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a Crop and Weed Ecol. Group, Wageningen Univ., P.O. Box 430, 6700 AK Wageningen, the Netherlands
b Lab. of Plant Breeding, Wageningen Univ., P.O. Box 386, 6700 AJ Wageningen, the Netherlands
c Plant Dynamics, Englaan 8, 6703 EW Wageningen, the Netherlands
* Corresponding author (xinyou.yin{at}wur.nl)
Received for publication May 1, 2001.
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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Abbreviations: AFLP, amplification fragment length polymorphism • G x E, genotype x environment interaction • h2, heritability • MAB, marker-assisted breeding • QTL, quantitative trait locus or loci • QTL x E, quantitative trait loci x environment interaction • RFLP, restriction fragment length polymorphism • RIL, recombinant inbred line • SLA, specific leaf area
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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Besides recent developments in genomics (such as genome sequencing) that will provide useful tools and massive amounts of information for plant breeding (Stuber et al., 1999; Miflin, 2000), an option of improving breeding efficiency is to develop and utilize a thorough understanding of morphophysiological factors that determine yield (Bindraban, 1997). However, only in a limited number of instances has plant physiology led to crop improvement; rather, its role in breeding so far has been to provide possible explanations for the improvements that have been achieved. Miflin (2000) suggested that this situation may change in the future if the links between physiology and genetics are established. This suggestion agrees with a main conclusion from an extensive survey (Jackson et al., 1996) that there is a general agreement among plant breeders and physiologists that physiological knowledge can be applied to improve breeding efficiency in the future.
As early as in the 1960s, Donald (1968) proposed an approach based much more explicitly on the design of plants or ideotypes for target environment, using known principles of physiology and agronomy. Rasmusson (1987) suggested improvements to Donald's approach, considering correlations between traits, and designed a barley (Hordeum vulgare L.) ideotype for the Midwest USA with desired changes in culm, leaf, and head characteristics. Rasmusson (1991) reported that while some characteristics appeared to afford little opportunities for obtaining gains, others did show more promise. Based on the concept of ideotype approach, International Rice Research Institute (IRRI) launched a program in 1989 to develop a "new plant type" rice that combines multiple innovations (Peng et al., 1999). While so far these new lines have not broken the yield barrier as hoped for, progress is being made at IRRI with refined ideotype designs (S. Peng, personal communication, 2002). A convincing example of ideotype approach is the breeding for the superhybrid rice by Professor Yuan Longping's group in China. Rather than count on heterosis alone to raise yields, he also incorporated morphophysiological characters such as long, narrow and erect top leaves and large panicles that hang close to ground, the characteristics that physiologists have expected to enhance efficiency of crop light capture (Setter et al., 1995). Field trials at four separate locations showed potential yields of the superhybrid were 15 to 20% higher than 10.5 t ha-1 for existing hybrids (Normile, 1999).
Using crop physiology in ideotype breeding can be more feasible than ever because of the development of dynamic process-based crop growth simulation models (crop models hereafter). These models quantify causality between relevant physiological processes and responses of these processes to environmental variables and therefore allow yield predictions not restricted to the environments where the model parameters are derived. As model parameters can represent certain genetic characteristics, crop modeling has been considered a useful tool to assist breeding (Loomis et al., 1979; Whisler et al., 1986; Boote et al., 1996). Shorter et al. (1991) proposed collaborative efforts among breeders, physiologists, and modelers, using models as a framework to integrate physiology with breeding. For such, understanding the inheritance of the model parameters is required (Stam, 1998).
An important development during the last decade in quantitative genetics was the ability to identify genome regions responsible for variation of a trait due to the advent of molecular markers (Paterson et al., 1988). The term QTL has come to refer to polygenes underlying a quantitative trait. Numerous studies have been reported on identifying QTL for various traits in humans, animals, and plants. Similar to other quantitative traits, individual input parameters of a crop model are amenable to QTL analysis (Yin et al., 1999b).
In this paper, we discuss potentials and limitations of crop modeling and QTL analysis in assisting plant breeding. The complementary aspects of crop modeling and QTL analysis are explored to develop an integrated approach for ideotype breeding.
