Optimization of training sets for genomic prediction of early-stage single crosses in maize

Training population optimization algorithms are useful for efficiently training genomic prediction models for single-cross performance, especially if the population is extended beyond only realized crosses to all possible single crosses. Genomic prediction of single-cross performance could allow effective evaluation of all possible single crosses between all inbreds developed in a hybrid breeding program. The objectives of the present study were to investigate the effect of different levels of relatedness on genomic predictive ability of single crosses, evaluate the usefulness of deterministic formula to forecast prediction accuracy in advance, and determine the potential for TRS optimization based on prediction error variance (PEVmean) and coefficient of determination (CDmean) criteria. We used 481 single crosses made by crossing 89 random recombinant inbred lines (RILs) belonging to the Iowa stiff stalk synthetic group with 103 random RILs belonging to the non-stiff stalk synthetic heterotic group. As expected, predictive ability was enhanced by ensuring close relationships between TRSs and target sets, even when TRS sizes were smaller. We found that designing a TRS based on PEVmean or CDmean criteria is useful for increasing the efficiency of genomic prediction of maize single crosses. We went further and extended the sampling space from that of all observed single crosses to all possible single crosses, providing a much larger genetic space within which to design a training population. Using all possible single crosses increased the advantage of the PEVmean and CDmean methods based on expected prediction accuracy. This finding suggests that it may be worthwhile using an optimization algorithm to select a training population from all possible single crosses to maximize efficiency in training accurate models for hybrid genomic prediction.

Selection of ideotype to increase yield potential of GM and non-GM rice cultivars

Using classical breeding, plant breeders envision a plant type whose yield they aim to enhance by selecting for individual traits to create model/ideal plants or ideotypes. To achieve this, those factors restricting yield need to be identified and controlled through the use of new technologies to achieve the desired ideotype. This study aimed to determine the ideotype of seven genetically modified (GM) and non-GM rice (Oryza sativa L.) cultivars. Field experiments were carried out in three isolated regions in the north of Iran under the Iranian bio-safety standard protocol. Four of the GM cultivars carried the cry1Ab gene in the vegetative stage while three non-GM cultivars served as the control. R² values showed that five, six and seven variables in Sari, Amol and Rasht regions accounted for 63 %, 52 % and 74 % of paddy yield variation, respectively. In the same three regions, paddy yield variation due to white heads accounted for 28.38 %, 8.45 % and 3.95 % of the total variation in paddy yield, respectively. The total estimated variation in paddy yield in Sari, Amol and Rasht was 1810.50, 2377.6 and 2176.47 kg ha^-1, respectively. Average data over the three regions indicated that highest loss in paddy yield was observed in non-GM 'Nemat', 'Khazar' and 'Tarom Hashemi'. GM cultivars derived from 'Khazar' showed significantly lower paddy yield loss than the non-GM parent. Dead heart, a condition that occurs in the vegetative stage in which the stem borer larva enters the stem and feeds on the growing shoot, causing the central shoot to dry, as well as white heads, which is a condition in which whole ear heads of adult plants become dry and yield chaffy grains, in all three regions were important variables contributing to paddy yield loss. In the future, producing GM rice resistant to striped stem borer with an active promoter in the reproductive growth stage might allow farmers to reduce a significant part of paddy yield loss resulting from white heads, which is directly negatively correlated with filled spikelets per panicle (R² = -0.57**), in order to achieve an ideotype.

Translational genomics for achieving higher genetic gains in groundnut

KEY MESSAGE

Groundnut has entered now in post-genome era enriched with optimum genomic and genetic resources to facilitate faster trait dissection, gene discovery and accelerated genetic improvement for developing climate-smart varieties. Cultivated groundnut or peanut (Arachis hypogaea), an allopolyploid oilseed crop with a large and complex genome, is one of the most nutritious food. This crop is grown in more than 100 countries, and the low productivity has remained the biggest challenge in the semiarid tropics. Recently, the groundnut research community has witnessed fast progress and achieved several key milestones in genomics research including genome sequence assemblies of wild diploid progenitors, wild tetraploid and both the subspecies of cultivated tetraploids, resequencing of diverse germplasm lines, genome-wide transcriptome atlas and cost-effective high and low-density genotyping assays. These genomic resources have enabled high-resolution trait mapping by using germplasm diversity panels and multi-parent genetic populations leading to precise gene discovery and diagnostic marker development. Furthermore, development and deployment of diagnostic markers have facilitated screening early generation populations as well as marker-assisted backcrossing breeding leading to development and commercialization of some molecular breeding products in groundnut. Several new genomics applications/technologies such as genomic selection, speed breeding, mid-density genotyping assay and genome editing are in pipeline. The integration of these new technologies hold great promise for developing climate-smart, high yielding and more nutritious groundnut varieties in the post-genome era.

