QTL analysis in plants; where are we now?

Genomic variation across distribution of Micro-Tom, a model cultivar of tomato (Solanum lycopersicum)

Micro-Tom is a cultivar of tomato (Solanum lycopersicum), which is known as a major crop and model plant in Solanaceae. Micro-Tom has phenotypic traits such as dwarfism, and substantial EMS-mutagenized lines have been reported. After Micro-Tom was generated in Florida, USA, it was distributed to research institutes worldwide and used as a genetic resource. In Japan, the Micro-Tom lines have been genetically fixed; currently, three lines have been re-distributed from three institutes, but many phenotypes among the lines have been observed. We have determined the genome sequence de novo of the Micro-Tom KDRI line, one of the Micro-Tom lines distributed from Kazusa DNA Research Institute (KDRI) in Japan, and have built chromosome-scale pseudomolecules. Genotypes among six Micro-Tom lines, including three in Japan, one in the United States, one in France, and one in Brazil showed phenotypic alternation. Here, we unveiled the swift emergence of genetic diversity in both phenotypes and genotypes within the Micro-Tom genome sequence during its propagation. These findings offer valuable insights crucial for the management of bioresources.

Insights into Salinity Tolerance in Wheat

Salt stress has a detrimental impact on food crop production, with its severity escalating due to both natural and man-made factors. As one of the most important food crops, wheat is susceptible to salt stress, resulting in abnormal plant growth and reduced yields; therefore, damage from salt stress should be of great concern. Additionally, the utilization of land in coastal areas warrants increased attention, given diminishing supplies of fresh water and arable land, and the escalating demand for wheat. A comprehensive understanding of the physiological and molecular changes in wheat under salt stress can offer insights into mitigating the adverse effects of salt stress on wheat. In this review, we summarized the genes and molecular mechanisms involved in ion transport, signal transduction, and enzyme and hormone regulation, in response to salt stress based on the physiological processes in wheat. Then, we surveyed the latest progress in improving the salt tolerance of wheat through breeding, exogenous applications, and microbial pathways. Breeding efficiency can be improved through a combination of gene editing and multiple omics techniques, which is the fundamental strategy for dealing with salt stress. Possible challenges and prospects in this process were also discussed.

Loci associated with cave-derived traits concentrate in specific regions of the Mexican cavefish genome

A major goal of modern evolutionary biology is connecting phenotypic evolution with its underlying genetic basis. The Mexican cavefish (Astyanax mexicanus), a characin fish species comprised of a surface ecotype and a cave-derived ecotype, is well suited as a model to study the genetic mechanisms underlying adaptation to extreme environments. Here we map 206 previously published quantitative trait loci (QTL) for cave-derived traits in A. mexicanus to the newest version of the surface fish genome assembly, AstMex3. This analysis revealed that QTL cluster in the genome more than expected by chance, and this clustering is not explained by the distribution of genes in the genome. To investigate whether certain characteristics of the genome facilitate phenotypic evolution, we tested whether genomic characteristics, such as highly mutagenic CpG sites, are reliable predictors of the sites of trait evolution but did not find any significant trends. Finally, we combined the QTL map with previously collected expression and selection data to identify a list of 36 candidate genes that may underlie the repeated evolution of cave phenotypes, including rgrb which is predicted to be involved in phototransduction. We found this gene has disrupted exons in all non-hybrid cave populations but intact reading frames in surface fish. Overall, our results suggest specific "evolutionary hotspots" in the genome may play significant roles in driving adaptation to the cave environment in Astyanax mexicanus and demonstrate how this compiled dataset can facilitate our understanding of the genetic basis of repeated evolution in the Mexican cavefish.

The Molecular Docking of MAX Fungal Effectors with Plant HMA Domain-Binding Proteins

Fungal effector proteins are important in mediating disease infections in agriculturally important crops. These secreted small proteins are known to interact with their respective host receptor binding partners in the host, either inside the cells or in the apoplastic space, depending on the localisation of the effector proteins. Consequently, it is important to understand the interactions between fungal effector proteins and their target host receptor binding partners, particularly since this can be used for the selection of potential plant resistance or susceptibility-related proteins that can be applied to the breeding of new cultivars with disease resistance. In this study, molecular docking simulations were used to characterise protein-protein interactions between effector and plant receptors. Benchmarking was undertaken using available experimental structures of effector-host receptor complexes to optimise simulation parameters, which were then used to predict the structures and mediating interactions of effector proteins with host receptor binding partners that have not yet been characterised experimentally. Rigid docking was applied for both the so-called bound and unbound docking of MAX effectors with plant HMA domain protein partners. All bound complexes used for benchmarking were correctly predicted, with 84% being ranked as the top docking pose using the ZDOCK scoring function. In the case of unbound complexes, a minimum of 95% of known residues were predicted to be part of the interacting interface on the host receptor binding partner, and at least 87% of known residues were predicted to be part of the interacting interface on the effector protein. Hydrophobic interactions were found to dominate the formation of effector-plant protein complexes. An optimised set of docking parameters based on the use of ZDOCK and ZRANK scoring functions were established to enable the prediction of near-native docking poses involving different binding interfaces on plant HMA domain proteins. Whilst this study was limited by the availability of the experimentally determined complexed structures of effectors and host receptor binding partners, we demonstrated the potential of molecular docking simulations to predict the likely interactions between effectors and their respective host receptor binding partners. This computational approach may accelerate the process of the discovery of putative interacting plant partners of effector proteins and contribute to effector-assisted marker discovery, thereby supporting the breeding of disease-resistant crops.

The genotype-phenotype relationship and evolutionary genetics in the light of the Metabolic Control Analysis

Metabolic control analysis has long been used as a systemic model of the genotype-phenotype (GP) relationship. By considering kinetic parameters and enzyme concentrations as reflecting the genotype level and metabolic fluxes or pools as phenotypes related to fitness, MCA has given a biological basis to the relationship between these two levels. The non-linear and concave relationship between enzymes and fluxes can account for common genetic effects that reductionist approaches have been powerless to explain, such as the dominance of active alleles over less active alleles, the various types of epistasis and heterosis, and reveals the structural links between these genetic effects. The summation property of the flux control coefficients accounts for the L-shaped distribution of Quantitative Trait Locus (QTL) effects, irrespective of other possible causes. Metabolic models of response to selection results in evolutionary scenarios that are markedly different from those derived from the classical infinitesimal model of quantitative genetics. In particular, evolution towards selective neutrality appears to be a consequence of the diminishing return of the flux-enzyme relationship. In this paper, we survey the historical and recent achievements of MCA in genetics, quantitative genetics and evolution, focusing on epistasis and the evolution of flux in relation to enzyme concentrations.

