Genomic prediction of hybrid crops allows disentangling dominance and epistasis

We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted “residual genetic” line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme—e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.     

Prediction of single-cross hybrid performance in maize using haplotype blocks associated with QTL for grain yield

Marker-based prediction of hybrid performance facilitates the identification of untested single-cross hybrids with superior yield performance. Our objectives were to (1) determine the haplotype block structure of experimental germplasm from a hybrid maize breeding program, (2) develop models for hybrid performance prediction based on haplotype blocks, and (3) compare hybrid performance prediction based on haplotype blocks with other approaches, based on single AFLP markers or general combining ability (GCA), under a validation scenario relevant for practical breeding. In total, 270 hybrids were evaluated for grain yield in four Dent × Flint factorial mating experiments. Their parental inbred lines were genotyped with 20 AFLP primer–enzyme combinations. Adjacent marker loci were combined into haplotype blocks. Hybrid performance was predicted on basis of single marker loci and haplotype blocks. Prediction based on variable haplotype block length resulted in an improved prediction of hybrid performance compared with the use of single AFLP markers. Estimates of prediction efficiency (R ² ) ranged from 0.305 to 0.889 for marker-based prediction and from 0.465 to 0.898 for GCA-based prediction. For inter-group hybrids with predominance of general over specific combining ability, the hybrid prediction from GCA effects was efficient in identifying promising hybrids. Considering the advantage of haplotype block approaches over single marker approaches for the prediction of inter-group hybrids, we see a high potential to substantially improve the efficiency of hybrid breeding programs. Tobias A. Schrag and Hans Peter Maurer contributed equally to this work.     

Usefulness of Multiparental Populations of Maize (Zea mays L.) for Genome-Based Prediction

The efficiency of marker-assisted prediction of phenotypes has been studied intensively for different types of plant breeding populations. However, one remaining question is how to incorporate and counterbalance information from biparental and multiparental populations into model training for genome-wide prediction. To address this question, we evaluated testcross performance of 1652 doubled-haploid maize (Zea mays L.) lines that were genotyped with 56,110 single nucleotide polymorphism markers and phenotyped for five agronomic traits in four to six European environments. The lines are arranged in two diverse half-sib panels representing two major European heterotic germplasm pools. The data set contains 10 related biparental dent families and 11 related biparental flint families generated from crosses of maize lines important for European maize breeding. With this new data set we analyzed genome-based best linear unbiased prediction in different validation schemes and compositions of estimation and test sets. Further, we theoretically and empirically investigated marker linkage phases across multiparental populations. In general, predictive abilities similar to or higher than those within biparental families could be achieved by combining several half-sib families in the estimation set. For the majority of families, 375 half-sib lines in the estimation set were sufficient to reach the same predictive performance of biomass yield as an estimation set of 50 full-sib lines. In contrast, prediction across heterotic pools was not possible for most cases. Our findings are important for experimental design in genome-based prediction as they provide guidelines for the genetic structure and required sample size of data sets used for model training.     

Determination of heterotic groups for tropical Indica hybrid rice germplasm

Key message

Two heterotic groups and four heterotic patterns were identified for IRRI hybrid rice germplasm to develop hybrid rice in the tropics based on SSR molecular data and field trials.

Abstract

Information on heterotic groups and patterns is a fundamental prerequisite for hybrid crop breeding; however, no such clear information is available for tropical hybrid rice breeding after more than 30 years of hybrid rice commercialization. Based on a study of genetic diversity using molecular markers, 18 parents representing hybrid rice populations historically developed at the International Rice Research Institute (IRRI) were selected to form diallel crosses of hybrids and were evaluated in tropical environments. Yield, yield heterosis and combining ability were investigated with the main objectives of (1) evaluating the magnitude of yield heterosis among marker-based parental groups, (2) examining the consistency between marker-based group and heterotic performance of hybrids, and (3) identifying foundational hybrid parents in discrete germplasm pools to provide a reference for tropical indica hybrid rice breeding. Significant differences in yield, yield heterosis and combining ability were detected among parents and among hybrids. On average, the hybrids yielded 14.8 % higher than the parents. Results revealed that inter-group hybrids yielded higher, with higher yield heterosis than intra-group hybrids. Four heterotic patterns within two heterotic groups based on current IRRI B- and R-line germplasm were identified. Parents in two marker-based groups were identified with limited breeding value among current IRRI hybrid rice germplasm because of their lowest contribution to heterotic hybrids. Heterotic hybrids are significantly correlated with high-yielding parents. The efficiency of breeding heterotic hybrids could be enhanced using selected parents within identified marker-based heterotic groups. This information is useful for exploiting those widely distributed IRRI hybrid rice parents.     

