Euphytica (2013) 192:145–153 DOI 10.1007/s10681-013-0929-8
Identification and validation of a yield-enhancing QTL cluster in rice (Oryza sativa L.) Touming Liu • Ting Yu • Yongzhong Xing
Received: 8 February 2013 / Accepted: 18 April 2013 / Published online: 30 April 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract Improvement of rice grain yield (YD) is an important goal in rice breeding. YD is determined by its related traits such as spikelet fertility (SF), 1,000grain weight (TGW), and the number of spikelets per panicle (SPP). We previously mapped quantitative trait loci (QTLs) for SPP and TGW using the recombinant inbred lines (RILs) derived from the crosses between Minghui 63 and Teqing. In this study, four QTLs for SF and four QTLs for YD were detected in the RILs. Comparison of the locations of QTLs for these three yield-related traits identified one QTL cluster in the interval between RM3400 and RM3646 on chromosome 3. The QTL cluster contained three QTLs, SPP3a, SF3 and TGW3a, but no YD QTL was located there. To validate the QTL cluster, a BC4F2 population was obtained, in which SPP3a, SF3 and TGW3a were simultaneously mapped to the same region. SPP3a, SF3 and TGW3a explained 36.3, 29.5 and 59.0 % of phenotype variance with additive effect of 16.4 spikelets, 6 % SF and 1.8 g grain weight,
T. Liu T. Yu Y. Xing (&) National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China e-mail:
[email protected] Present Address: T. Liu Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences, Changsha 410205, China
respectively. In the BC4F2 population, though the region has opposite effects on TGW and SPP/SF, a YD QTL YD3 identified in this cluster region can increase 4.6 g grains per plant, which suggests this QTL cluster is a yield-enhancing QTL cluster and can be targeted to improve rice yield by marker aided selection. Keywords Recombinant inbred lines Advanced backcross Rice yield Quantitative trait loci
Introduction Food shortage is becoming a serious global problem because the rate of world population growth currently exceeds the rate of increase in food production. To meet the food growing demands, it is necessary to improve rice grain yield (YD) because rice (Oryza sativa L.) is a staple food in many countries. In the past half century, rice yield has undergone two big leaps, primarily as the result of genetic improvement: improved harvest index and plant architecture through use of semi-dwarf genes, and production of hybrids that exploited heterosis. Consequently, rice yield was more than doubled in most parts of the world and even tripled in certain countries and regions. Rice yield is a complex trait that exhibits a low heritability (Xiong 1992), which creates a challenge to study it directly. Alternatively, yield components like 1,000-grain weight (TGW), the number of spikelets
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per panicle (SPP) and spikelet fertility (SF), directly contribute to YD. Although these yield related traits are also inherited quantitatively, they have comparatively higher heritability (Xing et al. 2001). Therefore, it is more feasible to focus on yield related traits rather than yield itself when dissected the genetic basis of yield. In the last two decades, with the development of the molecular marker and Quantitative trait loci (QTL) analysis approach, substantial attention has been paid to dissect the genetic basis of yield related traits such as TGW, SPP and SF, and a large number of QTLs have been reported (Zhuang et al. 2002; Septiningsih et al. 2003; Li et al. 2008; Fu et al. 2010; Liu et al. 2010a, 2011). Recently, based on the near