Understanding the genomic selection for crop improvement: current progress and future prospects

Although increased use of modern breeding techniques and technology has resulted in long-term genetic gain, the pace of genetic gain must be sped up to satisfy global agricultural demand. However, marker-assisted selection has proven its potential for improving qualitative traits with large effects regulated by one to few genes. Its contribution to the improvement of the quantitative traits regulated by a number of small-effect genes is modest. In this context, genomic selection (GS) has been regarded as the most promising method for genetically enhancing complicated features that are regulated by several genes, each of which has minor effects. By examining a population's phenotypes and high-density marker scores, genomic selection can forecast the breeding potential of individual lines. The fact that GS uses all marker data in the prediction model prevents skewed marker effect estimations and maximizes the amount of variation caused by small-effect QTL. It has the ability to speed up the breeding cycle and as a consequence of which superior genotypes are selected rapidly. Developing the best GS models while taking into account non-additive effects, genotype-by-environment interaction, and cost-effectiveness will enable the widespread implementation of GS in plants. These steps will also increase heritability estimation and prediction accuracy. This review focuses on the shift from conventional selection methods to GS, underlying statistical tools and methodologies, the state of GS research in agricultural plants, and prospects for its effective use in the creation of climate-resilient crops.

Versatile mapping-by-sequencing with Easymap v.2

Mapping-by-sequencing combines Next Generation Sequencing (NGS) with classical genetic mapping by linkage analysis to establish gene-to-phenotype relationships. Although numerous tools have been developed to analyze NGS datasets, only a few are available for mapping-by-sequencing. One such tool is Easymap, a versatile, easy-to-use package that performs automated mapping of point mutations and large DNA insertions. Here, we describe Easymap v.2, which also maps small insertion/deletions (InDels), and includes workflows to perform QTL-seq and variant density mapping analyses. Each mapping workflow can accommodate different experimental designs, including outcrossing and backcrossing, F₂, M₂, and M₃ mapping populations, chemically induced mutation and natural variant mapping, input files containing single-end or paired-end reads of genomic or complementary DNA sequences, and alternative control sample files in FASTQ and VCF formats. Easymap v.2 can also be used as a variant analyzer in the absence of a mapping algorithm and includes a multi-threading option.

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review

Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane

Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder’s equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.

Identification of QTLs Associated With Agronomic Traits in Tobacco via a Biparental Population and an Eight-Way MAGIC Population

Agronomic traits such as plant height (PH), leaf number (LN), leaf length (LL), and leaf width (LW), which are closely related to yield and quality, are important in tobacco (Nicotiana tabacum L.). To identify quantitative trait loci (QTLs) associated with agronomic traits in tobacco, 209 recombinant inbred lines (RILs) and 537 multiparent advanced generation intercross (MAGIC) lines were developed. The biparental RIL and MAGIC lines were genotyped using a 430 K single-nucleotide polymorphism (SNP) chip assay, and their agronomic traits were repeatedly evaluated under different conditions. A total of 43 QTLs associated with agronomic traits were identified through a combination of linkage mapping (LM) and association mapping (AM) methods. Among these 43 QTLs, three major QTLs, namely qPH13-3, qPH17-1, and qLW20-1, were repeatedly identified by the use of various genetically diverse populations across different environments. The candidate genes for these major QTLs were subsequently predicted. Validation and utilization of the major QTL qLW20-1 for the improvement of LW in tobacco were investigated. These results could be applied to molecular marker-assisted selection (MAS) for breeding important agronomic traits in tobacco.

Overview of Identified Genomic Regions Associated with Various Agronomic and Physiological Traits in Barley under Abiotic Stresses

Climate change has caused breeders to focus on varieties that are able to grow under unfavorable conditions, such as drought, high and low temperatures, salinity, and other stressors. In recent decades, progress in biotechnology and its related tools has provided opportunities to dissect and decipher the genetic basis of tolerance to various stress conditions. One such approach is the identification of genomic regions that are linked with specific or multiple characteristics. Cereal crops have a key role in supplying the energy required for human and animal populations. However, crop products are dramatically affected by various environmental stresses. Barley (Hordeum vulgare L.) is one of the oldest domesticated crops that is cultivated globally. Research has shown that, compared with other cereals, barley is well adapted to various harsh environmental conditions. There is ample literature regarding these responses to abiotic stressors, as well as the genomic regions associated with the various morpho-physiological and biochemical traits of stress tolerance. This review focuses on (i) identifying the tolerance mechanisms that are important for stable growth and development, and (ii) the applicability of QTL mapping and association analysis in identifying genomic regions linked with stress-tolerance traits, in order to help breeders in marker-assisted selection (MAS) to quickly screen tolerant germplasms in their breeding cycles. Overall, the information presented here will inform and assist future barley breeding programs.

Five plants per RIL for phenotyping traits of high or moderate heritability ensure the power of QTL mapping in a rice MAGIC population

Currently, the power of QTL mapping is mainly dependent on the quality of phenotypic data in a given population, regardless of the statistical method, as the quality of genotypic data is easily guaranteed in the laboratory. Increasing the sample size per line used for phenotyping is a good way to improve the quality of phenotypic data. However, accommodating a large-scale mapping population takes a large area of rice field, which frequently results in high costs and extra environmental noises. To acquire a reasonable small sample size without a penalty in mapping power, we conducted three experiments with a 4-way MAGIC population and measured phenotypes of 5, 10, and 20 plants per RIL. Three traits including heading date, plant height, and tillers per plant were focused. With SNP- and bin-based QTL mapping, 3 major and 3 minor QTLs for heading date with high heritability and 2 major QTLs for plant height with moderate heritability were commonly detected across the three experiments, but no QTL for tillers per plant with low heritability were commonly identified. In addition, bin-based QTL mapping was more powerful than SNP-based mapping and able to rank the genetic effects of parental alleles. Thus, 5 plants per RIL for phenotyping ensure the power of QTL mapping for traits of high or moderate heritability, and bin-based QTL mapping is recommended for multiparent populations.

Genetic architecture of variation in Arabidopsis thaliana rosettes

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

Genetic Bases of Complex Traits: From Quantitative Trait Loci to Prediction

Conceived as a general introduction to the book, this chapter is a reminder of the core concepts of genetic mapping and molecular marker-based prediction. It provides an overview of the principles and the evolution of methods for mapping the variation of complex traits, and methods for QTL-based prediction of human disease risk and animal and plant breeding value. The principles of linkage-based and linkage disequilibrium-based QTL mapping methods are described in the context of the simplest, single-marker, methods. Methodological evolutions are analysed in relation with their ability to account for the complexity of the genotype-phenotype relations. Main characteristics of the genetic architecture of complex traits, drawn from QTL mapping works using large populations of unrelated individuals, are presented. Methods combining marker-QTL association data into polygenic risk score that captures part of an individual's susceptibility to complex diseases are reviewed. Principles of best linear mixed model-based prediction of breeding value in animal- and plant-breeding programs using phenotypic and pedigree data, are summarized and methods for moving from BLUP to marker-QTL BLUP are presented. Factors influencing the additional genetic progress achieved by using molecular data and rules for their optimization are discussed.

