Logo of dnaresOxford JournalsDNA ResearchAbout this journalContact this journalSubscriptionsCurrent issueArchiveSearch
DNA Res. 2009 Jun; 16(3): 141–154.
Published online 2009 Apr 9. doi:  10.1093/dnares/dsp006
PMCID: PMC2695773

Towards the Understanding of Complex Traits in Rice: Substantially or Superficially?


Completion of the genome analysis followed by extensive comprehensive studies on a variety of genes and gene families of rice (Oryza sativa) resulted in rapid accumulation of information concerning the presence of many complex traits that are governed by a number of genes of distinct functions in this most important crop cultivated worldwide. The genetic and molecular biological dissection of many important rice phenotypes has contributed to our understanding of the complex nature of the genetic control with respect to these phenotypes. However, in spite of the considerable advances made in the field, details of genetic control remain largely unsolved, thereby hampering our exploitation of this useful information in the breeding of new rice cultivars. To further strengthen the field application of the genome science data of rice obtained so far, we need to develop more powerful genomics-assisted methods for rice breeding based on information derived from various quantitative trait loci (QTL) and related analyses. In this review, we describe recent progresses and outcomes in rice QTL analyses, problems associated with the application of the technology to rice breeding and their implications for the genetic study of other crops along with future perspectives of the relevant fields.

Key words: QTL, near-isogenic lines, chromosome segment substitution lines, marker-assisted selection, map-based cloning

1. Introduction

Rice (Oryza sativa L.) is one of the most important food crops in the world. It is the staple of diet for heavily populated Asian countries as well as many African countries in which overpopulation is becoming a critical issue. In spite of the successful introduction of resistance to biotic/abiotic stresses into a variety of rice cultivars that resulted in improved crop yield, benefits of the ‘green revolution’1 will soon be exhausted due to the population pressure. Recent progresses in the genomics of rice plant seem promising to speed up its breeding to yield further improved varieties in a shorter period of time. A major challenge in this technology, however, resides in the development of methodology to associate the genomic data with phenotypes of economical significance.

The engine used for plant breeding is the selection of naturally occurring variants. Since the beginning of agriculture of rice initiated some 10 000 years ago, the traits of rice plant have been continuously improved through the introduction of naturally occurring beneficial alleles by spontaneous and/or artificial crossings. In the last century, systematic plant breeding theory based on modern Mendelian genetics was widely practiced. A limitation in this approach is that even though we can control the resultant phenotypes to a certain extent, mechanisms governing the genetic control of quantitative traits such as crop yield have been only poorly understood and exploited.

Since the last decade, genetic dissection of ‘quantitative trait loci’ referred to as QTL has become a common approach, and has created a new paradigm in plant genetics. The notion emerged to understand the genetic basis of various ‘quantitative traits’ observed in cultivated plants such as crop yield that appeared to vary continuously among cultivars. Since such ‘quantitative traits’ are generally controlled by cooperatively acting genes of related functions, separate analysis of individual genes did not lead to successful characterization of the observed phenotypes. New methodology to categorically analyze the genetic loci involved in the phenotypes was thus necessary.

In conjunction with the completion of the rice genome sequencing project,2 QTL analysis has become able to contribute to our understanding of natural variations in rice, thereby producing an enormous amount of information concerning their basis, such as chromosomal location of genes, allelic effects, epistatic interactions and so on. To date, however, such QTL information has not been fully exploited in rice breeding programs due largely to the nature of QTL analysis. Since it is based on statistical analysis, its reliability differs from one QTL analysis to another; in addition, information concerning the interactions with other genes and the effect of environmental conditions is hardly available. In this review, we examine recent progresses in rice QTL analysis, describe the outcomes and problems in its application to current rice breeding and discuss its possible implications toward the analysis of other crops as well as its future prospects.

2. Genetic and molecular dissection of complex traits in rice

2.1. Brief overview of QTL information through ‘Gramene QTL’

Over the last decade, a large number of QTL have been generated using the linkage map constructed from DNA markers in the rice genome. The limited amount of information that anchors those different DNA marker used in different experiments makes it very difficult to compare the QTL generated by different researchers. In 2005, rice genome sequence has completely become available2 and the physical positions of numerous QTL could be determined based on the sequence information of flanking markers. This has permitted the development of a more comprehensive QTL database in rice.

Gramene QTL3 is the most popular rice QTL database owing to its comprehensive collection of QTL information, which can be accessed on a genome browser. In April 2008, Gramene QTL annotated 8646 QTL, which were extracted from 247 reports published between 1994 and 2006. These QTL are categorized into nine trait categories (TCs) encompassing 237 characters as subordinate categories. Of the 237 characters, the one representing the largest number of QTL is plant height (categorized into the TC of ‘vigor’) with 1011 QTL, followed by ‘days to heading’ with 618 QTL (TC of ‘development’), 353 QTL for ‘spikelet number’ (TC of ‘yield’), 330 QTL for ‘spikelet fertility’ (TC of ‘sterility or fertility’) and 253 QTL for ‘panicle length’ (TC of anatomy). Of a total of 8646 QTL, the physical position of 6293 QTL (73%) was determined on specific chromosomes (Table 1). The remaining 27% of QTL could be positioned on the linkage map but not on the physical map mainly due to the lack of physical locations of flanking DNA markers. Twenty-five percent of QTL that have a physical position have been mapped to an interval of >1 Mb, owing to the small number of markers used in the analysis. Nearly 32% of QTL with a physical position could be mapped to an interval of <1 kb on specific chromosomes. Some of these locations should be viewed with caution because in some cases only one linked marker has been described in associated papers; therefore, these positions have not been precisely determined.

Table 1

The number of Gramene QTL classified into each trait category

A total of 4090 QTL in Gramene QTL are physically distributed unevenly on 12 chromosomes (Fig. 1). There is a tendency for a cluster of QTL to occur in particular chromosomal regions. For example, several hundred QTL including 95 QTL related to vigor has been mapped to specific chromosomal region of 40 Mb of chromosome 1. It is known that the sd1 (semi-dwarf 1) gene is located in this region4 and the variation of plant type originating from sd1 allelic differences may be detected by several QTL analyses. Similarly, many QTL for yield in the region of distal end of chromosome 1 may be detected by the action of Gn1a (Grain-number 1a).5 On the basis of the nature of QTL analysis, it is unavoidable at present to report QTL redundantly. Metric characters such as plant height can be readily measured and have been usually used for the characterization of characters in several QTL analyses. Therefore, it is often the case that different trait characters can be identified as QTL on the same region of a specific chromosome.

