OsLG3 contributing to rice grain length and yield was mined by Ho-LAMap
- Jianping Yu†1,
- Haiyan Xiong†1,
- Xiaoyang Zhu†1,
- Hongliang Zhang1,
- Huihui Li2,
- Jinli Miao1,
- Wensheng Wang2,
- Zuoshun Tang3,
- Zhanying Zhang1,
- Guoxin Yao1,
- Qiang Zhang1,
- Yinghua Pan1, 4,
- Xin Wang1,
- M. A. R. Rashid1,
- Jinjie Li1,
- Yongming Gao2,
- Zhikang Li2,
- Weicai Yang3,
- Xiangdong Fu3 and
- Zichao Li1Email authorView ORCID ID profile
© Li et al. 2017
Received: 7 November 2016
Accepted: 10 March 2017
Published: 6 April 2017
Most agronomic traits in rice are complex and polygenic. The identification of quantitative trait loci (QTL) for grain length is an important objective of rice genetic research and breeding programs.
Herein, we identified 99 QTL for grain length by GWAS based on approximately 10 million single nucleotide polymorphisms from 504 cultivated rice accessions (Oryza sativa L.), 13 of which were validated by four linkage populations and 92 were new loci for grain length. We scanned the Ho (observed heterozygosity per locus) index of coupled-parents of crosses mapping the same QTL, based on linkage and association mapping, and identified two new genes for grain length. We named this approach as Ho-LAMap. A simulation study of six known genes showed that Ho-LAMap could mine genes rapidly across a wide range of experimental variables using deep-sequencing data. We used Ho-LAMap to clone a new gene, OsLG3, as a positive regulator of grain length, which could improve rice yield without influencing grain quality. Sequencing of the promoter region in 283 rice accessions from a wide geographic range identified four haplotypes that seem to be associated with grain length. Further analysis showed that OsLG3 alleles in the indica and japonica evolved independently from distinct ancestors and low nucleotide diversity of OsLG3 in indica indicated artificial selection. Phylogenetic analysis showed that OsLG3 might have much potential value for improvement of grain length in japonica breeding.
The results demonstrated that Ho-LAMap is a potential approach for gene discovery and OsLG3 is a promising gene to be utilized in genomic assisted breeding for rice cultivar improvement.
Rice (O. sativa L.) is a staple food and the world’s most important cereal crop. Grain yield is determined by three component traits, namely grain weight, number of grains per panicle, and number of panicles per plant. Grain size is a prime breeding target, as it affects both yield and quality. Therefore, the study of grain size is highly important for the improvement of rice yield and quality as well as for the understanding of the rice domestication process . Grain size is specified by its three dimensions (length, width, and thickness). Recently, although a number of quantitative trait loci (QTL) conferring grain length  have been isolated, and among them several QTL, such as GS3, GW2, GL3.1, TGW6, qSW5, and GW8, have been well studied [1, 3–7], molecular characterization of these and many more unknown genes is still largely unclear. Thus, it is of great significance to understand the underlying genetic and molecular bases of grain length .
Recent studies isolated and characterized genes involved in QTL using map-based cloning techniques based on linkage mapping [2, 6, 8–11]. However, for fine mapping, very large sample sizes are required; typically, thousands of individuals are needed and the fieldwork involved is extremely laborious, usually involving measurement of multiple traits at several time points across diverse environments. Genome-wide association analysis (GWAS) is generally considered an effective tool to infer causative links between genomic markers and phenotype in many crops [12–16]. However, in previous studies, the causal polymorphisms showed a weaker association than the peak single nucleotide polymorphism (SNP) in Arabidopsis thaliana and rice [12, 17]. Extensive computer simulations have shown that the power of GWAS is low for polygenic traits and that spurious associations can be expected . Linkage mapping was shown to be a valuable complementary approach to address these situations in maize and Arabidopsis [14, 16, 19]. Recently, there have been attempts to combine the vigor of linkage mapping with GWAS in rice [20–22]. GWAS should be performed in conjunction with genetic linkage analysis to detect relevant loci .
It is generally agreed that GWAS in rice cannot resolve a single gene due to the low rate of linkage disequilibrium decay. With the development of next generation sequencing technology, DNA sequencing has become easier and cheaper. A core collection of 3000 rice accessions from 89 countries were deep resequenced [23, 24], providing an unprecedented resource for genomic research. Herein, we propose a unified approach for gene discovery in rice from candidate region association mapping combing linkage mapping that limits the major problem caused by false positives. We not only describe a new strategy to identify previously unknown but agronomically important alleles based on deep-sequencing technology, but also report on the cloning and characterization of a dominant QTL, OsLG3, as a positive regulator of grain length. Natural variations in the OsLG3 promoter region confer grain length and weight and its favorable allele represents a valuable genetic resource for rice cultivar improvement.
Ninety-nine QTL for grain length were detected by GWAS based on high-density SNPs
Co-localized QTL for grain length by joining GWAS and multiple biparental linkage analysis
We used four segregating populations from cross involving six varieties to identify QTL for grain size and grain weight, including SLG-1 (SLG, with the largest grain), Chuanqi (CQ, with the smallest grain), Nipponbare (NIP, medium grain), Haobuka (HBK, large grain), IRAT109 (large grain), and Yuefu (YF, medium grain), which were selected from the MCC1 panel and showed high genetic differentiation from each other (Additional file 7: Figure S7 and Additional file 33: Table S10). The four segregating populations included BILs (backcross inbred lines) from Nipponbare and SLG-1, CSSLs (chromosome segregation substitution lines) from YF and IRAT109, BC1F2 lines from Chuanqi and SLG-1, and BC1F2 lines from Nipponbare and Haobuka. Substantial variation in grain size traits was observed among these populations (for example, 07DH010-14, Additional file 8: Figure S8). Thirty-four QTL for grain shape and weight were detected among the four groups (Additional file 9: Figure S9 and Additional file 33: Table S11). Among them, 13 QTL for grain length co-localized with QTL mapped by our GWAS (Fig. 1a, c). Of 34 QTL, 22 were detected in at least two combinations. Moreover, three QTL (qGL3-1, qGL3-2, and qGL3-3) for grain length were detected in four crosses (Fig. 1c).