Using Crop Databases to Explore Phenotypes: From QTL to Candidate Genes

Seeds, especially those of certain grasses and legumes, provide the majority of the protein and carbohydrates for much of the world’s population. Therefore, improvements in seed quality and yield are important drivers for the development of new crop varieties to feed a growing population. Quantitative Trait Loci (QTL) have been identified for many biologically interesting and agronomically important traits, including many seed quality traits. QTL can help explain the genetic architecture of the traits and can also be used to incorporate traits into new crop cultivars during breeding. Despite the important contributions that QTL have made to basic studies and plant breeding, knowing the exact gene(s) conditioning each QTL would greatly improve our ability to study the underlying genetics, biochemistry and regulatory networks. The data sets needed for identifying these genes are increasingly available and often housed in species- or clade-specific genetics and genomics databases. In this demonstration, we present a generalized walkthrough of how such databases can be used in these studies using SoyBase, the USDA soybean Genetics and Genomics Database, as an example.

Genetic Approaches to Enhance Multiple Stress Tolerance in Maize

The multiple-stress effects on plant physiology and gene expression are being intensively studied lately, primarily in model plants such as Arabidopsis, where the effects of six stressors have simultaneously been documented. In maize, double and triple stress responses are obtaining more attention, such as simultaneous drought and heat or heavy metal exposure, or drought in combination with insect and fungal infestation. To keep up with these challenges, maize natural variation and genetic engineering are exploited. On one hand, quantitative trait loci (QTL) associated with multiple-stress tolerance are being identified by molecular breeding and genome-wide association studies (GWAS), which then could be utilized for future breeding programs of more resilient maize varieties. On the other hand, transgenic approaches in maize have already resulted in the creation of many commercial double or triple stress resistant varieties, predominantly weed-tolerant/insect-resistant and, additionally, also drought-resistant varieties. It is expected that first generation gene-editing techniques, as well as recently developed base and prime editing applications, in combination with the routine haploid induction in maize, will pave the way to pyramiding more stress tolerant alleles in elite lines/varieties on time.

[NGS sequencing in barley breeding and genetic studies]

Barley (Hordeum vulgare L.) is the one of the most important cereal species used as food and feed crops, as well as for malting and alcohol production. At the end of the last century, traditional breeding techniques were complemented by the use of DNA markers. Molecular markers have also been used extensively for molecular genetic mapping and QTL analysis. In 2012, the barley genome sequencing was completed, which provided a broad range of new opportunities - from a more efficient search for candidate genes controlling economically important traits to genomic selection. The review summarizes the results of the studies performed after barley genome sequencing, which discovered new areas of barley genetics and breeding with high throughput screening and genotyping methods. During this period, intensive studies aimed at identification of barley genomic loci associated with economically important traits have been carried out; online databases and tools for working with barley genomic data and their deposition have appeared and are being replenished. In recent years, GWAS analysis has been used for large-scale phenotypegenotype association studies, which has been widely used in barley since 2010 due to the developed SNP-arrays, as well as genotyping methods based on direct NGS sequencing of selected fractions of the genome. To date, more than 80 papers have been published that describe the results of the GWAS analysis in barley. SNP identification associated with economically important traits and their transformation into CAPS or KASP markers convenient for screening selection material significantly expands the possibilities of marker-assisted selection of barley. In addition, the currently available information on potential target genes and the quality of the whole barley genome sequence provides a good base for applying genome editing technologies to create material for the creation of varieties with desired properties.

Integrated genomic selection for rapid improvement of crops

An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.

Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE

The goals of quantitative genetics differ according to its field of application. In plant breeding, the main focus of quantitative genetics is on identifying candidates with the best genotypic value for a target population of environments. Keeping quantitative genetics current requires keeping old concepts that remain useful, letting go of what has become archaic, and introducing new concepts and methods that support contemporary breeding. The core concept of continuous variation being due to multiple Mendelian loci remains unchanged. Because the entirety of germplasm available in a breeding program is not in Hardy-Weinberg equilibrium, classical concepts that assume random mating, such as the average effect of an allele and additive variance, need to be retired in plant breeding. Doing so is feasible because with molecular markers, mixed-model approaches that require minimal genetic assumptions can be used for best linear unbiased estimation (BLUE) and prediction. Plant breeding would benefit from borrowing approaches found useful in other disciplines. Examples include reliability as a new measure of the influence of genetic versus nongenetic effects, and operations research and simulation approaches for designing breeding programs. The genetic entities in such simulations should not be generic but should be represented by the pedigrees, marker data, and phenotypic data for the actual germplasm in a breeding program. Over the years, quantitative genetics in plant breeding has become increasingly empirical and computational and less grounded in theory. This trend will continue as the amount and types of data available in a breeding program increase.

Genomic regions associated with important seed quality traits in food-grade soybeans

BACKGROUND

The production of soy-based food products requires specific physical and chemical characteristics of the soybean seed. Identification of quantitative trait loci (QTL) associated with value-added traits, such as seed weight, seed protein and sucrose concentration, could accelerate the development of competitive high-protein soybean cultivars for the food-grade market through marker-assisted selection (MAS). The objectives of this study were to identify and validate QTL associated with these value-added traits in two high-protein recombinant inbred line (RIL) populations.

RESULTS

The RIL populations were derived from the high-protein cultivar 'AC X790P' (49% protein, dry weight basis), and two high-yielding commercial cultivars, 'S18-R6' (41% protein) and 'S23-T5' (42% protein). Fourteen large-effect QTL (R2 > 10%) were identified associated with seed protein concentration. Of these QTL, seven QTL were detected in both populations, and eight of them were co-localized with QTL associated with either seed sucrose concentration or seed weight. None of the protein-related QTL was found to be associated with seed yield in either population. Sixteen candidate genes with putative roles in protein metabolism were identified within seven of these protein-related regions: qPro_Gm02-3, qPro_Gm04-4, qPro_Gm06-1, qPro_Gm06-3, qPro_Gm06-6, qPro_Gm13-4 and qPro-Gm15-3.

CONCLUSION

The use of RIL populations derived from high-protein parents created an opportunity to identify four novel QTL that may have been masked by large-effect QTL segregating in populations developed from diverse parental cultivars. In total, we have identified nine protein QTL that were detected either in both populations in the current study or reported in other studies. These QTL may be useful in the curated selection of new soybean cultivars for optimized soy-based food products.