Figure 1

The location of Gramene QTL on rice chromosomes. In 6293 Gramene QTL with physical position of chromosome segment deduced from the sequences of corresponding DNA markers, 4090 QTL within the interval of 1 Mb or less were only plotted by using the central ...

On the other hand, very few QTL has been positioned in centromeric regions and in the short arm of chromosome 10. This may be due to the limited number of trials (247 reports) and bias in plant cross-combination that has been used in the QTL analysis. In many different reports, the same populations of recombinant inbred lines (RILs) were used for the analysis of traits. This can generate QTL with unequal distribution on the chromosomes. Furthermore, some QTL have been putatively determined as loci exhibiting epistatic interactions with other loci or environmental factors. The reliability of these QTL cannot be evaluated simply by information provided by each report. This issue is discussed in more detail later in this review.

In spite of the accumulated experience in QTL analysis and the relatively large number of QTL analyses performed so far, an overview of QTL information in the Gramene QTL database reveals that it may be premature to use some of that information in marker-assisted selection in rice breeding. In order to cross-reference QTL information in rice, researchers must pay much more attention to the designation of target traits, population size and the number of markers used in the analysis.

2.2. Natural variations can be a target of molecular dissection

In spite of the mixture of different levels of quality in QTL analyses, some of the QTL for morphological and physiological traits have been treated as a single Mendelian factor and cloned by using a map-based strategy (Table 2).6 These studies have also revealed the kinds of nucleotide changes involved in determining phenotypic differences. The cloning of causal genes/QTL for natural variations has allowed us to understand the genetic control mechanisms of naturally occurring complex variations. The clearest example is the analysis of flowering time, for which five QTL have been cloned: Hd1, Hd3a, Hd6, Ehd1 and Ghd7.711 Recently, Hd3a was found to act as a flowering promoter, florigen, which had been predicted previously but had not been proved.12 This series of studies has contributed to the description of a pathway of flowering time control in rice.13,14 As a result, this kind of scenario can be applied to other natural variations of agronomical interest. Recent progress in cloning causal genes involving major phenotypic change within natural variation, such as seed shattering, seed size and growth habit, has further elucidated the rice domestication process.11,1519 For example, the qSH1 gene, a major QTL of seed shattering in rice, encodes a BEL-type homeodomain and a single-nucleotide polymorphism (SNP) has been detected in this gene.15 This SNP, lying in the 5′ regulatory region of qSH1, generates a phenotype characterized by the loss of seed shattering. Furthermore, based on the analysis of several old landraces of Chinese and Japanese rice, this SNP might have arisen during the domestication process. It was demonstrated that a deletion of ∼1 kb has been a key event in changing from small to large seed during the japonica rice domestication process.19 It was also demonstrated that gene duplication and divergence in the Sub1 locus, which is involved in submergence tolerance, also have occurred both prior to and after rice domestication.20 Consequently, the accumulation of several domestication signatures among current and old rice accessions permitted the establishment of a model of the japonica rice domestication process.21

Table 2

QTLs with major effects on agronomic traits in rice

3. Questions in the genetic dissection of natural variation

3.1. How do we quantitatively score phenotype?

Some morphologically distinguishable quantitative traits, such as grain weight, days to heading or plant height, have been evaluated by QTL analysis, but it is sometimes difficult to characterize the phenotype of economically important traits. A typical example is the genetic analysis of yield potential, which is one of the most important traits in agriculture. Rice yield—the total amount of grain harvested—is broadly divided into two quantifiable components, source potential and sink size.22 Both of these traits must be determined to characterize the yield potential, but progress on the genetic analysis of source potential is far behind that of sink size.5,18,19,21 Since source potential, such as leaf photosynthesis or translocation, is physiological traits, scoring them is time-consuming, laborious and likely to be influenced by environmental conditions. Therefore, these traits are still not applicable to QTL analysis, which needs to score large numbers of segregated progeny at the same growth stage.

As another example, general growth habits such as culm length, panicle number, fertility ratio or dry matter weight each measured under drought stress conditions are often used as an index of the degree of drought resistance.2327 Chromosomal locations of these QTL occasionally overlap with the previously reported QTL for growth habits such as ‘days to heading’ (flowering time) or ‘culm length’ examined under normal conditions. We may detect only a QTL of drought avoidance even though we hope to find a QTL involving drought tolerance. So far, crop physiologists and geneticists have attempted to evaluate drought tolerance by measuring root morphology,28,29 but this might be a part of key factors in the trait in preventing drought stress. Scoring the phenotypic differences of non-morphological traits is a crucial issue for QTL analysis. Recent progress in plant physiology and biochemistry concurrently with the development of novel analytical tools will facilitate the investigation of target traits (see section 4.3).

3.2. QTL that have minor effects remain poorly investigated

QTL associated with complex traits have been identified and have already delivered crucial information to aid our understanding of natural variations. In general, a large number of QTL have been detected mainly by using primary mapping populations such as F2, RILs or backcross inbred lines (BILs). One of the major questions in QTL analysis is whether we can detect all QTL including those with minor effects. On the basis of the statistical nature of QTL analysis, some bias in the results are expected due to ‘noise’ inherent in the complex biological and environmental systems involved. We must consider that QTL associated with minor effects might not be detected in the analysis using primary mapping populations (false negatives). In addition, we must also recognize and define false positives.

The QTL analysis of ‘heading date’ is a good example of the difficulty of identifying QTL with minor effects. In primary QTL analysis using F2 populations between two varieties, ‘Nipponbare’ and ‘Kasalath’, five QTL have been detected.30 Further analysis using backcrossed progeny derived from the same cross-combination revealed at least 10 additional QTL.31 Some of these QTL are found to be masked by the QTL with relatively large phenotypic effects or an epistatic interaction.32,33 The trait of ‘spikelet number per panicle’ is another example. Nagata et al.34 estimated that 4 QTL controlled the number of ‘spikelet per panicle’ by using BILs derived from the cross between ‘Sasanishiki’ as a recurrent and ‘Habataki’ as a donor. Since then, Ando et al.35 have developed chromosomal segment substitution lines (CSSLs), which are a series of lines covering entire chromosomal regions of ‘Habataki’ dividing it into small segments but on the genetic background of ‘Sasanishiki’. They identified not only the same 4 QTL but also an additional 12 QTL with relatively small phenotypic effect. One of the common QTL located on chromosome 1 may correspond with Gn1a,5 but this did not fully explain the phenotypic difference between the parents. This example clearly demonstrates the importance of finding QTL with small effect to understand the genetic basis of spikelet number.