Joint analysis of days to flowering reveals independent temperate adaptations in maize

Domesticates are an excellent model for understanding biological consequences of rapid climate change. Maize (Zea mays ssp. mays) was domesticated from a tropical grass yet is widespread across temperate regions today. We investigate the biological basis of temperate adaptation in diverse structured nested association mapping (NAM) populations from China, Europe (Dent and Flint) and the United States as well as in the Ames inbred diversity panel, using days to flowering as a proxy. Using cross-population prediction, where high prediction accuracy derives from overall genomic relatedness, shared genetic architecture, and sufficient diversity in the training population, we identify patterns in predictive ability across the five populations. To identify the source of temperate adapted alleles in these populations, we predict top associated genome-wide association study (GWAS) identified loci in a Random Forest Classifier using independent temperate–tropical North American populations based on lines selected from Hapmap3 as predictors. We find that North American populations are well predicted (AUC equals 0.89 and 0.85 for Ames and USNAM, respectively), European populations somewhat well predicted (AUC equals 0.59 and 0.67 for the Dent and Flint panels, respectively) and that the Chinese population is not predicted well at all (AUC is 0.47), suggesting an independent adaptation process for early flowering in China. Multiple adaptations for the complex trait days to flowering in maize provide hope for similar natural systems under climate change.Subject terms: Evolutionary genetics, Quantitative trait     

Prediction of single-cross hybrid performance for grain yield and grain dry matter content in maize using AFLP markers associated with QTL

Prediction methods to identify single-cross hybrids with superior yield performance have the potential to greatly improve the efficiency of commercial maize (Zea mays L.) hybrid breeding programs. Our objectives were to (1) identify marker loci associated with quantitative trait loci for hybrid performance or specific combining ability (SCA) in maize, (2) compare hybrid performance prediction by genotypic value estimates with that based on general combining ability (GCA) estimates, and (3) investigate a newly proposed combination of the GCA model with SCA predictions from genotypic value estimates. A total of 270 hybrids was evaluated for grain yield and grain dry matter content in four Dent × Flint factorial mating experiments, their parental inbred lines were genotyped with 20 AFLP primer-enzyme combinations. Markers associated significantly with hybrid performance and SCA were identified, genotypic values and SCA effects were estimated, and four hybrid performance prediction approaches were evaluated. For grain yield, between 38 and 98 significant markers were identified for hybrid performance and between zero and five for SCA. Estimates of prediction efficiency (R ²) ranged from 0.46 to 0.86 for grain yield and from 0.59 to 0.96 for grain dry matter content. Models enhancing the GCA approach with SCA estimates resulted in the highest prediction efficiency if the SCA to GCA ratio was high. We conclude that it is advantageous for prediction of single-cross hybrids to enhance a GCA-based model with SCA effects estimated from molecular marker data, if SCA variances are of similar or larger importance as GCA variances.     

The strategy and potential utilization of temperate germplasm for tropical germplasm improvement: a case study of maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

The organization of maize (Zea mays L.) germplasm into genetically divergent heterotic groups is the foundation of a successful hybrid maize breeding program. In this study, 94 CIMMYT maize lines (CMLs) and 54 United States germplasm enhancement of maize (GEM) lines were assembled and characterized using 1,266 single nucleotide polymorphisms (SNPs) with high quality. Based on principal component analysis (PCA), the GEM lines and CMLs were clearly separated. In the GEM lines, there were two groups classified by PCA corresponding to the heterotic groups “stiff stalk” and “non-stiff stalk”. CMLs did not form obvious subgroups by PCA. The allelic frequency of each SNP differed in GEM lines and CMLs. In total, 3.6% alleles (46/1,266) of CMLs are absent in GEM lines and 4.4% alleles (56/1,266) of GEM lines are absent in CMLs. The performance of F1 plants (n = 654) produced by crossing between different groups based on pedigree information was evaluated at the breeding nurseries of two CIMMYT stations. Genomic estimated phenotypic values of plant height and days to anthesis for a testing set of 45 F1 crosses were predicted based on the training data of 600 F1 crosses using a best linear unbiased prediction method. The prediction accuracy benefitted from the adoption of the markers associated with quantitative trait loci for both traits; however, it does not necessarily increase with an increase in marker density. It is suggested that genomic selection combined with association analysis could improve prediction efficiency and reduce cost. For hybrid maize breeding in the tropics, incorporating GEM lines which have unique alleles and clear heterotic patterns into tropically adapted lines could be beneficial for enhancing heterosis in grain yields.     

Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture

Key Message

Genomic prediction using the Brassica 60 k genotyping array is efficient in oilseed rape hybrids. Prediction accuracy is more dependent on trait complexity than on the prediction model.

Abstract

In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F₁-hybrid performance, and selection of well-paired parents requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrids, in general, dramatically exceeds breeding capacity and budget. Integration of genomic selection (GS) could substantially increase the number of potential combinations that can be evaluated. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously predicted in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. In this study, we evaluated the usefulness of the Illumina Brassica 60 k SNP array for genomic prediction and compared three alternative approaches based on a homoscedastic ridge regression BLUP and three Bayesian prediction models that considered general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal doubled haploid lines and five male-sterile testers. Predictive ability was evaluated for seven agronomic traits. We demonstrate that the Brassica 60 k genotyping array is an adequate and highly valuable platform to implement genomic prediction of hybrid performance in oilseed rape. Furthermore, we present first insights into the application of established statistical models for prediction of important agronomical traits with contrasting patterns of polygenic control.

     

Trends in genetic diversity among European maize cultivars and their parental components during the past 50 years

It has been claimed that the system that delivers the products of plant breeding reduces the diversity of cultivated varieties leading to an increased genetic vulnerability. The main goal of our study was to monitor the temporal trends in genetic diversity over the past five decades among maize cultivars with the largest acreage in Central Europe. Our objectives were to (1) investigate how much of the genetic diversity present in important adapted open-pollinated varieties (OPVs) has been captured in the elite flint germplasm pool, (2) examine changes in the genetic diversity among the most important commercial hybrids as well as in their dent and flint parents, (3) analyze temporal changes in allele frequencies between the dent and flint parental inbreds, and (4) investigate linkage disequilibrium (LD) trends between pairs of loci within the set of parental dent and flint lines. We examined 30 individuals of five prominent OPVs from Central Europe, 85 maize hybrids of economic importance, and their dent and flint parental components with 55 SSRs. LD was significant at probability level P=0.01 for 20.2% of the SSR marker pairs in the 82 dent lines and for 17.2% in the 66 flint lines. The dent and flint heterotic groups were clearly separated already at the beginning of hybrid breeding in Central Europe. Furthermore, the genetic variation within and among varieties decreased significantly during the five decades. The five OPVs contain numerous unique alleles that were absent in the elite flint pool. Consequently, OPVs could present useful sources for broadening the genetic base of elite maize breeding germplasm.     

DNA methylation in cotton hybrids and their parents

The possible role of methylation in the performance of heterosis has been analyzed in many crops. To further study this possibility, we investigated both the differences in cytosine methylation patterns between cotton heterotic hybrid/nonheterotic hybrids and their parental lines and the change in methylation level from seedling stage to flowering stage by using the methylation-sensitive amplified polymorphism (MSAP) method. The results showed that the number of demethylation loci in highly heterotic hybrids was greater that in lowly heterotic hybrids, and the level of DNA cytosine methylation in cotton at the seedling stage is higher than that at the flowering stage. The altered methylation patterns at low-copy genomic regions can be confirmed by DNA gel blot analysis. A total of 39 fragments that showed different methylation patterns were cloned and sequenced. The methylation status of these genes was modified differentially in hybrid and parents, suggesting that these genes might play a role in the performance of heterosis.     

Deducing Hybrid Performance from Parental Metabolic Profiles of Young Primary Roots of Maize by Using a Multivariate Diallel Approach

Heterosis, the greater vigor of hybrids compared to their parents, has been exploited in maize breeding for more than 100 years to produce ever better performing elite hybrids of increased yield. Despite extensive research, the underlying mechanisms shaping the extent of heterosis are not well understood, rendering the process of selecting an optimal set of parental lines tedious. This study is based on a dataset consisting of 112 metabolite levels in young roots of four parental maize inbred lines and their corresponding twelve hybrids, along with the roots'' biomass as a heterotic trait. Because the parental biomass is a poor predictor for hybrid biomass, we established a model framework to deduce the biomass of the hybrid from metabolite profiles of its parental lines. In the proposed framework, the hybrid metabolite levels are expressed relative to the parental levels by incorporating the standard concept of additivity/dominance, which we name the Combined Relative Level (CRL). Our modeling strategy includes a feature selection step on the parental levels which are demonstrated to be predictive of CRL across many hybrid metabolites. We demonstrate that these selected parental metabolites are further predictive of hybrid biomass. Our approach directly employs the diallel structure in a multivariate fashion, whereby we attempt to not only predict macroscopic phenotype (biomass), but also molecular phenotype (metabolite profiles). Therefore, our study provides the first steps for further investigations of the genetic determinants to metabolism and, ultimately, growth. Finally, our success on the small-scale experiments implies a valid strategy for large-scale experiments, where parental metabolite profiles may be used together with profiles of selected hybrids as a training set to predict biomass of all possible hybrids.     

