Recurrent Genomic Selection Research Articles

Implementation of rapid cycle recurrent genomic selection for forage yield in perennial ryegrass

Implementation of rapid cycle recurrent genomic selection for forage yield in perennial ryegrass

Genetic dissection of green pod yield in dolichos bean, an orphan vegetable legume, using new molecular markers.

In the era of genomic-assisted breeding for crop improvement, developing new molecular markers and validating them for use in breeding programs are the prelude. Dolichos bean is one of the most important vegetable legume crops owing to its nutrient-rich green pods used as vegetables. Limitations in genomic resources, including molecular markers, restrict the accelerated improvement of the crop. In the present investigation, a set of 430 new simple sequence repeat markers was developed from sequence information of a reference variety. These markers included di- and tri-nucleotide repeats. The markers were assayed on an association panel, which was evaluated for green pod yield over 5years. A multi-locus model, FarmCPU, was used to assess the marker-trait association analysis. A total of 106 marker-trait associations were identified using an efficient mixed-model approach. Tri-nucleotide repeats were more informative and predominantly associated with trait. Among these markers, 17 were associated with a high level of significance. Markers LP-D-68 and LP-D-14 were identified with a high level of significance in 5-year pooled data and explained 12.70% and 12% of the phenotypic variance, respectively. These markers associated with a high level of confidence have significant scope for use in marker-assisted selection programmes. Other associated markers may be utilized for improving parents through marker-assisted recurrent selection or genomic selection programs.

Recurrent selection for Fusarium head blight resistance in a durum wheat population

AbstractFusarium head blight (FHB) is a devastating fungal disease of wheat. Since early 1990s, frequent FHB epidemics in major wheat‐growing regions have caused massive economic losses. Developing FHB‐resistant varieties is key to minimize the loss caused by the disease. In durum wheat (Triticum turgidum L. ssp. durum), however, it is challenged by lack of resistant sources. Introgression of resistance genes from wild relatives and bread wheat has resulted in some durum wheat lines with moderate resistance. Due to its complex genetic nature, integrating more resistance genes with moderate and even minor effects promises to provide high and durable FHB resistance. In this study, a base population was developed using diverse resistant lines and elite durum wheat breeding lines as founders and went through three cycles of phenotypic selection from 2019 to 2022, with FHB severity decreased by 34%. Six S0:1 lines in Cycle 2 and Cycle 3 populations exhibited significantly lower FHB severity than the moderately susceptible check variety ND Riveland. The results suggested that recurrent phenotypic selection could effectively improve FHB resistance. Analysis of the founders and Cycle 2 and Cycle 3 populations with 2706 single nucleotide polymorphism markers indicated that there was little decrease in genetic variation due to selection. Genome‐wide association analysis found no markers significantly associated with FHB severity. Genomic prediction accuracies based on cross‐validation for FHB severity, plant height, and days‐to‐flowering were 0.51, 0.69, and 0.61, respectively. Recurrent genomic selection can shorten the select cycle to 4 months and therefore is promising to accelerate genetic improvement of FHB severity.

Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population

Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.

Toward combining qualitative race-specific and quantitative race-nonspecific disease resistance by genomic selection

Key messageA novel genomic selection strategy offers the unique opportunity to develop qualitative race-specific resistant varieties that possess high levels of the more durable quantitative race-nonspecific resistance in their genetic background.Race-specific qualitative resistance genes (R-genes) are conferring complete resistance in many pathosystems, but are frequently overcome by new virulent pathogen races. Once the deployed R-genes are overcome, a wide variation of quantitative disease resistance (QDR) can be observed in a set of previously race-specific, i.e., completely resistant genotypes—a phenomenon known as “vertifolia effect.” This race-nonspecific QDR is considered to be more durable in the long term, but provides merely a partial protection against pathogens. This simulation study aimed to detangle race-specific R-gene-mediated resistance of pending selection candidates and the QDR in their genetic background by employing different genomic selection strategies. True breeding values that reflected performance data for rust resistance in wheat were simulated, and used in a recurrent genomic selection based on several prediction models and training population designs. Using training populations that were devoid of race-specific R-genes was thereby pivotal for an efficient improvement of QDR in the long term. Marker-assisted preselection for the presence of R-genes followed by a genomic prediction for accumulating the many small to medium effect loci underlying QDR in the genetic background of race-specific resistant genotypes appeared furthermore to be a promising approach to select simultaneously for both types of resistance. The practical application of such a knowledge-driven genomic breeding strategy offers the opportunity to develop varieties with multiple layers of resistance, which have the potential to prevent intolerable crop losses under epidemic situations by displaying a high level of QDR even when race-specific R-genes have been overcome by evolving pathogen populations.

