Hybrid maize breeding with doubled haploids: III. Efficiency of early testing prior to doubled haploid production in two-stage selection for testcross performance

Genome optimization via virtual simulation to accelerate maize hybrid breeding

The employment of doubled-haploid (DH) technology in maize has vastly accelerated the efficiency of developing inbred lines. The selection of superior lines has to rely on genotypes with genomic selection (GS) model, rather than phenotypes due to the high expense of field phenotyping. In this work, we implemented 'genome optimization via virtual simulation (GOVS)' using the genotype and phenotype data of 1404 maize lines and their F1 progeny. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' or 'advantageous alleles' in a genetic pool. Such a virtually optimized genome, although can never be developed in reality, may help plot the optimal route to direct breeding decisions. GOVS assists in the selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. The assumption is that the more fragments of optimal genotypes a line contributes to the assembly, the higher the likelihood of the line favored in the F1 phenotype, e.g. grain yield. Compared to traditional GS method, GOVS-assisted selection may avoid using an arbitrary threshold for the predicted F1 yield to assist selection. Additionally, the selected lines contributed complementary sets of advantageous alleles to the virtual genome. This feature facilitates plotting the optimal route for DH production, whereby the fewest lines and F1 combinations are needed to pyramid a maximum number of advantageous alleles in the new DH lines. In summary, incorporation of DH production, GS and genome optimization will ultimately improve genomically designed breeding in maize. Short abstract: Doubled-haploid (DH) technology has been widely applied in maize breeding industry, as it greatly shortens the period of developing homozygous inbred lines via bypassing several rounds of self-crossing. The current challenge is how to efficiently screen the large volume of inbred lines based on genotypes. We present the toolbox of genome optimization via virtual simulation (GOVS), which complements the traditional genomic selection model. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' in a breeding population, and then assists in selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. Availability of GOVS (https://govs-pack.github.io/) to the public may ultimately facilitate genomically designed breeding in maize.

Applications of Doubled Haploids in Plant Breeding and Applied Research

Manifold and diverse applications of doubled haploid (DH) plants have emerged in academy and in the plant breeding industry since the first discovery of a haploid mutant in the Jimson Weed (Datura stramonium), followed by the first reports about anther culture in the same species, maternal haploids by wide crosses in tobacco (Nicotiana tabacum L.) and barley (Hordeum vulgare L.), interspecific hybridization, ovary culture (gynogenesis), isolated microspore culture, and more recently the CENH3 approach in thale cress (Arabidopsis thaliana L.) and other species. Research and development efforts were and are still significant in both user groups. Luckily, often academic and industrial partners cooperate in challenging and sometimes voluminous projects worldwide. Not only to develop innovative DH protocols and technologies per se, but also to exploit the advantages of DH plants in a huge variety of research and development experiments. This review concentrates not on the DH technologies per se, but on the application of DHs in plant-related research and development projects.

Genome optimization for improvement of maize breeding

We propose a new model to improve maize breeding that incorporates doubled haploid production, genomic selection, and genome optimization. Breeding 4.0 has been considered the next era of plant breeding. It is clear that the Breeding 4.0 era for maize will feature the integration of multi-disciplinary technologies including genomics and phenomics, gene editing and synthetic biology, and Big Data and artificial intelligence. The breeding approach of passively selecting ideal genotypes from designated genetic pools must soon evolve to virtual design of optimized genomes by pyramiding superior alleles using computational simulation. An optimized genome expressing optimal phenotypes, which may never actually be created, can function as a blueprint for breeding programs to use minimal materials and hybridizations to achieve maximum genetic gain. We propose a new breeding pipeline, "genomic design breeding," that incorporates doubled haploid production, genomic selection, and genome optimization and is facilitated by different scales of trait predictions and decision-making models. Successful implementation of the proposed model will facilitate the evolution of maize breeding from "art" to "science" and eventually to "intelligence," in the Breeding 4.0 era.