Hybrid wheat: quantitative genetic parameters and consequences for the design of breeding programs

Key message

Commercial heterosis for grain yield is present in hybrid wheat but long-term competiveness of hybrid versus line breeding depends on the development of heterotic groups to improve hybrid prediction.

Abstract

Detailed knowledge of the amount of heterosis and quantitative genetic parameters are of paramount importance to assess the potential of hybrid breeding. Our objectives were to (1) examine the extent of midparent, better-parent and commercial heterosis in a vast population of 1,604 wheat (Triticum aestivum L.) hybrids and their parental elite inbred lines and (2) discuss the consequences of relevant quantitative parameters for the design of hybrid wheat breeding programs. Fifteen male lines were crossed in a factorial mating design with 120 female lines, resulting in 1,604 of the 1,800 potential single-cross hybrid combinations. The hybrids, their parents, and ten commercial wheat varieties were evaluated in multi-location field experiments for grain yield, plant height, heading time and susceptibility to frost, lodging, septoria tritici blotch, yellow rust, leaf rust, and powdery mildew at up to five locations. We observed that hybrids were superior to the mean of their parents for grain yield (10.7 %) and susceptibility to frost (?7.2 %), leaf rust (?8.4 %) and septoria tritici blotch (?9.3 %). Moreover, 69 hybrids significantly (P < 0.05) outyielded the best commercial inbred line variety underlining the potential of hybrid wheat breeding. The estimated quantitative genetic parameters suggest that the establishment of reciprocal recurrent selection programs is pivotal for a successful long-term hybrid wheat breeding.     

Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects

Identifying high performing hybrids is an essential part of every maize breeding program. Genomic prediction of maize hybrid performance allows to identify promising hybrids, when they themselves or other hybrids produced from their parents were not tested in field trials. Using simulations, we investigated the effects of marker density (10, 1, 0.3 marker per mega base pair, Mbp(-1)), convergent or divergent parental populations, number of parents tested in other combinations (2, 1, 0), genetic model (including population-specific and/or dominance marker effects or not), and estimation method (GBLUP or BayesB) on the prediction accuracy. We based our simulations on marker genotypes of Central European flint and dent inbred lines from an ongoing maize breeding program. To simulate convergent or divergent parent populations, we generated phenotypes by assigning QTL to markers with similar or very different allele frequencies in both pools, respectively. Prediction accuracies increased with marker density and number of parents tested and were higher under divergent compared with convergent parental populations. Modeling marker effects as population-specific slightly improved prediction accuracy under lower marker densities (1 and 0.3?Mbp(-1)). This indicated that modeling marker effects as population-specific will be most beneficial under low linkage disequilibrium. Incorporating dominance effects improved prediction accuracies considerably for convergent parent populations, where dominance results in major contributions of SCA effects to the genetic variance among inter-population hybrids. While the general trends regarding the effects of the aforementioned influence factors on prediction accuracy were similar for GBLUP and BayesB, the latter method produced significantly higher accuracies for models incorporating dominance.     

Metabolic robustness in young roots underpins a predictive model of maize hybrid performance in the field

Heterosis has been extensively exploited for yield gain in maize (Zea mays L.). Here we conducted a comparative metabolomics‐based analysis of young roots from in vitro germinating seedlings and from leaves of field‐grown plants in a panel of inbred lines from the Dent and Flint heterotic patterns as well as selected F₁ hybrids. We found that metabolite levels in hybrids were more robust than in inbred lines. Using state‐of‐the‐art modeling techniques, the most robust metabolites from roots and leaves explained up to 37 and 44% of the variance in the biomass from plants grown in two distinct field trials. In addition, a correlation‐based analysis highlighted the trade‐off between defense‐related metabolites and hybrid performance. Therefore, our findings demonstrated the potential of metabolic profiles from young maize roots grown under tightly controlled conditions to predict hybrid performance in multiple field trials, thus bridging the greenhouse–field gap.     

Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids

Key message

A new genomic model that incorporates genotype?×?environment interaction gave increased prediction accuracy of untested hybrid response for traits such as percent starch content, percent dry matter content and silage yield of maize hybrids.

Abstract

The prediction of hybrid performance (HP) is very important in agricultural breeding programs. In plant breeding, multi-environment trials play an important role in the selection of important traits, such as stability across environments, grain yield and pest resistance. Environmental conditions modulate gene expression causing genotype?×?environment interaction (G?×?E), such that the estimated genetic correlations of the performance of individual lines across environments summarize the joint action of genes and environmental conditions. This article proposes a genomic statistical model that incorporates G?×?E for general and specific combining ability for predicting the performance of hybrids in environments. The proposed model can also be applied to any other hybrid species with distinct parental pools. In this study, we evaluated the predictive ability of two HP prediction models using a cross-validation approach applied in extensive maize hybrid data, comprising 2724 hybrids derived from 507 dent lines and 24 flint lines, which were evaluated for three traits in 58 environments over 12 years; analyses were performed for each year. On average, genomic models that include the interaction of general and specific combining ability with environments have greater predictive ability than genomic models without interaction with environments (ranging from 12 to 22%, depending on the trait). We concluded that including G?×?E in the prediction of untested maize hybrids increases the accuracy of genomic models.

     

Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production

Key message

Genetic and phenotypic analysis of two complementary maize panels revealed an important variation for biomass yield. Flowering and biomass QTL were discovered by association mapping in both panels.

Abstract

The high whole plant biomass productivity of maize makes it a potential source of energy in animal feeding and biofuel production. The variability and the genetic determinism of traits related to biomass are poorly known. We analyzed two highly diverse panels of Dent and Flint lines representing complementary heterotic groups for Northern Europe. They were genotyped with the 50 k SNP-array and phenotyped as hybrids (crossed to a tester of the complementary pool) in a western European field trial network for traits related to flowering time, plant height, and biomass. The molecular information revealed to be a powerful tool for discovering different levels of structure and relatedness in both panels. This study revealed important variation and potential genetic progress for biomass production, even at constant precocity. Association mapping was run by combining genotypes and phenotypes in a mixed model with a random polygenic effect. This permitted the detection of significant associations, confirming height and flowering time quantitative trait loci (QTL) found in literature. Biomass yield QTL were detected in both panels but were unstable across the environments. Alternative kinship estimator only based on markers unlinked to the tested SNP increased the number of significant associations by around 40 % with a satisfying control of the false positive rate. This study gave insights into the variability and the genetic architectures of biomass-related traits in Flint and Dent lines and suggests important potential of these two pools for breeding high biomass yielding hybrid varieties.     

Prediction of hybrid performance in grain sorghum using RFLP markers

Heterosis is an important component of hybrid yield performance. Identifying high yielding hybrids is expensive and involves testing large numbers of hybrid combinations in multi-environment trials. Molecular marker diversity has been proposed as a more efficient method of selecting superior combinations. The aim of this study was to investigate the value of molecular marker-based distance information to identify high yielding grain sorghum hybrids in Australia. Data from 48 trials were used to produce hybrid performance-estimates for four traits (yield, height, maturity and stay green) for 162 hybrid combinations derived from 70 inbred parent lines. Each line was screened with 113 mapped RFLP markers. The Rogers distances between the parents of each hybrid were calculated from the marker information on a genome basis and individually for each of the ten linkage groups of sorghum. Some of the inbred parents were related so the hybrids were classified into 75 groups with each group containing individual hybrids that showed similar patterns of Rogers distances across linkage groups. Correlations between hybrid-group performance and hybrid-group Rogers distances were calculated. A significant correlation was observed between whole genome-based Rogers distance and yield ( r = 0.42). This association is too weak to be of value for identifying superior hybrid combinations. One reason for the generally poor association between parental genetic diversity and yield may be that important QTLs influencing heterosis are located in particular chromosome regions and not distributed evenly over the genome. Variation in the sign and magnitude of correlations between Rogers distance and hybrid-group performance for particular linkage groups observed in this study support this hypothesis. The concept of using diversity on individual linkage groups to predict performance was explored. Using data from just two linkage groups 38% of the variation in hybrid performance for grain yield could be explained. A model combining phenotypic trait data and parental diversity on particular linkage groups explained 71% of the variation in grain yield and has potential for use in the selection of heterotic hybrids.