Gene-based modelling for rice: an opportunity to enhance the simulation of rice growth and development?

Process-based crop simulation models require employment of new knowledge for continuous improvement. To simulate growth and development of different genotypes of a given crop, most models use empirical relationships or parameters defined as genetic coefficients to represent the various cultivar characteristics. Such a loose introduction of different cultivar characteristics can result in bias within a simulation, which could potentially integrate to a high simulation error at the end of the growing season when final yield at maturity is predicted. Recent advances in genetics and biomolecular analysis provide important opportunities for incorporating genetic information into process-based models to improve the accuracy of the simulation of growth and development and ultimately the final yield. This improvement is especially important for complex applications of models. For instance, the effect of the climate change on the crop growth processes in the context of natural climatic and soil variability and a large range of crop management options (e.g., N management) make it difficult to predict the potential impact of the climate change on the crop production. Quantification of the interaction of the environmental variables with the management factors requires fine tuning of the crop models to consider differences among different genotypes. In this paper we present this concept by reviewing the available knowledge of major genes and quantitative trait loci (QTLs) for important traits of rice for improvement of rice growth modelling and further requirements. It is our aim to review the assumption of the adequacy of the available knowledge of rice genes and QTL information to be introduced into the models. Although the rice genome sequence has been completed, the development of gene-based rice models still requires additional information than is currently unavailable. We conclude that a multidiscipline research project would be able to introduce this concept for practical applications.     

Evolutionary dynamics and adaptive benefits of deleterious mutations in crop gene pools

Mutations with deleterious consequences in nature may be conditionally deleterious in crop plants. That is, while some genetic variants may reduce fitness under wild conditions and be subject to purifying selection, they can be under positive selection in domesticates. Such deleterious alleles can be plant breeding targets, particularly for complex traits. The difficulty of distinguishing favorable from unfavorable variants reduces the power of selection, while favorable trait variation and heterosis may be attributable to deleterious alleles. Here, we review the roles of deleterious mutations in crop breeding and discuss how they can be used as a new avenue for crop improvement with emerging genomic tools, including HapMaps and pangenome analysis, aiding the identification, removal, or exploitation of deleterious mutations.     

From plant immunity to crop disease resistance

Plant diseases caused by diverse pathogens lead to a serious reduction in crop yield and threaten food security worldwide. Genetic improvement of plant immunity is considered as the most effective and sustainable approach to control crop diseases. In the last decade, our understanding of plant immunity at both molecular and genomic levels has improved greatly. Combined with advances in biotechnologies, particularly clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9-based genome editing, we can now rapidly identify new resistance genes and engineer disease-resistance crop plants like never before. In this review, we summarize the current knowledge of plant immunity and outline existing and new strategies for disease resistance improvement in crop plants. We also discuss existing challenges in this field and suggest directions for future studies.     

Cassava breeding: opportunities and challenges

Although cassava is a major food crop, its scientific breeding began only recently compared with other crops. Significant progress has been achieved, particularly in Asia where cassava is used mainly for industrial processes and no major biotic constraints affect its productivity. Cassava breeding faces several limitations that need to be addressed. The heterozygous nature of the crop and parental lines used to generate new segregating progenies makes it difficult to identify parents with good breeding values. Breeding so far has been mainly based on a mass phenotypic recurrent selection. There is very little knowledge on the inheritance of traits of agronomic relevance. Several approaches have been taken to overcome the constraints in the current methodologies for the genetic improvement of cassava. Evaluations at early stages of selection allow for estimates of general combining ability effect or breeding values of parental lines. Inbreeding by sequential self-pollination facilitates the identification of useful recessive traits, either already present in the Manihot gene pool or induced by mutagenesis.     

Darwinian agriculture: when can humans find solutions beyond the reach of natural selection?