3.3. Epistatic and environmental interactions: easy to estimate but hard to confirm

To thoroughly understand the genetic control of complex traits, the important issues to be clarified are the epistatic interaction among the genes involved [genotype by genotype (G × G) interaction] and the interaction between gene expression and environmental conditions [environmental (G × E) interaction]. Statistical analyses such as two-way ANOVA and multiple regressions have been often used to reveal G × G interactions mainly on yield-related traits including heterosis, by using primary mapping populations.28,3646 These analyses undoubtedly have provided a clue to understand the complex nature of genetic control mechanisms. Note that very few of the indications of such interactions have been proved by further analysis. It has been sometimes reported that a large number of epistatic interaction pairs distributed whole genome were observed. For example, 195 and 328 digenic interactions were detected in the indica/japonica and indica/indica combinations, respectively.47 Although some of these interactions may be important, it is possible that many false positives are also being included. In general, 100–300 plants or lines have been used in most of the QTL analyses reported. So is the presence of false positives caused by the bias inherent in a small population size, by experimental errors in phenotyping or by some other cause? How do we define a real digenic interaction? This may be particularly true in the case of interaction involving more than three loci. A large primary mapping population (e.g. >500) may be required to avoid the generation of false positives. Again, note that it is very hard to distinguish between real interactions and experimental noise in statistical analysis.

Meanwhile, estimation of the G × E interaction needs to be clarified in order to use in a breeding program significant alleles revealed by QTL analysis. G × E is frequently evaluated by year–year and/or location–location variations using mapping populations that are available for replication, such as doubled haploid lines (DHLs), RILs or BILs. Since an indirect methodology of QTL mapping with a ‘QTL by environment’ (QE) interaction was proposed,48 many examples of chromosomal regions involving G × E have been reported for rice.43,49,50 The Gramene QTL database3 handles all locations detected with interaction effects as independent QTL. The largest entry contains 611 QTL information sources,43 which may imply that some epistatic pairs are possibly false positives. To minimize the risk of such false positives, rigorous statistical conditions using repetitive experiments in one environment should be employed in the experimental design.51

3.4. A proposal: confirmation is more important than estimation

Above we have pointed out that there are three problems that must be solved for a better understanding of the genetic dissection of natural variation in rice: (i) trait evaluation; (ii) detection of minor QTL and (iii) G × G and G × E interactions. On the basis of the nature of the analytical methods including the statistics used in QTL mapping of primary populations, it might be difficult to solve these problems. One practical solution is to design and develop plant materials with a uniform genetic background for validation of the results obtained in the primary analysis. Basically, advanced backcross progeny would be used to prove the genetic effect of major QTL.52 This type of validation of a genetic effect can be performed on QTL with minor effects. The use of CSSLs, single-segment substitution lines (SSSLs) or introgression lines (ILs) will be a practical solution for trait evaluation and detection of minor QTL.35,5357 As described in section 3.2, Ando et al.35 demonstrated the power of CSSLs to find QTL that have a minor effect. However, CSSLs cannot estimate candidate pairs of G × G interactions because they do not have enough genotype combinations for any two loci. To overcome this disadvantage, the combined use of a primary population (F2s, RILs, DHLs) and a backcrossed population (CSSLs, SSSLs, ILs) is recommended. Successful proof should be obtained by evaluation of the phenotypic difference of both near-isogenic lines (NILs) of the QTL and their combined line after estimations of G × G by primary population.58,59 Because CSSLs or ILs normally have one chromosomal region substituted in the recurrent genetic background, they can be used as NILs themselves or as starting material to develop NILs. These types of plant materials are particularly useful to discriminate between real QTL and false positives, and to prove G × E interactions.6062

Figure 2 illustrates the roles of a primary population, such as F2 or RIL, and a backcrossed population, such as CSSL or NIL. Putative QTL, G × E and G × G interactions are statistically estimated from a primary population. These QTL can be validated based on phenotypic differences between the recurrent parent and the CSSL whose substitution segment corresponds with the detected QTL region in the primary analysis. Moreover, G × E or G × G interactions can be validated by evaluation of phenotypic performance of the CSSLs. Finally, the information integrated from these analyses allows us to proceed to further studies, such as map-based cloning for biological characteristics or marker-assisted breeding. We are developing several kinds of CSSLs on the japonica rice genetic background; we select donor cultivars based on their geographical distribution in the world. Although the process of development is laborious and time-consuming, the resulting CSSLs will contribute to exploration of new economically important alleles as well as clarifying genetic control of natural variation.

Figure 2

Scheme for the use of primary and backcrossed populations to investigate genetically complex traits in rice. (A) Two kinds of experimental populations—primary populations such as RILs and backcrossed population such as CSSLs—can be developed ...

We propose that the best route forward is to identify QTL at the molecular level and to check their expression in each QTL combination. In the case of flowering time, which is one of the most successfully advanced traits in rice quantitative genetics, knowledge of the identified QTL is being integrated in order to construct the relevant gene network.13,14 Map-based cloning of the QTL is currently becoming one of the standard strategies as a first step of this kind of work. Application of the developed CSSLs will provide a route to accelerate this process.

4. Rice QTL for practical use

4.1. Rice varieties developed by marker assisted selection

Once the genes controlling traits with economical and agricultural interest have been identified, there will be opportunities to utilize the resulting information such as the DNA sequence and chromosomal location of these genes in breeding programs. Since the paradigm of marker-assisted-selection (MAS) has emerged, a great deal of effort has been invested in the practice of MAS. Several examples have already been reported for the development of NILs with particular traits in elite rice varieties. For example, submergence by deep water causes severe stress to rice in south-east Asia, where flooding occurs during the monsoon season. A major QTL, Submergence 1 (Sub1), was detected near the centromere of chromosome 9 and was eventually cloned (Table 2).63,64 When the Sub1A allele was introgressed by MAS into an elite cultivar widely grown in Asia, the resultant lines showed promising agronomic performance in yield and other agronomic traits, as well as tolerance to submergence.65,66 Heading date is an example where a series of NILs with different heading dates were developed in the genetic background of an elite cultivar, ‘Koshihikari’, in Japan.67 In these cases, relatively small chromosome segments (∼600 kb) were introduced by MAS. A further example is insect resistance, one of QTL controlling brown plant hopper resistance, which has been identified in a wild relative of rice, Oryza officinalis. This QTL has been introduced into the elite Japanese cultivar, ‘Hinohikari’, by MAS (Hirabayashi et al., unpublished data). In these MAS, it is necessary not only to perform background selection by DNA markers distributed in non-target chromosomal regions, but the size of the introgressed chromosome segment should be minimized to avoid unfavorable trait association (linkage drag). To accelerate the effectiveness of MAS, the precise map location of target QTL must be determined and several flanking markers developed.