Doubled haploid technology for line development in maize: technical advances and prospects

KEY MESSAGE

Increased efficiencies achieved in different steps of DH line production offer greater benefits to maize breeding programs. Doubled haploid (DH) technology has become an integral part of many commercial maize breeding programs as DH lines offer several economic, logistic and genetic benefits over conventional inbred lines. Further, new advances in DH technology continue to improve the efficiency of DH line development and fuel its increased adoption in breeding programs worldwide. The established method for maize DH production covered in this review involves in vivo induction of maternal haploids by a male haploid inducer genotype, identification of haploids from diploids at the seed or seedling stage, chromosome doubling of haploid (D0) seedlings and finally, selfing of fertile D0 plants. Development of haploid inducers with high haploid induction rates and adaptation to different target environments have facilitated increased adoption of DH technology in the tropics. New marker systems for haploid identification, such as the red root marker and high oil marker, are being increasingly integrated into new haploid inducers and have the potential to make DH technology accessible in germplasm such as some Flint, landrace, or tropical material, where the standard R1-nj marker is inhibited. Automation holds great promise to further reduce the cost and time in haploid identification. Increasing success rates in chromosome doubling protocols and/or reducing environmental and human toxicity of chromosome doubling protocols, including research on genetic improvement in spontaneous chromosome doubling, have the potential to greatly reduce the production costs per DH line.

Haploid male fertility and spontaneous chromosome doubling evaluated in a diallel and recurrent selection experiment in maize

Mainly additive gene action governed inheritance of haploid male fertility, although epistatic effects were also significant. Recurrent selection for haploid male fertility resulted in substantial improvement in this trait. The doubled haploid (DH) technology offers several advantages in maize breeding compared to the traditional method of recurrent selfing. However, there is still great potential for improving the success rate of DH production. Currently, the majority of haploid plants are infertile after chromosome doubling treatment by antimitotic agents such as colchicine and cannot be selfed for production of DH lines. Improvement in haploid male fertility (HMF) by selection for a higher spontaneous chromosome doubling rate (SDR) has the potential to increase DH production efficiency. To investigate the gene action governing SDR in two breeding populations, we adapted the quantitative-genetic model of Eberhart and Gardner (in Biometrics 22:864-881. https://doi.org/10.2307/2528079 , 1966) for the case of haploid progeny from ten DH lines and corresponding diallel crosses. Furthermore, we carried out three cycles of recurrent selection for SDR in two additional populations to evaluate the selection gain for this trait. Additive genetic effects predominated in both diallel crosses, but epistatic effects were also significant. Entry-mean heritability of SDR observed for haploid progeny of these populations exceeded 0.91, but the single-plant heritability relevant to selection was low, ranging from 0.11 to 0.19. Recurrent selection increased SDR from approximately 5-50%, which suggests the presence of few QTL with large effects. This improvement in HMF is greater than the effect of standard colchicine treatment, which yields at maximum 30% fertile haploids. Altogether, the results show the great potential of spontaneous chromosome doubling to streamline development DH lines and to enable new breeding schemes with more efficient allocation of resources.

Technological advances in maize breeding: past, present and future

Maize has for many decades been both one of the most important crops worldwide and one of the primary genetic model organisms. More recently, maize breeding has been impacted by rapid technological advances in sequencing and genotyping technology, transformation including genome editing, doubled haploid technology, parallelled by progress in data sciences and the development of novel breeding approaches utilizing genomic information. Herein, we report on past, current and future developments relevant for maize breeding with regard to (1) genome analysis, (2) germplasm diversity characterization and utilization, (3) manipulation of genetic diversity by transformation and genome editing, (4) inbred line development and hybrid seed production, (5) understanding and prediction of hybrid performance, (6) breeding methodology and (7) synthesis of opportunities and challenges for future maize breeding.

Optimum design of family structure and allocation of resources in association mapping with lines from multiple crosses

Family mapping is based on multiple segregating families and is becoming increasingly popular because of its advantages over population mapping. Athough much progress has been made recently, the optimum design and allocation of resources for family mapping remains unclear. Here, we addressed these issues using a simulation study, resample model averaging and cross-validation approaches. Our results show that in family mapping, the predictive power and the accuracy of quatitative trait loci (QTL) detection depend greatly on the population size and phenotyping intensity. With small population sizes or few test environments, QTL results become unreliable and are hampered by a large bias in the estimation of the proportion of genotypic variance explained by the detected QTL. In addition, we observed that even though good results can be achieved with low marker densities, no plateau is reached with our full marker complement. This suggests that higher quality results could be achieved with greater marker densities or sequence data, which will be available in the near future for many species.