The efficiency of Ho-LAMap for detection of causal genes and simulation studies
Similar to other reports of GWAS in rice [12, 13, 29], the peak SNPs were not part of the causal genes except for GS3, even though we narrowed the QTL regions of several hundred kilobases (Additional file 6: Figure S6). However, analysis of the causal alleles of some cloned genes indicated that most of the varieties shared the same causal variations [5–7], although the varieties did not uniformly share the same non-causal variations. Based on this, we propose a strategy – Ho-LAMap that joins GWAS and multiple biparental linkage analysis – to distinguish the causal gene from other genes confounded with population structure. The principles of Ho-LAMap are depicted in Additional file 12: Figure S12 and Additional file 33: Notes S3. Firstly, we cross diverse founder varieties (i.e., varieties that are significantly different from reference parent for grain traits) with a reference parent (usually a small grain variety). After backcrossing or several self-pollinated generations, advanced populations are prepared for QTL mapping and these serve to identify the regions in the genome that are most likely to carry the causal genes. Primary fine mapping will help to ensure the boundary of the QTL. Association mapping using the GLM (Q) model in the QTL interval will identify almost all SNPs that correlate significantly with the target trait. In crosses that have detected the targeted QTL, the majority of SNPs within the QTL interval will segregate in a 1:1 founder varieties:reference parent ratio. However, the SNP responsible for the change of phenotype will be the same in all founder parents detecting the targeted QTL. If we define the Ho (observed heterozygosity per locus) index as the ratio between the number of heterozygous crosses corresponding to each SNP locus and the total number of crosses which have the targeted QTL, we expect this index to equal 1 near the causal SNP and 0.5 for the unlinked loci. Ho indices can be scanned across the genome to find the region with a Ho index of 1, and presumably harboring the gene responsible for the change of phenotype. In this method, we just need to sequence several parents and do SNP mapping. Our proposed name for this approach is Ho index unified linkage and association mapping (Ho-LAMap) as applied to rice.
Among three co-localized QTL, qGL3-1 encompassed the cloned gene GS3  and thus was used to validate the efficiency of Ho-LAMap. In the mapping region of qGL3-1 overlapping between GWAS and multiple biparental linkage analysis, we detected 1763 SNPs with significant association signals. For each identified locus, we obtained the Ho index between each pair of parents for the four combinations, and plotted the Ho indices in the overlapping mapped region (Fig. 1d). The Ho index of GS3 was 1 and those for almost all other loci were below 0.5, and even 0. The results confirmed that this approach allowed us to rapidly identify the causal gene according to GWAS combined with primarily mapped QTL common among multiple biparental crosses.
Given the success Ho-LAMap in identifying genes controlling quantitative traits based on common QTL and GWAS, we are more generally interested in the possible factors affecting the efficiency of Ho-LAMap in detecting causal genes. Simulation studies (see Methods) on several known genes, such as GS3, TGW6, etc., were carried out to estimate (1) the number of crosses required (N), (2) the minor allele frequency of the targeted gene, and (3) the subgroup that selected parents were from. Our results suggest that the minor allele frequency is perhaps the most important factor for effectiveness of Ho-LAMap and that parents selected from an appropriate subgroup would increase the efficiency (Fig. 1e and Additional file 13: Figure S13). Whereas the resolving power increased with the number of crosses we concluded that, in application of Ho-LAMap to complex quantitative traits in rice, N ≥ 4 should be required for genes such as TGW6 (Additional file 14: Figure S14 and Additional file 33: Table S12).
Identification of two new grain length loci via Ho-LAMap
Two transformation constructs were prepared to test this hypothesis. The first, OE, contained the cDNA of OsLG3 from Nipponbare (short grain) driven by the 35S promoter; the second, contained RNAi-OsLG3, the fragment of OsLG3 coming from Nipponbare. All of the transgenic lines that overexpressed the Nipponbare OsLG3 allele showed increased grain length and grain weight, compared to transgene-negative plants (Fig. 2c and Additional file 18: Figure S18a–c). All transgenic RNAi-OsLG3 plants formed grains that were substantially shorter and lighter than those from transgene-negative plants (Fig. 2c and Additional file 18: Figure S18d, e). Thus, a longer grain could be caused by the variations in the promoter region.
OsLG3 regulates grain length by altering cell number but does not influence grain quality
Although most of the morphological characteristics of NIL(SLG) were similar to those of Nipponbare (Additional file 19: Figure S19), the spikelet hulls of NIL(SLG) plants before fertilization were longer than those of Nipponbare plants. We conducted scanning electron microscopy study of outer and inner surfaces of glumes from NIL(SLG) and NIP (Additional file 20: Figure S20). There was little, if any, difference in cell length in either the palea or lemma, but total cell numbers of outer and inner epidermal cells in the longitudinal direction in NIL(SLG) were more than their equivalents in Nipponbare. We also investigated the expression of the key genes determining cell cycle time [7, 31] such as CDKA1, CYCD3, MCM3, CYCA2.1, CYCA2.2, CYCA2.3, CAK1, and H1. The transcript levels of most of these genes were considerably higher in NIL(SLG) plants relative to Nipponbare plants (Additional file 21: Figure S21), suggesting that the increase in cell number in NIL(SLG) might result from elevated expression of genes promoting cell proliferation. In addition, compared with Nipponbare, NIL(SLG) had a 4.2% higher grain length to width ratio, but had the same grain chalkiness level and the same starch granule appearance in transverse sections of grains (Additional file 22: Figure S22). These observations suggest that OsLG3 might promote longitudinal growth by increasing cell proliferation while not influencing grain quality (Fig. 2d–f and Additional file 23: Figure S23 and Additional file 24: Figure S24).
Expression patterns of OsLG3 and its transcription activator activity
As mentioned above, OsLG3 transcripts were detected in various tissues by qRT-PCR analysis (Fig. 2b and Additional file 17: Figure S17). To determine the spatial expression pattern of OsLG3 in detail, we generated transgenic plants containing OsLG3 promoter::GUS fusions (ProOsLG3::GUS). We observed GUS activity in all of the analyzed tissues/organs (roots, stems, sheaths, leaves and panicles) (Additional file 25: Figure S25). Higher GUS activity was detected in developing panicles, spikelet hulls during spikelet development and roots (Additional file 25: Figure S25). These results indicate that OsLG3 is a temporally and spatially expressed gene.