MAS also offers a new concept in plant breeding. Once NILs representing a particular trait of economic value are independently developed, gene pyramiding can be performed by simple crossing between such NILs.68 In order to develop a new line with lodging resistance and high yield, the combination of two genes controlling the characteristics, ‘semi-dwarf’ and ‘number of grain’ was successfully introduced into ‘Koshihikari’.5 This concept can also be applied to multiple genes controlling specific traits. Four QTL derived from upland rice, control a partial resistance to rice blast and have been successfully pyramidized into lowland rice cultivars by MAS (Fukuoka et al., unpublished data).

4.2. Genetic dissection of natural variations observed among closely related cultivars

In general, performance in QTL analysis and map-based cloning is strongly dependent on sequence difference between two parental lines. In fact, most of the QTL listed in Table 2 have been detected and cloned by using distant crosses, such as indica and japonica. On the other hand, there is considerable phenotypic variation between japonica cultivars.69,70 These variations between closely related cultivars have often been major targets for selection in rice breeding programs. This is particularly true in the breeding of cultivars adapted to regional environmental conditions. As a result, these natural variations are important targets for genetic and molecular dissection. Given this, it has been difficult to detect and clone QTL controlling variations among closely related cultivars, owing to the lack of informative DNA markers. The completion of whole-genome sequencing has dramatically changed this situation. It has allowed us to identify effectively microsatellite motifs and convert them to simple sequence repeats (SSR) markers.2 This situation has allowed us to perform mapping of QTL using progeny derived from a cross between genetically closely related cultivars. Fujino et al.71 cloned the QTL qLTG3-1 for low temperature germinabilty using progeny between temperate japonica crosses. This is the first example of the molecular cloning of genes identified between genetically related cultivars. Recently, the use of SSR markers facilitated the detection of QTL with complex inheritance. For example, QTL controlling grain quality72,73 and eating quality (preference)7476 among temperate japonica rice have been effectively detected by the combination of SSR genotyping and phenotyping of their RILs. Again, these phenotypic differences are current targets in rice breeding in Japan. Therefore, these techniques of genetic dissection and eventually molecular dissection will contribute not only to the understanding of genetic control of those traits, but will also facilitate MAS in rice breeding.

4.3. Impact of technological innovation

In the genetic dissection of natural variations, novel and effective genotyping and phenotyping need to be employed. For genotyping, recent innovations in sequencing technology have enabled us to perform sequence analysis on a massive scale.77,78 This so-called next-generation sequencing technology has opened new opportunities for polymorphism discovery in the rice genome.79 In Arabidopsis, this system has been applied to two natural accessions to perform genome-wide SNP and InDel discovery.80 This method is expected to provide unique opportunities to identify nucleotide polymorphisms between genetically closely related strains, such as Japanese cultivars. Furthermore, the simultaneous genotyping thousands of SNPs on a genome-wide scale has also been recently developed.81 The combination of these two technologies has facilitated genetic dissection of complex traits by QTL mapping and whole-genome association studies. So far, most QTL that have been identified are morphological traits, such as heading date, plant height, seed size, grain number and so on, which can be scored easily. Identification of novel QTL depends on the application of novel phenotyping methods. To further mine natural variations in rice, phenotypic performance should be measured by different and unique methods. Recently, several studies revealed that allelic differences at QTL are the result of subtle variations in the activity of plant hormones such as gibberellic acid and cytokinin in particular tissues. These variations affect plant height and seed number, which contribute to yield performance4,5 Of particular interest will be an understanding of the genetic factors involved in causing these variations in the levels of plant hormones. Recently, simultaneous surveys of different plant hormones and their derivatives have been established.82 Therefore, quantitative variations in a series of plant hormones can be a direct target for QTL analysis. In addition, recent progress with a high throughput monitoring system allowed us to reveal naturally occurring variations in metabolite profiles in plant and this approach could also be a target for QTL mapping in Arabidopsis and tomato.83,84 In rice, a core collection has been subjected to metabolite profiling: metabolite levels in seed between accessions varies considerably.85,86 Therefore, genetic dissection by QTL analysis can be applied to the rice metabolite profiles. This approach will provide a unique opportunity to understand more thoroughly naturally occurring variation and to improve rice cultivars.

5. How rice QTL contribute to functional analysis of genes in other cereals

Conservation and colinearity in genome structure in cereals have been recognized for nearly 20 years. Using genetic markers, such as restriction fragment length polymorphism markers, syntenic correspondence of chromosomal regions between wheat, barley and rice has been identified.8789 Devos and Gale90 established the cereal genome circle, which clearly illustrates the ancestral genome and the evolutional relationship of chromosomal regions between cereals. In this regard, the genome sequence of rice has contributed greatly to the structural and functional analysis of the genomes of other cereals. As the amount of information on the genes and QTL associated with natural variations has increased for rice, a critical question is whether this information can be transferred to the analysis of gene function in other cereals. Since the map location of many QTL has been determined in rice and other cereals, several studies have predicted orthologous relationships among corresponding genes in other species.91,92 However, few proofs of these relationships that have been published so far. One case in particular is the analysis of flowering time in cereals. Comparative QTL studies in cereals have advanced using the knowledge we have accumulated in rice and in the highly studied Arabidopsis plant.9395 QTL cloning of rice has enhanced our capacity to find candidate ortholog genes in other cereals. Comparative QTL studies for forage grasses will present technical challenges because of their large genome size and the difficulty of molecular dissection due to their allogamous nature. The QTL for flowering time, disease resistance and sterility could be targets of comparative QTL studies.96,97 As yet, there is no clear evidence that such a scenario of gene discovery can be extended to traits with agricultural importance, such as yield performance, environmental stress tolerance and other morphological characteristics of agronomical interest. Despite a large number of studies on complex traits of agronomical importance, very limited numbers of genes have been identified at the molecular level. Once we have the sequence of target QTL in rice, then this information can be transferred to other cereals by identifying and cloning their orthologs. Then comparative studies, such as linkage mapping of those genes and confirmation of co-localization of putative QTL of interest in other cereals, can be performed. The existing approximate chromosomal location of rice QTL can provide a pointer toward a syntenic relationship with corresponding target traits in other species, but further study cannot be done until each relevant QTL is mapped and cloned. In addition, phenotypic traits of agronomical importance such as yield performance and environmental stress tolerance are controlled by a complex network of multiple genes. Therefore, it is not necessary to share the same genes to generate natural variations. In fact, since Hd1, the rice ortholog of the Arabidopsis CONSTANS (CO) gene, has been isolated as a major QTL controlling heading date,7 several studies have been performed to characterize the CO/Hd1 ortholog relationship in other cereals such as wheat, barley and maize.98100 These studies demonstrate that the ortholog of CO in these cereals might be involved in genetic control of photoperiodic flowering. Interestingly, no clear evidence has been demonstrated to prove that such orthologs are involved in natural variations in flowering time in these species. This may imply that conservation in genetic control mechanisms of particular traits does not necessarily imply conservation in the contribution of orthologs to generate natural variation between cereals.