Genetic Associations in Four Decades of Multienvironment Trials Reveal Agronomic Trait Evolution in Common Bean

Multienvironment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET data sets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean (Phaseolus vulgaris) grown for > 70 years in the United States and Canada, consisting of 20-50 entries each year at 10-20 locations. The CDBN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association studies (GWAS) using best linear unbiased predictions derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2 million SNPs) for 327 CDBN genotypes. The value of this approach was confirmed by the discovery of three candidate genes and genomic regions previously identified in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect significant correlated effects, found significant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover significant genetic effects and to define genomic regions associated with crop improvement.

Genomic Breeding of Green Super Rice Varieties and Their Deployment in Asia and Africa

KEY MESSAGE

The "Green Super Rice" (GSR) project aims to fundamentally transform crop production techniques and promote the development of green agriculture based on functional genomics and breeding of GSR varieties by whole-genome breeding platforms. Rice (Oryza sativa L.) is one of the leading food crops of the world, and the safe production of rice plays a central role in ensuring food security. However, the conflicts between rice production and environmental resources are becoming increasingly acute. For this reason, scientists in China have proposed the concept of Green Super Rice for promoting resource-saving and environment-friendly rice production, while still achieving a yield increase and quality improvement. GSR is becoming one of the major goals for agricultural research and crop improvement worldwide, which aims to mine and use vital genes associated with superior agronomic traits such as high yield, good quality, nutrient efficiency, and resistance against insects and stresses; establish genomic breeding platforms to breed and apply GSR; and set up resource-saving and environment-friendly cultivation management systems. GSR has been introduced into eight African and eight Asian countries and has contributed significantly to rice cultivation and food security in these countries. This article mainly describes the GSR concept and recent research progress, as well as the significant achievements in GSR breeding and its application.

Genome-based breeding approaches in major vegetable crops

Vegetable crops are major nutrient sources for humanity and have been well-cultivated since thousands of years of domestication. With the rapid development of next-generation sequencing and high-throughput genotyping technologies, the reference genome of more than 20 vegetables have been well-assembled and published. Resequencing approaches on large-scale germplasm resources have clarified the domestication and improvement of vegetable crops by human selection; its application on genetic mapping and quantitative trait locus analysis has led to the discovery of key genes and molecular markers linked to important traits in vegetables. Moreover, genome-based breeding has been utilized in many vegetable crops, including Solanaceae, Cucurbitaceae, Cruciferae, and other families, thereby promoting molecular breeding at a single-nucleotide level. Thus, genome-wide SNP markers have been widely used, and high-throughput genotyping techniques have become one of the most essential methods in vegetable breeding. With the popularization of gene editing technology research on vegetable crops, breeding efficiency can be rapidly increased, especially by combining the genomic and variomic information of vegetable crops. This review outlines the present genome-based breeding approaches used for major vegetable crops to provide insights into next-generation molecular breeding for the increasing global population.

Genetic and genomic research for the development of an efficient breeding system in heterostylous self-incompatible common buckwheat (Fagopyrum esculentum)

Common buckwheat (Fagopyrum esculentum Moench; 2n = 2x = 16) is an annual crop that is cultivated widely around the world and contains an abundance of nutrients and bioactive compounds. However, the yield of buckwheat is low compared to that of other major crops, and it contains proteins that cause allergic reactions in some people. Much research has aimed to improve or eliminate these undesirable traits, and some major advances have recently been made. Here, we review recent advances in buckwheat breeding materials, tools, and methods, including the development of self-compatible lines, genetic maps, a buckwheat genome database, and an efficient breeding strategy. We also describe emerging breeding methods for high-value lines.

Genomics-assisted breeding for pigeonpea improvement

The review outlines advances in pigeonpea genomics, breeding and seed delivery systems to achieve yield gains at farmers' field. Pigeonpea is a nutritious and stress-tolerant grain legume crop of tropical and subtropical regions. Decades of breeding efforts in pigeonpea have resulted in development of a number of high-yielding cultivars. Of late, the development of CMS-based hybrid technology has allowed the exploitation of heterosis for yield enhancement in this crop. Despite these positive developments, the actual on-farm yield of pigeonpea is still well below its potential productivity. Growing needs for high and sustainable pigeonpea yields motivate scientists to improve the breeding efficiency to deliver a steady stream of cultivars that will provide yield benefits under both ideal and stressed environments. To achieve this objective in the shortest possible time, it is imperative that various crop breeding activities are integrated with appropriate new genomics technologies. In this context, the last decade has seen a remarkable rise in the generation of important genomic resources such as genome-wide markers, high-throughput genotyping assays, saturated genome maps, marker/gene-trait associations, whole-genome sequence and germplasm resequencing data. In some cases, marker/gene-trait associations are being employed in pigeonpea breeding programs to improve the valuable yield and market-preferred traits. Embracing new breeding tools like genomic selection and speed breeding is likely to improve genetic gains. Breeding high-yielding pigeonpea cultivars with key adaptation traits also calls for a renewed focus on systematic selection and utilization of targeted genetic resources. Of equal importance is to overcome the difficulties being faced by seed industry to take the new cultivars to the doorstep of farmers.