We further tested the activity of a set of truncations and deletions of OsLG3/OsAP2-125. It showed that the C-terminal 168–334 aa region is sufficient to activate the reporter, whereas the N-terminal truncated OsLG3/OsAP2-125 proteins are not (Fig. 3b). These results show that transcription activation activity of OsLG3/OsAP2-125 resides in its C-terminal regions and that DNA-binding activity resides in its N-terminal regions containing the AP2 domain [32, 33].
Validation of functional variations in promoter region and origin of OsLG3
Geographically, 32 temperate japonica cultivars with Hap4 were from northern China, Russia, Korea, and Japan, or higher elevation zones (Fig. 4f). In contrast, samples with Hap1, including 133 indica and 10 javanica cultivars, were from South China, Southeast Asia, East Africa and South America. This group also included 19 glutinous temperate japonica from the Yunnan-Guizhou Plateau or mixtures. The 26 O. rufipogon samples with Hap1 were from Southeast Asia and south China, whereas the 8 Hap4 O. rufipogon samples originated from Guangxi province in China (Additional file 32: Table S1). These observations indicate that a north–south differentiation in OsLG3 occurred between indica and japonica during rice domestication.
We analyzed the genetic diversity  in OsLG3 and its flanking regions (~400 kb) to determine whether OsLG3 had undergone artificial selection during domestication of indica. Clearly decreased nucleotide diversity was observed at the OsLG3 locus in indica, but not in temperate or tropical japonica accessions (Fig. 4g). The overall average nucleotide diversity of the 20 kb OsLG3 flanking regions was higher than that of OsLG3 in indica, but also significantly lower than in wild rice (Fig. 4h). The diversity in an approximately 100-kb region around OsLG3 in indica was lower than that in japonica and wild rice. These results suggest that the decreased diversity in indica resulted from selection.
To gain deeper insights into the evolution of OsLG3, we analyzed all polymorphic sites in the entire gene region of a larger population. We identified 7 haplotypes in 504 diverse cultivars (3 indica and 4 japonica haplotypes) and 13 haplotypes in 15 wild accessions. A minimum-spanning tree of haplotypes revealed two distinct clusters – separate cultivar and wild rice haplotype clusters – in OsLG3. The cultivars divided into two subgroups, indica and japonica (Fig. 4i). The wild accessions contained both large and small grain haplotypes. Only some O. rufipogon accessions from China had small grain haplotypes, which might have been inherited to ancient japonica. All indica haplotypes had large grain OsLG3 haplotypes and one haplotype was present in two wild accessions, indicating the large grain haplotypes in indica might be derived from an O. rufipogon accession with a large grain haplotype. The large grain allele of OsLG3 in javanica and some glutinous template japonica from the Yunnan-Guizhou Plateau or admixed accessions may be derived from indica sources (Fig. 4f, i). Our results implied that OsLG3 alleles in indica and japonica independently originated from distinct ancestors, consistent with previous conclusions that the two subspecies were domesticated from distinct ancestral gene pools .
Genetic interactions with other related genes and potential utilization in rice breeding improvement
Grain size traits of rice are complex and generally controlled by multiple genes. Genetic control of these characteristics has been investigated over the last decade. To date, a total of 11 QTL for grain size (including GS3, GL3.1, GW6a, TGW6, GL7, GLW7, GS5, GW5, GW8, GS2, and GW2) have been cloned from natural rice varieties by a classic map-based approach. Despite these efforts, the mechanisms that establish the final size of grain remain poorly understood. In this research, we conducted GWAS for rice grain length based on high density SNPs (an average of 1 SNP per 40 bp) from 504 accessions and identified 99 QTL for grain length. This provides important information for cloning novel grain length genes in the future. Our findings confirmed that Ho-LAMap was able to mine genes rapidly over a wide range of experimental variables using deep-sequence data and that natural variations in the OsLG3 promoter region conferred grain length and weight.
In rice, GS3, GL3.1, GW2, GW5, and GW6a controlled grain size by regulating cell proliferation, whereas GLW7 did so by regulating cell expansion. GS2, GS5, GL7/GW7, and GW8 influenced both cell division and cell expansion . In this study, we reported a novel transcriptional regulatory factor that controlled grain length by influencing cell proliferation. Expression analysis showed that elevated expression levels of OsLG3 increased the expression levels of many key genes involved in the cell cycle. The glumes from NIL(SLG) had the same cell length with NIP, but cell numbers of epidermal cells in the longitudinal direction in NIL(SLG) were more than those in Nipponbare. Thus, these findings indicate that OsLG3 contributes to grain length by promoting cell division.
Many QTL related to rice grain size or weight were reported to enhance the grain yield. Among them, GW2, GS2, and GW5 increased grain size and yield to a large extent; however, they caused a sudden increase in the percentage and degree of chalkiness, resulting in a sharp decrease in grain quality. Our results revealed that the introduction of SLG allele of OsLG3 into existing elite japonica varieties might increase the grain length, weight and yield without disturbing the grain quality. According to the pedigree records, Yunguang8 is one of the first two-line hybrid rice most widely grown in Yunnan province of China because of its particularly high yield and good appearance quality. Its parents are the Yunhui11 and Nongken58S, a formerly widely used good-quality PTGMS line in China. A resequencing study showed that Yunguang8 carried the beneficial allele of OsLG3 deriving from Yunhui11 and the gw8 Basmati allele deriving from Nongken58S, respectively (Additional file 32: Table S1). Thus, the combination of the OsLG3 SLG and gw8 Basmati alleles provides a good example, which could have been employed by breeders, for simultaneously improving rice yield and grain quality over what is currently achievable.