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CROP MODELING AS A TOOL TO ASSIST PLANT BREEDING |
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TOPNOTESABSTRACTINTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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Given the expectation that crop models based on physiologically sound mechanisms have the potential to quantify and integrate crop yield responses to genetic and environmental factors, physiologists and modelers have explored potential uses of crop models in various aspects of breeding:
A common endpoint of these studies, based on model simulations, is suggestions that breeders may use. Given that direct experimental confirmation and objective comparisons of modeled suggestions with those already used in breeding programs are rare, Stam (1998), from a geneticist's and breeder's point of view, expressed his concerns about this model-based approach. First, a practical problem of breeding is that the majority of model input traits to be assessed are difficult to accurately measure. Second, the inheritance of the model input traits is largely unknown. For example, in designing an ideotype by modeling, it is assumed, either tacitly or explicitly, that these traits can be combined at will in a single genotype. Such an assumption ignores the possible existence of constraints and correlations among the traits. Constraints might be imposed simply by the fact that little genetic variation exists in the genetic material available for selection. Thus, models may not identify those traits for which gain via breeding may be easiest (Jackson et al., 1996). Correlations between the traits, due either to a tight linkage between QTL or to a single QTL that affects multiple traits (pleiotropy), may seriously hamper the realization of an ideotype. After all, plant breeding is genetic improvement; knowledge of the genetic basis of phenotypic variation, whether described in terms of conventional agronomic traits or model input traits, is crucial for a successful breeding program (Stam, 1998). To assist the development of efficient breeding strategies, crop modeling requires understanding of the inheritance of the factors that determine crop growth (Shorter et al., 1991).
White and Hoogenboom (1996) presented a model for bean (Phaseolus vulgaris L.) in which the genetic control of model parameters was considered. They applied linear regression to estimate values of more than 20 model input traits from information about alleles (variants at a gene locus) of seven known genes in the cultivars studied. This approach, however, assumes that all the traits were controlled by pleiotropic effects of the seven genes, ignoring possible additional trait-specific genes. Advances in quantitative genetics, by mapping trait-specific QTL, can help to broaden insight in the genetic basis of crop traits.
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QTL MAPPING AND ITS APPLICATIONS |
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TOPNOTESABSTRACTINTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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To map quantitative traits, supplementary information from recognizable single-gene loci is required. First attempts to link a quantitative trait to a major gene locus in plants date back to Sax (1923), who studied seed weight and color in an F2 of a cross in bean. Seed color involved the segregation of a single gene, P/p. Seed weight differed among the three color genotypes (Fig. 1), indicating that either the P/p locus had a pleiotropic effect on seed weight or there was a QTL for weight closely linked to the P/p locus.
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Until the mid-1980s, most of the mapped genes were mutant genes with clear phenotypes; mapping gradually progressed by looking at progeny from crosses between the carrier of the new gene and genotypes carrying already mapped gene(s) (e.g., Woodward, 1957). With the advent of DNA markers, we are in the position of analyzing a large number of recognizable loci segregating simultaneously in the same cross. To handle the large number of loci, a variety of software (e.g., Stam, 1993) has been developed to establish the overall map that gives the best fit to the combined data. An example of such a map, using AFLP produced from a cross between two barley cultivars, is illustrated in Fig. 2 for chromosome 3. Needless to say, information about the position of mutant loci is important for building a DNA-marker map because previously mapped mutant loci (e.g., the denso locus in Fig. 2) can be used as anchor markers that assign new markers to different chromosomal groups. A growing number of map databases in plants now become accessible through the web sites (e.g., http://www.nal.usda.gov/pgdic; verified 11 Sept. 2002).
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Two complementary uses of the QTL approach have emerged: the fundamental and the applied (Prioul et al., 1997). The first use, which is of interest to physiologists, targets QTL by determining their contribution to physiological components of macroscopic traits. Not only does the QTL approach provide unequivocal answers to a range of physiological questions, it also generates new insight into the causality between components that would have been difficult to obtain by conventional physiological approaches (e.g., Simko et al., 1997). The importance of the QTL approach is shown in a special issue of New Phytologist [1997, 137(1)], which was entirely devoted to proselytizing physiologists to take a genetic approach.
The second use of the QTL studies, which is of interest to breeders, is marker-assisted breeding (MAB). This approach uses markers for tagging QTL of interest so as to pyramid favorable QTL alleles and break their linkage with undesirable alleles (Lee, 1995; Ordon et al., 1998; Ribaut and Hoisington, 1998). An apparent use of MAB is the marker-steered introgression with valuable single genes from exotic donors to enhance elite breeding material (Stam, 1998), which allows faster recovery of the recipient-parent genome than the conventional recurrent backcrossing (Ribaut and Hoisington, 1998). As alien species or landraces are rich in resistance genes and resistances are simply inherited relative to yield traits, the application of markers for tagging of resistance genes in major crops has progressed rapidly (Ordon et al., 1998).