Fine mapping and gene cloning in the post-NGS era: advances and prospects

Improvement in traits of agronomic importance is the top breeding priority of crop improvement programs. Majority of these agronomic traits show complex quantitative inheritance. Identification of quantitative trait loci (QTLs) followed by fine mapping QTLs and cloning of candidate genes/QTLs is central to trait analysis. Advances in genomic technologies revolutionized our understanding of genetics of complex traits, and genomic regions associated with traits were employed in marker-assisted breeding or cloning of QTLs/genes. Next-generation sequencing (NGS) technologies have enabled genome-wide methodologies for the development of ultra-high-density genetic linkage maps in different crops, thus allowing placement of candidate loci within few kbs in genomes. In this review, we compare the marker systems used for fine mapping and QTL cloning in the pre- and post-NGS era. We then discuss how different NGS platforms in combination with advanced experimental designs have improved trait analysis and fine mapping. We opine that efficient genotyping/sequencing assays may circumvent the need for cumbersome procedures that were earlier used for fine mapping. A deeper understanding of the trait architectures of agricultural significance will be crucial to accelerate crop improvement.

Integrating genomics for chickpea improvement: achievements and opportunities

Integration of genomic technologies with breeding efforts have been used in recent years for chickpea improvement. Modern breeding along with low cost genotyping platforms have potential to further accelerate chickpea improvement efforts. The implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea.

Impacts of genomic research on soybean improvement in East Asia

It has been commonly accepted that soybean domestication originated in East Asia. Although East Asia has the historical merit in soybean production, the USA has become the top soybean producer in the world since 1950s. Following that, Brazil and Argentina have been the major soybean producers since 1970s and 1990s, respectively. China has once been the exporter of soybean to Japan before 1990s, yet she became a net soybean importer as Japan and the Republic of Korea do. Furthermore, the soybean yield per unit area in East Asia has stagnated during the past decade. To improve soybean production and enhance food security in these East Asian countries, much investment has been made, especially in the breeding of better performing soybean germplasms. As a result, China, Japan, and the Republic of Korea have become three important centers for soybean genomic research. With new technologies, the rate and precision of the identification of important genomic loci associated with desired traits from germplasm collections or mutants have increased significantly. Genome editing on soybean is also becoming more established. The year 2019 marked a new era for crop genome editing in the commercialization of the first genome-edited plant product, which is a high-oleic-acid soybean oil. In this review, we have summarized the latest developments in soybean breeding technologies and the remarkable progress in soybean breeding-related research in China, Japan, and the Republic of Korea.

Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax

Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 ± 0.016), PRO (0.893 ± 0.023), YLD (0.892 ± 0.030), and DTM (0.730 ± 0.062), and by SS+SM for LIN (0.837 ± 0.053), LIO (0.835 ± 0.049), and IOD (0.835 ± 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.

Diversity Analysis of Sweet Potato Genetic Resources Using Morphological and Qualitative Traits and Molecular Markers

The European Union (EU) market for sweet potatoes has increased by 100% over the last five years, and sweet potato cultivation in southern European countries is a new opportunity for the EU to exploit and introduce new genotypes. In view of this demand, the origins of the principal Italian sweet potato clones, compared with a core collection of genotypes from Central and Southern America, were investigated for the first time. This was accomplished by combining a genetic analysis, exploiting 14 hypervariable microsatellite markers, with morphological and chemical measurements based on 16 parameters. From the molecular analyses, Italian accessions were determined to be genetically very similar to the South American germplasm, but they were sub-clustered into two groups. This finding was subsequently confirmed by the morphological and chemical measurements. Moreover, the analysis of the genetic structure of the population suggested that one of the two groups of Italian genotypes may have descended from one of the South American accessions, as predicted on the basis of the shared morphological characteristics and molecular fingerprints. Overall, the combination of two different characterization methods, genetic markers and agronomic traits, was effective in differentiating or clustering the sweet potato genotypes, in agreement with their geographical origin or phenotypic descriptors. This information could be exploited by both breeders and farmers to detect and protect commercial varieties, and hence for traceability purposes.