An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2

In the genetic system that regulates complex traits, metabolites, gene expression levels, RNA editing levels and DNA methylation, a series of small and linked genes exist. To date, however, little is known about how to design an efficient framework for the detection of these kinds of genes. In this article, we propose a genome-wide composite interval mapping (GCIM) in F₂. First, controlling polygenic background via selecting markers in the genome scanning of linkage analysis was replaced by estimating polygenic variance in a genome-wide association study. This can control large, middle and minor polygenic backgrounds in genome scanning. Then, additive and dominant effects for each putative quantitative trait locus (QTL) were separately scanned so that a negative logarithm P-value curve against genome position could be separately obtained for each kind of effect. In each curve, all the peaks were identified as potential QTLs. Thus, almost all the small-effect and linked QTLs are included in a multi-locus model. Finally, adaptive least absolute shrinkage and selection operator (adaptive lasso) was used to estimate all the effects in the multi-locus model, and all the nonzero effects were further identified by likelihood ratio test for true QTL identification. This method was used to reanalyze four rice traits. Among 25 known genes detected in this study, 16 small-effect genes were identified only by GCIM. To further demonstrate GCIM, a series of Monte Carlo simulation experiments was performed. As a result, GCIM is demonstrated to be more powerful than the widely used methods for the detection of closely linked and small-effect QTLs.

Performance of a Set of Eggplant (Solanum melongena) Lines With Introgressions From Its Wild Relative S. incanum Under Open Field and Screenhouse Conditions and Detection of QTLs

Introgression lines (ILs) of eggplant (Solanum melongena) represent a resource of high value for breeding and the genetic analysis of important traits. We have conducted a phenotypic evaluation in two environments (open field and screenhouse) of 16 ILs from the first set of eggplant ILs developed so far. Each of the ILs carries a single marker-defined chromosomal segment from the wild eggplant relative S. incanum (accession MM577) in the genetic background of S. melongena (accession AN-S-26). Seventeen agronomic traits were scored to test the performance of ILs compared to the recurrent parent and of identifying QTLs for the investigated traits. Significant morphological differences were found between parents, and the hybrid was heterotic for vigour-related traits. Despite the presence of large introgressed fragments from a wild exotic parent, individual ILs did not display differences with respect to the recipient parent for most traits, although significant genotype × environment interaction (G × E ) was detected for most traits. Heritability values for the agronomic traits were generally low to moderate. A total of ten stable QTLs scattered across seven chromosomes was detected. For five QTLs, the S. incanum introgression was associated with higher mean values for plant- and flower-related traits, including vigour prickliness and stigma length. For one flower- and four fruit-related-trait QTLs, including flower peduncle and fruit pedicel lengths and fruit weight, the S. incanum introgression was associated with lower mean values for fruit-related traits. Evidence of synteny to other previously reported in eggplant populations was found for three of the fruit-related QTLs. The other seven stable QTLs are new, demonstrating that eggplant ILs are of great interest for eggplant breeding under different environments.

Genomics-assisted breeding in minor and pseudo-cereals

Minor and pseudo-cereals, which can grow with lower input and often produce specific nutrients compared to major cereal crops, are attracting worldwide attention. Since these crops generally have a large genetic diversity in a breeding population, rapid genetic improvement can be possible by the application of genomics-assisted breeding methods. In this review, we discuss studies related to biparental quantitative trait locus (QTL) mapping, genome-wide association study, and genomic selection for minor and pseudo-cereals. Especially, we focus on the current progress in a pseudo-cereal, buckwheat. Prospects for the practical utilization of genomics-assisted breeding in minor and pseudo-cereals are discussed including the issues to overcome especially for these crops.

Bin-based genome-wide association analyses improve power and resolution in QTL mapping and identify favorable alleles from multiple parents in a four-way MAGIC rice population

A whole genome bin map was developed for a MAGIC population. Association studies for heading date at bin level exhibited powerful QTL mapping and identified favorable alleles. The presumed advantages of multiparent advanced generation intercross (MAGIC) population in quantitative trait locus (QTL) mapping were not fully utilized in the previous studies in which genome-wide association studies (GWAS) were conducted at only single nucleotide polymorphism level. In this study, we genotyped a rice four-way MAGIC population of 247 F₇ lines and their parents by sequencing. A total of 5934 bins with an average length of 65 kb were constructed and covered 97% of the genome. The MAGIC population showed low population structure and balanced parental contributions. A bin-based GWAS for heading date identified 4 QTLs in three environments. Three major QTLs were mapped exactly to the bins where the major heading date genes DTH3, Ghd7.1 and Ghd8 were located. Multiple comparisons showed that different parental alleles had varied genetic effects. Like DTH3, the alleles of the Guichao 2/YJSM, IR34 and Cypress had larger, intermediate and no effects, respectively. Based on comparative sequencing of 8 known heading date genes undetected in this MAGIC population, only Ghd7 exhibited diverse function among parents. The failure in Ghd7 mapping was well explained by its interaction with Hd1 because Ghd7 had no effects on heading date when combined with the nonfunctional hd1 carried by all four parents. Overall, bin-based GWAS have more mapping power and higher resolution with a MAGIC population and provide favorable alleles to breeders. The use of more diversified parents is encouraged to develop a MAGIC population for detecting more QTLs for important agronomical traits.

Identification of New QTL Contributing to Barley Yellow Dwarf Virus-PAV (BYDV-PAV) Resistance in Wheat

Barley yellow dwarf (BYD) is a major virus disease which dramatically reduces wheat yield. Introducing BYD resistance genes into commercial varieties has been proven to be effective in reducing damage caused by barley yellow dwarf virus (BYDV). However, only one major resistance gene is readily deployable for breeding; Bdv2 derived from Thinopyrum intermedium is deployed as a chromosomal translocation. In this study, a double haploid (DH) population was developed from a cross between XuBYDV (introduced from China showing very good resistance to BYD) and H-120 (a BYD-sensitive Chinese accession), and was used to identify QTL for BYD resistance. The population was genotyped using an Infinium iSelect bead chip array targeting 90K gene-based SNPs. The disease resistance of DH lines inoculated with BYDV was assessed at the heading stage. The infections were assessed by tissue blot immunoassay (TBIA). Three new QTL were identified on chromosomes 5A, 6A, and 7A for both symptom and TBIA, with all three resistance alleles being inherited from XuBYDV. Some DH lines with the resistance alleles from all three QTL showed high level resistance to BYD. These new QTL will be useful in breeding programs for pyramiding BYD resistance genes.

Impact of Climate Change on Crops Adaptation and Strategies to Tackle Its Outcome: A Review

Agriculture and climate change are internally correlated with each other in various aspects, as climate change is the main cause of biotic and abiotic stresses, which have adverse effects on the agriculture of a region. The land and its agriculture are being affected by climate changes in different ways, e.g., variations in annual rainfall, average temperature, heat waves, modifications in weeds, pests or microbes, global change of atmospheric CO₂ or ozone level, and fluctuations in sea level. The threat of varying global climate has greatly driven the attention of scientists, as these variations are imparting negative impact on global crop production and compromising food security worldwide. According to some predicted reports, agriculture is considered the most endangered activity adversely affected by climate changes. To date, food security and ecosystem resilience are the most concerning subjects worldwide. Climate-smart agriculture is the only way to lower the negative impact of climate variations on crop adaptation, before it might affect global crop production drastically. In this review paper, we summarize the causes of climate change, stresses produced due to climate change, impacts on crops, modern breeding technologies, and biotechnological strategies to cope with climate change, in order to develop climate resilient crops. Revolutions in genetic engineering techniques can also aid in overcoming food security issues against extreme environmental conditions, by producing transgenic plants.