Progress in genetic improvement of crop yield potential has slowed since 1985. Simultaneously, more sustainable management of agricultural ecosystems is needed. A better understanding of natural selection can help solve both problems. We illustrate this point with two specific examples. First, the genetic legacy of crop plants has been refined by millions of years of natural selection, often driven by competition among plants. We therefore suggest that most simple, tradeoff-free options to increase competitiveness (e.g., increased gene expression, or minor modifications of existing plant genes) have already been tested by natural selection. Further genetic improvement of crop yield potential over the next decade will mainly involve tradeoffs, either between fitness in past versus present environments, or between individual competitiveness and the collective performance of plant communities. Eventually, we may develop the ability to predict the consequences of genetic alterations so radical that they have not yet been tested by natural selection. Second, natural selection acts mainly at the level of genes, individuals, and family groups, rather than ecosystems as a whole. Consequently, there is no reason to expect the structure of natural ecosystems (diversity, spatial, or temporal patterns) to be a reliable blueprint for agricultural ecosystems. Natural ecosystems are nonetheless an important source of information that could be used to improve agriculture.     

Feasibility,limitation and possible solutions of RNAi‐based technology for insect pest control

Abstract Numerous studies indicate that target gene silencing by RNA interference (RNAi) could lead to insect death. This phenomenon has been considered as a potential strategy for insect pest control, and it is termed RNAi‐mediated crop protection. However, there are many limitations using RNAi‐based technology for pest control, with the effectiveness target gene selection and reliable double‐strand RNA (dsRNA) delivery being two of the major challenges. With respect to target gene selection, at present, the use of homologous genes and genome‐scale high‐throughput screening are the main strategies adopted by researchers. Once the target gene is identified, dsRNA can be delivered by micro‐injection or by feeding as a dietary component. However, micro‐injection, which is the most common method, can only be used in laboratory experiments. Expression of dsRNAs directed against insect genes in transgenic plants and spraying dsRNA reagents have been shown to induce RNAi effects on target insects. Hence, RNAi‐mediated crop protection has been considered as a potential new‐generation technology for pest control, or as a complementary method of existing pest control strategies; however, further development to improve the efficacy of protection and range of species affected is necessary. In this review, we have summarized current research on RNAi‐based technology for pest insect management. Current progress has proven that RNAi technology has the potential to be a tool for designing a new generation of insect control measures. To accelerate its practical application in crop protection, further study on dsRNA uptake mechanisms based on the knowledge of insect physiology and biochemistry is needed.     

Gene discovery in cereals through quantitative trait loci and expression analysis in water-use efficiency measured by carbon isotope discrimination

Drought continues to be a major constraint on cereal production in many areas, and the frequency of drought is likely to increase in most arid and semi-arid regions under future climate change scenarios. Considerable research and breeding efforts have been devoted to investigating crop responses to drought at various levels and producing drought-resistant genotypes. Plant physiology has provided new insights to yield improvement in drought-prone environments. Crop performance could be improved through increases in water use, water-use efficiency (WUE) and harvest index. Greater WUE can be achieved by coordination between photosynthesis and transpiration. Carbon isotope discrimination (Δ(13) C) has been demonstrated to be a simple but reliable measure of WUE, and negative correlation between them has been used to indirectly estimate WUE under selected environments. New tools, such as quantitative trait loci (QTL) mapping and gene expression profiling, are playing vital roles in dissecting drought resistance-related traits. The combination of gene expression and association mapping could help identify candidate genes underlying the QTL of interest and complement map-based cloning and marker-assisted selection. Eventually, improved cultivars can be produced through genetic engineering. Future efficient and effective breeding progress in cereals under targeted drought environments will come from the integrated knowledge of physiology and genomics.     