isogenic lines (NILs), which can eliminate the genetic background noise and visualize a QTL as a Mendelian factor. Many TGW QTLs (Li et al. 2004; Xie et al. 2006, 2008) and SPP QTLs (Tian et al. 2006; Xing et al. 2008; Liu et al. 2009) have been fine-mapped. Some QTLs for these yield related traits were cloned. GS3, a major QTL for TGW and grain length, was delimited to a 7.9 kb region, and the candidate gene was predicted to encode a putative transmembrane protein (Fan et al. 2006). GW2, encodes a previously unknown RING-type E3 ubiquitin ligase, is a major QTL for rice grain width and TGW (Song et al. 2007). Two major QTLs, qSW5 and GS5, play important roles in regulating grain width and grain weight on chromosome 5, and were respectively cloned (Shomura et al. 2008; Li et al. 2011). The first cloned QTL for SPP/GPP, Gn1a, encodes a cytokinin oxidase and negatively regulates SPP in rice by breaking down the cytokinin (Ashikari et al. 2005). Ghd7, a major QTL for SPP/GPP and heading date, was fine-mapped to a 0.2 cM region on chromosome 7 and finally cloned (Xing et al. 2008; Xue et al. 2008). Ghd8 and Ghd7.1, two QTLs for SPP/GPP, which expressed the similar phenotype change to that caused by Ghd7, have been recently cloned (Yan et al. 2011, 2013). Relative to SPP and TGW, which were frequently focused, SF was paid less attention and few studies reported for this trait (Wang et al. 2008). Significant correlation was frequently reported among SPP, TGW, SF and YD (Septiningsih et al. 2003; Zhang et al. 2006; Xing et al. 2008; Fu et al. 2010). In our previous study, QTL mapping for TGW and SPP (Liu et al. 2010b) was accomplished in recombinant inbred line (RIL) population derived
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Euphytica (2013) 192:145–153 Table 1 The heritability of SF, SPP, TGW and YD in RILs and each trait performance of two parents Traits
H2 (%)
SF
77.6
86.1
75.1
SPP
87.3
196.7
136.0
TGW
93.6
22.0
28.2
YD
58.9
36.2
28.1
Teqing
Minghui 63
SF spikelet fertility, SPP the number of spikelets per panicle, TGW 1,000-grain weight, YD grain yield, H2 broad heritability
from two indica rice varieties, Teqing and Minghui 63, with striking differences in SPP, TGW, SF and YD (Table 1). In this study, QTL mapping for SF and YD was executed and the genetics basis of correlation among four traits was dissected by comparing the locations of QTLs detected in this study with the QTLs for TGW and SPP (Liu et al. 2010b). Furthermore, a BC4F2 population segregating in the small region between RM3400 and RM3646 on chromosome 3 was developed. The QTLs in the cluster were validated in the BC4F2 population.
Materials and methods Field experiment for RILs A RIL population was employed to identify QTL controlling SF, YD, SPP and TGW. This population consisted of 190 RILs derived by single-seed descent from a cross between two parents, Minghui 63, with large TGW but small SPP and low SF, and Teqing, with small TGW but large SPP and high SF. For the field experiment, the seeds were sown in a seedling bed in May 2005 and 2006. The RILs (F7 and F8) and two parents were transplanted to a bird-net-equipped field in the experimental farm of Huazhong Agricultural University in Wuhan, China, in the 2005 and 2006 rice growing seasons. Field experiments were carried out following a randomized complete block design with two replicates. Fourteen seedlings (*25 days old) for each family line were transplanted into a two-row plot, with a distance of 16.5 cm between plants within a row and 26.4 cm between rows. The 10 plants in the middle of the two rows of each plot were harvested individually to score SPP, SF, YD and TGW.