Natural variation in acyl editing is a determinant of seed storage oil composition

Seeds exhibit wide variation in the fatty acid composition of their storage oil. However, the genetic basis of this variation is only partially understood. Here we have used a multi-parent advanced generation inter-cross (MAGIC) population to study the genetic control of fatty acid chain length in Arabidopsis thaliana seed oil. We mapped four quantitative trait loci (QTL) for the quantity of the major very long chain fatty acid species 11-eicosenoic acid (20:1), using multiple QTL modelling. Surprisingly, the main-effect QTL does not coincide with FATTY ACID ELONGASE 1 and a parallel genome wide association study suggested that LYSOPHOSPHATIDYLCHOLINE ACYLTRANSFERASE 2 (LPCAT2) is a candidate for this QTL. Regression analysis also suggested that LPCAT2 expression and 20:1 content in seeds of the 19 MAGIC founder accessions are related. LPCAT is a key component of the Lands cycle; an acyl editing pathway that enables acyl-exchange between the acyl-Coenzyme A and phosphatidylcholine precursor pools used for microsomal fatty acid elongation and desaturation, respectively. We Mendelianised the main-effect QTL using biparental chromosome segment substitution lines and carried out complementation tests to show that a single cis-acting polymorphism in the LPCAT2 promoter causes the variation in seed 20:1 content, by altering the LPCAT2 expression level and total LPCAT activity in developing siliques. Our work establishes that oilseed species exhibit natural variation in the enzymic capacity for acyl editing and this contributes to the genetic control of storage oil composition.

Biotechnological and Digital Revolution for Climate-Smart Plant Breeding

Climate change, associated with global warming, extreme weather events, and increasing incidence of weeds, pests and pathogens, is strongly influencing major cropping systems. In this challenging scenario, miscellaneous strategies are needed to expedite the rate of genetic gains with the purpose of developing novel varieties. Large plant breeding populations, efficient high-throughput technologies, big data management tools, and downstream biotechnology and molecular techniques are the pillars on which next generation breeding is based. In this review, we describe the toolbox the breeder has to face the challenges imposed by climate change, remark on the key role bioinformatics plays in the analysis and interpretation of big “omics” data, and acknowledge all the benefits that have been introduced into breeding strategies with the biotechnological and digital revolution.

Introgression of Physiological Traits for a Comprehensive Improvement of Drought Adaptation in Crop Plants

Burgeoning population growth, industrial demand, and the predicted global climate change resulting in erratic monsoon rains are expected to severely limit fresh water availability for agriculture both in irrigated and rainfed ecosystems. In order to remain food and nutrient secure, agriculture research needs to focus on devising strategies to save water in irrigated conditions and to develop superior cultivars with improved water productivity to sustain yield under rainfed conditions. Recent opinions accruing in the scientific literature strongly favor the adoption of a "trait based" crop improvement approach for increasing water productivity. Traits associated with maintenance of positive tissue turgor and maintenance of increased carbon assimilation are regarded as most relevant to improve crop growth rates under water limiting conditions and to enhance water productivity. The advent of several water saving agronomic practices notwithstanding, a genetic enhancement strategy of introgressing distinct physiological, morphological, and cellular mechanisms on to a single elite genetic background is essential for achieving a comprehensive improvement in drought adaptation in crop plants. The significant progress made in genomics, though would provide the necessary impetus, a clear understanding of the "traits" to be introgressed is the most essential need of the hour. Water uptake by a better root architecture, water conservation by preventing unproductive transpiration are crucial for maintaining positive tissue water relations. Improved carbon assimilation associated with carboxylation capacity and mesophyll conductance is important in sustaining crop growth rates under water limited conditions. Besides these major traits, we summarize the available information in literature on classifying various drought adaptive traits. We provide evidences that Water-Use Efficiency when introgressed with moderately higher transpiration, would significantly enhance growth rates and water productivity in rice through an improved photosynthetic capacity.

Genetic Mapping Populations for Conducting High-Resolution Trait Mapping in Plants

Fine mapping of quantitative trait loci (QTL) is the route to more detailed molecular characterization and functional studies of the relationship between polymorphism and trait variation. It is also of direct relevance to breeding since it makes QTL more easily integrated into marker-assisted breeding and into genomic selection. Fine mapping requires that marker-trait associations are tested in populations in which large numbers of recombinations have occurred. This can be achieved by increasing the size of mapping populations or by increasing the number of generations of crossing required to create the population. We review the factors affecting the precision and power of fine mapping experiments and describe some contemporary experimental approaches, focusing on the use of multi-parental or multi-founder populations such as the multi-parent advanced generation intercross (MAGIC) and nested association mapping (NAM). We favor approaches such as MAGIC since these focus explicitly on increasing the amount of recombination that occurs within the population. Whatever approaches are used, we believe the days of mapping QTL in small populations must come to an end. In our own work in MAGIC wheat populations, we started with a target of developing 1,000 lines per population: that number now looks to be on the low side. Graphical Abstract.

Mapping QTLs Controlling Flowering Time and Important Agronomic Traits in Pearl Millet

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a staple crop for the people of arid and semi-arid regions of the world. It is fast gaining importance as a climate resilient nutricereal. Exploiting the bold seeded, semi-dwarf, and early flowering genotypes in pearl millet is a key breeding strategy to enhance yield, adaptability, and for adequate food in resource-poor zones. Genetic variation for agronomic traits of pearl millet inbreds can be used to dissect complex traits through quantitative trait locus (QTL) mapping. This study was undertaken to map a set of agronomically important traits like flowering time (FT), plant height (PH), panicle length (PL), and grain weight (self and open-pollinated seeds) in the recombinant inbred line (RIL) population of ICMB 841-P3 × 863B-P2 cross. Excluding grain weight (open pollinated), heritabilities for FT, PH, PL, grain weight (selfed) were in high to medium range. A total of six QTLs for FT were detected on five chromosomes, 13 QTLs for PH on six chromosomes, 11 QTLs for PL on five chromosomes, and 14 QTLs for 1,000-grain weight (TGW) spanning five chromosomes. One major QTL on LG3 was common for FT and PH. Three major QTLs for PL, one each on LG1, LG2, and LG6B were detected. The large effect QTL for TGW (self) on LG6B had a phenotypic variance (R²) of 62.1%. The R² for FT, TGW (self), and PL ranged from 22.3 to 59.4%. A total of 21 digenic interactions were discovered for FT (R² = 18-40%) and PL (R² = 13-19%). The epistatic effects did not reveal any significant QTL × QTL × environment (QQE) interactions. The mapped QTLs for flowering time and other agronomic traits in present experiment can be used for marker-assisted selection (MAS) and genomic selection (GS) breeding programs.

