Transmission of important chromosomal regions under selection revealed in rice pedigree breeding programs

Genetic analysis across a whole plant genome based on pedigree information offers considerable potential for enhancing genetic gain from plant breeding programs through quantitative trait loci (QTL) mapping and marker-assisted selection. Here, we report its application for graphically genotyping varieties used in Chinese japonica rice (Oryza sativa L.) pedigree breeding programs. We identified 34 important chromosomal regions from the founder parent that are under selection in the breeding programs, and by comparing donor genomic regions that are under selection with QTL locations of agronomic traits, we found that QTL clustered in important genomic regions, in accordance with association analyses of natural populations and other previous studies. The convergence of genomic regions under selection with QTL locations suggests that donor genomic regions harboring key genes/QTL for important agronomic traits have been selected by plant breeders since the 1950s from the founder rice plants. The results provide better understanding of the effects of selection in breeding programs on the traits of rice cultivars. They also provide potentially valuable information for enhancing rice breeding programs through screening candidate parents for targeted molecular markers, improving crop yield potential and identifying suitable genetic material for use in future breeding programs.     

Accelerating the Switchgrass (Panicum virgatum L.) Breeding Cycle Using Genomic Selection Approaches

Switchgrass (Panicum virgatum L.) is a perennial grass undergoing development as a biofuel feedstock. One of the most important factors hindering breeding efforts in this species is the need for accurate measurement of biomass yield on a per-hectare basis. Genomic selection on simple-to-measure traits that approximate biomass yield has the potential to significantly speed up the breeding cycle. Recent advances in switchgrass genomic and phenotypic resources are now making it possible to evaluate the potential of genomic selection of such traits. We leveraged these resources to study the ability of three widely-used genomic selection models to predict phenotypic values of morphological and biomass quality traits in an association panel consisting of predominantly northern adapted upland germplasm. High prediction accuracies were obtained for most of the traits, with standability having the highest ten-fold cross validation prediction accuracy (0.52). Moreover, the morphological traits generally had higher prediction accuracies than the biomass quality traits. Nevertheless, our results suggest that the quality of current genomic and phenotypic resources available for switchgrass is sufficiently high for genomic selection to significantly impact breeding efforts for biomass yield.     

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.

     

Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy

Genetic correlations between quantitative traits measured in many breeding programs are pervasive. These correlations indicate that measurements of one trait carry information on other traits. Current single-trait (univariate) genomic selection does not take advantage of this information. Multivariate genomic selection on multiple traits could accomplish this but has been little explored and tested in practical breeding programs. In this study, three multivariate linear models (i.e., GBLUP, BayesA, and BayesCπ) were presented and compared to univariate models using simulated and real quantitative traits controlled by different genetic architectures. We also extended BayesA with fixed hyperparameters to a full hierarchical model that estimated hyperparameters and BayesCπ to impute missing phenotypes. We found that optimal marker-effect variance priors depended on the genetic architecture of the trait so that estimating them was beneficial. We showed that the prediction accuracy for a low-heritability trait could be significantly increased by multivariate genomic selection when a correlated high-heritability trait was available. Further, multiple-trait genomic selection had higher prediction accuracy than single-trait genomic selection when phenotypes are not available on all individuals and traits. Additional factors affecting the performance of multiple-trait genomic selection were explored.     

Predicting future biomass yield in Miscanthus using the carbohydrate metabolic profile as a biomarker

In perennial energy crop breeding programmes, it can take several years before a mature yield is reached when potential new varieties can be scored. Modern plant breeding technologies have focussed on molecular markers, but for many crop species, this technology is unavailable. Therefore, prematurity predictors of harvestable yield would accelerate the release of new varieties. Metabolic biomarkers are routinely used in medicine, but they have been largely overlooked as predictive tools in plant science. We aimed to identify biomarkers of productivity in the bioenergy crop, Miscanthus, that could be used prognostically to predict future yields. This study identified a metabolic profile reflecting productivity in Miscanthus by correlating the summer carbohydrate composition of multiple genotypes with final yield 6 months later. Consistent and strong, significant correlations were observed between carbohydrate metrics and biomass traits at two separate field sites over 2 years. Machine‐learning feature selection was used to optimize carbohydrate metrics for support vector regression models, which were able to predict interyear biomass traits with a correlation (R) of >0.67 between predicted and actual values. To identify a causal basis for the relationships between the glycome profile and biomass, a ¹³C‐labelling experiment compared carbohydrate partitioning between high‐ and low‐yielding genotypes. A lower yielding and slower growing genotype partitioned a greater percentage of the ¹³C pulse into starch compared to a faster growing genotype where a greater percentage was located in the structural biomass. These results supported a link between plant performance and carbon flow through two rival pathways (starch vs. sucrose), with higher yielding plants exhibiting greater partitioning into structural biomass, via sucrose metabolism, rather than starch. Our results demonstrate that the plant metabolome can be used prognostically to anticipate future yields and this is a method that could be used to accelerate selection in perennial energy crop breeding programmes.     