Results from rapid-cycle recurrent genomic selection in spring bread wheat.

Genomic selection (GS) in wheat breeding programs is of great interest for predicting the genotypic values of individuals, where both additive and nonadditive effects determine the final breeding value of lines. While several simulation studies have shown the efficiency of rapid-cycling GS strategies for parental selection or population improvement, their practical implementations are still lacking in wheat and other crops. In this study, we demonstrate the potential of rapid-cycle recurrent GS (RCRGS) to increase genetic gain for grain yield (GY) in wheat. Our results showed a consistent realized genetic gain for GY after 3 cycles of recombination (C1, C2, and C3) of bi-parental F1s, when summarized across 2 years of phenotyping. For both evaluation years combined, genetic gain through RCRGS reached 12.3% from cycle C0 to C3 and realized gain was 0.28 ton ha-1 per cycle with a GY from C0 (6.88 ton ha-1) to C3 (7.73 ton ha-1). RCRGS was also associated with some changes in important agronomic traits that were measured (days to heading, days to maturity, and plant height) but not selected for. To account for these changes, we recommend implementing GS together with multi-trait prediction models.

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat

AbstractCereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement.

New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

BackgroundRecurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. Usually, generations are discrete in recurrent selection, which means that breeding candidates are evaluated and considered for selection for only one cycle. Alternately, generations can overlap, with breeding candidates considered for selection as parents for multiple cycles. With recurrent genomic selection but not phenotypic selection, candidates can be re-evaluated by using genomic estimated breeding values without additional phenotyping of the candidates themselves. Therefore, it may be that candidates with true high breeding values discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by stochastic simulation.ResultsWith phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these individuals erroneously believed to have high value were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as individuals from previous breeding cycles typically had truly lower breeding values than candidates from the current generation. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low.ConclusionMaintaining discrete generations in recurrent phenotypic selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

Empirical comparison of genomic and phenotypic selection for resistance to Fusarium ear rot and fumonisin contamination in maize.

GS and PS performed similarly in improving resistance to FER and FUM content. With cheaper and faster genotyping methods, GS has the potential to be more efficient than PS. Fusarium verticillioides is a common maize (Zea mays L.) pathogen that causes Fusarium ear rot (FER) and produces the mycotoxin fumonisin (FUM). This study empirically compared phenotypic selection (PS) and genomic selection (GS) for improving FER and FUM resistance. Three intermating generations of recurrent GS were conducted in the same time frame and from a common base population as two generations of recurrent PS. Lines sampled from each PS and GS cycle were evaluated in three North Carolina environments in 2020. We observed similar cumulative responses to GS and PS, representing decreases of about 50% of mean FER and FUM compared to the base population. The first cycle of GS was more effective than later cycles. PS and GS both achieved about 70% of predicted total gain from selection for FER, but only about 26% of predicted gains for FUM, suggesting that heritability for FUM was overestimated. We observed a 20% decrease in genetic marker variation from PS and 30% decrease from GS. Our greatest challenge was our inability to quickly obtain dense and consistent set of marker genotypes across generations of GS. Practical implementation of GS in individual small-scale breeding programs will require cheaper and faster genotyping methods, and such technological advances will present opportunities to significantly optimize selection and mating schemes for future GS efforts beyond what we were able to achieve in this study.

Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions.

Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F2 (60 out of 629 plants) and the F5 stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha−1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.

Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice (Oryza sativa L.).