Although there is a strong potential contribution, it may be premature to apply rice QTL information to the comparative analysis of gene function of cereals. To make comparative analysis most effective, more comprehensive understanding of rice complex traits and the identification of genes involved in natural variations of agronomical importance must be achieved.

6. Conclusions

Remarkable progress has been achieved in the genetic and molecular dissection of naturally occurring variations in the last decade. Several QTL with major effects have been introgressed into modern rice cultivars by MAS. In addition, some of the QTL involved in sink size and plant statue have been already cloned; therefore, it is expected that these findings will increase the probability of modulating and enhancing cloned genes at the molecular level, thereby increasing yield potential. However, natural variations generated by these major QTLs have already often been recognized and properly utilized by breeders in conventional breeding. It will be necessary to discover new alleles for the improvement of current rice cultivars. In general, traits with relevance to rice breeding, such as yield performance and stress tolerance, have been found to be controlled by minor QTL. We propose that the major research focus in this area should be in the unused and unrecognized variations that have occurred after domestication. QTL with minor effects are often hidden by a large phenotypic variation of major QTL. This may be due not only to the complexity of genetic control but also to the reliability of the evaluation of phenotype. In order to overcome these difficulties, it will be necessary to design and develop appropriate plant materials and to apply phenotyping methods with high reliability. Furthermore, genome-wide genotyping using SNP typing arrays should provide a more robust analytical method to analyze such complex traits. Therefore, the integration of all resources, such as the tools and analytical methods already discussed and plant materials, will be necessary to facilitate further dissection of the complex traits of agricultural importance. We have made considerable progress already, but it is essentially the starting point for a deeper understanding of naturally occurring variations and their utilization in rice breeding.


Work in QTL-GRS is mainly supported by funds (Integrated Research Project for Plant, Insect and Animals using Genome Technology and Genomics for Agricultural Innovation) from the Ministry of Agriculture, Forestry and Fisheries, Japan.


We thank members of the QTL Genomics Research Center (QTL-GRS) at NIAS for helpful discussions and suggestions in the preparation of this manuscript.


Edited by Katsumi Isono.