A major challenge for MAB is to deal with traits controlled by multiple interactive and environment-dependent QTL, such as yield and yield-relating traits that often have a low h2. Genetic simulation studies (e.g., Van Berloo and Stam, 1998) have shown that MAB can be superior to the conventional phenotype-based approach for traits of low h2, and there is some evidence that marker-facilitated backcrossing can be employed to manipulate and improve grain yield in maize (Zea mays L.) (Stuber et al., 1999). However, in most cases, the superiority of MAB has not been convincingly demonstrated experimentally (Ribaut and Hoisington, 1998). Manipulating these traits is difficult because of their intrinsic complexities: polygenic control, epistasis, and G x E. Existing QTL detection methods do not seem to have the required precision to deal with these complexities. With traits like yield that have a low h2, many QTL may be segregating. The QTL with major effects are easily manipulated by empirical breeding practices and may already be fixed in many breeding lines. It would be more productive to use marker technology as a means for placing greater emphasis on those QTL that show only relative minor effects (Stuber et al., 1999). The location of minor QTL identified by existing mapping methods may have wide confidence intervals. The most likely location of a useful QTL may appear to be between a pair of markers, but it could actually be as far as 20 cM away (Kearsey and Farquhar, 1998). While recent multiple-QTL methods (e.g., Jansen, 1995) can reduce confidence intervals of QTL locations (Fig. 4A) and resolve two or more linked QTL, the efficacy of these methods depends on whether markers are evenly distributed in the map. In principle, epistasis of QTL can be included within the frame of these multiple-QTL methods. However, the rapid increase in the number of parameters, difficulties to decide which interactions to include, and the computational burden force us to assume the absence of epistasis. Methods have been developed to evaluate QTL x environment interactions (QTL x E) using multiple-environment data (e.g., Jiang and Zeng, 1995; Van Eeuwijk et al., 2000), but the information obtained cannot be applied to predict phenotypes in independent environments (Stratton, 1998).
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COMBINING CROP MODELING AND QTL MAPPING: AN EXPLORATIVE STUDY |
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TOPNOTESABSTRACTINTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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Physiological aspects of a trait, which have so far received little attention from geneticists in QTL analysis, were considered, using SLA as the example (Yin et al., 1999a). The SLA was measured six times: one conducted at the same developmental stage for all RILs (at flowering), four at specific days before flowering, and the last one at 14 d after flowering. When the SLA of each measurement time was directly subjected to analysis, one to three QTL were detected. The denso gene was found to affect SLA strongly at all measurement times, e.g., 27 d after emergence (Fig. 4A), except at flowering. If the SLA of the different RILs was corrected for differences in physiological age at the time of measurement, using the phenology submodel in SYP-BL, QTL were detected for SLA at only three stages. Moreover, the effect of the denso gene was no longer significant during the preflowering stages, e.g., at developmental stage 0.35 (Fig. 4B). The effect of the denso gene on the SLA detected in the first instance was therefore the artifact of its direct effect on the preflowering duration that can be seen in Fig. 3. This result suggests potential use of physiology and modeling in QTL analysis. Any further roles of physiology or modeling should be explored, especially given that any great change in the reliability of QTL detection methods can hardly be achieved in future (Kearsey and Farquhar, 1998).
Next, the identified QTL were coupled to the SYP-BL model by replacing the original measured input trait values with those predicted from the QTL effects (Yin et al., 2000a). This replacement generated a QTL-based model for barley, QTL-BL. Yields predicted by both models correlated with the observed values, despite substantial unexplained variation (Fig. 5). The QTL-BL model predicted yield differences slightly better than the SYP-BL model. Similar results were obtained when the models were applied to a season independent from the one in which the original input traits used for QTL analysis were measured (Yin et al., 2000a). The slightly better performance of QTL-BL could be due to less random noise in the QTL-based values because the random error in measured model input traits was partly removed by QTL analysis statistics. However, this advantage of the QTL-BL model is obtained at the cost of ignoring some genetic effects because all of the QTL detected for a trait often do not fully explain its genetic variation. Nonetheless, the correlation between SYP-BL- and QTL-BL-predicted yields was high (r > 0.88), indicating that QTL information can successfully replace measured parameters (Yin et al., 2000a).