Rice is a major cereal and a model system for plant biology, however, the origin of cultivated rice and its domestication history have long been debated. During the past decades, several models were proposed for rice domestication. Asian cultivated rice contains five genetically distinct ecotype groups, namely indica, aus, temperate japonica, tropical japonica, and aromatic. A simple single origin of cultivated rice suggested that the cultivated rice was primarily domesticated from the annual genotypes, and the two subspecies indica and japonica were regarded to be molded after domestication [37, 38]. Londo et al.  reported that indica and japonica were domesticated independently. Another hypothesis indicated that japonica rice was first domesticated and indica rice was subsequently developed from crosses between japonica rice and another clade of O. rufipogon . Recently, Civáň et al.  suggested that there were three geographically separate domestications of Asian rice and concluded that rice domestication was a multiregional process separately producing the indica, japonica, and aus types of rice. Some well-characterized domestication genes (Sh4, Prog1, Bh4, and qSH1) in rice were found to be fixed in both subspecies with the same alleles, thus supporting the hypothesis of a single domestication origin [42–45]. However, our findings revealed that OsLG3 alleles in indica and japonica independently originated from distinct ancestors (Fig. 4i). In view of the fact that grain size is a target trait for both domestication and artificial breeding, this conclusion was consistent with the hypothesis that indica and japonica were domesticated independently. Although rice is a self-crossing species, in this study, gene flow was detected from domesticated to two (out of the total 15) wild accessions (Fig. 4i). This is in agreement with a previous report of gene flow from domesticated to wild crop in another self-crossing species .
As demonstrated by our study, grain length is a complex trait associated with population structure in rice (Additional file 4: Figure S4a and Additional file 5: Figure S5). Although we detected two major QTL using the CMLM model (Additional file 34: Table S5), a large amount of middle effect and minor effect QTL, even many other major QTL for grain length (e.g., GW2, OsLG3, TGW6, gw8), could not be identified (Additional file 3: Figure S3). This indicates that not all associations that are eliminated in the CMLM model are false. GWAS incorporating Q and K in a CMLM controls P value inflation well, but leads to false negatives, missing some potentially important true associations. It is possibly due to the confounding between the covariates and test marker weakening the signals of Quantitative Trait Nucleotides (QTNs), resulting in false negatives in CMLM . Recently, Liu et al.  proposed a new method, FarmCPU (Fixed and random model Circulating Probability Unification), to control false positives as well as CMLM with reduction in false negatives. FarmCPU iteratively performs marker tests with pseudo QTNs as covariates in a fixed effect model and optimization on pseudo QTNs in a random effect model. To some extent, this process removes the confounding between testing markers and kinship, prevents model over-fitting, and controls false positives simultaneously. We reanalyzed our data using FarmCPU. Seven, four, and two QTL were identified by FarmCPU, including the first three PCs, two PCs, and one PC as covariates, respectively; further, these QTL hit two (GS3 and SSG6), two (GS3 and GW6a), and one (GS3) known genes, respectively (Additional file 28: Figure S28), and overlapped with five, three, and one published QTL (Additional file 38: Table S15). When using FarmCPU including the first three PCs, we detected five and five QTL in the indica and japonica subpopulations, respectively, and these QTL hit two (GS3 and GW5) and one (TGW6) cloned genes, respectively (Additional file 29: Figure S29), overlapping with four and two of the published QTL (Additional file 38: Table S15). FarmCPU outperformed CMLM with respect to controlling inflation of P values (Additional file 28: Figure S28e–h), identifying new QTL for rice grain length, and overlapping with known loci (Additional file 28: Figure S28a–d and Additional file 34: Table S5 and Additional file 38: Table S15). Unlike the FarmCPU, our association analysis for the rice grain size traits in Ho-LAMap employed GLM (Q) to try to avoid false negatives. There is no doubt that FarmCPU also could be integrated with Ho-LAMap in identifying new QTL because of its improved statistical power.
The era of deep-sequencing of vast arrays of germplasm has arrived and DNA sequencing has become quicker and cheaper. Thousands of rice accessions have been deep-sequenced  and thousands of QTL on grain size (also other agronomic traits) have been mapped in rice (http://archive.gramene.org/db/qtl/qtl_display?trait_category=Yield). However, how to rapidly identify the genes associated with agronomic traits utilizing these large amounts of sequence data and the linkage mapped QTL information in rice remains a challenge.
Ho-LAMap is an attractive method for rapid, cost-effective identification of natural variations underlying QTL based on co-localization in multiple populations (Additional file 30: Figure S30 and Additional file 33: Notes S3). Coupled parents of populations having different genetic backgrounds can be divided into two classes similar to generating contrasting genetic pools for analysis of individual traits. Using the Ho index, we effectively distinguish a large number of background interference signals (i.e., false positives) from all (Additional file 12: Figure S12). Furthermore, candidate region association mapping conducted on SNPs located in the overlapping intervals of targeted QTL avoids false-positive loci located outside the critical region.
Deep sequencing of more than 3000 core rice collections with very broad genetic diversity and collected worldwide has been accomplished and our team has completed construction of approximately 100 recombinant inbred line populations based on accessions from our MCC panel. We expect that Ho-LAMap will facilitate gene isolation and rice breeding through molecular design by reducing the time for the construction of large NIL-F2 populations and fine mapping of QTL thus avoiding extremely laborious, time-consuming, and expensive fieldwork required for identification and cloning of agronomically important genes (Additional file 31: Figure S31) such as those for stress resistance.
Field planting and measurement of grain and yield traits
Rice plants were examined under natural field conditions at the two Experimental Stations of China Agricultural University, Beijing and Hainan, China. The planting density was 13.3 cm between plants in rows that were 23.3 cm apart. Field management, including irrigation, fertilizer application, and pest control, followed normal agricultural practices. Harvested rice grains were air-dried and stored at room temperature for at least 3 months before testing. Fully filled grains were used for measuring grain width, length, and weight. Ten randomly chosen grains from each plant were lined up width-wise along a Vernier caliper to measure grain width and then arranged length-wise to measure grain length. Grain weight was based on 200 grains and converted to 1000-grain weight.
Plant material and backcross populations
The extra large-grain rice accession SLG-1 (Oryza sativa L. ssp. japonica) and another two large-grain varieties, Haobuka and IRAT109, were selected from more than 7000 germplasm and used as desirable donor parents. The smallest grain accession, Chuanqi (ssp. indica), and two medium grain accessions, Nipponbare and Yuefu (ssp. japonica), were selected as recurrent parents. Four hybrid combinations (BILs from Nipponbare and SLG-1; CSSLs from Yuefu and IRAT109; BC1F2 from Chuanqi and SLG-1; and BC1F2 from Nippobare and Haobuka) were created by the six varieties.
