Maize Doubled Haploid Research Articles

Doubled Haploid Technology: Generation of Doubled Haploid Maize Lines Using Haploid Inducers.

Doubled haploid (DH) technology allows for the development of completely homozygous lines from heterozygous plants in only two generations. This approach has been widely adopted in maize breeding programs, as it expedites the generation of inbred lines compared to traditional methods. The DH approach is based on the use of maize genotypes that have the ability to induce haploid seeds when used as the pollen parent. The most common method for producing maize haploid plants for the generation of DH lines is in vivo maternal haploid induction. The process involves pollination with a haploid inducer maize line to generate haploid seeds. Then, haploids are screened for and identified (typically via the expression of a particular marker gene), germinated, treated with an exogenous doubling agent to induce genome duplication, and transplanted to the field. Following successful self-pollination, seeds harvested from the ear represent fully homozygous lines. The seed set at this stage, however, is often low, necessitating one or two additional rounds of self-pollination to increase the number of fully homozygous inbred lines. Here, we describe a protocol for the generation of maize DH lines using maternal haploid-inducing maize lines. We outline the steps for setting up the donor material, performing induction crosses, selecting haploids based on two different marker alleles, treating seedlings with colchicine to double the genome, transplanting the treated seedlings to the field, and self-pollinating the treated plants.

A novel in vivo genome editing doubled haploid system for Zea mays L.

Doubled haploid (DH) technologies accelerate maize inbred development. Recently, methods using CRISPR-Cas have created gene-edited maize DH populations, albeit with relatively low editing frequencies. Restoring fertility via haploid chromosome doubling remains a critically important production constraint. Thus, improved editing and chromosome doubling outcomes are needed. Here we obtained maternally derived diploid embryos in vivo by ectopically co-expressing Zea mays BABY BOOM and cyclin D-like gene products within unfertilized egg cells. When combined with gene editing, the in vivo method enables the production of mature seed with a maternally derived, gene-edited diploid embryo without requiring in vitro tissue culture methods nor the use of a chemical chromosome doubling agent. In summary, we report a novel approach for creating gene-edited maize DH populations that we expect can accelerate genetic gain in a scalable, cost-effective manner.

Genomic prediction of the performance of tropical doubled haploid maize lines under artificial Striga hermonthica (Del.) Benth. infestation.

Striga hermonthica (Del.) Benth., a parasitic weed, causes substantial yield losses in maize production in sub-Saharan Africa. Breeding for Striga resistance in maize is constrained by limited genetic diversity for Striga resistance within the elite germplasm and phenotyping capacity under artificial Striga infestation. Genomics-enabled approaches have the potential to accelerate identification of Striga resistant lines for hybrid development. The objectives of this study were to evaluate the accuracy of genomic selection for traits associated with Striga resistance and grain yield (GY) and to predict genetic values of tested and untested doubled haploid maize lines. We genotyped 606 doubled haploid lines with 8,439 rAmpSeq markers. A training set of 116 doubled haploid lines crossed to 2 testers was phenotyped under artificial Striga infestation at 3 locations in Kenya. Heritability for Striga resistance parameters ranged from 0.38-0.65 while that for GY was 0.54. The prediction accuracies for Striga resistance-associated traits across locations, as determined by cross-validation (CV) were 0.24-0.53 for CV0 and from 0.20 to 0.37 for CV2. For GY, the prediction accuracies were 0.59 and 0.56 for CV0 and CV2, respectively. The results revealed 300 doubled haploid lines with desirable genomic estimated breeding values for reduced number of emerged Striga plants (STR) at 8, 10, and 12 weeks after planting. The genomic estimated breeding values of doubled haploid lines for Striga resistance-associated traits in the training and testing sets were similar in magnitude. These results highlight the potential application of genomic selection in breeding for Striga resistance in maize. The integration of genomic-assisted strategies and doubled haploid technology for line development coupled with forward breeding for major adaptive traits will enhance genetic gains in breeding for Striga resistance in maize.

Evaluation of Doubled Haploid Maize Hybrids under Normal and Drought Condition

This experiment was conducted to evaluate the performance of doubled haploid maize hybrids under normal and drought condition. Fifteen doubled haploid maize hybrids were sown in Research Area of Plant Breeding and Genetics, University of Agriculture Faisalabad by using Randomized Complete Block Design during spring 2019. The experimental area was divided into two blocks. Both blocks contain two replications of 15 hybrids. One out of these two blocks was treated with normal irrigations and second block was treated with drought. Data was recorded for various growth and yield related traits. To estimate the performance of doubled haploid maize hybrids under normal and drought conditions the recorded data was subjected to ANOVA by using the STATISTIX 8.1 software. LSD mean comparison test at 0.05% level of significance for hybrids and hybrids × treatment interaction was also calculated. Analysis of variance showed the significant difference among all the hybrids and also in hybrid × treatment interaction. Hybrids DH-26S × 3B and DH-100A × 21 showed maximum 100 grain weight (31.9g) under drought condition. Hybrid DH-100A × 21 showed maximum biomass (278.9g) under drought condition. Genetic advancement and heritability percentage were also calculated for all parameter and listed in the tables. The results showed that maximum genetic advancement was found in biomass; (56), (47) respectively under normal and drought condition.