Molecular mapping and genomics of soybean seed protein: a review and perspective for the future

KEY MESSAGE

Genetic improvement of soybean protein meal is a complex process because of negative correlation with oil, yield, and temperature. This review describes the progress in mapping and genomics, identifies knowledge gaps, and highlights the need of integrated approaches. Meal protein derived from soybean [Glycine max (L) Merr.] seed is the primary source of protein in poultry and livestock feed. Protein is a key factor that determines the nutritional and economical value of soybean. Genetic improvement of soybean seed protein content is highly desirable, and major quantitative trait loci (QTL) for soybean protein have been detected and repeatedly mapped on chromosomes (Chr.) 20 (LG-I), and 15 (LG-E). However, practical breeding progress is challenging because of seed protein content's negative genetic correlation with seed yield, other seed components such as oil and sucrose, and interaction with environmental effects such as temperature during seed development. In this review, we discuss rate-limiting factors related to soybean protein content and nutritional quality, and potential control factors regulating seed storage protein. In addition, we describe advances in next-generation sequencing technologies for precise detection of natural variants and their integration with conventional and high-throughput genotyping technologies. A syntenic analysis of QTL on Chr. 15 and 20 was performed. Finally, we discuss comprehensive approaches for integrating protein and amino acid QTL, genome-wide association studies, whole-genome resequencing, and transcriptome data to accelerate identification of genomic hot spots for allele introgression and soybean meal protein improvement.

Development and Genetic Characterization of Advanced Backcross Materials and An Introgression Line Population of Solanum incanum in a S. melongena Background

Advanced backcrosses (ABs) and introgression lines (ILs) of eggplant (Solanum melongena) can speed up genetics and genomics studies and breeding in this crop. We have developed the first full set of ABs and ILs in eggplant using Solanum incanum, a wild eggplant that has a relatively high tolerance to drought, as a donor parent. The development of these ABs and IL eggplant populations had a low efficiency in the early stages, because of the lack of molecular markers and genomic tools. However, this dramatically improved after performing genotyping-by-sequencing in the first round of selfing, followed by high-resolution-melting single nucleotide polymorphism genotyping in subsequent selection steps. A set of 73 selected ABs covered 99% of the S. incanum genome, while 25 fixed immortal ILs, each carrying a single introgressed fragment in homozygosis, altogether spanned 61.7% of the S. incanum genome. The introgressed size fragment in the ILs contained between 0.1 and 10.9% of the S. incanum genome, with a mean value of 4.3%. Sixty-eight candidate genes involved in drought tolerance were identified in the set of ILs. This first set of ABs and ILs of eggplant will be extremely useful for the genetic dissection of traits of interest for eggplant, and represents an elite material for introduction into the breeding pipelines for developing new eggplant cultivars adapted to the challenges posed by the climate-change scenario.

Quantitative trait loci from identification to exploitation for crop improvement

Advancement in the field of genetics and genomics after the discovery of Mendel's laws of inheritance has led to map the genes controlling qualitative and quantitative traits in crop plant species. Mapping of genomic regions controlling the variation of quantitatively inherited traits has become routine after the advent of different types of molecular markers. Recently, the next generation sequencing methods have accelerated the research on QTL analysis. These efforts have led to the identification of more closely linked molecular markers with gene/QTLs and also identified markers even within gene/QTL controlling the trait of interest. Efforts have also been made towards cloning gene/QTLs or identification of potential candidate genes responsible for a trait. Further new concepts like crop QTLome and QTL prioritization have accelerated precise application of QTLs for genetic improvement of complex traits. In the past years, efforts have also been made in exploitation of a number of QTL for improving grain yield or other agronomic traits in various crops through markers assisted selection leading to cultivation of these improved varieties at farmers' field. In present article, we reviewed QTLs from their identification to exploitation in plant breeding programs and also reviewed that how improved cultivars developed through introgression of QTLs have improved the yield productivity in many crops.

Oxidative discolouration in whole-head and cut lettuce: biochemical and environmental influences on a complex phenotype and potential breeding strategies to improve shelf-life

Lettuce discolouration is a key post-harvest trait. The major enzyme controlling oxidative discolouration has long been considered to be polyphenol oxidase (PPO) however, levels of PPO and subsequent development of discolouration symptoms have not always correlated. The predominance of a latent state of the enzyme in plant tissues combined with substrate activation and contemporaneous suicide inactivation mechanisms are considered as potential explanations for this phenomenon. Leaf tissue physical properties have been associated with subsequent discolouration and these may be influenced by variation in nutrient availability, especially excess nitrogen and head maturity at harvest. Mild calcium and irrigation stress has also been associated with a reduction in subsequent discolouration, although excess irrigation has been linked to increased discolouration potentially through leaf physical properties. These environmental factors, including high temperature and UV light intensities, often have impacts on levels of phenolic compounds linking the environmental responses to the biochemistry of the PPO pathway. Breeding strategies targeting the PAL and PPO pathway biochemistry and environmental response genes are discussed as a more cost-effective method of mitigating oxidative discolouration then either modified atmosphere packaging or post-harvest treatments, although current understanding of the biochemistry means that such programs are likely to be limited in nature and it is likely that they will need to be deployed alongside other methods for the foreseeable future.

Identification of QTLs controlling grain protein concentration using a high-density SNP and SSR linkage map in barley (Hordeum vulgare L.)

BACKGROUND

Grain protein concentration (GPC) is a major determinant of quality in barley (Hordeum vulgare L.). Breeding barley cultivars with high GPC has practical value for feed and food properties. The aim of the present study was to identify quantitative trait loci (QTLs) for GPC that could be detected under multiple environments.

RESULTS

A population of 190 recombinant inbred lines (RILs) deriving from a cross between Chinese landrace ZGMLEL with high GPC (> 20%) and Australian cultivar Schooner was used for linkage and QTL analyses. The genetic linkage map spanned 2353.48 cM in length with an average locus interval of 2.33 cM. GPC was evaluated under six environments for the RIL population and the two parental lines. In total, six environmentally stable QTLs for GPC were detected on chromosomes 2H (1), 4H (1), 6H (1), and 7H (3) and the increasing alleles were derived from ZGMLEL. Notably, the three QTLs on chromosome 7H (QGpc.ZiSc-7H.1, QGpc.ZiSc-7H.2, and QGpc.ZiSc-7H.3) that linked in coupling phase were firstly identified. Moreover, the genetic effects of stable QTLs on chromosomes 2H, 6H and 7H were validated using near isogenic lines (NILs).