The Nipponbare x SLG-1 cross: Four BC4F2 populations for primary QTL mapping was constructed through selective backcrossing of lines which had large grain at each backcross generation. Three BC4F3 populations for further QTL mapping were derived from some BC4F2 plants without the known large-effect QTL (gs3 and GW2). Using a similar strategy, we developed another two BC1F2 populations: one from SLG-1 crossed with Chuanqi, the other one from the cross between Nipponbare and Haobuka.
The Yuefu x IRAT109 cross: Yuefu was crossed with IRAT109 and backcrossed with Yuefu for five generations, before selfing to produce BC5F3 plants that were genotyped using 176 simple sequence repeat markers evenly distributed across all 12 rice chromosomes. Finally, we generated a fixed population of 271 CSSLs, with each containing an average three chromosome segments from the donor in the Yuefu genetic background.
Selection of germplasm, sequencing and SNP identification
The 506 worldwide accessions (Additional file 32: Table S1) contributed by the Chinese Academy of Agricultural Sciences included a mini-core collection of 248 accessions selected from a core collection of 932 accessions established from 61,470 O. sativa accessions preserved in the China National Crop Gene Bank , and 256 accessions selected based on isozyme diversity , used as parental lines in the international rice molecular breeding network, plus two accessions with the largest and smallest grains, respectively.
Genomic DNA was prepared from bulk-harvested leaves of a single young plant for each sampled accession by a modified CTAB method either at the International Rice Research Institute or at the Chinese Academy of Agricultural Sciences. Genomic DNA samples were then shipped to BGI-Shenzhen and were used to construct Illumina index libraries following the manufacturer’s protocol. Following quality control, at least 3 μg of genomic DNA of each sample was randomly fragmented by sonication and size-fractionated by electrophoresis. DNA fragments of approximately 500 bp were purified from each of 24 accessions and were labeled independently using distinct 6 bp nucleotide multiplex identifiers, followed by pooling prior to library construction for next generation sequencing. Each sequencing library was sequenced in six or more lanes on the HiSeq2000 platform and 90-bp paired-end reads were generated. Subsequently, the reads from each sample were extracted based on their unique nucleotide multiplex identifiers as 83 bp reads (90 – 6 – 1, where 1 is the ligation base “T”). To ensure high quality, raw data were filtered by deleting reads having adapter contamination or containing more than 50% low quality bases (quality value ≤ 5).
SNP calling for each sample were performed independently using the UnifiedGenotyper package in GATK with a minimum phred-scaled confidence threshold of 50, and a minimum phred-scaled confidence threshold for emitting variants at 10. To ensure the quality of variant calling, the conditions for every site in a genome were set at > 20 for mapping quality, > 50 for variant quality, and > 2 for the number of supporting reads for every base.
Using IRGSP-1.0 as the reference, the 3000 sequenced genomes had an average depth of approximately 14×, ranging from approximately 4× to greater than 60×, and yielded a combined total of approximately 17 TB of high quality sequence data. Of the 3000 entries, 504 accessions used in this study had an average sequence depth of 14.9×. Burrow–Wheeler alignment followed by variant calling using GATK identified approximately 10 million SNPs.
The SNPs finally identified in this study were 3,585,228 controlled by MAF ≥ 5% and missing rates ≤ 30%.
GWAS studies and candidate-region association mapping
GWAS of indica, japonica, and the full population were conducted using their corresponding genotype datasets. In each panel, only the SNPs with MAF > 5% were used for association analyses. Three models – the naive model, GLM (Q), and CMLM – were used for association analysis . The Q matrix and K matrix were estimated by STRUCTURE 2.0 and GAPIT function in R , respectively, and the R version relies on the version of EMMA.
QTL mapping and fine mapping
A genetic map was constructed using MapMaker3.0/EXP version 3.0 . QTL analysis was performed by IciMapping3.1  along with the composite interval mapping method, and the threshold was obtained by 1000 permutations. The substitution mapping strategy was used for fine mapping .
The significant SNPs within the QTL regions on both sides of physical position of each of seven well-characterized genes (i.e., GS3, GS5, GW5, GW8, OsLG3, GL7, and TGW6) and phenotypes of grain length, grain width, and grain weight were used in our simulation studies. The parents of biparental populations used for QTL mapping were randomly selected from the two tails of the population by extreme trait performance. Since there are two alleles for each SNP, say A and a, let p H denote the allele frequency of A in one tail of the population with high trait performance, and p L denote the allele frequency of A in the other tail with low trait performance. That is to say, the allele frequency of A for one parent was p H , and that for the other parent was p L . If the SNP locus is polymorphic between two parents, it could be significantly associated with the trait of interest. The power of Ho-LAMP method can be estimated as p H (1 – p L ) + (1 – p H ) p L . To evaluate the effect of population structure on the power of Ho-LAMP method, we conducted simulation experiments for all 504 rice cultivars, Japonica cultivars, and Indica cultivars as groups. We also tried percentages of 0.05, 0.10, and 0.15 to select sub-populations with extreme phenotypic performance on both sides. Results were largely similar; therefore, 0.05 was used as the percentage cut-off to determine the size of the two tails of the population.
Simulation of cross number also used significant SNPs of some of these well-characterized genes (i.e., GW8, GS3, and TGW6). Firstly, 504 accessions were divided into two parts by functional alleles of each gene, called A and a. Randomly selected similar numbers of varieties from each part were used to calculate the Ho-index for functional alleles in the candidate region. We simulated 1000 repeats and counted the times when only the functional allele had the maximum Ho-index, denoted as one time and three replicates for each gene. We evaluated the power as the proportion of detecting the target gene successfully in 1000 replications of simulation. Finally, we set different cross numbers (2–50) to carry out the simulation and defined the ideal number of crosses for each gene. The same process was applied to the indica and japonica populations, respectively.
Vector construction and plant transformation
Full-length cDNA of OsLG3 was amplified from the first-strand cDNA of IRAT109 with specific primers to generate the overexpression construct (Additional file 33: Table S17) using PrimeSTAR HS DNA Polymerase (TaKaRa). The sequence-confirmed PCR fragment was digested with Kpn I and Pac I, and inserted into the vector pMDC32 under the control of the Cauliflower Mosaic virus (CaMV) 35S promoter.