Correlation and path coefficient analyses of different morpho-physiological traits in doubled haploid maize hybrids under normal and drought stress

Correlation and path coefficient analyses of different morpho-physiological traits in doubled haploid maize hybrids under normal and drought stress

Methods for evaluating effects of transgenes for quantitative traits

AbstractTransgenes that improve quantitative traits have traditionally been evaluated in one or a few genetic backgrounds across multiple environments. However, testing across multiple genetic backgrounds can be equally important to accurately quantify the value of a transgene for breeding objectives. Creating near‐isogenic lines across a wide germplasm space is costly and time consuming, which renders it impractical during early stages of testing. In this experiment, we evaluate three approaches to sample the genetic space while concurrently testing across environments. We created both transgenic and non‐transgenic doubled haploid maize (Zea mays L.) lines, F2:3 lines, and bulk F3 families to determine if all methods resulted in similar estimation of transgene value and to identify the number of yield trial plots from each method necessary to obtain a stable estimate of the transgene value. With one exception, the three methods consistently estimated a similar effect of the transgene. We suggest that bulked F3 lines topcrossed to a tester inbred is the most effective method to estimate the value of a transgene across both genetic space and environments.

Application of Auxins in Haploid Embryo Induction in Hexaploidy Wheat

Doubled haploid (DH) plant production plays a crucial role in modern plant breeding programs, offering an efficient means to generate homozygous lines from heterozygous parents within a single generation. Different types of auxins have been utilized in wheat cross with maize DH production, with 2,4-D being the most widely used and effective hormone, followed by dicamba. Other auxins, including picloram, Indole-3-acetic acid (IAA), phenylacetic acid (PAA), silver nitrate, 1-naphthaleneacetic acid (NAA), kinetin, 6-benzyladenine (BA), and zearalenone, have also been tested for their potential role in haploid embryo induction. Various methods have been explored for the application of 2,4-D, such as spray, tiller injection, dipping, and spikelet culture methods. 2,4-D found to be most effective auxin treatment both alone and with combination with other phytohormones.

Multi-Trait Genomic Prediction Improves Accuracy of Selection among Doubled Haploid Lines in Maize

Recent advances in maize doubled haploid (DH) technology have enabled the development of large numbers of DH lines quickly and efficiently. However, testing all possible hybrid crosses among DH lines is a challenge. Phenotyping haploid progenitors created during the DH process could accelerate the selection of DH lines. Based on phenotypic and genotypic data of a DH population and its corresponding haploids, we compared phenotypes and estimated genetic correlations between the two populations, compared genomic prediction accuracy of multi-trait models against conventional univariate models within the DH population, and evaluated whether incorporating phenotypic data from haploid lines into a multi-trait model could better predict performance of DH lines. We found significant phenotypic differences between DH and haploid lines for nearly all traits; however, their genetic correlations between populations were moderate to strong. Furthermore, a multi-trait model taking into account genetic correlations between traits in the single-environment trial or genetic covariances in multi-environment trials can significantly increase genomic prediction accuracy. However, integrating information of haploid lines did not further improve our prediction. Our findings highlight the superiority of multi-trait models in predicting performance of DH lines in maize breeding, but do not support the routine phenotyping and selection on haploid progenitors of DH lines.

Discriminant analysis of maize haploid seeds using near-infrared hyperspectral imaging integrated with multivariate methods

Doubled haploid (DH) technology is one of the most practical and innovative methods used to effectively accelerate the process of maize breeding. Quickly and precisely separating haploid seeds from maize kernels with a large number of diploids is crucial to improve the efficiency of maize DH breeding. This study explored the applicability of near-infrared (NIR) hyperspectral imaging (HSI) to identify the maize haploid and diploid of seeds. The effects of maize seed marker methods (colour and oil content markers) and embryo orientations (embryo and non-embryo) on the identification model were considered. Three variable selection methods, namely, sub-window permutation analysis (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variables elimination (UVE), were used to obtain optimal wavelengths related to the difference index of maize haploid and diploid seeds. While the full wavelength was replaced by the optimal wavelengths, the identification accuracies for the maize haploid and diploid seeds of SPA-PLSDA, CARS-PLSDA and UVE-PLSDA were 90%, 90% and 89.38%, respectively. The 20 most optimal wavelengths were determined following a comprehensive analysis of the three selection methods and the PLSDA model was built. The model achieved an accuracy of 90.31%, exceeding that of the PLSDA model with the full 216 wavelengths (89.06% accuracy). The results demonstrate the suitability of the model for two common colour and oil content markers, and both embryo and non-embryo maize seeds. This approach significantly contributes to the efficient and broad practicability of maize haploid screening solutions. • NIR-HSI imaging technology was applied for identification of haploid and diploid in maize seeds. • Three variable selection methods (i.e. SPA, CARS, UVE) were used for determination of optimum wavelengths. • The classification model was built by PLSDA algorithm, which considered different marker methods (color marker and oil content marker) and embryo orientations (embryo and non-embryo) of maize seed. • Applicability of the developed analytical method for classifying haploid and diploid of the maize seeds was demonstrated.