Novel Genomic Tools and Modern Genetic and Breeding Approaches for Crop Improvement

In recent past, genomic tools especially molecular markers have been extensively used for understanding genome dynamics as well for applied aspects in crop breeding. Several new genomics technologies such as next generation sequencing (NGS), high-throughput marker genotyping, -omics technologies have emerged as powerful tools for understanding genome variation in crop species at DNA, RNA as well as protein level. These technologies promise to provide an insight into the way gene(s) are expressed and regulated in cell and to unveil metabolic pathways involved in trait(s) of interest for breeders not only in model-/major- but even for under-resourced crop species which were once considered “orphan” crops. In parallel, genetic variation for a species present not only in cultivated genepool but even in landraces and wild species can be harnessed by using new genetic approaches such as advanced-backcross QTL (AB-QTL) analysis, introgression libraries (ILs), multi-parent advanced generation intercross (MAGIC) population and association genetics. The gene(s) or genomic regions, responsible for trait(s) of interest, identified either through conventional linkage mapping or above mentioned approaches can be introgressed or pyramided to develop superior genotypes through molecular breeding approaches such as marker-assisted back crossing (MABC), marker assisted recurrent selection (MARS) and genome wide selection (GWS). This article provides an overview on some recent genomic tools and novel genetic and breeding approaches as mentioned above with a final aim of crop improvement.     

Integration of genomic tools to assist breeding in the <Emphasis Type="Italic">japonica</Emphasis> subspecies of rice

The cultivated rice (Oryza sativa L.) has two subspecies, indica and japonica. The japonica rice germplasm has a narrower genetic diversity compared to the indica subspecies. Rice breeders aim to develop new varieties with a higher yield potential, with enhanced resistances to biotic and abiotic stresses, and improved adaptation to environmental changes. In order to face some of these challenges, japonica rice germplasm will have to be diversified and new breeding strategies developed. Indica rice improvement could also profit from more “genepool mingling” for which japonica rice could play an important role. Interesting traits such as low-temperature tolerance, and wider climate adaptation could be introgressed into the indica subspecies. In the past decade, huge developments in rice genomics have expanded our available knowledge on this crop and it is now time to use these technologies for improving and accelerating rice breeding research. With the full sequence of the rice genome, breeders may take advantage of new genes. Also new genes may be discovered from the genepool of wild relatives, or landraces of the genus Oryza, and incorporated into elite japonica cultivars in a kind of “gene revolution” program. Expectedly, new technologies that are currently being optimized, aiming for novel gene discovery or for tracking the regions under selection, will be suggested as new breeding approaches. This paper revisits breeding strategies successfully employed in indica rice, and discusses their application in japonica rice improvement (e.g. ideotype breeding, wide hybridization and hybrid performance).     

Allele mining in crops: Prospects and potentials

Enormous sequence information is available in public databases as a result of sequencing of diverse crop genomes. It is important to use this genomic information for the identification and isolation of novel and superior alleles of agronomically important genes from crop gene pools to suitably deploy for the development of improved cultivars. Allele mining is a promising approach to dissect naturally occurring allelic variation at candidate genes controlling key agronomic traits which has potential applications in crop improvement programs. It helps in tracing the evolution of alleles, identification of new haplotypes and development of allele-specific markers for use in marker-assisted selection. Realizing the immense potential of allele mining, concerted allele mining efforts are underway in many international crop research institutes. This review examines the concepts, approaches and applications of allele mining along with the challenges associated while emphasizing the need for more refined ‘mining’ strategies for accelerating the process of allele discovery and its utilization in molecular breeding.     

Strategies for Improving Drought Resistance in Grain Legumes

This review distills recent information on drought resistance characteristics of grain legumes with a view toward developing appropriate genetic enhancement strategies for water-limited environments. First, the possible adaptations that allow grain legumes to better cope with drought stress are summarized. It is suggested that there are considerable gains to be made in increasing yield and yield stability in environments characterized by terminal drought stress by further exploiting drought escape, by shortening crop duration. Many traits conferring dehydration avoidance and dehydration tolerance are available, but integrated traits, expressing at a higher level of organization, are suggested to be more useful in crop improvement programs. Possible genetic improvement strategies are outlined, ranging from empirical selection for yield in droughted environments to a physiological genetic approach. It is suggested that in view of recent advances in understanding drought resistance mechanisms, the latter strategy is becoming more feasible. It is concluded that use of this recently derived knowledge in a systematic manner can lead to significant gains in yield and yield stability of the world's major grain legumes, as they are mainly grown (and will continue to be grown) under rain-fed conditions.     