To generate the RNA interference (RNAi) construct, a fragment targeting the 294 bp coding region of OsLG3 was amplified with specific primers P3 and P4, digested with Kpn I and BamH I and then Spe I and Sac I sites, and subsequently inserted into the pTCK303 vector as previously described . Primers used in these experiments are listed in Additional file 33: Table S17. To generate transgenic plants, the constructs were transformed into Nipponbare by Agrobacterium-mediated transformation . Positive transgenic plants were selected by germinating transgenic seeds on 1/2 MS medium containing 50 mg/L hygromycin (Roche, Germany).
RNA extraction and expression analysis
For expression analysis, rice leaves were flash-frozen in liquid nitrogen, total RNAs were extracted using RNAiso Plus (TaKaRa) according to the manufacturer’s instructions; 4 μg of the DNase-treated RNA were reverse transcribed using M-MLV reverse transcriptase (TaKaRa). The resulting cDNA samples were diluted five times and used as templates for PCR.
qRT-PCR was performed in an Applied Biosystems 7500 Real Time PCR system (ABI, USA) using SYBR Premix Ex Taq™ II (TaKaRa) as previously described [55, 56]. The gene-specific primers used for real-time PCR are listed in Additional file 33: Table S16. qRT-PCR was performed in triplicate for each sample, and the Actin1 gene was used as the internal reference for data normalization using the 2–ΔΔt method .
Sub-cellular localization of OsLG3 and transactivation activity assay
To generate the 35S::OsLG3-GFP construct, the full-length ORF of OsLG3 without the terminal codon was amplified with the corresponding primers (Additional file 33: Table S17). The amplified fragments were digested with Kpn I and Pac I, and then cloned into the pMDC83 vector and fused with the GFP reporter gene driven by the CaMV 35S promoter. The recombinant constructs of the 35S:OsLG3-GFP fusion and GFP alone were transiently transfected into onion epidermal cells, using a PDS-1000/He system (Bio-Rad, USA) at 1100 psi. After incubation at 25 °C for 24 h, the fluorescence signal was examined through a confocal laser scanning microscope FV1000 (Olympus, USA) with excitation at 488 nm and emission at 525 nm.
For the transactivation assay, plasmids pGBKT7-OsLG3 (full length coding region of OsLG3), pGBKT7-OsLG3-N (N-terminal of OsLG3, amino acids 1–108), pGBKT7-OsLG3-C (C-terminal of OsLG3, amino acids 109–209) were constructed. The pGBKT7 vector was used as a negative control and pGBKT7-53 was used as a positive control. These constructs were introduced into yeast strain AH109 by LiAc-mediated yeast transformations, and screened on selective medium plates without tryptophan (SD/–Trp). The PCR-verified transformants were transferred to SD medium without tryptophan/histidine/adenine (SD/–Trp/–His/–Ade) for 3 days. Transactivation activities were performed according to the in vivo agar plate assay (x-α-gal in medium).
Milled rice grains for scanning electron microscopy were transversely cut in the middle with a knife and coated with gold under vacuum conditions. The morphology of starch granules in the belly part of the endosperm was examined with a scanning electron microscope (Hitachi, S-570, China Atomic Energy Research Institute) at an accelerating voltage of 12 kV. The analysis was based on at least three biological replications of mounted specimens. All procedures were carried out according to the manufacturer’s protocol.
Nucleotide diversity analysis and minimum spanning tree
The average nucleotide diversities (π) of indica, temperate japonica, tropical japonica, and wild rice subpopulations were estimated in non-overlapping 10 kb windows using an in-house Perl script; missing data positions were included, with a modification of population size .
Five hundred and four diverse cultivated rice and 15 O. rufipogon accessions from around the world (Additional file 32: Table S1) were used to construct a minimum spanning tree for OsLG3. Arlequin version 3.5  was used to define the haplotypes and calculate the minimum spanning tree among haplotypes. Arlequin’s distance matrix output was used in Hapstar-0.7  to draw a minimum spanning tree.
Phylogenetic and genetic interaction analysis
The phylogenetic tree of 480 varieties was constructed based on SNPs and indels that were proven FNPs (listed in Additional file 32: Table S1) by MAGE 6.0. The EvolView  online tool was used for visualizing the phylogenetic tree. Grain length was measured by Vernier calipers and the violin map for genetic interaction analysis was constructed in R. Multiple comparisons were made by Tukey’ honest significant difference in R. Landraces and raw data are listed in Additional file 32: Table S1.
We thank Professor Robert A McIntosh (University of Sydney) for critical reading and suggested revision of the manuscript, Yan Liu for QTL analysis, Yanfa Chen and Haifeng Guo for preparing samples, and Jianyin Xie and Yan Zhao for help with data analysis.
This work was supported by the China National Key Technologies R&D Program (2015BAD02B01, 2016YFD0100101, and 2013BAD01B02-15), 948 project of the MOA (2011-G2B and 2011-G1 (2)-25), Key Program of GuangXi Academy of Agricultural Sciences (Gui nong ke 2016JZ05), Fund of Chairman of Guangxi autonomous region (1517-03), and Basal Research Fund of GuangXi Academy of Agricultural Sciences (2015YT14).
Availability of data and materials
The data on 504 accessions (Additional file 32: Table S1) in our study are a part of the 3000 rice genomes project. All SNPs and indels of the accessions in our study can be found at http://www.rmbreeding.cn/snp3k. We re-sequenced the OsLG3 region of 27 wild rice accessions (Additional file 32: Table S1). Sequence data for another 10 wild rice accessions were obtained from ftp://rice:email@example.com/BGI/rice. All data generated or analyzed during this study are included in this published article and its supplementary information files. Raw data can be found in Additional file 39.