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Development of a NIL-F2 population A QTL cluster harboring three QTLs, SF3, TGW3a and SPP3a, were identified in the interval between RM3400 and RM3646 in the RIL population. To develop NILs for the targeted interval, a cross was made between Minghui 63 and Teqing. Teqing segments containing QTL cluster were introgressed into Minghui 63 by successive backcrosses with molecular marker aided selection. In this process, two flanking markers of the QTL cluster, RM3400 and RM3646, were used to select the positive backcross progeny for continuously backcrossing. A total of 97 SSR polymorphic markers evenly distributed on 12 chromosomes were used for genetic background screening in the BC4F1 generation. The positive individual BC4F1-7 that contained Teqing fragment in the target QTL cluster, but had highly similar genetic constitution to the recurrent parent, Minghui 63, were selected to produce BC4F2 by selfing. Field experiment for the BC4F2 population A BC4F2 population of 228 plants derived from BC4F1-7 was planted in Wuhan, China, in May 2007. Twelve seedlings (*25 days old) per row were transplanted in the experimental farm of Huazhong Agricultural University, with a distance of 16.5 cm between plants within a row and 26.4 cm between rows. In total, 170 plants except those growing along the boundary were individually harvested for trait measurement at the ripening stage. Trait measurement SF was calculated as the grain number per plant (GPP) divided by SPP; SPP and GPP were calculated as the total number of spikelets and the total number of grains from the whole plant divided by the number of tillers, respectively; TGW was calculated as the grain weight per plant divided by the number of grains multiplied by 1,000; the grains from the entire plant was weighed as the YD value. DNA extraction and marker genotyping DNA was extracted from fresh leaves of BC4F2 population at the seedling stage by employing the CTAB method (Murray and Thompson 1980). SSR
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markers were identified from the Gramene database ( http://www.gramene.org/), and the SSR primers were designed according to this public database (McCouch et al. 2002; Temnykh et al. 2000). A SSR marker assay was conducted as described by Wu and Tanksley (1993). Data analysis The genetic linkage map was constructed in our previous work (Liu et al. 2010b). Composite interval mapping for SF and YD was performed using the program Windows QTL CARTOGRAPHER 2.0 (Wang et al. 2001–2003). Window size was set at 10 cM. Forward stepwise regression was used to find significant markers as cofactors. The experiment-wise LOD threshold significance level was determined by computing 1,000 permutations (P \ 0.05). These permutations can account for non-normality in marker distribution and trait values. The threshold of LOD values was 2.85–2.89 in 2005 and 2006, respectively. Heritability was calculated in RILs according to the formula described by Liu et al. (2010b). Interval QTL mapping was performed with BC4F2 data using the program Mapmaker/QTL 1.1 (Lincoln et al. 1993).
Results Trait variations in RILs and the BC4F2 population The two parents, Minghui 63 and Teqing, showed highly significant differences in the traits of SF, SPP and TGW. Minghui 63 exhibited a small panicle of 136 spikelets, a low SF of 75 % but a large TGW of about 28 g, and Teqing had a large panicle of 197 spikelets, a high SF of 86 % but a small TGW of 22 g (Table 1). The yield of Teqing was 36.2 g, higher than 28.1 g of Minghui 63. TGW and SPP had high heritability of 93.6 and 87.3 %, respectively (Liu et al. 2010b); while SF and YD displayed relative low heritability of 77.6 and 58.9 %, respectively. A large variation in RILs was observed for SF and YD in 2005 and 2006, and their phenotypic values showed a normal frequency distribution (Fig. 1). In the BC4F2 population, all three yield-related traits and YD showed a large variation (Table 2). On the basis of genotypes at RM3400, 170 plants were divided as three groups: Teqing homozygote (TT), heterozygote