Hybrid rice varieties can outyield the best inbred varieties by 15 – 30% with appropriate management. However, hybrid rice requires more inputs and management than inbred rice to realize a yield advantage in high-yielding environments. The development of stress-tolerant hybrid rice with lowered input requirements could increase hybrid rice yield relative to production costs. We used genomic prediction to evaluate the combining abilities of 564 stress-tolerant lines used to develop Green Super Rice with 13 male sterile lines of the International Rice Research Institute for yield-related traits. We also evaluated the performance of their F1 hybrids. We identified male sterile lines with good combining ability as well as F1 hybrids with potential further use in product development. For yield per plant, accuracies of genomic predictions of hybrid genetic values ranged from 0.490 to 0.822 in cross-validation if neither parent or up to both parents were included in the training set, and both general and specific combining abilities were modeled. The accuracy of phenotypic selection for hybrid yield per plant was 0.682. The accuracy of genomic predictions of male GCA for yield per plant was 0.241, while the accuracy of phenotypic selection was 0.562. At the observed accuracies, genomic prediction of hybrid genetic value could allow improved identification of high-performing single crosses. In a reciprocal recurrent genomic selection program with an accelerated breeding cycle, observed male GCA genomic prediction accuracies would lead to similar rates of genetic gain as phenotypic selection. It is likely that prediction accuracies of male GCA could be improved further by targeted expansion of the training set. Additionally, we tested the correlation of parental genetic distance with mid-parent heterosis in the phenotyped hybrids. We found the average mid-parent heterosis for yield per plant to be consistent with existing literature values at 32.0%. In the overall population of study, parental genetic distance was significantly negatively correlated with mid-parent heterosis for yield per plant (r = −0.131) and potential yield (r = −0.092), but within female families the correlations were non-significant and near zero. As such, positive parental genetic distance was not reliably associated with positive mid-parent heterosis.

Strategies and considerations for implementing genomic selection to improve traits with additive and non-additive genetic architectures in sugarcane breeding.

Simulations highlight the potential of genomic selection to substantially increase genetic gain for complex traits in sugarcane. The success rate depends on the trait genetic architecture and the implementation strategy. Genomic selection (GS) has the potential to increase the rate of genetic gain in sugarcane beyond the levels achieved by conventional phenotypic selection (PS). To assess different implementation strategies, we simulated two different GS-based breeding strategies and compared genetic gain and genetic variance over five breeding cycles to standard PS. GS scheme 1 followed similar routines like conventional PS but included three rapid recurrent genomic selection (RRGS) steps. GS scheme 2 also included three RRGS steps but did not include a progeny assessment stage and therefore differed more fundamentally from PS. Under an additive trait model, both simulated GS schemes achieved annual genetic gains of 2.6-2.7% which were 1.9 times higher compared to standard phenotypic selection (1.4%). For a complex non-additive trait model, the expected annual rates of genetic gain were lower for all breeding schemes; however, the rates for the GS schemes (1.5-1.6%) were still greater than PS (1.1%). Investigating cost-benefit ratios with regard to numbers of genotyped clones showed that substantial benefits could be achieved when only 1500 clones were genotyped per 10-year breeding cycle for the additive genetic model. Our results show that under a complex non-additive genetic model, the success rate of GS depends on the implementation strategy, the number of genotyped clones and the stage of the breeding program, likely reflecting how changes in QTL allele frequencies change additive genetic variance and therefore the efficiency of selection. These results are encouraging and motivate further work to facilitate the adoption of GS in sugarcane breeding.

Within-breed selection is sufficient to improve terminal crossbred beef marbling: a review of reciprocal recurrent genomic selection

Reciprocal recurrent selection is the selection of purebreds for crossbred performance and takes advantage of additive and non-additive variance by using pedigreed progeny performance records. Developed in maize, the adoption of this approach in livestock breeding has been limited to the pork and poultry industries; genomic selection may facilitate its extension into the beef industry by replacing pedigree. The literature regarding the relative importance of additive versus non-additive variance and reciprocal recurrent genomic selection models was reviewed. The potential for using reciprocal recurrent genomic selection in a terminal Wagyu × Angus cross scenario was examined. Non-additive variance is more important for fitness traits and accounts for a small proportion of variance related to production traits such as marbling. In general, reciprocal recurrent selection was not significantly better at improving performance of crossbreds than was traditional selection within parental breeds using only additive variance in the studies examined. Simulation studies showed benefits of including dominance or breed-specific allele effects in prediction models but advantages were small as more realistic simulations were examined. On the basis of the evidence, it is likely that in a terminal two-way cross-beef scenario utilising Wagyu sires and Angus dams, where selection emphasis is on marbling, selection of purebreds on the basis of additive variance will allow substantial progress to be realised.