JY and ZiL designed the research, and together with HX and XZ, performed most of experiments and analyzed the data. HL conducted the simulation analysis. GY and QZ constructed the genetic populations. JM, YP, ZZ, and XW performed part of the experiments. WW, ZT, YG, and ZhL conceived and supervised the project. JY, XF, HZ, JL, and ZiL conceived the experiment and wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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- Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, et al. Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet. 2008;40(8):1023–8.View ArticlePubMedGoogle Scholar
- Huang R, Jiang L, Zheng J, Wang T, Wang H, Huang Y, et al. Genetic bases of rice grain shape: so many genes, so little known. Trends Plant Sci. 2012;18(4):218–26.View ArticlePubMedGoogle Scholar
- Mao H, Sun S, Yao J, Wang C, Yu S, Xu C, et al. Linking differential domain functions of the GS3 protein to natural variation of grain size in rice. Proc Natl Acad Sci U S A. 2010;107(45):19579–84.View ArticlePubMedPubMed CentralGoogle Scholar
- Song XJ, Huang W, Shi M, Zhu MZ, Lin HX. A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet. 2007;39(5):623–30.View ArticlePubMedGoogle Scholar
- Zhang X, Zhang H. Rare allele of OsPPKL1 associated with grain length causes extra-large grain and a significant yield increase in rice. Proc Natl Acad Sci U S A. 2012;109(52):21534.View ArticlePubMedPubMed CentralGoogle Scholar
- Ishimaru K, Hirotsu N, Madoka Y, Murakami N, Hara N, Onodera H, et al. Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield. Nat Genet. 2013;45(6):707–11.View ArticlePubMedGoogle Scholar
- Wang S, Wu K, Yuan Q, Liu X, Liu Z, Lin X, et al. Control of grain size, shape and quality by OsSPL16 in rice. Nat Genet. 2012;44(8):950.View ArticlePubMedGoogle Scholar
- Frary A, Nesbitt TC, Frary A, Grandillo S, Knaap E, Cong B, et al. fw2.2: A quantitative trait locus key to the evolution of tomato fruit size. Science. 2000;289(5476):85–8.View ArticlePubMedGoogle Scholar
- Li Y, Fan C, Xing Y, Yun P, Luo L, Yan B, et al. Chalk5 encodes a vacuolar H + -translocating pyrophosphatase influencing grain chalkiness in rice. Nat Genet. 2014;46(4):398–404.View ArticlePubMedGoogle Scholar
- Ma Y, Dai X, Xu Y, Luo W, Zheng X, Zeng D, et al. COLD1 confers chilling tolerance in rice. Cell. 2015;160(6):1209–21.View ArticlePubMedGoogle Scholar
- Liu Y, Wu H, Chen H, Liu Y, He J, Kang H, et al. A gene cluster encoding lectin receptor kinases confers broad-spectrum and durable insect resistance in rice. Nat Biotech. 2015;33(3):301–5.View ArticleGoogle Scholar
- Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet. 2010;42(11):961–7.View ArticlePubMedGoogle Scholar
- Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, et al. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet. 2012;44(1):32–9.View ArticleGoogle Scholar
- Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, et al. Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet. 2011;43(2):159–62.View ArticlePubMedGoogle Scholar
- Jia G, Huang X, Zhi H, Zhao Y, Zhao Q, Li W, et al. A haplotype map of genomic variations and genome-wide association studies of agronomic traits in foxtail millet (Setaria italica). Nat Genet. 2013;45(8):957–61.View ArticlePubMedGoogle Scholar
- Kump KL, Bradbury PJ, Wisser RJ, Buckler ES, Belcher AR, Oropeza-Rosas MA, et al. Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population. Nat Genet. 2011;43(2):163–8.View ArticlePubMedGoogle Scholar
- Atwell S, Huang YS, Vilhjalmsson BJ, Willems G, Horton M, Li Y, et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010;465(7298):627–31.View ArticlePubMedPubMed CentralGoogle Scholar
- Payseur BA, Place M. Prospects for association mapping in classical inbred mouse strains. Genetics. 2007;175(4):1999–2008.View ArticlePubMedPubMed CentralGoogle Scholar
- Nemri A, Atwell S, Tarone AM, Huang YS, Zhao K, Studholme DJ, et al. Genome-wide survey of Arabidopsis natural variation in downy mildew resistance using combined association and linkage mapping. Proc Natl Acad Sci. 2010;107(22):10302–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang Q, Xie W, Xing H, Yan J, Meng X, Li X, et al. Genetic architecture of natural variation in rice chlorophyll content revealed by a genome-wide association study. Mol Plant. 2015;8(6):946–57.View ArticlePubMedGoogle Scholar
- Lou Q, Chen L, Mei H, Wei H, Feng F, Wang P, et al. Quantitative trait locus mapping of deep rooting by linkage and association analysis in rice. J Exp Bot. 2015;66(15):4749–57.View ArticlePubMedPubMed CentralGoogle Scholar
- Crowell S, Korniliev P, Falcão A, Ismail A, Gregorio G, Mezey J, et al. Genome-wide association and high-resolution phenotyping link Oryza sativa panicle traits to numerous trait-specific QTL clusters. Nat Commun. 2016;7:10527.View ArticlePubMedPubMed CentralGoogle Scholar
- The 3,000 rice genomes project. GigaScience. 2014;3(1):7.Google Scholar
- Alexandrov N, Tai S, Wang W, Mansueto L, Palis K, Fuentes RR, et al. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res. 2015;43(Database issue):D1023.View ArticlePubMedGoogle Scholar
- Zhang H, Zhang D, Wang M, Sun J, Qi Y, Li J, et al. A core collection and mini core collection of Oryza sativa L. in China. Theor Appl Genet. 2011;122(1):49–61.View ArticlePubMedGoogle Scholar
- Yu SB, Xu WJ, Vijayakumar CH, Ali J, Fu BY, Xu JL, et al. Molecular diversity and multilocus organization of the parental lines used in the International Rice Molecular Breeding Program. Theor Appl Genet. 2003;108(1):131–40.View ArticlePubMedGoogle Scholar
- Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38(2):203–8.View ArticlePubMedGoogle Scholar
- Zhang Z, Ersoz E, Lai C-Q, Todhunter RJ, Tiwari HK, Gore MA, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 2010;42(4):355–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhao K, Tung CW, Eizenga GC, Wright MH, Ali ML, Price AH, et al. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun. 2011;2:467.View ArticlePubMedPubMed CentralGoogle Scholar