Assessment of Stalk Properties to Predict Juice Yield in Sorghum

Sweet sorghum is an outstanding feedstock choice for bioethanol production, but the gap between theoretical and commercial ethanol yields must be reduced to improve economic viability. Extractable juice yield is a primary limiting factor for higher ethanol yield, but current phenotyping techniques to measure juice yield in sorghum can be laborious. Therefore, alternative approaches to measuring juice yield during selection are needed. The objectives of this study were to investigate the relationship between stalk-related traits and juice yield and to assess the ability to predict juice yield using agronomic traits and stalk properties across and within a diverse set of sorghum ideotypes (photoinsensitive, photosensitive, biomass, grain, and sweet types). Stalk weight, stalk volume, stalk diameter, and plant height had significantly strong associations with juice yield, which were consistent across different sorghum ideotypes. The direct and indirect effects of multiple predictive traits on juice yield varied greatly with the distinct sorghum subsets. However, equation modeling demonstrated that juice yield is satisfactorily predicted by jointly assessing stalk weight and stalk moisture. Moreover, alternative prediction models involving distinct combinations of agronomic and stalk-related traits had similarly good prediction accuracy. Altogether, this suggests that several prediction models can be used to accelerate phenotyping for juice yield, which will improve the selection process. Overall, the results indicate that increasing sorghum juice yield via indirect selection is possible, but the choice of prediction model depends on the ideotypes and resources available in a breeding program.     

Accuracy of genomic selection for age at puberty in a multi‐breed population of tropically adapted beef cattle

Genomic selection is becoming a standard tool in livestock breeding programs, particularly for traits that are hard to measure. Accuracy of genomic selection can be improved by increasing the quantity and quality of data and potentially by improving analytical methods. Adding genotypes and phenotypes from additional breeds or crosses often improves the accuracy of genomic predictions but requires specific methodology. A model was developed to incorporate breed composition estimated from genotypes into genomic selection models. This method was applied to age at puberty data in female beef cattle (as estimated from age at first observation of a corpus luteum) from a mix of Brahman and Tropical Composite beef cattle. In this dataset, the new model incorporating breed composition did not increase the accuracy of genomic selection. However, the breeding values exhibited slightly less bias (as assessed by deviation of regression of phenotype on genomic breeding values from the expected value of 1). Adding additional Brahman animals to the Tropical Composite analysis increased the accuracy of genomic predictions and did not affect the accuracy of the Brahman predictions.     

Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection

Reliable selection criteria are required for young riding horses to increase genetic gain by increasing accuracy of selection and decreasing generation intervals. In this study, selection strategies incorporating genomic breeding values (GEBVs) were evaluated. Relevant stages of selection in sport horse breeding programs were analyzed by applying selection index theory. Results in terms of accuracies of indices (r_TI) and relative selection response indicated that information on single nucleotide polymorphism (SNP) genotypes considerably increases the accuracy of breeding values estimated for young horses without own or progeny performance. In a first scenario, the correlation between the breeding value estimated from the SNP genotype and the true breeding value (= accuracy of GEBV) was fixed to a relatively low value of r_mg = 0.5. For a low heritability trait (h² = 0.15), and an index for a young horse based only on information from both parents, additional genomic information doubles r_TI from 0.27 to 0.54. Including the conventional information source ‘own performance’ into the before mentioned index, additional SNP information increases r_TI by 40%. Thus, particularly with regard to traits of low heritability, genomic information can provide a tool for well-founded selection decisions early in life. In a further approach, different sources of breeding values (e.g. GEBV and estimated breeding values (EBVs) from different countries) were combined into an overall index when altering accuracies of EBVs and correlations between traits. In summary, we showed that genomic selection strategies have the potential to contribute to a substantial reduction in generation intervals in horse breeding programs.     