CONCLUSIONS

Collectively, the identified QTLs expanded our knowledge about the genetic basis of GPC in barley and could be selected to develop cultivars with high grain protein concentration.

Adapting Genotyping-by-Sequencing for Rice F2 Populations

Rapid and cost-effective genotyping of large mapping populations can be achieved by sequencing a reduced representation of the genome of every individual in a given population, and using that information to generate genetic markers. A customized genotyping-by-sequencing (GBS) pipeline was developed to genotype a rice F2 population from a cross of Oryza sativa ssp. japonica cv. Nipponbare and the African wild rice species O. longistaminata While most GBS pipelines aim to analyze mainly homozygous populations, we attempted to genotype a highly heterozygous F2 population. We show how species- and population-specific improvements of established protocols can drastically increase sample throughput and genotype quality. Using as few as 50,000 reads for some individuals (134,000 reads on average), we were able to generate up to 8154 informative SNP markers in 1081 F2 individuals. Additionally, the effects of enzyme choice, read coverage, and data postprocessing are evaluated. Using GBS-derived markers, we were able to assemble a genetic map of 1536 cM. To demonstrate the usefulness of our GBS pipeline, we determined quantitative trait loci (QTL) for the number of tillers. We were able to map four QTL to chromosomes 1, 3, 4, and 8, and partially confirm their effects using introgression lines. We provide an example of how to successfully use GBS with heterozygous F2 populations. By using the comparatively low-cost MiSeq platform, we show that the GBS method is flexible and cost-effective, even for smaller laboratories.

Insight into MAS: A Molecular Tool for Development of Stress Resistant and Quality of Rice through Gene Stacking

Rice yield is subjected to severe losses due to adverse effect of a number of stress factors. The most effective method of controlling reduced crop production is utilization of host resistance. Recent technological advances have led to the improvement of DNA based molecular markers closely linked to genes or QTLs in rice chromosome that bestow tolerance to various types of abiotic stresses and resistance to biotic stress factors. Transfer of several genes with potential characteristics into a single genotype is possible through the process of marker assisted selection (MAS), which can quicken the advancement of tolerant/resistant cultivars in the lowest number of generations with the utmost precision through the process of gene pyramiding. Overall, this review presented various types of molecular tools including MAS that can be reasonable and environmental friendly approach for the improvement of abiotic and biotic stress resistant rice with enhanced quality.

Improvement in nitrogen fixation capacity could be part of the domestication process in soybean

Biological nitrogen fixation (BNF) in soybeans is a complex process involving the interplay between the plant host and the symbiotic rhizobia. As nitrogen supply has a crucial role in growth and development, higher nitrogen fixation capacity would be important to achieve bigger plants and larger seeds, which were important selection criteria during plant domestication by humans. To test this hypothesis, we monitored the nitrogen fixation-related performance in 31 cultivated and 17 wild soybeans after inoculation with the slow-growing Bradyrhizobium diazoefficiens sp. nov. USDA110 and the fast-growing Sinorhizobium (Ensifer) fredii CCBAU45436. Our results showed that, in general, cultivated soybeans gave better performance in BNF. Electron microscopic studies indicated that there was an exceptionally high accumulation of poly-β-hydroxybutyrate bodies in bacteroids in the nodules of all wild soybeans tested, suggesting that the C/N balance in wild soybeans may not be optimized for nitrogen fixation. Furthermore, we identified new quantitative trait loci (QTLs) for total ureides and total nodule fresh weight by employing a recombinant inbred population composed of descendants from a cross between a cultivated and a wild parent. Using nucleotide diversity (θπ), divergence index (Fst) and distribution of fixed single-nucleotide polymorphisms as parameters, we found that some regions in the total ureides QTL on chromosome 17 and the total nodule fresh weight QTL on chromosome 12 exhibited very low diversity among cultivated soybeans, suggesting that these were traits specially selected during the domestication and breeding process.

Genetic and Quantitative Trait Locus Analysis for Bio-Oil Compounds after Fast Pyrolysis in Maize Cobs

Fast pyrolysis has been identified as one of the biorenewable conversion platforms that could be a part of an alternative energy future, but it has not yet received the same attention as cellulosic ethanol in the analysis of genetic inheritance within potential feedstocks such as maize. Ten bio-oil compounds were measured via pyrolysis/gas chromatography-mass spectrometry (Py/GC-MS) in maize cobs. 184 recombinant inbred lines (RILs) of the intermated B73 x Mo17 (IBM) Syn4 population were analyzed in two environments, using 1339 markers, for quantitative trait locus (QTL) mapping. QTL mapping was performed using composite interval mapping with significance thresholds established by 1000 permutations at α = 0.05. 50 QTL were found in total across those ten traits with R2 values ranging from 1.7 to 5.8%, indicating a complex quantitative inheritance of these traits.

Dissecting quantitative trait variation in the resequencing era: complementarity of bi-parental, multi-parental and association panels

Quantitative trait loci (QTL) have been identified using traditional linkage mapping and positional cloning identified several QTLs. However linkage mapping is limited to the analysis of traits differing between two lines and the impact of the genetic background on QTL effect has been underlined. Genome-wide association studies (GWAs) were proposed to circumvent these limitations. In tomato, we have shown that GWAs is possible, using the admixed nature of cherry tomato genomes that reduces the impact of population structure. Nevertheless, GWAs success might be limited due to the low decay of linkage disequilibrium, which varies along the genome in this species. Multi-parent advanced generation intercross (MAGIC) populations offer an alternative to traditional linkage and GWAs by increasing the precision of QTL mapping. We have developed a MAGIC population by crossing eight tomato lines whose genomes were resequenced. We showed the potential of the MAGIC population when coupled with whole genome sequencing to detect candidate single nucleotide polymorphisms (SNPs) underlying the QTLs. QTLs for fruit quality traits were mapped and related to the variations detected at the genome sequence and expression levels. The advantages and limitations of the three types of population, in the context of the available genome sequence and resequencing facilities, are discussed.

Combining powers of linkage and association mapping for precise dissection of QTL controlling resistance to gray leaf spot disease in maize (Zea mays L.)

BACKGROUND

Gray Leaf Spot (GLS causal agents Cercospora zeae-maydis and Cercospora zeina) is one of the most important foliar diseases of maize in all areas where the crop is being cultivated. Although in the USA the situation with GLS severity is not as critical as in sub-Saharan Africa or Brazil, the evidence of climate change, increasing corn monoculture as well as the narrow genetic base of North American resistant germplasm can turn the disease into a serious threat to US corn production. The development of GLS resistant cultivars is one way to control the disease. In this study we combined the high QTL detection power of genetic linkage mapping with the high resolution power of genome-wide association study (GWAS) to precisely dissect QTL controlling GLS resistance and identify closely linked molecular markers for robust marker-assisted selection and trait introgression.