- Itoh JI, Nonomura KI, Ikeda K, Yamaki S, Inukai Y, Yamagishi H, et al. Rice plant development: from zygote to spikelet. Plant Cell Physiol. 2005;46(1):23.View ArticlePubMedGoogle Scholar
- Berckmans B, De VL. Transcriptional control of the cell cycle. Curr Opin Plant Biol. 2009;12(5):599–605.View ArticlePubMedGoogle Scholar
- Weigel D. The APETALA2 domain is related to a novel type of DNA binding domain. Plant Cell. 1995;7(4):388–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Hao D, Ohme-Takagi M, Sarai A. Unique mode of GCC box recognition by the DNA-binding domain of ethylene-responsive element-binding factor (ERF domain) in plant. J Biol Chem. 1998;273(41):26857–61.View ArticlePubMedGoogle Scholar
- Asano K, Matsuoka M. Artificial selection for a green revolution gene during japonica rice domestication. Proc Natl Acad Sci U S A. 2011;108(27):11034–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Kovach MJ, Sweeney MT, McCouch SR. New insights into the history of rice domestication. Trends Genet. 2007;23(11):578–87.View ArticlePubMedGoogle Scholar
- Li N, Li Y. Signaling pathways of seed size control in plants. Curr Opin Plant Biol. 2016;33:23–32.View ArticlePubMedGoogle Scholar
- Sharma S, Tripathy S, Biswal J. Origin of asian cultivated rice and its ecotypic differentiation. Indian J Genet Plant Breed. 1997;57(4):339–60.Google Scholar
- Chang T-T. The origin, evolution, cultivation, dissemination, and diversification of Asian and African rices. Euphytica. 1976;25(1):425–41.View ArticleGoogle Scholar
- Londo JP, Chiang Y-C, Hung K-H, Chiang T-Y, Schaal BA. Phylogeography of Asian wild rice, Oryza rufipogon, reveals multiple independent domestications of cultivated rice. Oryza sativa Proc Natl Acad Sci. 2006;103(25):9578–83.View ArticlePubMedGoogle Scholar
- Huang X, Kurata N, Wei X, Wang Z, Wang A, Zhao Q, et al. A map of rice genome variation reveals the origin of cultivated rice. Nature. 2012;490:497–501.View ArticlePubMedGoogle Scholar
- Civáň P, Craig H, Cox CJ, Brown TA. Three geographically separate domestications of Asian rice. Nat Plants. 2015;1:15164.View ArticlePubMedPubMed CentralGoogle Scholar
- Li C, Zhou A, Sang T. Rice domestication by reducing shattering. Science. 2006;311(5769):1936–9.View ArticlePubMedGoogle Scholar
- Tan L, Li X, Liu F, Sun X, Li C, Zhu Z, et al. Control of a key transition from prostrate to erect growth in rice domestication. Nat Genet. 2008;40(11):1360–4.View ArticlePubMedGoogle Scholar
- Zhu B-F, Si L, Wang Z, Zhu YZJ, Shangguan Y, Lu D, et al. Genetic control of a transition from black to straw-white seed hull in rice domestication. Plant Physiol. 2011;155(3):1301–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang LB, Zhu Q, Wu ZQ, Ross‐Ibarra J, Gaut BS, Ge S, et al. Selection on grain shattering genes and rates of rice domestication. New Phytol. 2009;184(3):708–20.View ArticlePubMedGoogle Scholar
- Chen LJ, Lee DS, Song ZP, Suh HS, Lu BR. Gene flow from cultivated rice (Oryza sativa) to its weedy and wild relatives. Ann Bot. 2004;93(1):67–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu X, Huang M, Fan B, Buckler ES, Zhang Z. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet. 2016;12(2):e1005767.View ArticlePubMedPubMed CentralGoogle Scholar
- Li Z, Rutger J. Geographic distribution and multilocus organization of isozyme variation of rice (Oryza sativa L.). Theor Appl Genet. 2000;101(3):379–87.View ArticleGoogle Scholar
- Team RC. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013. p. 2014.Google Scholar
- Lincoln S, Daly M, Lander E. Constructing genetics maps with MAPMAKER/EXP 3.0. Whitehead Technical Report. Cambridge, MA: MAPMAKER; 1992.Google Scholar
- Li H, Ye G, Wang J. A modified algorithm for the improvement of composite interval mapping. Genetics. 2007;175(1):361–74.View ArticlePubMedPubMed CentralGoogle Scholar
- Paterson AH, DeVerna JW, Lanini B, Tanksley SD. Fine mapping of quantitative trait loci using selected overlapping recombinant chromosomes, in an interspecies cross of tomato. Genetics. 1990;124(3):735–42.PubMedPubMed CentralGoogle Scholar
- Wang Z, Chen C, Xu Y, Jiang R, Han Y, Xu Z, et al. A practical vector for efficient knockdown of gene expression in rice (Oryza sativa L.). Plant Mol Biol Rep. 2004;22(4):409–17.View ArticleGoogle Scholar
- Hiei Y, Ohta S, Komari T, Kumashiro T. Efficient transformation of rice (Oryza sativa L.) mediated by Agrobacterium and sequence analysis of the boundaries of the T-DNA. Plant J. 1994;6(2):271–82.View ArticlePubMedGoogle Scholar
- Duan J, Zhang M, Zhang H, Xiong H, Liu P, Ali J, et al. OsMIOX, a myo-inositol oxygenase gene, improves drought tolerance through scavenging of reactive oxygen species in rice (Oryza sativa L.). Plant Sci. 2012;196:143–51.View ArticlePubMedGoogle Scholar
- Xiong H, Li J, Liu P, Duan J, Zhao Y, Guo X, et al. Overexpression of OsMYB48-1, a novel MYB-related transcription factor, enhances drought and salinity tolerance in rice. PLoS One. 2014;9(3), e92913.View ArticlePubMedPubMed CentralGoogle Scholar
- Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2 − ΔΔCT method. Methods. 2001;25(4):402–8.View ArticlePubMedGoogle Scholar
- Thornton K. Libsequence: a C++ class library for evolutionary genetic analysis. Bioinformatics. 2003;19(17):2325–7.Google Scholar
- Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10(3):564–7.View ArticlePubMedGoogle Scholar
- Teacher AG, Griffiths DJ. HapStar: automated haplotype network layout and visualization. Mol Ecol Resour. 2011;11(1):151–3.View ArticlePubMedGoogle Scholar
- Zhang H, Gao S, Lercher MJ, Hu S, Chen WH. EvolView, an online tool for visualizing, annotating and managing phylogenetic trees. Nucleic Acids Res. 2012;40(Web Server issue):W569.View ArticlePubMedPubMed CentralGoogle Scholar