Breeding for Yield Potential and Stress Adaptation in Cereals

The need to accelerate breeding for increased yield potential and better adaptation to drought and other abiotic stresses is an issue of increasing urgency. As the population continues to grow rapidly, the pressure on resources (mainly untouched land and water) is also increasing, and potential climate change poses further challenges. We discuss ways to improve the efficiency of crop breeding through a better physiological understanding by both conventional and molecular methods. Thus the review highlights the physiological basis of crop yield and its response to stresses, with special emphasis on drought. This is not just because physiology forms the basis of proper phenotyping, one of the pillars of breeding, but because a full understanding of physiology is also needed, for example, to design the traits targeted by molecular breeding approaches such as marker-assisted selection (MAS) or plant transformation or the way these traits are evaluated. Most of the information in this review deals with cereals, since they include the world's main crops, however, examples from other crops are also included. Topics covered by the review include the conceptual framework for identifying secondary traits associated with yield potential and stress adaptation, and how to measure these secondary traits in practice. The second part of the review deals with the real role of molecular breeding for complex traits from a physiological perspective. This part examines current developments in MAS and quantitative trait loci (QTL) detection as well as plant transformation. Emphasis is placed on the current limitations of these molecular approaches to improving stress adaptation and yield potential. The essay ends by presenting some ideas regarding future avenues for crop breeding given the current and possible future challenges, and on a multidisciplinary approach where physiological knowledge and proper phenotyping play a major role.     

Whole-genome strategies for marker-assisted plant breeding

Molecular breeding for complex traits in crop plants requires understanding and manipulation of many factors influencing plant growth, development and responses to an array of biotic and abiotic stresses. Molecular marker-assisted breeding procedures can be facilitated and revolutionized through whole-genome strategies, which utilize full genome sequencing and genome-wide molecular markers to effectively address various genomic and environmental factors through a representative or complete set of genetic resources and breeding materials. These strategies are now increasingly based on understanding of specific genomic regions, genes/alleles, haplotypes, linkage disequilibrium (LD) block(s), gene networks and their contribution to specific phenotypes. Large-scale and high-density genotyping and genome-wide selection are two important components of these strategies. As components of whole-genome strategies, molecular breeding platforms and methodologies should be backed up by high throughput and precision phenotyping and e-typing (environmental assay) with strong support systems such as breeding informatics and decision support tools. Some basic strategies are discussed in this article, including (1) seed DNA-based genotyping for simplifying marker-assisted selection (MAS), reducing breeding cost and increasing scale and efficiency, (2) selective genotyping and phenotyping, combined with pooled DNA analysis, for capturing the most important contributing factors, (3) flexible genotyping systems, such as genotyping by sequencing and arraying, refined for different selection methods including MAS, marker-assisted recurrent selection and genomic selection (GS), (4) marker-trait association analysis using joint linkage and LD mapping, and (5) sequence-based strategies for marker development, allele mining, gene discovery and molecular breeding.     

A global barley panel revealing genomic signatures of breeding in modern Australian cultivars

The future of plant cultivar improvement lies in the evaluation of genetic resources from currently available germplasm. Today’s gene pool of crop genetic diversity has been shaped during domestication and more recently by breeding. Recent efforts in plant breeding have been aimed at developing new and improved varieties from poorly adapted crops to suit local environments. However, the impact of these breeding efforts is poorly understood. Here, we assess the contributions of both historical and recent breeding efforts to local adaptation and crop improvement in a global barley panel by analysing the distribution of genetic variants with respect to geographic region or historical breeding category. By tracing the impact that breeding had on the genetic diversity of Hordeum vulgare (barley) released in Australia, where the history of barley production is relatively young, we identify 69 candidate regions within 922 genes that were under selection pressure. We also show that modern Australian barley varieties exhibit 12% higher genetic diversity than historical cultivars. Finally, field-trialling and phenotyping for agriculturally relevant traits across a diverse range of Australian environments suggests that genomic regions under strong breeding selection and their candidate genes are closely associated with key agronomic traits. In conclusion, our combined data set and germplasm collection provide a rich source of genetic diversity that can be applied to understanding and improving environmental adaptation and enhanced yields.     