1. Khush G. S. Green revolution: preparing for the 21st century. Genome. 1999;42:646–655. [PubMed]
2. International Rice Genome Sequencing Project. The map-based sequence of the rice genome. Nature. 2005;436:793–800. [PubMed]
3. Jaiswal P., Ware D., Ni J. Gramene: development and integration of trait and gene ontologies for rice. Comp. Funct. Genomics. 2002;3:132–136. [PMC free article] [PubMed]
4. Sasaki A., Ashikari M., Ueguchi-Tanaka M., et al. A mutant gibberellin-synthesis gene in rice. Nature. 2002;416:701–702. [PubMed]
5. Ashikari M., Sakakibara H., Lin S. Y., et al. Cytokinin oxidase regulates rice grain production. Science. 2005;309:741–745. [PubMed]
6. Yamamoto T., Yano M. In: Rice Biology in the Genomics Era. Biotechnology in Agriculture and Forestry. Hirano H. Y., editor. Vol. 62. Berlin: Springer-Verlag; 2008. pp. 295–308.
7. Yano M., Katayose Y., Ashikari M., et al. Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the Arabidopsis flowering time gene CONSTANS. Plant Cell. 2000;12:2473–2483. [PMC free article] [PubMed]
8. Kojima S., Takahashi Y., Kobayashi Y., et al. Hd3a, a rice ortholog of the Arabidopsis FT gene, promotes transition to flowering downstream of Hd1 under short-day conditions. Plant Cell Physiol. 2002;43:1096–1105. [PubMed]
9. Takahashi Y., Shomura A., Sasaki T., Yano M. Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the α subunit of protein kinase CK2. Proc. Natl Acad. Sci. USA. 2001;98:7922–7927. [PMC free article] [PubMed]
10. Doi K., Izawa T., Fuse T., et al. Ehd1, a B-type response regulator in rice, confers short-day promotion of flowering and controls FT-like gene expression independently of Hd1. Genes Dev. 2004;18:926–936. [PMC free article] [PubMed]
11. Xue W., Xing Y., Weng X., et al. Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat. Genet. 2008;40:761–767. [PubMed]
12. Tamaki S., Matsuo S., Wong H. L., Yokoi S., Shimamoto K. Hd3a protein is a mobile flowering signal in rice. Science. 2007;316:1033–1036. [PubMed]
13. Izawa T. Daylength measurements by rice plants in photoperiodic short-day flowering. Inter. Rev. Cytol. 2007;256:191–222. [PubMed]
14. Tsuji H., Tamaki S., Komiya R., Shimamoto K. Florigen and the photoperiodic control of flowering in rice. Rice. 2008;1:25–35.
15. Konishi S., Izawa T., Lin S. Y., et al. An SNP caused loss of seed shattering during rice domestication. Science. 2006;312:1392–1396. [PubMed]
16. Tan L., Li X., Liu F., et al. Control of a key transition from prostrate to erect growth in rice domestication. Nat. Genet. 2008;40:1360–1364. [PubMed]
17. Jin J., Huang W., Gao J. P., et al. Genetic control of rice plant architecture under domestication. Nat. Genet. 2008;40:1365–1369. [PubMed]
18. Song X. J., Huang W., Shi M., Zhu M. Z., Lin H. X. A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat. Genet. 2007;39:623–630. [PubMed]
19. Shomura A., Izawa T., Ebana K., et al. Deletion in a gene associated with grain size increased yields during rice domestication. Nat. Genet. 2008;40:1023–1028. [PubMed]
20. Fukao T., Harris T., Bailey-Serres J. Evolutionary analysis of the Sub1 gene cluster that confers submergence tolerance to domesticated rice. Ann. Bot. 2009;103:143–150. [PMC free article] [PubMed]
21. Konishi S., Ebana K., Izawa T. Inference of the japonica rice domestication process from the distribution of six functional nucleotide polymorphisms of domestication-related genes in various landraces and modern cultivars. Plant Cell Physiol. 2008;49:1283–1293. [PubMed]
22. Yoshida S., Parao F. T. Proceedings of the Symposium on Climate & Rice, International Rice Research Institute, Los Baños, The Philippines. 1976. pp. 471–494.
23. Hemamalini G. S., Shashidhar H. E., Hittalmani S. Molecular marker assisted tagging of morphological and physiological traits under two contrasting moisture regimes at peak vegetative stage in rice (Oryza Sativa L.) Euphytica. 2000;112:69–78.
24. Babu R. C., Nguyen B. D., Chamarerk V., et al. Genetic analysis of drought resistance in rice by molecular markers: association between secondary traits and field performance. Crop Sci. 2003;43:1457–1469.
25. Lafitte H. R., Price A. H., Courtois B. Yield response to water deficit in an upland rice mapping population: associations among traits and genetic markers. Theor. Appl. Genet. 2004;109:1237–1246. [PubMed]
26. Xu J. L., Lafitte H. R., Gao Y. M., Fu B. Y., Torres R., Li Z. K. QTLs for drought escape and tolerance identified in a set of random introgression lines of rice. Theor. Appl. Genet. 2005;111:1642–1650. [PubMed]
27. Zou G. H., Mei H. W., Liu H. Y., et al. Grain yield responses to moisture regimes in a rice population: association among traits and genetic markers. Theor. Appl. Genet. 2005;112:106–113. [PubMed]
28. Kamoshita A., Wade J., Ali L., et al. Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions. Theor. Appl. Genet. 2002;104:880–893. [PubMed]
29. Nguyen T. T., Klueva N., Chamareck V., et al. Saturation mapping of QTL regions and identification of putative candidate genes for drought tolerance in rice. Mol. Gen. Genet. 2004;272:35–46. [PubMed]
30. Yano M., Harushima Y., Nagamura N., Kurata Y., Minobe T., Sasaki T. Identification of quantitative trait loci controlling heading date in rice using a high-density linkage map. Theor. Appl. Genet. 1997;95:1025–1032.
31. Yano M., Kojima S., Takahashi Y., Lin H., Sasaki T. Genetic control of flowering time in rice, a short-day plant. Plant Physiol. 2001;127:1425–1429. [PMC free article] [PubMed]
32. Yamamoto T., Lin H., Sasaki T., Yano M. Identification of heading date quantitative trait locus Hd6 and characterization of its epistatic interactions with Hd2 in rice using advanced backcross progeny. Genetics. 2000;154:885–891. [PMC free article] [PubMed]
33. Lin H., Ashikari M., Yamanouchi U., Sasaki T., Yano M. Identification and characterization of a quantitative trait locus, Hd9, controlling heading date in rice. Breed. Sci. 2002;52:35–41.
34. Nagata K., Fukuta Y., Shimizu H., Yagi T., Terao T. Quantitative trait loci for sink size and ripening traits in rice (Oryza Sativa L.) Breed. Sci. 2002;52:259–273.
35. Ando T., Yamamoto T., Shimizu T., et al. Genetic dissection and pyramiding of quantitative traits for panicle architecture by using chromosomal segment substitution lines in rice. Theor. Appl. Genet. 2008;116:881–890. [PubMed]
36. Xiao J., Li J., Yuan L., Tanksley S. D. Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers. Genetics. 1995;140:745–754. [PMC free article] [PubMed]
37. Yu S. B., Li J. X., Xu C. G., Gao Y. F., Li X. H. Importance of epistasis as the genetic basis of heterosis in an elite rice hybrid. Proc. Natl Acad. Sci. USA. 1997;94:9226–9231. [PMC free article] [PubMed]
38. Li Z. K., Pinson S., Park W., Paterson A., Stansel J. Epistasis for three grain yield components in rice (Oryza Sativa L.) Genetics. 1997;145:453–465. [PMC free article] [PubMed]