RESULTS

Using genetic linkage analysis with a small bi-parental mapping population, we identified four GLS resistance QTL on chromosomes 1, 6, 7, and 8, which were validated by GWAS. GWAS enabled us to dramatically increase the resolution within the confidence intervals of the above-mentioned QTL. Particularly, GWAS revealed that QTLGLSchr8, detected by genetic linkage mapping as a locus with major effect, was likely represented by two QTL with smaller effects. Conducted in parallel, GWAS of days-to-silking demonstrated the co-localization of flowering time QTL with GLS resistance QTL on chromosome 7 indicating that either QTLGLSchr7 is a flowering time QTL or it is a GLS resistance QTL that co-segregates with the latter. As a result, this genetic linkage - GWAS hybrid mapping system enabled us to identify one novel GLS resistance QTL (QTLGLSchr8a) and confirm with more refined positions four more previously mapped QTL (QTLGLSchr1, QTLGLSchr6, QTLGLSchr7, and QTLGLSchr8b). Through the novel Single Donor vs. Elite Panel method we were able to identify within QTL confidence intervals SNP markers that would be suitable for marker-assisted selection of gray leaf spot resistant genotypes containing the above-mentioned GLS resistance QTL.

CONCLUSION

The application of a genetic linkage - GWAS hybrid mapping system enabled us to dramatically increase the resolution within the confidence interval of GLS resistance QTL by-passing labor- and time-intensive fine mapping. This method appears to have a great potential to accelerate the pace of QTL mapping projects. It is universal and can be used in the QTL mapping projects in any crops.

The Genetics of Nitrogen Use Efficiency in Crop Plants

In the past 50 years, the application of synthetic nitrogen (N) fertilizer to farmland resulted in a dramatic increase in crop yields but with considerable negative impacts on the environment. New solutions are therefore needed to simultaneously increase yields while maintaining, or preferably decreasing, applied N to maximize the nitrogen use efficiency (NUE) of crops. In this review, we outline the definition of NUE, the selection and development of NUE crops, and the factors that interact with NUE. In particular, we emphasize the challenges of developing crop plants with enhanced NUE, using more classical genetic approaches based on utilizing existing allelic variation for NUE traits. The challenges of phenotyping, mapping quantitative trait loci (QTLs), and selecting candidate genes for NUE improvement are described. In addition, we highlight the importance of different factors that lead to changes in the NUE components of nitrogen uptake efficiency (NUpE) and nitrogen utilization efficiency (NUtE).

Identification of Putative Molecular Markers Associated with Root Traits in Coffea canephora Pierre ex Froehner

Coffea canephora exhibit poor root system and are very sensitive to drought stress that affects growth and production. Deeper root system has been largely empirical as better avoidance to soil water limitation in drought condition. The present study aimed to identify molecular markers linked to high root types in Coffea canephora using molecular markers. Contrasting parents, L1 valley with low root and S.3334 with high root type, were crossed, and 134 F1 individuals were phenotyped for root and associated physiological traits (29 traits) and genotyped with 41 of the 320 RAPD and 9 of the 55 SSR polymorphic primers. Single marker analysis was deployed for detecting the association of markers linked to root associated traits by SAS software. There were 13 putative RAPD markers associated with root traits such as root length, secondary roots, root dry weight, and root to shoot ratio, in which root length associated marker OPS1850 showed high phenotypic variance of 6.86%. Two microsatellite markers linked to root length (CPCM13400) and root to shoot ratio (CM211300). Besides, 25 markers were associated with more than one trait and few of the markers were associated with positively related physiological traits and can be used in marker assisted trait selection.

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

BACKGROUND AND AIMS

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress.

METHODS

Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions.

KEY RESULTS

To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait 'total crop nitrogen uptake' (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10-36 % more yield than those based on markers for yield per se.

CONCLUSIONS

This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for specific environments whilst also allowing for prioritization. Crop modelling is thus a powerful tool for marker design for improved rice yields and for ideotyping under contrasting conditions.

Comparison and Confirmation of Quantitative Trait Loci Conferring Partial Resistance to Rice Sheath Blight on Chromosome 9

Sheath blight (SB), caused by Rhizoctonia solani, is one of the worst rice (Orzya sativa) diseases worldwide. Resistance to the SB disease in rice is a complex trait controlled by quantitative trait loci (QTLs). Through map integration, we found several previously identified SB resistance (SBR) QTLs reported in inconsistent regions on the long arm of chromosome 9. Five of them were detected on 'Jasmine 85' (J85), 'Minghui 63' (MH63), and 'Lemont' (LMNT) rice and were designated qSB-9^J85-1, qSB-9^J85-2, qSB-9^MH63-1, qSB-9^MH63-2, and qSB-9^LMNT, respectively, in the present study. To further verify and physically map the five potential SBR QTLs, we introduced these SBR QTLs into a common susceptible variety (LMNT) and developed a few chromosomal segment substitution lines through marker-assisted selection. After artificial inoculation with the SB fungus, we were able to validate qSB-9^J85-2 but not the other four SBR QTLs; whereas, on MH63, an SBR QTL designated qSB-9^MH63-3 was confirmed in the region defined by markers Y83 and Y91.8 that included qSB-9^J85-2, covering approximately 1,235 kb. Both qSB-9^J85-2 and qSB-9^MH63-3 appeared to be dominant resistance genes and contributed to similar levels to SB resistance, reducing SB disease severity by approximately 1.0 on a 0-to-9 SB disease rating system. After comparing with another confirmed SBR QTL (qSB-9^TQ) from 'Teqing' rice (TQ), we conclude that qSB-9^J85-2, qSB-9^MH63-3, and qSB-9^TQ are probably controlled by the same allelic resistance genes. These results will accelerate the utilization of this major SBR QTL and its map-based cloning.

Genetic control of functional traits related to photosynthesis and water use efficiency in Pinus pinaster Ait. drought response: integration of genome annotation, allele association and QTL detection for candidate gene identification

BACKGROUND

Understanding molecular mechanisms that control photosynthesis and water use efficiency in response to drought is crucial for plant species from dry areas. This study aimed to identify QTL for these traits in a Mediterranean conifer and tested their stability under drought.

RESULTS

High density linkage maps for Pinus pinaster were used in the detection of QTL for photosynthesis and water use efficiency at three water irrigation regimes. A total of 28 significant and 27 suggestive QTL were found. QTL detected for photochemical traits accounted for the higher percentage of phenotypic variance. Functional annotation of genes within the QTL suggested 58 candidate genes for the analyzed traits. Allele association analysis in selected candidate genes showed three SNPs located in a MYB transcription factor that were significantly associated with efficiency of energy capture by open PSII reaction centers and specific leaf area.

CONCLUSIONS

The integration of QTL mapping of functional traits, genome annotation and allele association yielded several candidate genes involved with molecular control of photosynthesis and water use efficiency in response to drought in a conifer species. The results obtained highlight the importance of maintaining the integrity of the photochemical machinery in P. pinaster drought response.