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.     

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

In recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High‐throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis‐related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio‐engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype‐to‐gene identification pipeline.     

Engineering plants for tomorrow: how high-throughput phenotyping is contributing to the development of better crops

High-throughput plant phenotyping has been advancing at an accelerated rate as a response to the need to fill the gap between genomic information and the plasticity of the plant phenome. During the past decade, North America has seen a stark increase in the number of phenotyping facilities, and these groups are actively contributing to the generation of high-dimensional, richly informative datasets about the phenotype of model and crop plants. As both phenomic datasets and analysis tools are made publicly available, the key to engineering more resilient crops to meet global demand is closer than ever. However, there are a number of bottlenecks that must yet be overcome before this can be achieved. In this paper, we present an overview of the most commonly used sensors that empower digital phenotyping and the information they provide. We also describe modern approaches to identify and characterize plants that are resilient to common abiotic and biotic stresses that limit growth and yield of crops. Of interest to researchers working in plant biochemistry, we also include a section discussing the potential of these high-throughput approaches in linking phenotypic data with chemical composition data. We conclude by discussing the main bottlenecks that still remain in the field and the importance of multidisciplinary teams and collaboration to overcome those challenges.     

Towards genomic selection in apple (Malus ×domestica Borkh.) breeding programmes: Prospects, challenges and strategies

The apple genome sequence and the availability of high-throughput genotyping technologies have initiated a new era where SNP markers are abundant across the whole genome. Genomic selection (GS) is a statistical approach that utilizes all available genome-wide markers simultaneously to estimate breeding values or total genetic values. For breeding programmes, GS is a promising alternative to the traditional marker-assisted selection for manipulating complex polygenic traits often controlled by many small-effect genes. Various factors, such as genetic architecture of selection traits, population size and structure, genetic evaluation systems, density of SNP markers and extent of linkage disequilibrium, have been shown to be the key drivers of the accuracy of GS. In this paper, we provide an overview of the status of these aspects in current apple-breeding programmes. Strategies for GS for fruit quality and disease resistance are discussed, and an update on an empirical genomic selection study in a New Zealand apple-breeding programme is provided, along with a foresight of expected accuracy from such selection.     

Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.     

Genetic basis of yield as viewed from a crop physiologist's perspective

The final yield of a crop is the product of growth during the growing season and a number of developmental processes occurring throughout the life cycle of a crop, with most genes influencing the final outcome to a degree. However, recent advances in molecular biology have developed the potential to identify and map many genes or QTLs related to various important traits, including yield, plant adaptation and tolerance to stresses. Significant G×E interactions for yield have been identified, as have interactions associated with QTLs for yield. However, there is little evidence available to confirm that a QTL for yield from a parental line in one mapping population may improve yield when transferred into an adapted, high‐yielding line of another population. In order to narrow the apparent gap between the genotype and the phenotype with regard to yield, it is important to identify key traits related to yield and then attempt to identify and locate the genes controlling them. The partitioning of the developmental time to anthesis into different phases: from sowing to the onset of stem elongation and from then to anthesis, as a relatively simple physiological attribute putatively related to yield, is discussed. If the relationship holds in a wider range of conditions and the genetic factors responsible are located then the genetic basis of yield should be identified. There has also been significant progress in crop simulation modelling. Using knowledge of crop physiology and empirical relationships these models can simulate the performance of crops, including the G×E interactions. Such models require information regarding the genetic basis of yield, which are included in the form of genetic coefficients. Essentially models are constructed as decision‐making tools for management but may be of use in detecting prospective traits for selection within a breeding programme. Problems associated with this approach are discussed. This review discusses the need to use crop physiology approaches to analyse components of yield in order to reliably identify the genetic basis of yield.     

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture,Training Population Composition,Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite,Tropical Rice Breeding Lines

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute''s (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.     

