F17. In these populations, the same pattern of genotypic differentiation was also detected for GL and/or GW (Supplemental Fig. 2). These results are in accordance with the allelic variation ofreported by Wang L L et al (2015), suggesting that this QTL was segregated in populations W14-1, W14-2, W16-1, W17-1, W17-2 and W18-2.

Validation of qTGW1.2a and delimitation of this QTL into a 300.4-kb region

Two-way ANOVA was performed to test the phenotypic differences between the two genotypic groups in each population. In the first experiment in which three NIL populations in BC2F12were tested, genotypic effects were all non-significant on GL but highly significant (< 0.0001) on TGW and GW in two populations, W14-1 and W14-2 (Table 3). In addition, the enhancing alleles for TGW and GW in both populations were all derived from MY46, which are in consistent with the effects ofreported by Wang L L et al (2015). In W14-1, the additive effects were 0.23 g for TGW and 0.013 mm for GW, explaining 26.15% and 21.60% of the phenotypic variance (2), respectively. In W14-2, the additive effects were 0.36 g for TGW and 0.026 mm for GW, with2values of 41.65% and 42.38%, respectively. As for the remaining population, W14-3, genotypic effects were non-significant on TGW and GL but significant on GW (= 0.0038). The MY46 allele decreased GW by 0.008 mm, with a low2value of 6.00%.Apparently, the variation detected in the W14-3 population was not due to. It is thus concluded thatwas located within the segregating regions of W14-1 and W14-2, but outside the segregating region of W14-3. As shown in Fig. 2-A, this is an interval flanked by InDel markers Wn32886 and Wn33252, corresponding to a 366.1-kb region in the Nipponbare genome.

Table 2. Descriptive statistics of three grain-size traits in each population.

TGW, 1000-grain weight; GL, Grain length; GW, Grain width; SD, Standard deviation; CV, Coefficient of variation.

The effect ofwas further validated in the second experiment in which two NIL populations in BC2F14were tested. Genotypic effects on the three traits were not significant in the W16-2 population but all significant (< 0.01) in W16-1 with the enhancing alleles derived from MY46 (Table 3). The additive effects were 0.38 g for TGW, 0.029 mm for GL and 0.016 mm for GW, contributing 31.23%, 20.34% and 32.37% to the phenotypic variance, respectively. Obviously,was located within the segregating region of W16-1 but outside the segregating region of W16-2. As shown in Fig. 2-B, this is an interval flanked by InDel markers Wn32886 and Wn33186, corresponding to a 300.4-kb region in the Nipponbare genome.

Fine-mapping qTGW1.2a into a 77.5-kb region

Mapping ofwas continued using NIL populations in the BC2F16and BC2F17generations. Among the three BC2F16populations, genotypic effects on the three traits were not significant in W17-3 but all significant (< 0.001) in the other two populations with the enhancing alleles derived from MY46 (Table 3). In W17-1, the additive effects were 0.29 g for TGW, 0.019 mm for GL and 0.021 mm for GW, with2values of 35.39%, 11.55% and 35.80%, respectively. In W17-2, the additive effects were 0.28 g for TGW, 0.034 mm for GL and 0.011 mm for GW, with2values of 33.59%, 28.15% and 16.21%, respectively. These results indicate thatwas located within the common segregating regions of W17-1 and W17-2 but outside the segregating region of W17-3. As shown in Fig. 2-C, this is an interval flanked by InDel markers Wn33011 and Wn33186, corresponding to a 175.1-kb region in the Nipponbare genome.

Fig. 1. Distribution of 1000-grain weight in 11 near isogenic line populations of rice.

In the last experiment in which three NIL populations in BC2F17were tested, genotypic effects were all non-significant on GL but highly significant (< 0.0001) on TGW and GW in one population, W18-2 (Table 3). The additive effects detected in W18-2 were 0.26 g for TGW and 0.021 mm for GW, contributing 36.65% and 25.49% to the phenotypic variance, respectively. Again, the enhancing alleles were both derived from MY46. Thus,was located within the segregating region of W18-2 but outside the segregating regions of W18-1 and W18-3. As shown in Fig. 2-D, this is an interval flanked by InDel markers Wn33011 and Wn33089, corresponding to a 77.5-kb region in the Nipponbare genome.