39. Li Z. K., Luo L. J., Mei H. W., Shu Q. Y., Wang D. L. Overdominant epistatic loci are the primary basis of inbreeding depression and heterosis in rice. I. Biomass and grain yield. Genetics. 2001;158:1737–1753. [PMC free article] [PubMed]
40. Luo L. J., Li Z. K., Mei H. W., Shu Q. Y., Tabien R. Overdominant epistatic loci are the primary basis of inbreeding depression and heterosis in rice. II. Grain yield components. Genetics. 2001;158:1755–1771. [PMC free article] [PubMed]
41. Hua J. P., Xing Y. Z., Xu C. G., Sun X. L., Yu S. B., Zhang Q. Genetic dissection of an elite rice hybrid revealed that heterozygotes are not always advantageous for performance. Genetics. 2002;162:1885–1895. [PMC free article] [PubMed]
42. Hua J., Xing Y., Wu W., et al. Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid. Proc. Natl Acad. Sci. USA. 2003;100:2574–2579. [PMC free article] [PubMed]
43. Li Z. K., Yu S. B., Lafitte H. R., et al. QTL x environment interactions in rice. I. Heading date and plant height. Theor. Appl. Genet. 2003;108:141–153. [PubMed]
44. You A., Lu X., Jin H., et al. Identification of quantitative trait loci across recombinant inbred lines and testcross populations for traits of agronomic importance in rice. Genetics. 2006;172:1287–1300. [PMC free article] [PubMed]
45. Wan X., Weng J., Zhai H., et al. Quantitative trait loci (QTL) analysis for rice grain width and fine mapping of an identified QTL allele gw-5 in a recombination hotspot region on chromosome 5. Genetics. 2008;179:2239–2252. [PMC free article] [PubMed]
46. Garcia A. A. F., Wang S., Melchinger A. E., Zeng Z. B. Quantitative trait loci mapping and the genetic basis of heterosis in maize and rice. Genetics. 2008;180:1707–1724. [PMC free article] [PubMed]
47. Li L., Lu K., Chen Z., Mu T., Hu Z., Li X. Dominance, overdominance and epistasis condition the heterosis in two heterotic rice hybrids. Genetics. 2008;180:1725–1742. [PMC free article] [PubMed]
48. Wang D. L., Zhu J., Li Z. K., Paterson A. H. Mapping QTLs with epistatic effects and QTL times environment interactions by mixed linear model approaches. Theor. Appl. Genet. 1999;99:1255–1264.
49. Yan J., Zhu J., He C., Benmoussa M., Wu P. Molecular marker-assisted dissection of genotype x environment interaction for plant type traits in rice (Oryza Sativa L.) Crop Sci. 1999;39:538–544.
50. Hittalmani S., Huang N., Courtois B., et al. Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia. Theor. Appl. Genet. 2003;107:679–690. [PubMed]
51. MacMillan K., Emrich K., Piepho H. P., Mullins C. E., Price A. H. Assessing the importance of genotype x environment interaction for root traits in rice using a mapping population II: conventional QTL analysis. Theor. Appl. Genet. 2006;113:953–964. [PubMed]
52. Tanksley S. D., Grandillo S., Fulton T. M., et al. Advanced backcross QTL analysis in a cross between an elite processing line of tomato and its wild relative L. pimpinellifolium. Theor. Appl. Genet. 1996;92:213–224. [PubMed]
53. Kubo T., Aida Y., Nakamura K., Tsunematsu H., Doi K., Yoshimura A. Reciprocal chromosome segment substitution series derived from Japonica and Indica cross of rice (Oryza Sativa L.) Breed. Sci. 2002;52:319–325.
54. Ebitani T., Takeuchi Y., Nonoue Y., Yamamoto T., Takeuchi K., Yano M. Construction and evaluation of chromosome segment substitution lines carrying overlapping chromosome segments of indica rice cultivar ‘Kasalath’ in a genetic background of japonica elite cultivar ‘Koshihikari. Breed. Sci. 2005;55:65–73.
55. Xiao Y. H., Liu L. L., Jiang L., et al. Development of the chromosome segment substitution lines (CSSLs) derived from a hybrid rice cross, Pai64S/93-11 with super high yield potential. Rice Genetics Newsletter. 2005;22
56. Xi Z. Y., He F. H., Zeng R. Z., et al. Development of a wide population of chromosome single-segment substitution lines in the genetic background of an elite cultivar of rice (Oryza Sativa L.) Genome. 2006;4:476–484. [PubMed]
57. Takai T., Nonoue Y., Yamamoto S., et al. Development of chromosome segment substitution lines derived from backcross between indica donor rice cultivar ‘Nona Bokra’ and japonica recipient cultivar ‘Koshihikari. Breed. Sci. 2007;57:257–261.
58. Lin H. X., Yamamoto T., Sasaki T., Yano M. Characterization and detection of epistatic interactions of 3 QTLs, Hd1, Hd2, and Hd3, controlling heading date in rice using nearly isogenic lines. Theor. Appl. Genet. 2000;101:1021–1028.
59. Matsubara K., Ando T., Mizubayashi T., Ito S., Yano M. Identification and linkage mapping of complementary recessive genes causing hybrid breakdown in an intraspecific rice cross. Theor. Appl. Genet. 2007;115:179–186. [PubMed]
60. Wan X. Y., Wan J. M., Su C. C., et al. QTL detection for eating quality of cooked rice in a population of chromosome segment substitution lines. Theor. Appl. Genet. 2004;110:71–79. [PubMed]
61. Wan X. Y., Wan J. M., Weng J. F., et al. Stability of QTLs for rice grain dimension and endosperm chalkiness characteristics across eight environments. Theor. Appl. Genet. 2005;110:1334–1346. [PubMed]
62. Liu G., Zhang Z., Zhu H., et al. Detection of QTLs with additive effects and additive-by-environment interaction effects on panicle number in rice (Oryza Sativa L.) with single-segment substitution lines. Theor. Appl. Genet. 2008;116:923–931. [PubMed]
63. Xu K., Mackill D. J. A major locus for submergence tolerance mapped on rice chromosome 9. Mol. Breed. 1996;2:219–224.
64. Xu K., Xu X., Ronald P. C., Mackill D. J. A high-resolution linkage map of the vicinity of the rice submergence tolerance locus Sub1. Mol. Gen. Genet. 2000;263:681–689. [PubMed]
65. Neeraja C. N., Maghirang-Rodriguez R., Pamplona A., et al. A marker-assisted backcross approach for developing submergence-tolerant rice cultivars. Theor. Appl. Genet. 2007;115:767–776. [PubMed]
66. Septiningsih E., Pamplona A., Sanchez D., et al. Development of submergence-tolerant rice cultivars: the Sub1 locus and beyond. Ann. Bot. 2008;103:151–160. [PMC free article] [PubMed]
67. Takeuchi Y., Ebitani T., Yamamoto T., et al. Development of isogenic lines of rice cultivar Koshihikari with early and late heading by marker-assisted selection. Breed. Sci. 2006;56:405–413.
68. Ashikari M., Matsuoka M. Identification, isolation and pyramiding of quantitative trait loci for rice breeding. Trends Plant Sci. 2006;11:344–350. [PubMed]
69. Kojima Y., Ebana K., Fukuoka S., Nagamine T., Kawase M. Development of an RFLP-based rice diversity research set of germplasm. Breed. Sci. 2005;55:431–440.
70. Ebana K., Kojima Y., Fukuoka S., Nagamine T., Kawase M. Development of mini core collection of Japanese rice landrace. Breed. Sci. 2008;58:281–291.
71. Fujino K., Sekiguchi H., Matsuda Y., Sugimoto K., Ono K., Yano M. Molecular identification of a major quantitative trait locus, qLTG3-1, controlling low-temperature germinability in rice. Proc. Natl Acad. Sci. USA. 2008;105:12623–12628. [PMC free article] [PubMed]
72. Kobayashi A., Genliang B., Shenghai Y., Tomita K. Detection of quantitative loci for white-back and basal-white kernels under high temperature stress in japonica rice varieties. Breed. Sci. 2007;57:107–116.