Evaluation and retrospective optimization of genomic selection for yield and disease resistance in spring barley

The general applicability of genomic selection (GS) to plant breeding and principles guiding its use have been established by simulation and empirical cross-validation studies. More recently, studies have demonstrated genetic gains over multiple cycles of selection in a variety of crop species. In this study, we provide additional evidence for the effectiveness of GS in an actual breeding program by demonstrating significant gains of 186.1 kg ha^?1 and ??1.85 ppm for grain yield and deoxynivalenol, respectively, two unfavorably correlated quantitative traits, across 3 cycles of selection in a spring six-row barley breeding population. With its general effectiveness established, the next step is to increase the accuracy of predictions used in GS and thereby increase genetic gains. For this, we first showed that updating the training population (TP) with phenotyped lines from recent breeding cycles, specifically selected lines, had an overall positive effect on prediction accuracy. Additionally, we investigated four recently proposed algorithms that seek to optimize the composition of a TP. Overall, the optimization algorithms improved prediction accuracy when compared to a randomly selected TP subset of the same size, but which algorithm performed best was dependent on the trait being predicted and other factors discussed within. This retrospective investigation highlights the importance of maintaining and optimizing the TP when using GS in applied breeding to maximize prediction accuracy, thereby maximizing gain from selection and resource utilization efficiency.     

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical P<0.05) were identified across the three lines. With GS, 70 selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167–0.198) and BLUP (0.105–0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.     

Efficiency of multi-trait,indirect, and trait-assisted genomic selection for improvement of biomass sorghum

Key message

We compare genomic selection methods that use correlated traits to help predict biomass yield in sorghum, and find that trait-assisted genomic selection performs best.

Abstract

Genomic selection (GS) is usually performed on a single trait, but correlated traits can also help predict a focal trait through indirect or multi-trait GS. In this study, we use a pre-breeding population of biomass sorghum to compare strategies that use correlated traits to improve prediction of biomass yield, the focal trait. Correlated traits include moisture, plant height measured at monthly intervals between planting and harvest, and the area under the growth progress curve. In addition to single- and multi-trait direct and indirect GS, we test a new strategy called trait-assisted GS, in which correlated traits are used along with marker data in the validation population to predict a focal trait. Single-trait GS for biomass yield had a prediction accuracy of 0.40. Indirect GS performed best using area under the growth progress curve to predict biomass yield, with a prediction accuracy of 0.37, and did not differ from indirect multi-trait GS that also used moisture information. Multi-trait GS and single-trait GS yielded similar results, indicating that correlated traits did not improve prediction of biomass yield in a standard GS scenario. However, trait-assisted GS increased prediction accuracy by up to \(50\%\) when using plant height in both the training and validation populations to help predict yield in the validation population. Coincidence between selected genotypes in phenotypic and genomic selection was also highest in trait-assisted GS. Overall, these results suggest that trait-assisted GS can be an efficient strategy when correlated traits are obtained earlier or more inexpensively than a focal trait.

     

Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis.As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging(LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional(3 D) data accurately,and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China,we developed a high-throughput crop phenotyping platform, named Crop 3 D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3 D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs,functions and testing results of the Crop 3 D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.     

Practical application of genomic selection in a doubled-haploid winter wheat breeding program

Crop improvement is a long-term, expensive institutional endeavor. Genomic selection (GS), which uses single nucleotide polymorphism (SNP) information to estimate genomic breeding values, has proven efficient to increasing genetic gain by accelerating the breeding process in animal breeding programs. As for crop improvement, with few exceptions, GS applicability remains in the evaluation of algorithm performance. In this study, we examined factors related to GS applicability in line development stage for grain yield using a hard red winter wheat (Triticum aestivum L.) doubled-haploid population. The performance of GS was evaluated in two consecutive years to predict grain yield. In general, the semi-parametric reproducing kernel Hilbert space prediction algorithm outperformed parametric genomic best linear unbiased prediction. For both parametric and semi-parametric algorithms, an upward bias in predictability was apparent in within-year cross-validation, suggesting the prerequisite of cross-year validation for a more reliable prediction. Adjusting the training population’s phenotype for genotype by environment effect had a positive impact on GS model’s predictive ability. Possibly due to marker redundancy, a selected subset of SNPs at an absolute pairwise correlation coefficient threshold value of 0.4 produced comparable results and reduced the computational burden of considering the full SNP set. Finally, in the context of an ongoing breeding and selection effort, the present study has provided a measure of confidence based on the deviation of line selection from GS results, supporting the implementation of GS in wheat variety development.     