Impact of selective genotyping in the training population on accuracy and bias of genomic selection

Estimating marker effects based on routinely generated phenotypic data of breeding programs is a cost-effective strategy to implement genomic selection. Truncation selection in breeding populations, however, could have a strong impact on the accuracy to predict genomic breeding values. The main objective of our study was to investigate the influence of phenotypic selection on the accuracy and bias of genomic selection. We used experimental data of 788 testcross progenies from an elite maize breeding program. The testcross progenies were evaluated in unreplicated field trials in ten environments and fingerprinted with 857 SNP markers. Random regression best linear unbiased prediction method was used in combination with fivefold cross-validation based on genotypic sampling. We observed a substantial loss in the accuracy to predict genomic breeding values in unidirectional selected populations. In contrast, estimating marker effects based on bidirectional selected populations led to only a marginal decrease in the prediction accuracy of genomic breeding values. We concluded that bidirectional selection is a valuable approach to efficiently implement genomic selection in applied plant breeding programs.

Gene stacking strategies with doubled haploids derived from biparental crosses: theory and simulations assuming a finite number of loci

Recent progress in genotyping and doubled haploid (DH) techniques has created new opportunities for development of improved selection methods in numerous crops. Assuming a finite number of unlinked loci (ℓ) and a given total number (n) of individuals to be genotyped, we compared, by theory and simulations, three methods of marker-assisted selection (MAS) for gene stacking in DH lines derived from biparental crosses: (1) MAS for high values of the marker score (T, corresponding to the total number of target alleles) in the F(2) generation and subsequently among DH lines derived from the selected F(2) individual (Method 1), (2) MAS for augmented F(2) enrichment and subsequently for T among DH lines from the best carrier F(2) individual (Method 2), and (3) MAS for T among DH lines derived from the F(1) generation (Method 3). Our objectives were to (a) determine the optimum allocation of resources to the F(2) ([Formula: see text]) and DH generations [Formula: see text] for Methods 1 and 2 by simulations, (b) compare the efficiency of all three methods for gene stacking by simulations, and (c) develop theory to explain the general effect of selection on the segregation variance and interpret our simulation results. By theory, we proved that for smaller values of ℓ, the segregation variance of T among DH lines derived from F(2) individuals, selected for high values of T, can be much smaller than expected in the absence of selection. This explained our simulation results, showing that for Method 1, it is best to genotype more F(2) individuals than DH lines ([Formula: see text]), whereas under Method 2, the optimal ratio [Formula: see text] was close to 0.5. However, for ratios deviating moderately from the optimum, the mean [Formula: see text] of T in the finally selected DH line ([Formula: see text]) was hardly reduced. Method 3 had always the lowest mean [Formula: see text] of [Formula: see text] except for small numbers of loci (ℓ = 4) and is favorable only if a small number of loci are to be stacked in one genotype and/or saving one generation is of crucial importance in cultivar development. Method 2 is under most circumstances the superior method, because it generally showed the highest mean [Formula: see text] and lowest SD of [Formula: see text] for the finally selected DH.

Best linear unbiased prediction and optimum allocation of test resources in maize breeding with doubled haploids

With best linear unbiased prediction (BLUP), information from genetically related candidates is combined to obtain more precise estimates of genotypic values of test candidates and thereby increase progress from selection. We developed and applied theory and Monte Carlo simulations implementing BLUP in 2 two-stage maize breeding schemes and various selection strategies. Our objectives were to (1) derive analytical solutions of the mixed model equations under two breeding schemes, (2) determine the optimum allocation of test resources with BLUP under different assumptions regarding the variance component ratios for grain yield in maize, (3) compare the progress from selection using BLUP and conventional phenotypic selection based on mean performance solely of the candidates, and (4) analyze the potential of BLUP for further improving the progress from selection. The breeding schemes involved selection for testcross performance either of DH lines at both stages (DHTC) or of S(1) families at the first stage and DH lines at the second stage (S(1)TC-DHTC). Our analytical solutions allowed much faster calculations of the optimum allocations and superseded matrix inversions to solve the mixed model equations. Compared to conventional phenotypic selection, the progress from selection was slightly higher with BLUP for both optimization criteria, namely the selection gain and the probability to select the best genotypes. The optimum allocation of test resources in S(1)TC-DHTC involved ≥ 10 test locations at both stages, a low number of crosses (≤ 6) each with 100-300 S(1) families at the first stage, and 500-1,000 DH lines at the second stage. In breeding scheme DHTC, the optimum number of test candidates at the first stage was 5-10 times larger, whereas the number of test locations at the first stage and the number of test candidates at the second stage were strongly reduced compared to S(1)TC-DHTC.