Morphological and molecular characterization of sweet,grain and forage sorghum (Sorghum bicolor L.) genotypes grown under temperate climatic conditions

Abstract

In the present study, we used 12 genotypes of sorghum originated from different countries (five sweet, four grain and three forage). These different genotypes and types of sorghum were evaluated for the agro-morphological traits that are associated with the estimated sugar and bioethanol yield to estimate their phenotypic diversity. Analysis of variance showed significant differences between different types of sorghum for all the evaluated traits. Sweet sorghum genotypes, however, showed better performance with respect to all studied traits than the other genotypes. A positive significant correlation was observed between plant height, leaf number, leaf area, biomass yield, cane and bagasse yields, and the predicted bioethanol yield. Both, cluster and principal component analysis were performed to group the genotypes according to their agro-morphological and molecular similarity coefficients. For analytical approaches, the Iranian grain and forage genotypes clustered separately from the other genotypes. The clustering patterns obtained from the molecular dominant markers had higher discriminatory power than using morphological characters to separate sweet genotypes from the forage and grain sorghum ones. The results clearly indicated that sweet sorghum can be grown in Germany and maintains its superiority in biomass production and sugar yield over grain and forage sorghum types.     

Genomic prediction ability for yield-related traits in German winter barley elite material

Key message

Genomic prediction was evaluated in German winter barley breeding lines. In this material, prediction ability is strongly influenced by population structure and main determinant of prediction ability is the close genetic relatedness of the breeding material.

Abstract

To ensure breeding progress under changing environmental conditions the implementation and evaluation of new breeding methods is of crucial importance. Modern breeding approaches like genomic selection may significantly accelerate breeding progress. We assessed the potential of genomic prediction in a training population of 750 genotypes, consisting of multiple six-rowed winter barley (Hordeum vulgare L.) elite material families and old cultivars, which reflect the breeding history of barley in Germany. Crosses of parents selected from the training set were used to create a set of double-haploid families consisting of 750 genotypes. Those were used to confirm prediction ability estimates based on a cross-validation with the training set material using 11 different genomic prediction models. Population structure was inferred with dimensionality reduction methods like discriminant analysis of principle components and the influence of population structure on prediction ability was investigated. In addition to the size of the training set, marker density is of crucial importance for genomic prediction. We used genome-wide linkage disequilibrium and persistence of linkage phase as indicators to estimate that 11,203 evenly spaced markers are required to capture all QTL effects. Although a 9k SNP array does not contain a sufficient number of polymorphic markers for long-term genomic selection, we obtained fairly high prediction accuracies ranging from 0.31 to 0.71 for the traits earing, hectoliter weight, spikes per square meter, thousand kernel weight and yield and show that they result from the close genetic relatedness of the material. Our work contributes to designing long-term genetic prediction programs for barley breeding.

     

Genomic diversity among sorghum genotypes with resistance to sorghum shoot fly, Atherigona soccata

Host plant resistance is one of the important components for management of sorghum shoot fly, Atherigona soccata. The levels of resistance in cultivated germplasm are low to moderate, and therefore, it is important to identify sorghum genotypes with diverse mechanisms of resistance based on physico-chemical and or molecular markers. We assessed the genetic diversity of 15 sorghum genotypes with different levels of resistance/susceptibility to shoot fly, A. soccata using 93 sorghum simple sequence repeat (SSR) primer pairs and simultaneously characterized for 15 morpho-biochemical traits associated with shoot fly resistance. Of these 93 SSR primer pairs, amplification products from 79, thought to correspond to single-copy loci distributed across all ten sorghum chromosome pairs, showed good polymorphism across the 15 sorghum genotypes. The polymorphic information content (PIC) values of these 79 SSR markers ranged from 0.06 to 0.86. The Principal Coordinate Analyses (PCoA) and cluster analyses based on dissimilarity matrices derived from SSR based allelic variation (Neighbor-Joining distance) and variation in 15 morpho-biochemical traits (based on Gower??s distance), revealed grouping of most susceptible genotypes in single cluster. The improved breeding lines grouped with resistant or susceptible genotypes, based on shared pedigree. Based on these results, three resistant accessions viz., IS 1054, IS 1057 and IS 4664 were found diverse to IS 18551, which is widely used as shoot fly resistance donor. These diverse sources, after further characterization for resistance mechanisms, can be used in breeding programs for improving shoot fly resistance.     