Genomics to accelerate genetic improvement in tilapia

Selective breeding of tilapia populations started in the early 1990s and over the past three decades tilapia has become one of the most important farmed freshwater species, being produced in more than 125 countries around the globe. Although genome assemblies have been available since 2011, most of the tilapia industry still depends on classical selection techniques using mass spawning or pedigree information to select for growth traits with reported genetic gains of up to 20% per generation. The involvement of international breeding companies and research institutions has resulted in the rapid development and application of genomic resources in the last few years. GWAS and genomic selection are expected to contribute to uncovering the genetic variants involved in economically relevant traits and increasing the genetic gain in selective breeding programs, respectively. Developments over the next few years will probably focus on achieving a deep understanding of genetic architecture of complex traits, as well as accelerating genetic progress in the selection for growth-, quality- and robustness-related traits. Novel phenotyping technologies (i.e. phenomics), lower-cost whole-genome sequencing approaches, functional genomics and gene editing tools will be crucial in future developments for the improvement of tilapia aquaculture.     

Traits and selection strategies to improve root systems and water uptake in water-limited wheat crops

Wheat yields globally will depend increasingly on good management to conserve rainfall and new varieties that use water efficiently for grain production. Here we propose an approach for developing new varieties to make better use of deep stored water. We focus on water-limited wheat production in the summer-dominant rainfall regions of India and Australia, but the approach is generally applicable to other environments and root-based constraints. Use of stored deep water is valuable because it is more predictable than variable in-season rainfall and can be measured prior to sowing. Further, this moisture is converted into grain with twice the efficiently of in-season rainfall since it is taken up later in crop growth during the grain-filling period when the roots reach deeper layers. We propose that wheat varieties with a deeper root system, a redistribution of branch root density from the surface to depth, and with greater radial hydraulic conductivity at depth would have higher yields in rainfed systems where crops rely on deep water for grain fill. Developing selection systems for mature root system traits is challenging as there are limited high-throughput phenotyping methods for roots in the field, and there is a risk that traits selected in the lab on young plants will not translate into mature root system traits in the field. We give an example of a breeding programme that combines laboratory and field phenotyping with proof of concept evaluation of the trait at the beginning of the selection programme. This would greatly enhance confidence in a high-throughput laboratory or field screen, and avoid investment in screens without yield value. This approach requires careful selection of field sites and years that allow expression of deep roots and increased yield. It also requires careful selection and crossing of germplasm to allow comparison of root expression among genotypes that are similar for other traits, especially flowering time and disease and toxicity resistances. Such a programme with field and laboratory evaluation at the outset will speed up delivery of varieties with improved root systems for higher yield.     

Will genomic selection be a practical method for plant breeding?

Background

Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information available on GS for practical use.

Scope

In this review, GS is discussed from a practical breeding viewpoint. Statistical approaches employed in GS are briefly described, before the recent progress in GS studies is surveyed. GS practices in plant breeding are then reviewed before future prospects are discussed.

Conclusions

Statistical concepts used in GS are discussed with genetic models and variance decomposition, heritability, breeding value and linear model. Recent progress in GS studies is reviewed with a focus on empirical studies. For the practice of GS in plant breeding, several specific points are discussed including linkage disequilibrium, feature of populations and genotyped markers and breeding scheme. Currently, GS is not perfect, but it is a potent, attractive and valuable approach for plant breeding. This method will be integrated into many practical breeding programmes in the near future with further advances and the maturing of its theory.Key words: Genomic selection, plant breeding, marker assisted selection, genetic model, linkage disequilibrium