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Fig. 1 Frequency distribution of SF and yield per plant in the RIL population
Table 2 Descriptive statistics of the traits in the BC4F2 population and trait performance of three genotypes at the target region of QTL Trait
BC4F2 Range
SF (%) SPP TGW (g) YD (g)
Genotypes of the targeted QTL regiona
P value Mean ± SD
TT
MT
MM
49.0-92.0
75.9 ± 9.1
B 0.001
80.0 ± 8.2a
79.27 ± 8.3a
67.95 ± 7.4b
111.6-209.3
151.3 ± 18.9
B 0.001
168.4 ± 17.9a
154.1 ± 16.2b
134.7 ± 8.5c
20.4-28.2
23.7 ± 1.47
B 0.001
22.7 ± 0.9a
23.3 ± 1.0a
25.7 ± 1.2b
8.3-54.9
23.5 ± 9.0
B 0.05
24.4 ± 9.1a
23.2 ± 7.8a
19.8 ± 6.3b
SF spikelet fertility, SPP the number of spikelets per panicle, TGW 1,000-grain weight, YD grain yield per plant a
The genotypes of the targeted QTL region on basis of the genotype of RM3400
(MT) and Minghui 63 homozygote (MM). No significant difference was observed between plants of TT and MT for SF, TGW and YD, whereas significant difference of SF, TGW and YD was observed between groups of TT and MT, and group MM (Table 2). This suggested that Teqing allele of these QTLs for SF, TGW and YD in the BC4F2 population was dominance. As for SPP, significant difference was observed among three genotype groups (Table 2), which suggest the gene for SPP acted in semi-dominance. Correlation between yield and its related traits
Table 3 Correlation among SPP, TGW, SF and YD in the RILs and BC4F2 population Traits
SPP
SPP TGW
TGW
SF
YD
-0.399**
-0.224**
0.354**
-0.181**
0.198**
-0.669**
SF
0.459**
-0.463**
YD
0.566**
-0.260**
0.484** 0.579**
The correlation coefficients in the RILs and BC4F2 population are presented above and below the diagonal, respectively SF spikelet fertility, SPP the number of spikelets per panicle, TGW 1,000-grain weight, YD grain yield per plant ** Significant correlation at P \ 0.01
A significant positive correlation was observed between YD and yield-related traits of SPP and SF in both RILs and the BC4F2 population (Table 3). Between YD and TGW, a significant positive and negative correlation was observed in RILs and the BC4F2 population, respectively (Table 3). Among three yield-related traits, significant negative correlation between TGW and SPP, SF and TGW was observed in two populations (Table 3). A significant negative and positive correlation was observed between SF and SPP in RILs and the BC4F2 population, respectively (Table 3).
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QTLs for SF and YD in RILs One and four SF QTLs were identified in 2005 and 2006, respectively (Table 4; Fig. 2). SF3 was commonly detected in both years, whereas the other three QTLs (SF1, SF5 and SF12) were detected only in 2006 (Table 4; Fig. 2). Except SF5, Teqing alleles at all remaining detected QTLs increased the trait value. Four YD QTLs were detected on chromosomes 4, 6, 9 and 11. Minghui 63 alleles at YD4 and YD6 increased yield, whereas Teqing alleles at YD9 and YD11
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Table 4 QTL identified for SF and YD in the RIL population derived from the cross between Teqing and Minghui 63 Traits
QTL
Chr.
Interval
2005 LOD
SF (%)
YD (g)
SF1
1
RM1387–RM3362
SF3
3
RM3400–RM3646
SF5
5
SF12
12
YD4
4
2006 A
Var. (%)
LOD
A
Var. (%)
7.0
5.0
18.4
4.8
3.6
9.6
RM574A–RM3381
2.9
-3.5
8.9
RM270–RM1227
4.0
3.4
8.2
RM518–RM185
3.7
-1.5
10.3
4.2
1.3
8.7
7.1
3.9
16.2
YD6
6
RM540–RM6604
2.8
-1.4
5.6
YD9
9
RM3249–RM215
2.7
1.5
5.9
YD11
11
RM6105–RM224
Additive effect, positive additive indicates the Teqing allele increased the trait; percentage of total phenotypic variance explained by the QTL SF spikelet fertility, YD grain yield per plant
increased yield. All the four QTLs were detected only in one year (Table 4; Fig. 2). Our previous studies identified five and four QTLs for SPP and TGW (Liu et al. 2010b). SF3 were colocated with SPP3a and TGW3a, forming a QTL cluster in the interval between RM3400 and RM3646 on chromosome 3 (Fig. 2). But no YD QTL was identified in this region.
RM3400. YD3 explained 13.2 % of yield variance with an additive effect of 2.3 g. Teqing alleles at all the other QTLs except TGW3a increased the trait value, which was the same as that detected in RILs.