Selection on Optimal Haploid Value Increases Genetic Gain and Preserves More Genetic Diversity Relative to Genomic Selection

Doubled haploids are routinely created and phenotypically selected in plant breeding programs to accelerate the breeding cycle. Genomic selection, which makes use of both phenotypes and genotypes, has been shown to further improve genetic gain through prediction of performance before or without phenotypic characterization of novel germplasm. Additional opportunities exist to combine genomic prediction methods with the creation of doubled haploids. Here we propose an extension to genomic selection, optimal haploid value (OHV) selection, which predicts the best doubled haploid that can be produced from a segregating plant. This method focuses selection on the haplotype and optimizes the breeding program toward its end goal of generating an elite fixed line. We rigorously tested OHV selection breeding programs, using computer simulation, and show that it results in up to 0.6 standard deviations more genetic gain than genomic selection. At the same time, OHV selection preserved a substantially greater amount of genetic diversity in the population than genomic selection, which is important to achieve long-term genetic gain in breeding populations.     

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

In modern dairy cattle breeding, genomic breeding programs have the potential to increase efficiency and genetic gain. At the same time, the requirements and the availability of genotypes and phenotypes present a challenge. The set-up of a large enough reference population for genomic prediction is problematic for numerically small breeds but also for hard to measure traits. The first part of this study is a review of the current literature on strategies to overcome the lack of reference data. One solution is the use of combined reference populations from different breeds, different countries, or different research populations. Results reveal that the level of relationship between the merged populations is the most important factor. Compiling closely related populations facilitates the accurate estimation of marker effects and thus results in high accuracies of genomic prediction. Consequently, mixed reference populations of the same breed, but from different countries are more promising than combining different breeds, especially if those are more distantly related. The use of female reference information has the potential to enlarge the reference population size. Including females is advisable for small populations and difficult traits, and maybe combined with genotyping females and imputing those that are un-genotyped.The efficient use of imputation for un-genotyped individuals requires a set of genotyped related animals and well-considered selection strategies which animals to choose for genotyping and phenotyping. Small populations have to find ways to derive additional advantages from the cost-intensive establishment of genomic breeding schemes. Possible solutions may be the use of genomic information for inbreeding control, parentage verification, within-herd selection, adjusted mating plans or conservation strategies.The second part of the paper deals with the issue of high-quality phenotypes against the background of new, difficult and hard to measure traits. The use of contracted herds for phenotyping is recommended, as additional traits, when compared to standard traits used in dairy cattle breeding can be measured at set moments in time. This can be undertaken even for the recording of health traits, thus resulting in complete contemporary groups for health traits. Future traits to be recorded and used in genomic breeding programs, at least partly will be traits for which traditional selection based on widespread phenotyping is not possible. Enabling phenotyping of sufficient numbers to enable genomic selection will rely on cooperation between scientists from different disciplines and may require multidisciplinary approaches.     

Assessment of Genotype Ranking in Long-term Biomass Production of Salix Based on Juvenile Plant Traits: Breeding Implications

Willow (Salix spp.) is among the most promising energy crops to be grown on agricultural land and breeding research to increase biomass yield of this perennial crop is performed in Europe and North America. Biomass willows are grown in short rotation and harvests are performed every 3 to 5 years (i.e., at 3- to 5-year cutting cycles) for a period of up to 25 years. However, breeding programs to improve long-term biomass yield are often relying on the results of short-term screening studies performed on juvenile plants. A pre-requisite for successful breeding of perennial energy crops is thus the identification of relevant juvenile plant traits indicative of long-term plant performance under field conditions. In this study a number of juvenile plant traits, measured at various Salix genotypes grown in a short-term experiment were evaluated in terms of their capacity to predict the long-term performance in biomass production after the first and second cutting cycle. The objective was to develop a simple model linking juvenile plant traits such as shoot biomass, total leaf area and leaf nitrogen (N) concentration to the long-term biomass productivity of field-grown plants. A two-component regression model combining juvenile shoot biomass and leaf N concentration provided the highest prediction accuracy (coefficients of determination around 0.8). The model based on two easy-to-measure juvenile plant traits clearly has implications for willow breeding programs. The implications for breeding are discussed in the light of the possibilities and limitations associated with the chosen approach.     