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EXPECTATIONS AND FUTURE PERSPECTIVE |
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TOPNOTESABSTRACTINTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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If models are capable of predicting G x E in a population, they can assist QTL analysis to resolve QTL x E, a major problem that hampers the use of MAB in practical breeding (Lee, 1995; Ribaut and Hoisington, 1998). The QTL x E is commonly seen when growing a mapping population under a range of environments. An example of this is flowering time in Arabidopsis spp., examined under various daylength and vernalization regimes (Jansen, 1995). It turned out that daylength and/or vernalization influence the effect of some QTL, indicating QTL x E in a statistical sense. However, this information on interaction cannot be applied to new environments (Stratton, 1998). From a physiologists' or modelers' point of view, the impact of environments has to be minimized to identify the true genetic effect. Phenology models separate different aspects of flowering responses to photothermal environments (Atkinson and Porter, 1996). Parameters in a physiologically robust phenology model are genetically determined and are not altered by environment but predict flowering date of genotypes in a wide range of environments (Roberts et al., 1996). It is therefore expected that the QTL and their effects, detected for model parameters, will not be environment dependent.
When crop models enter a high-precision stage at which critical processes are quantified and integrated at the biochemical level, they could be used to resolve epistasis, a classical difficulty in genetics. Epistasis is often found for phenotypes that are achieved through interactive and interrelated metabolic and ontogenetic pathways (Lee, 1995). It might be reduced or even disappear if input traits of a model that accounts for interrelations among relevant processes are subjected to analysis. Such possibility agrees with the awareness of geneticists that epistasis can often be removed by a physiologically based scaling of trait values (Kearsey and Pooni, 1996). It should be acknowledged, however, that use of crop models to resolve epistasis may be a more difficult task than to resolve G x E. It could be demonstrated first for relatively simple traits (such as time to flowering) or in species with simple genetic makeup (such as Arabidopsis spp.) through simulating relevant biochemical pathways.
Integration of Crop Modeling and QTL Mapping into a Breeding Strategy
When crop models advance to the level of reliably predicting genotype difference, crop modeling could be integrated into the framework of MAB for an improved breeding approach (Fig. 6). Within this integrated approach, the crop model is evaluated if it predicts yield differences among genotypes in a genetic population under diverse environments; thus, G x E is interpreted in terms of a biological, as opposite to statistical, model. Mapping is performed on input traits of the model to dissect their variation into individual QTL, which in turn, will be coupled to the model. Once the physiological and genetic bases of yield responses to environments are adequately quantified, ideotypes can be proposed for a specific environment (Atkinson and Porter, 1996) in terms of the allelic constitution of the QTL for model input traits that determine yield. This approach overcomes the limitations in designing ideotypes by using models that ignore genetic constraints and correlations among the traits. Information obtained can be applied to any environment because of the high ability of extrapolation of crop modeling. With this integrated approach, epistasis may also be considered (Fig. 6), but resolving epistasis needs a long-term strategy.
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Traditionally, physiologists have worked with only a few genotypes but measured many characteristics or processes to understand crop responses to environments. In contrast, geneticists and breeders usually score a few traits on many genotypes (often >100) of a segregating population and rely on selection and statistics to move the population mean in the desired direction. This fundamental difference has often led geneticists and breeders to be skeptical of using physiological knowledge. On one hand, our proposed integrated approach does provide an excellent opportunity for collaboration among physiologists, modelers, geneticists, and breeders. On the other hand, implementation of such integrated approach needs large experiments, assessing many traits in many genotypes. To reduce this difficulty, the crop model should be developed such that its input parameters can be quickly assessed or through the way by which tissue can be harvested and frozen for later analysis. Options from genetic studies such as selective mapping (Xu and Vogl, 2000) should also be considered. Reducing the size of a mapping experiment with little sacrifice of the power of QTL detection, as the common interest of geneticists and physiologists, may represent a specific research area for their collaborations.
Use of Marker Technology in Modeling Cultivar Difference
Using regression analysis, Virk et al. (1996) has shown that variation of many agronomic traits in rice germplasm is associated with allelic variation of markers, indicating that marker-trait association is present not only in segregating populations but also across a crop germplasm or cultivar collection. If this result turns out to be generally true, QTL-based modeling may be applicable to a germplasm collection, for which important markers identified by, for example, multiple regression, are used as the surrogate of QTL. Because the chance that a specific marker maps to different genome positions in different populations within a species is small (e.g., Waugh et al., 1997), we could use markers identified from a germplasm collection to infer the position of QTL controlling the trait. This opportunity is especially true when integrated marker maps based on across-population data are becoming available (e.g., Haanstra et al., 1999). The applicability of marker information across germplasm or cultivar collection would allow the genetically based crop modeling to be performed without recourse to the use of a mapping population.
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CONCLUSIONS |
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TOPNOTESABSTRACTINTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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TOPNOTESABSTRACTINTRODUCTIONCROP MODELING AS A...QTL MAPPING AND ITS...COMBINING CROP MODELING AND...EXPECTATIONS AND FUTURE...CONCLUSIONSREFERENCES |
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