Discussion QTL cluster leading to traits correlation
Validation of the QTL cluster in BC4F2 population Genotype investigation at 97 marker loci evenly distributed on rice genome showed that the plant BC4F1-7 was heterozygous only at the region of QTL cluster. The flanking markers of the QTL cluster, RM3400 and RM3646, were used to genotype 170 BC4F2 individuals, and then a local linkage group covering 6.3 cM was constructed. Three yield-related QTLs (SF3, SPP3a and TGW3a) and one yield QTL (YD3) were simultaneously identified in the target region (Table 5). The LOD peak position for TGW3a was 2.3 cM from RM3400, and it explained 59.0 % of the TGW variance with an additive effect of 1.6 g. The LOD peak position for SPP3a was located at the locus of 0.3 cM from RM3400, and it explained 36.3 % of phenotype variance with an additive effect of 16.4 spikelets. The LOD peak position for SF3 was 2.3 cM from RM3400, and it explained 29.5 % of the SF variance with an additive effect of 6 %. Although no QTL was detected for YD in target region in RILs, YD3 was identified in the region with BC4F2 population. The LOD peak position for YD3 was 2.3 cM from
Grain yield is one of the most valuable traits in rice production. YD per plant is determined by its related traits including TGW, SPP and SF. Previous studies showed significant positive or negative correlation was frequently investigated among SPP, TGW and SF (Septiningsih et al. 2003; Zhang et al. 2006; Xing et al. 2008; Fu et al. 2010). In this study, significant correlation among three traits was observed in the RILs derived from the crosses between Minghui 63 and Teqing. Together with previous results of QTL analysis for SPP and TGW (Liu et al. 2010b), we found that there were three QTL clusters controlling these correlated traits (Fig. 2), which suggest that QTL cluster was the genetic basis of correlation among traits SPP, SF and TGW. Actually, the QTL cluster affecting SPP, SF and TGW was frequently detected in primary mapping population or NILs if there is a significant correlation among these traits (Thomson et al. 2003; Septiningsih et al. 2003; Zhang et al. 2006; Xing et al. 2008; Xie et al. 2008). Hence, QTL cluster is the important genetic basis of correlation among related traits.
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Fig. 2 Molecular linkage map showing the position of QTL for SF, SPP, TGW and GPP in RILs. The black and white arrows indicate that the QTL was detected in both environments and single environment, respectively
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Table 5 Effects of the QTL for SF, SPP, GPP, TGW and YD in the BC4F2 population Trait
QTL
QTL position
LOD
A
D
D/A
SF (%)
SF3
RM3400 ? 2.3 cM
10.6
6.0
SPP
SPP3a
RM3400 ? 0.3 cM
13.1
16.4
ns
TGW (g) YD (g)
TGW3a YD3
RM3400 ? 2.3 cM RM3400 ? 2.3 cM
25.8 6.1
-1.6 2.3
-0.9 1.1
5.3
Var (%)
0.89
29.5 36.3
0.58 0.49
59.0 13.2
Additive effect, positive value means the Teqing allele increases the trait. Dominance effect. ns not significant at the level of 5 %. Percentage of total phenotypic variance explained by the QTL SF spikelet fertility, SPP the number of spikelets per panicle, TGW 1,000-grain weight, YD grain yield per plant
Significant correlation between YD and related traits was observed in RILs in this study. However, it is surprising that none of four yield QTLs except YD9 detected nearby TGW9 was mapped to the region where QTLs for the related traits located. It is well known that yield per plant is the product of these three components of GPP, TGW and the number of tillers per plant. Looking through the details of QTL mapping, we found that some QTLs for other components were exactly located in the regions of yield QTLs seated. For example, in the regions of YD6 and YD11, qGPP6c and qGPP11 were detected respectively (Liu et al. 2010a). In addition, when we decreased the stringent threshold of LOD to 2.0, GPP4 (LOD = 2.3) was identified in the region of YD4. On the other hand, it is also surprising that no yield QTL fell into the region of QTL for yield components. But it seems to be a common feature in QTL mapping for a complex trait and its components (Liu et al. 2010b; Bai et al. 2010). The major reason for such seemingly surprising results is that the QTL has opposite effects on negatively correlated component traits, which counteract their effects on yield. In consequence, effect of the QTL on yield is decreasing and cannot be easily detected. Moreover, the heritability of yield is low due to easily influenced by genetic background and environmental factors, which leads to a large experimental error of the yield data. This amplified experimental error reduced the statistical power of QTL detection. For example, YD3 was not detected in RIL population but was identified in NILs because NILs eliminated the noise of genetic background. In addition, the yield related trait is one of the yield components, significant but limited effects of single components might not cause a significant effect on yield, the most complex trait and led to a failed QTL detection for yield.