Recurrent genomic selection for wheat grain fructans

AbstractFructans are carbohydrates found in many plants, including wheat (Triticum aestivum L.), and they serve physiological roles in both plants and humans. Genomic selection (GS) could facilitate the rapid development of climate‐resilient, nutritionally improved wheat cultivars, such as high‐fructan cultivars, while decreasing resource‐intensive phenotyping requirements. However, few empirical studies have examined GS for nutritional quality breeding. Although GS can accelerate gain from selection, loss of genetic variation and inbreeding may limit the potential for long‐term gain. The objectives of this study were (a) to determine realized gain from GS for wheat grain fructan content with simple truncated selection (TS) and optimized contribution selection (OCS) methods, (b) to determine if gains agree with theoretical expectations, and (c) to compare impacts of selection on inbreeding, genetic variance, and indirect selection on agronomic characteristics. Over 2 yr, two cycles of GS were performed with equal contribution TS and inbreeding‐constrained OCS selection. Genomic selection with TS and OCS led to a 25 ± 12% and 34 ± 6.4% increase in wheat grain fructan content, respectively. Although positive gains from selection were observed for both populations, OCS populations exhibited these gains while simultaneously retaining greater genetic variance and lower inbreeding levels relative to TS populations. Selection for wheat grain fructan content did not change plant height but significantly decreased days to heading in OCS populations. In this study, GS effectively improved the nutritional quality of wheat, and OCS controlled the rate of inbreeding.

Genomic selection helps accelerate popcorn population breeding

AbstractRecurrent selection is a method for developing new popcorn (Zea mays L.) cultivars. We aimed to determine the selection accuracy and genetic gains for different selection strategies: estimates based exclusively on phenotypic data (PhEN), estimates based on phenotypic and genotypic data (PhEN + GEN), and estimates based exclusively on single nucleotide polymorphism (SNP) marker genotyping (GEN). For the GEN strategy, we tested, via simulation, the possibility of reducing the number of SNPs and increasing the training population. The traits evaluated were 100‐grain weight, ear height, grain yield, popping expansion, plant height, and popping volume. Field trials were undertaken with 98 S1 progenies at two locations in an incomplete block design with three replications. The progenies’ parents were genotyped with a panel of ∼10,507 SNPs. As predicted by the GEN strategy at different selection intensities, the average annual genetic gain for the different traits were 29.1 and 25.2% higher than those of PhEN and GEN + PhEN for 98 candidates; 148.3 and 140.9% higher for 500; and 187.9 and 179.4% higher for 1,000 selection candidates, respectively. Recurrent genomic selection may result in high genetic gain, provided that: (a) phenotyping is accurate; (b) selection intensity is explored by genotyping several progenies and increasing the number of candidates; (c) genomic selection is used for early selection; and (d) the model is adjusted for a few more cycles of phenotyping. The simulation suggests that desirable values of genetic gain may be obtained by reducing the number of SNPs and increasing the training population size.

COMPARATIVE ANALYSIS OF BAYESIAN AND FREQUENCY-BASED METHODS IN GENOMIC SELECTION FOR POPCORN POPULATION BREEDING AND OPTIMIZATION OF SNP MARKER DENSITY

Bayesian methods and frequency-based approaches such GBLUP are used to estimate genomic genetic values in recurrent genomic selection. An important factor in genetic gain is prediction accuracy; therefore, the objective of the present study was to estimate the prediction accuracy of the following methods: GBLUP, Bayes A, Bayes B, Bayes Cπ, Bayes Lasso, and RKHS. After establishing the best method, different densities of SNP markers were tested. The experiment was implemented using an incomplete block design with three repetitions in two locations. Ninety-eight individuals were evaluated using 10,507 SNPs; the assessed traits were grain yield, popping expansion, and popcorn volume. The analyses were performed using R software and a ten-fold cross-validation system. The methods were compared using the t-test, via correlation networks and according to the time required to perform the analysis. The obtained results showed that the methods did not differ statistically with regard to selection accuracy, with high correlation estimates (<0.98); however, the GBLUP test stood out for its short analysis time and simplicity of execution. With regard to the distinct scenarios of SNP density tested with the GBLUP method, it was concluded that the use of ~ 4,800 SNPs led to results similar to those obtained with 10,507 SNPs. The GBLUP method is recommended for its greater speed in generating analytical results and for exhibiting robustness similar to that of other tested methods with the use of a small panel of SNPs.

Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection.

The implementation of genomic selection in recurrent breeding programs raises the concern that a higher inbreeding rate could compromise the long-term genetic gain. An optimized mating strategy that maximizes the performance in progeny and maintains diversity for long-term genetic gain is therefore essential. The optimal cross-selection approach aims at identifying the optimal set of crosses that maximizes the expected genetic value in the progeny under a constraint on genetic diversity in the progeny. Optimal cross-selection usually does not account for within-family selection, i.e., the fact that only a selected fraction of each family is used as parents of the next generation. In this study, we consider within-family variance accounting for linkage disequilibrium between quantitative trait loci to predict the expected mean performance and the expected genetic diversity in the selected progeny of a set of crosses. These predictions rely on the usefulness criterion parental contribution (UCPC) method. We compared UCPC-based optimal cross-selection and the optimal cross-selection approach in a long-term simulated recurrent genomic selection breeding program considering overlapping generations. UCPC-based optimal cross-selection proved to be more efficient to convert the genetic diversity into short- and long-term genetic gains than optimal cross-selection. We also showed that, using the UCPC-based optimal cross-selection, the long-term genetic gain can be increased with only a limited reduction of the short-term commercial genetic gain.