Hybrid maize breeding with doubled haploids: V. Selection strategies for testcross performance with variable sizes of crosses and S(1) families

In hybrid maize (Zea mays L.) breeding, doubled haploids (DH) are increasingly replacing inbreds developed by recurrent selfing. Doubled haploids may be developed directly from S(0) plants in the parental cross or via S(1) families. In both these breeding schemes, we examined 2 two-stage selecting strategies, i.e., considering or ignoring cross and family structure while selection among and within parental crosses and S(1) families. We examined the optimum allocation of resources to maximize the selection gain DeltaG and the probability P(q) of identifying the q% best genotypes. Our specific objectives were to (1) determine the optimum number and size of crosses and S(1) families, as well as the optimum number of test environments and (2) identify the superior selection strategy. Selection was based on the evaluation of testcross progenies of (1) DH lines in both stages (DHTC) and (2) S(1) families in the first stage and of DH lines within S(1) families in the second stage (S(1)TC-DHTC) with uniform and variable sizes of crosses and S(1) families. We developed and employed simulation programs for selection with variable sizes of crosses and S(1) families within crosses. The breeding schemes and selection strategies showed similar relative efficiency for both optimization criteria DeltaG and P (0.1%). As compared with DHTC, S(1)TC-DHTC had larger DeltaG and P (0.1%), but a higher standard deviation of DeltaG. The superiority of S(1)TC-DHTC was increased when the selection was done among all DH lines ignoring their cross and family structure and using variable sizes of crosses and S(1) families. In DHTC, the best selection strategy was to ignore cross structures and use uniform size of crosses.

Should maize doubled haploids be induced among F(1) or F (2) plants?

Maize (Zea mays L.) doubled haploid lines are typically produced from F(1) plants. Studies have suggested that the low frequency of recombinants in doubled haploids may reduce the response to selection. My objective was to determine if, for sustaining long-term response, doubled haploids should be induced in F(1) or F(2) plants during maize inbred development. In simulation experiments, I examined the response to multiple cycles of testcross selection among doubled haploid lines derived from F(1) plants (denoted by DH), doubled haploid lines derived from F(2) plants (DH(F2)), and recombinant inbred (RI) lines derived by single-seed descent. For a trait controlled by 100 or more quantitative trait loci (QTL), the cumulative responses to selection were up to 4-6% larger among DH(F2) lines than among DH lines. The cumulative responses were up to 5-8% larger among RI lines than among DH lines. The QTL become unlinked as the number of QTL in a finite genome decreases, and the responses among RI, DH, and DH(F2) lines were equal or nearly equal when only 20 QTL controlled the trait. Metabolic-flux epistasis reduced the differences in the response among RI, DH, and DH(F2) lines. Overall, the results indicated that doubled haploids should be induced from F(2) plants rather than from F(1) plants. If year-round nurseries are used and new F(1) crosses for inbred development are initially created on a speculative basis, the development of doubled haploids from F(2) rather than F(1) plants should not cause a delay in inbred development.

Hybrid maize breeding with doubled haploids. IV. Number versus size of crosses and importance of parental selection in two-stage selection for testcross performance

Parental selection influences the gain from selection and the optimum allocation of test resources in breeding programs. We compared two hybrid maize (Zea mays L.) breeding schemes with evaluation of testcross progenies: (a) doubled haploid (DH) lines in both stages (DHTC) and (b) S(1) families in the first stage and DH lines within S(1) families in the second stage (S(1)TC-DHTC). Our objectives were to (1) determine the optimum allocation regarding the number of crosses, S(1) families, DH lines, and test locations, (2) investigate the impact of parental selection on the optimum allocation and selection gain (DeltaG), and (3) compare the maximum DeltaG achievable with each breeding scheme. Selection gain was calculated by numerical integration. Different assumptions were made regarding the budget, variance components, correlation between the mean phenotypic performance of the parents and the mean genotypic value of the testcross performance of their progenies (rho( P )), and the composition of the finally selected test candidates. In comparison with randomly chosen crosses, maximum DeltaG was largely increased with parental selection in both breeding schemes. With an increasing correlation rho( P ), this superiority increased strongly, while the optimum number of crosses decreased in favor of an increased number of test candidates within crosses. Thus, concentration on few crosses among the best parental lines might be a promising approach for short-term success in advanced cycle breeding. Breeding scheme S(1)TC-DHTC led to a larger DeltaG but had a longer cycle length than DHTC. However, with further improvements in the DH technique and the realization of more than two generations per year, early testing of S(1) families prior to production of DH lines would become very attractive in hybrid maize breeding.