Genome Editing in Agriculture: Technical and Practical Considerations

The advent of precise genome-editing tools has revolutionized the way we create new plant varieties. Three groups of tools are now available, classified according to their mechanism of action: Programmable sequence-specific nucleases, base-editing enzymes, and oligonucleotides. The corresponding techniques not only lead to different outcomes, but also have implications for the public acceptance and regulatory approval of genome-edited plants. Despite the high efficiency and precision of the tools, there are still major bottlenecks in the generation of new and improved varieties, including the efficient delivery of the genome-editing reagents, the selection of desired events, and the regeneration of intact plants. In this review, we evaluate current delivery and regeneration methods, discuss their suitability for important crop species, and consider the practical aspects of applying the different genome-editing techniques in agriculture.

Necrotrophic Exploitation and Subversion of Plant Defense: A Lifestyle or Just a Phase, and Implications in Breeding Resistance

Breeding disease-resistant plants is a critical, environmentally friendly component of any strategy to sustainably feed and clothe the 9.8 billion people expected to live on Earth by 2050. Here, I review current literature detailing plant defense responses as they relate to diverse biological outcomes; disease resistance, susceptibility, and establishment of mutualistic plant-microbial relationships. Of particular interest is the degree to which these outcomes are a function of plant-associated microorganisms' lifestyles; biotrophic, hemibiotrophic, necrotrophic, or mutualistic. For the sake of brevity, necrotrophic pathogens and the necrotrophic phase of pathogenicity are emphasized in this review, with special attention given to the host-specific pathogens that exploit defense. Defense responses related to generalist necrotrophs and mutualists are discussed in the context of excellent reviews by others. In addition, host evolutionary trade-offs of disease resistance with other desirable traits are considered in the context of breeding for durable disease resistance.

Genetic diversity in soybean genotypes using phenotypic characters and enzymatic markers

The objective of this study was to evaluate the genetic diversity of soybean cultivars by adopting phenotypic traits and enzymatic markers, the relative contribution of agronomic traits to diversity, as well as diversity between the level of technology used in soybean cultivars and genetic breeding programs in which cultivars were inserted. The experiments were conducted on the field at the Center for Scientific and Technological Development in crop-livestock production and the Electrophoresis Laboratory of Lavras Federal University. The agronomic traits adopted were grain yield, plant height, first legume insertion, plant lodging, the mass of one thousand seeds, and days for complete maturation, in which the Euclidean distance, grouped by Tocher and UPGMA criteria, was obtained. After electrophorese gels for enzymatic systems, dehydrogenase alcohol, esterase, superoxide dismutase, and peroxidase were performed. The genetic similarity estimative was also obtained between genotypes by the Jaccard coefficient with subsequent grouping by the UPGMA method. The formation of two groups was shown using phenotypic characters in the genetic diversity study and individually discriminating the cultivar 97R73 RR. The character with the greatest contribution to the genetic divergence was grain yield with contribution higher than 90.0%. To obtain six different groups, individually discriminating the cultivars CG 8166 RR, FPS Jupiter RR, and BRS MG 780 RR, enzymatic markers were used. Cultivars carrying the RR technology presented more divergence than conventional cultivars and IPRO cultivars.

Genetic parameters and selection of soybean lines based on selection indexes

Defining selection criteria is important to obtain promising genotypes in a breeding program. The objective of this study was to estimate genetic parameters for agronomic traits and to perform soybean line selection using selection indices. The experiment was conducted at an experimental area located at Capim Branco farm, belonging to the Federal University of Uberlândia. A total of 37 soybean genotypes were evaluated in randomized complete block design with three replicates, in which twelve agronomic traits were evaluated. Analysis of variance, the Scott-Knott test at the 1 and 5% level of probability, and selection index analyses were performed. There was genetic variability for all agronomic traits, with medium to high levels of genotype determination coefficient. Twelve lines with a total cycle up to 110 days were observed and grouped with the cultivars MSOY 6101 and UFUS 7910. Three lines, UFUS FG 03, UFUS FG 20, and UFUS FG 31, were highlighted regarding grain yield with higher values than the national average of 3072 kg/ha. The direct selection enabled the highest trait individual gains. The Williams (1962) index and the Smith (1936) and Hazel (1943) index presented the highest selection gain for the grain yield character. The genotype-ideotype distance index and the index of the sum of ranks of Mulamba and Mock (1978) presented higher values of total selection gain. The lines UFUS FG 12, UFUS FG 14, UFUS FG 18, UFUS FG 25, and UFUS FG 31 were distinguished as superior genotypes by direct selection methods and selection indexes.

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles; however, optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.