Plant genome sequencing: applications for crop improvement

DNA sequencing technology is undergoing a revolution with the commercialization of second generation technologies capable of sequencing thousands of millions of nucleotide bases in each run. The data explosion resulting from this technology is likely to continue to increase with the further development of second generation sequencing and the introduction of third generation single‐molecule sequencing methods over the coming years. The question is no longer whether we can sequence crop genomes which are often large and complex, but how soon can we sequence them? Even cereal genomes such as wheat and barley which were once considered intractable are coming under the spotlight of the new sequencing technologies and an array of new projects and approaches are being established. The increasing availability of DNA sequence information enables the discovery of genes and molecular markers associated with diverse agronomic traits creating new opportunities for crop improvement. However, the challenge remains to convert this mass of data into knowledge that can be applied in crop breeding programs.     

The GP problem: quantifying gene-to-phenotype relationships

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.     

Genetic improvement of Purslane (Portulaca oleracea L.) and its future prospects

Common purslane (Portulaca oleracea), also known as pigweed, fatweed, pusle, and little hogweed, is an annual succulent herb in the family Portulacaceae that is found in most corners of the globe. From the ancient ages purslane has been treated as a major weed of vegetables as well as other crops. However, worldwide researchers and nutritionists have studied this plant as a potential vegetable crop for humans as well as animals. Purslane is a nutritious vegetable with high antioxidant properties and recently has been recognized as the richest source of α-linolenic acid, essential omega-3 and 6 fatty acids, ascorbic acid, glutathione, α-tocopherol and β-carotene. The lack of vegetable sources of ω-3 fatty acids has resulted in a growing level of attention to introduce purslane as a new cultivated vegetable. In the rapid-revolutionizing worldwide atmosphere, the ability to produce improved planting material appropriate to diverse and varying rising conditions is a supreme precedence. Though various published reports on morphological, physiological, nutritional and medicinal aspects of purslane are available, research on the genetic improvement of this promising vegetable crop are scant. Now it is necessary to conduct research for the genetic improvement of this plant. Genetic improvement of purslane is also a real scientific challenge. Scientific modernization of conventional breeding with the advent of advance biotechnological and molecular approaches such as tissue culture, protoplast fusion, genetic transformation, somatic hybridization, marker-assisted selection, qualitative trait locus mapping, genomics, informatics and various statistical representation have opened up new opportunities of revising the relationship between genetic diversity, agronomic performance and response to breeding for varietal improvement. This review is an attempt to amalgamate the assorted scientific information on purslane propagation, cultivation, varietal improvement, nutrient analyses, medicinal uses and to describe prospective research especially for genetic improvement of this crop.     

Molecular approaches for genetic improvement of seed quality and characterization of genetic diversity in soybean: a critical review

Soybean is an economically important leguminous crop. Genetic improvements of soybeans have focused on enhancement of seed and oil yield, development of varieties suited to different cropping systems, and breeding resistant/tolerant varieties for various biotic and abiotic stresses. Plant breeders have used conventional breeding techniques for the improvement of these traits in soybean. The conventional breeding process can be greatly accelerated through the application of molecular and genomic approaches. Molecular markers have proved to be a new tool in soybean breeding by enhancing selection efficiency in a rapid and time-bound manner. An overview of molecular approaches for the genetic improvement of soybean seed quality parameters, considering recent applications of marker-assisted selection and ‘omics’ research, is provided in this article.     