Validation of a yield-enhancing QTL cluster Three yield-related QTLs SF3, SPP3a and TGW3a were identified in the region between RM3400 and RM3646 in the RILs. Of them, TGW3a was detected in the region that is closely linked to GS3, a major QTL for TGW and grain length, which was fine mapped and cloned (Fan et al. 2006; Mao et al. 2010). Several previous studies reported QTLs for SF and SPP near the QTL cluster detected in the present study (Redon˜a and Mackill 1998; Zhuang et al. 2002; Septiningsih et al. 2003). Xing et al. (2001, 2002) identified the QTL cluster containing four QTLs for SPP, TGW, SF and YD. However, except the QTL of TGW, the other QTLs in this cluster have not been further studied. In this study, the QTL cluster was further validated using NILs. Precise genetic effect of each QTL in this QTL cluster was estimated without the noise of genetic background. The result indicated that this region has opposite pleiotropic effect on SPP/SF and TGW. That is, the region simultaneously increased SPP and SF but decreased TGW and vice versa, which was similar to TGW3b and SPP3b (Liu et al. 2010b). However, YD3 was identified in the cluster in the NILs, which suggested the QTL cluster had effects on the final YD per plant. QTLs for rice breeding Liu et al. (2010b) had identified a pleiotropic region containing SPP3b and TGW3b, which had an opposite effect on SPP and TGW. On the basis of some evidences from mutants and cloned genes, SPP3b and TGW3b were deduced as the same gene with pleiotropic effects. Similarly, a QTL cluster was narrowed down to a 258 kb region between RID711 and RM6389 on chromosome 7, and this region has
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pleiotropic effects on SPP and TGW (Bai et al. 2010). In the present study, the region between RM3400 and RM3646 had opposite pleiotropic effects on SPP and TGW. TGW3a was detected in the region that is closely linked to GS3 (Fan et al. 2006). Sequencing analysis of GS3 of Minghui 63 and Chuan 7 showed that there was a nonsense mutation in Chuan 7 in which a single nucleotide A is replaced with C in the second exon of GS3 (Fan et al. 2006). Based on the mutation, a functional marker, SF28, was developed to distinguish functional alleles from non-functional alleles. Genotyping assay with SF28 showed that both parents in the mapping population, Teqing and Minghui 63, had a functional allele and nonfunctional allele, respectively (Fan et al. 2009). TGW3a detected in this study is very likely the gene of GS3. However, GS3 was not reported to associate with SPP and SF. Hence, TGW3a is likely having no effects on SPP and SF, but closely linked to SPP3a and SF3. Anyway, the QTL cluster would be valuable for rice breeding because it can increase YD per plant of 4.6 g though it has an opposite effect on SPP and TGW. We can develop high yield varieties by breaking the linkage drag with marker-aided selection (MAS), which pyramid the Minghui 63 allele of TGW3a and the Teqing allele of SPP3a and SF3. Acknowledgments The authors kindly thank farm technician Mr. Wang JB for his excellent fieldwork. This study was supported by a grant National Key Program of Basic Development (2007 CB109001), National Special Program for Research of Transgenic Plants of China (2011ZX08009-001), Agriculture Public Welfare Scientific Research Project (201303008).
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