Genome-wide association and genomic prediction for yield and component traits of Miscanthus sacchariflorus

Accelerating biomass improvement is a major goal of Miscanthus breeding. The development and implementation of genomic-enabled breeding tools, like marker-assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of Miscanthus breeding. The present study conducted genome-wide association (GWA) and genomic prediction of biomass yield and 14 yield-components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single-nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported Miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield-component traits in M. sacchariflorus that may facilitate marker-assisted breeding for biomass yield. Genomic prediction accuracy for the yield-related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus. This study indicated that MAS and genomic prediction will likely be beneficial for conducting population-improvement of M. sacchariflorus.     

Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments

With the recent development of genomic resources and high‐throughput phenotyping platforms, the 21st century is primed for major breakthroughs in the discovery, understanding and utilization of plant genetic variation. Significant advances in agriculture remain at the forefront to increase crop production and quality to satisfy the global food demand in a changing climate all while reducing the environmental impacts of the world's food production. Sorghum, a resilient C₄ grain and grass important for food and energy production, is being extensively dissected genetically and phenomically to help connect the relationship between genetic and phenotypic variation. Unlike genetically modified crops such as corn or soybean, sorghum improvement has relied heavily on public research; thus, many of the genetic resources serve a dual purpose for both academic and commercial pursuits. Genetic and genomic resources not only provide the foundation to identify and understand the genes underlying variation, but also serve as novel sources of genetic and phenotypic diversity in plant breeding programs. To better disseminate the collective information of this community, we discuss: (i) the genomic resources of sorghum that are at the disposal of the research community; (ii) the suite of sorghum traits as potential targets for increasing productivity in contrasting environments; and (iii) the prospective approaches and technologies that will help to dissect the genotype–phenotype relationship as well as those that will apply foundational knowledge for sorghum improvement.     

Diverse approaches to achieving grain yield in wheat

Artificial selection (domestication and breeding) leaves a strong footprint in plant genomes. Second generation high throughput DNA sequencing technologies make it possible to sequence the gene complement of a plant genome within 3 to 5 months, and the costs of doing so are declining very quickly. This makes it practical to identify genomic regions that have undergone very strong selection. Available reference sequences of important crops such as rice, maize, and sorghum will promote the wide use of re-sequencing strategies in these crops. Marker/trait associations, especially haplotype (or haplotype block) association analyses, will help the precise mapping of important genomic regions and location of favored alleles or haplotypes for breeding. This mini-review examines a genomics approach to defining yield traits in wheat.     

Using Genetic Distance to Infer the Accuracy of Genomic Prediction

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either F_ST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.     

Assessment of sorghum genetic resources of Ethiopia for anthracnose (Colletotrichum sublineolum Henn.) resistance and agronomic traits

Sorghum anthracnose caused by Colletotrichum sublineolum Henn. is one of the key diseases limiting sorghum production and productivity. Development of anthracnose‐resistant sorghum genotypes possessing yield‐promoting agronomic traits is an important breeding goal in sorghum improvement programs. The objective of this study was to determine the responses of diverse sorghum genetic resources for anthracnose resistance and agronomic traits to identify desirable lines for breeding. A total of 366 sorghum collections and three standard checks were field evaluated during the 2016 and 2017 cropping seasons. Lines were artificially inoculated with a virulent pure isolate of the pathogen. Anthracnose disease severity was assessed to calculate the area under disease progress curve (AUDPC). Agronomic traits such as panicle length (PL), panicle width (PW), head weight (HW) and thousand grain weight (TGW) were measured. Lines showed highly significant differences (p < .001) for anthracnose severity, AUDPC and agronomic traits. Among the collections 32 lines developed levels of disease severity between 15% and 30% in both seasons. The following sorghum landraces were selected: 71708, 210903, 74222, 73955, 74685, 74670, 74656, 74183, 234112, 69412, 226057, 214852, 71420, 71484, 200126, 71557, 75120, 71547, 220014, 228179, 16212, 16173, 16133, 69088, 238388, 16168 and 71570. These landraces had a relatively low anthracnose severity possessing farmer‐preferred agronomic traits. The selected genotypes are useful genetic resources to develop anthracnose‐resistant sorghum cultivars.