Applications of biotechnology and genomics in potato improvement

Potato is the third most important global food crop and the most widely grown noncereal crop. As a species highly amenable to cell culture, it has a long history of biotechnology applications for crop improvement. This review begins with a historical perspective on potato improvement using biotechnology encompassing pathogen elimination, wide hybridization, ploidy manipulation and applications of cell culture. We describe the past developments and new approaches for gene transfer to potato. Transformation is highly effective for adding single genes to existing elite potato clones with no, or minimal, disturbances to their genetic background and represents the only effective way to produce isogenic lines of specific genotypes/cultivars. This is virtually impossible via traditional breeding as, due to the high heterozygosity in the tetraploid potato genome, the genetic integrity of potato clones is lost upon sexual reproduction as a result of allele segregation. These genetic attributes have also provided challenges for the development of genetic maps and applications of molecular markers and genomics in potato breeding. Various molecular approaches used to characterize loci, (candidate) genes and alleles in potato, and associating phenotype with genotype are also described. The recent determination of the potato genome sequence has presented new opportunities for genomewide assays to provide tools for gene discovery and enabling the development of robustly unique marker haplotypes spanning QTL regions. The latter will be useful in introgression breeding and whole‐genome approaches such as genomic selection to improve the efficiency of selecting elite clones and enhancing genetic gain over time.     

Integrating genetic information into plant breeding programmes: how will we produce varieties from molecular variation,using bioinformatics?

The development of new varieties of crop plants is ongoing for plant breeders and progress since the Green Revolution has been steady, if not dramatic. With the recent sequencing of Arabidopsis thaliana and of rice the development of both physical and informational resources has entered a new phase. This paper examines the state of plant bioinformatics as it is now and as it is likely to develop in the future. It also looks rather further forward to what crop scientists might want from bioinformatics, before examining the likely physiological targets for sustainability traits and the prospects for their improvement in wheat. Wheat is taken as the focus crop because it is potentially one of the most dif. Cult to work with in molecular terms, both because of its large hexaploid genome size and because of its considerable genetic distance from the most information rich plant species, Arabidopsis. Finally, we examine how these tools might be used to track down the underlying genes controlling sustainability traits and how these may then be exploited in plant breeding programmes using marker‐ assisted selection.     

Genetic contributions to agricultural sustainability

The current tools of enquiry into the structure and operation of the plant genome have provided us with an understanding of plant development and function far beyond the state of knowledge that we had previously. We know about key genetic controls repressing or stimulating the cascades of gene expression that move a plant through stages in its life cycle, facilitating the morphogenesis of vegetative and reproductive tissues and organs. The new technologies are enabling the identification of key gene activity responses to the range of biotic and abiotic challenges experienced by plants. In the past, plant breeders produced new varieties with changes in the phases of development, modifications of plant architecture and improved levels of tolerance and resistance to environmental and biotic challenges by identifying the required phenotypes in a few plants among the large numbers of plants in a breeding population. Now our increased knowledge and powerful gene sequence-based diagnostics provide plant breeders with more precise selection objectives and assays to operate in rationally planned crop improvement programmes. We can expect yield potential to increase and harvested product quality portfolios to better fit an increasing diversity of market requirements. The new genetics will connect agriculture to sectors beyond the food, feed and fibre industries; agri-business will contribute to public health and will provide high-value products to the pharmaceutical industry as well as to industries previously based on petroleum feedstocks and chemical modification processes.     

Studies on some genetic parameters of rice (Oryza sativa L.)

Summary In order to utilize the available useful variation in breeding rice to improve yield and quality of grain, the performance of a wide range of rice germ plasm comprising 30 varieties was evaluated in northern India, a major rice growing belt. Plant performance revealed high genetic divergence and phenotypic variability in the crop, with the maximum range of variation being for grain number per panicle and the minimum for grain dimensions. There were also significant differences among varietal means for ten phenotypic traits. Genotypic and phenotypic variance contributed profoundly to the variance of the phenotypic traits studied, but, since genetic variability in the traits related to yield was considerable, there is scope for further improvement in yielding ability. Grain number per panicle, number of effective tillers per plant and culm length exhibit high heritability, and genotypic coefficient of variation and therefore a high genetic advance. Thus, selection for these traits would be effective in crop improvement. Moreover, grain number per panicle shows a significant positive correlation with yield, and this trait could profitably form a reliable index for the yielding capacity of this crop.