Influence of Genotype and Environment on Wheat Grain Fructan Content

Fructans are naturally occurring plant polymers composed of fructose molecules. Approximately 15% of flowering plant species contain fructans, including wheat (Triticum aestivum L.). Fructans serve as carbon stores in plants and exhibit potentially beneficial effects on human health. The main objectives of this study were to examine the effects of genotype and environment on winter wheat grain fructan content and to assess the feasibility of using genomic selection for grain fructan content. Total grain fructan content was determined for 288 winter wheat genotypes grown across 2 yr at three locations each year. Observed variation in wheat grain fructan content was significantly influenced by genotype, environment, and genotype × environment interactions. The high genetic correlation, small impact of genotype × environment interactions on genomic predictability, and lack of significant hits in a genome‐wide association study suggest that genomic selection is a suitable tool in breeding for wheat grain fructan content. The results of this study will be useful for implementing recurrent genomic selection in winter wheat and guiding future decisions regarding breeding methodologies for total fructan content in wheat. This study provides a deeper understanding of the effects of genotype, environment, and genotype × environment interaction on fructan content, which will have implications for breeders seeking to develop nutritionally improved, climate‐resilient wheat cultivars.

Reciprocal recurrent genomic selection: an attractive tool to leverage hybrid wheat breeding.

Using a two-part breeding strategy based on a population improvement and a product development component can leverage hybrid wheat breeding. Despite the technological advance of methods to facilitate hybrid breeding in self-pollinating crops, line breeding is still the dominating breeding strategy. This is likely due to a higher long-term selection gain in line compared to hybrid breeding. In this respect, recent studies on two-part strategies splitting the breeding program into a population improvement and a product development component could mark a trend reversal. Here, an overview of experimental and simulation-based studies exploring the possibilities to integrate genome-wide prediction into recurrent selection is given. Furthermore, possibilities to make use of recurrent selection for inter-population improvement are discussed. Current findings of simulation studies and quantitative genetic considerations suggest that long-term selection gain of hybrid breeding can be increased by implementing a two-part selection strategy based on reciprocal recurrent genomic selection. This would strengthen the competitiveness of hybrid versus line breeding facilitating to develop outstanding hybrid varieties also for self-pollinating plants such as wheat.

Optimal cross selection for long-term genetic gain in two-part programs with rapid recurrent genomic selection

Key messageOptimal cross selection increases long-term genetic gain of two-part programs with rapid recurrent genomic selection. It achieves this by optimising efficiency of converting genetic diversity into genetic gain through reducing the loss of genetic diversity and reducing the drop of genomic prediction accuracy with rapid cycling.This study evaluates optimal cross selection to balance selection and maintenance of genetic diversity in two-part plant breeding programs with rapid recurrent genomic selection. The two-part program reorganises a conventional breeding program into a population improvement component with recurrent genomic selection to increase the mean value of germplasm and a product development component with standard methods to develop new lines. Rapid recurrent genomic selection has a large potential, but is challenging due to genotyping costs or genetic drift. Here we simulate a wheat breeding program for 20 years and compare optimal cross selection against truncation selection in the population improvement component with one to six cycles per year. With truncation selection we crossed a small or a large number of parents. With optimal cross selection we jointly optimised selection, maintenance of genetic diversity, and cross allocation with AlphaMate program. The results show that the two-part program with optimal cross selection delivered the largest genetic gain that increased with the increasing number of cycles. With four cycles per year optimal cross selection had 78% (15%) higher long-term genetic gain than truncation selection with a small (large) number of parents. Higher genetic gain was achieved through higher efficiency of converting genetic diversity into genetic gain; optimal cross selection quadrupled (doubled) efficiency of truncation selection with a small (large) number of parents. Optimal cross selection also reduced the drop of genomic selection accuracy due to the drift between training and prediction populations. In conclusion optimal cross selection enables optimal management and exploitation of population improvement germplasm in two-part programs.