73. Tabata M., Hirabayashi H., Takeuchi Y., Ando I., Iida Y., Ohsawa R. Mapping of quantitative trait loci for the occurrence of white-back kernels associated with high temperatures during the ripening period of rice (Oryza Sativa L.) Breed. Sci. 2007;57:47–52.
74. Takeuchi Y., Hori K., Suzuki K., et al. Major QTLs for eating quality of an elite Japanese rice cultivar, Koshihikari, on the short arm of chromosome 3. Breed. Sci. 2008;58:437–445.
75. Kobayashi A., Tomita K. QTL detection for stickiness of cooked rice using recombinant inbred lines derived from crosses between japonica rice cultivars. Breed. Sci. 2008;58:419–426.
76. Wada T., Ogata T., Tsubone M., Uchimura Y., Matsue Y. Mapping of QTLs for eating quality and physicochemical properties of the japonica rice ‘Koshihikari. Breed. Sci. 2008;58:427–435.
77. Blow N. The personal side of genomics. Nature. 2007;449:627–630. [PubMed]
78. Hutchison C. A., III DNA sequencing: bench to beside and beyond. Nucleic Acids Res. 2007;35:6227–6237. [PMC free article] [PubMed]
79. Lister R., Gregory B. D., Ecker J. R. Next is now: new technologies for sequencing of genomes, transcriptomes and beyond. Curr. Opin. Plant Biol. 2009;12:107–118. [PMC free article] [PubMed]
80. Ossowski S., Schneeberger K., Clark R., Lanz C., Warthmann N., Weigel D. Sequencing of natural strains of Arabidopsis thaliana with short reads. Genome Res. 2008;18:2024–2033. [PMC free article] [PubMed]
81. Steemers F. K., Gunderson K. L. Whole genome genotyping technologies on the BeadArray™ platform. Biotechnol. J. 2007;2:41–49. [PubMed]
82. Hirano K., Aya K., Hobo T., et al. Comprehensive transcriptome analysis of phytohormone biosynthesis and signaling genes in microspore/pollen and tapetum of rice. Plant Cell Physiol. 2008;49:1429–1450. [PMC free article] [PubMed]
83. Keurentjes J., Koornneef M., Vreugdenhil D. Quantitative genetics in the age of omics. Curr. Opin. Plant Biol. 2007;11:123–128. [PubMed]
84. Schauer N., Semel Y., Balbo I., et al. Mode of inheritance of primary metabolic traits in tomato. Plant Cell. 2008;20:509–523. [PMC free article] [PubMed]
85. Kusano M., Fukushima A., Kobayashi M., et al. Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2007;855:71–79. [PubMed]
86. Oikawa A., Matsuda F., Kusano M., Okazaki Y., Saito K. Rice metabolomics. Rice. 2008;1:63–71.
87. Ahn S., Anderson J. A., Sorrells M. E., Tanksley S. D. Homoeologous relationships of rice, wheat and maize chromosomes. Mol. Gen. Genet. 1993;241:483–490. [PubMed]
88. Ahn S., Tanksley S. D. Comparative linkage maps of the rice and maize genomes. Proc. Natl Acad. Sci. USA. 1993;90:7980–7984. [PMC free article] [PubMed]
89. Kurata N., Moore G., Nagamura Y., Foote T., Yano M., Minobe Y., Gale M. Conservation of genome structure between rice and wheat. Nat. Biotechnol. 1994;12:276–278.
90. Devos K. M., Gale M. D. Comparative genetics in the grasses. Plant Mol. Biol. 1997;35:3–15. [PubMed]
91. Paterson A. H., Lin Y. R., Li Z., et al. Convergent domestication of cereal crops by independent mutations at corresponding genetic loci. Science. 1995;269:1714–1718. [PubMed]
92. Sarma R. N., Gill B. S., Sasaki T., Galiba G., Sutka J., Laurie D. A., Snape J. W. Comparative mapping of the wheat chromosome 5A Vrn-A1 region with rice and its relationship to QTL for flowering time. Theor. Appl. Genet. 1998;97:103–109.
93. Chardon F., Virlon B., Moreau L., et al. Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics. 2004;168:2169–2185. [PMC free article] [PubMed]
94. Yu J. K., La Rota M., Kantety R. V., Sorrells M. E. EST derived SSR markers for comparative mapping in wheat and rice. Mol. Genet. Gen. 2004;271:742–751. [PubMed]
95. Andersen J. R., Jensen L. B., Asp T., Lubberstedt T. Vernalization response in perennial ryegrass (Lolium perenne L.) involves orthologues of diploid wheat (Triticum monococcum) VRN1 and rice (Oryza Sativa) Hd1. Plant Mol. Biol. 2006;60:481–494. [PubMed]
96. Jo Y. K., Barker R., Pfender W., Warnke S., Sim S. C., Jung G. Comparative analysis of multiple disease resistance in ryegrass and cereal crops. Theor. Appl. Genet. 2008;117:531–543. [PubMed]
97. Armstead I. P., Turner L. B., Marshall A. H., Humphreys M. O., King I. P., Thorogood D. Identifying genetic components controlling fertility in the outcrossing grass species perennial ryegrass (Lolium perenne) by quantitative trait loci analysis and comparative genetics. New Phytol. 2008;178:559–571. [PubMed]
98. Nemoto Y., Kisaka M., Fuse T., Yano M., Ogihara Y. Characterization and functional analysis of three wheat genes with homology to the CONSTANS flowering time gene in transgenic rice. Plant J. 2003;36:82–93. [PubMed]
99. Griffiths S., Dunford R. P., Coupland G., Laurie D. A. The evolution of CONSTANS-like gene families in barley, rice, and Arabidopsis. Plant Physiol. 2003;131:1855–1867. [PMC free article] [PubMed]
100. Miller T. A., Muslin E. H., Dorweiler J. E. A maize CONSTANS-like gene, conz1, exhibits distinct diurnal expression patterns in varied photoperiods. Planta. 2008;227:1377–1388. [PubMed]
101. Xu K., Xu X., Fukao T., et al. Sub1A is an ethylene-response-factor-like gene that confers submergence tolerance to rice. Nature. 2006;442:705–708. [PubMed]
102. Ueda T., Sato T., Hidema J., et al. qUVR-10, a major quantitative trait locus for ultraviolet-B resistance in rice, encodes cyclobutane pyrimidine dimer photolyase. Genetics. 2005;171:1941–1950. [PMC free article] [PubMed]
103. Ren Z. H., Gao J. P., Li L. G., et al. A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nat. Genet. 2005;37:1141–1146. [PubMed]
104. Li C., Zhou A., Sang T. Rice domestication by reducing shattering. Science. 2006;311:1936–1939. [PubMed]
105. Fan C., Xing Y., Mao H., et al. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor. Appl. Genet. 2006;112:1164–1171. [PubMed]
106. Nishimura A., Ashikari M., Lin S. Y., et al. Isolation of a rice regeneration quantitative trait loci gene and its application to transformation systems. Proc. Natl Acad. Sci. USA. 2005;102:11940–11944. [PMC free article] [PubMed]

Articles from DNA Research: An International Journal for Rapid Publication of Reports on Genes and Genomes are provided here courtesy of Oxford University Press
PubReader format: click here to try


Save items

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


  • Compound
    PubChem chemical compound records that cite the current articles. These references are taken from those provided on submitted PubChem chemical substance records. Multiple substance records may contribute to the PubChem compound record.
  • MedGen
    Related information in MedGen
  • PubMed
    PubMed citations for these articles
  • Substance
    PubChem chemical substance records that cite the current articles. These references are taken from those provided on submitted PubChem chemical substance records.

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...