Discussion

In the past decade, isolation and functional characterization of QTLs for yield traits in rice have achieved considerable progresses, especially for grain weight and grain size (Bai et al, 2012; Li et al, 2018). However, QTLs that have been cloned only contribute to a small proportion of those identified in primary mapping studies. For grain weight in rice, more than 500 QTLs distributed over all regions of the 12 chromosomes are documented (http://www.gramene.org). The 16 cloned QTLs for grain weight and grain size only occupy a small region of eight chromosomes and no QTLs for this trait on chromosomes 1, 10, 11 and 12 have been cloned. In the present study, a QTL for grain weight and grain size in rice,, was delimitated into a 77.5-kb region flanked by InDel markers Wn33011 and Wn33089 on the long arm of chromosome 1. In the six populations segregating this QTL, which were in four generations and tested across four years, namely W14-1, W14-2, W16-1, W17-1, W17-2 and W18-2, replacement of the ZS97 allele with the MY46 allele resulted in increases of grain weight and grain size. The genetic effects were large and stable for TGW and GW, whereas the effects were smaller and less consistent for GL. Evidently,was more responsible for grain width than for grain length in controlling grain weight. These results provide a good foundation for the cloning and functional analysis of.

Table 3. Phenotypic difference between the two genotypic groups in each population.

TGW, 1000-grain weight; GL, Grain length; GW, Grain width;, Additive effect of replacing a Zhenshan 97 allele with a Milyang 46 allele;2, Proportion of phenotypic variance explained by the QTL effect.

It has been well recognized that complex traits such as grain yield and its component traits were controlled by many genes that vary greatly in the magnitude of the genetic effects and the genetic relationship with other genes (Mackay et al, 2009). However, only QTLs that were shown to have large effects in primary mapping are commonly selected as candidates for gene cloning. Since this type of QTL is limited, it is not uncommon that a major-effect QTL is simultaneously targeted for molecular characterization by different groups, for example//(Hu et al, 2018; Xia et al, 2018; Ying et al, 2018). In recent years, more attentions have been paid to QTLs with small effects. For heading date that has always been in the frontier of QTL analysis in rice, a number of minor-effect QTLs were cloned, demonstrating that QTLs with small effects also play an important role in regulating the eco-geographical adaptation and yield trait in rice (Matsubara et al, 2012; Wu et al, 2013; Chen et al, 2018). In the present study and the previous one (Wang L L et al, 2015), the minor-effect QTLwas shown to have a stable effect across different years, generations and environments. Molecular characterization of this type of minor-effect QTLs, which have a direct and consistent influence on grain yield, would be important for constructing a regulation network underlying the development of rice grains.

According to Rice Genome Annotation Project (http://rice.plantbiology.msu.edu), there are 13 annotated genes in the 77.5-kb region for(Supplemental Table 2). Four of them encode transposon or retrotransposon proteins, including LOC_Os01g57120 and LOC_Os01g57190 for unclassified transposon proteins, LOC_Os01g57160 for an En/Spm sub-class CACTA transposon protein, and LOC_Os01g57130 for a Ty3-gypsy subclass retrotransposon protein. Four others also encode proteins with known functional domains, including LOC_Os01g57150 for a SR protein related family member, LOC_Os01g57210 for a katanin p80 WD40 repeat-containing subunit B1 homolog 1 protein, LOC_Os01g57220 for a secretory carrier-associated membrane protein, and LOC_Os01g57230 for a protein with the BTBN1 domain. Among the other five annotated genes, three (LOC_Os01g57170, LOC_Os01g57240 and LOC_Os01g57250) encode uncharacterized expressed protein and two (LOC_Os01g57140 and LOC_Os01g57180) encode hypothetical proteins. Work is underway to identify the most probable candidate gene for

Acknowledgements

This work was funded by the National Key R&D Program of China (Grant No. 2017YFD0100305), the National Natural Science Foundation of China (Grant No. 31521064), and a project of the China National Rice Research Institute (Grant No. 2017RG001-2).

Supplemental DATA

The following materials are available in the online version of this article at http://www.sciencedirect.com/science/ journal/16726308; http://www.ricescience.org.

Supplemental Table 1. InDel markers used in this study.

Supplemental Table 2. Annotated genes in the 77.5-kb region for.

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

Supplemental Fig. 2. Distribution of grain length and grain width in 11 near isogenic line populations of rice.

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20 February 2019;

26 April 2019

Zhuang Jieyun (zhuangjieyun@caas.cn; ZhuangJYcn@163.com)

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