Genomic prediction in doubled haploid maize (Zea mays) populations under water stress at flowering and well‐watered conditions using high‐density single‐nucleotide polymorphisms

AbstractThe most important applications of genomic selection (GS) in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. A total of 300 hybrids were generated using doubled haploid lines crossed to single known tester. The test hybrids and checks were evaluated for drought tolerance, grain yield and yield attributes under well‐watered (WW) and water stress at flowering (WSF) conditions during rabi 2018 at Hyderabad and Aurangabad locations. The study was further deep dived and practiced GS using 3352 single‐nucleotide polymorphism (SNP) markers. An extension of the genomic best linear unbiassed predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross‐validation methods were applied to quantify prediction accuracy. Our results showed that the highest cross‐validation prediction accuracy for grain yield was 0.47 under WSF condition in TPS3, whereas under WW condition, prediction accuracy was 0.44 in TPS2, which is statistically on par with WSF condition. Among the secondary traits, the peak GS accuracies recorded for the traits anthesis silking interval (0.52) and ears per plant (0.48) under WSF. Under both the water regimes, anthesis silking interval and plant height recorded higher prediction accuracy when compared with grain yield. Hence, GS could be practiced for anthesis silking interval and ears per plant under stress condition in maize. Further while optimizing the population size, it was revealed from the study that increasing size of the population increases GS accuracy and TPS2 considered as optimum size of population for GS prediction.

Overexpression of Modified CENH3 in Maize Stock6-Derived Inducer Lines Can Effectively Improve Maternal Haploid Induction Rates.

Maize (Zea mays) doubled haploid (DH) breeding is a technology that can efficiently generate inbred lines with homozygous genetic backgrounds. Haploids are usually produced through in vivo induction by haploid inducer lines in maize. Currently, two approaches are usually used to develop maize haploid inducer lines. One is through the conventional breeding improvement based on the Stock6 germplasm, and this strategy is extensively used to induce maternal haploids in commercial maize DH breeding. Another strategy, newly developed but less utilized so far, is by genetic manipulation of the Centromeric Histone3 (CENH3) in regular lines. However, whether both approaches can be combined to develop the haploid inducer line with higher maternal haploid induction rate (HIR) has not been reported. In this study, we manipulated the Stock6-derived inducer lines by overexpressing maize CENH3 fused with different fluorescent protein tags and found that the engineered Stock6-derived lines showed an obvious increase in the maternal HIR. Intriguingly, this above strategy could be further improved by substituting a tail-altered CENH3 for the full-length CENH3 in the tagged expression cassette, resulting in a maternal HIR up to 16.3% that was increased by ~6.1% than Stock6-derived lines control. These results suggested that integration of two in vivo haploid induction methods could rapidly and effectively improve the maternal HIRs of maize Stock6-derived inducer lines, and provided a potentially feasible solution for further optimizing the process of commercial maize DH breeding.

Doubled Haploid Technology in Maize (Zea mays): Status and Applications

Maize (Zea mays L.) is the third most important staple crop after rice and wheat with enormous diversity and adaptation ability. Hybrid breeding is the most important approach for developing high yielding cultivars in maize. It relies upon the generation of pure inbred lines with desirable traits in quick span to achieve higher genetic gains. Rapidly rising global population and climate change necessitates the development of innovative technologies that can help to safeguard the food security in future. Doubled Haploid (DH) technology is the best approach for rapid development of new inbred lines and has contributed immensely in the rapid generation of inbred lines and hybrid development. In addition, the use of molecular markers with DH technology resulted into mapping of genomic regions for different traits. The recent development in identification of alternative markers for haploid selection and genome editing approaches will further strengthen the DH technology for commercial maize breeding. This review describes important landmarks of maize DH technology, its applications, and recent advances in utilization of emerging technologies, viz. CRIPSR-cas and genomics approaches for DH technology

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci.

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.

Application of Genomic Selection at the Early Stage of Breeding Pipeline in Tropical Maize.

In maize, doubled haploid (DH) line production capacity of large-sized maize breeding programs often exceeds the capacity to phenotypically evaluate the complete set of testcross candidates in multi-location trials. The ability to partially select DH lines based on genotypic data while maintaining or improving genetic gains for key traits using phenotypic selection can result in significant resource savings. The present study aimed to evaluate genomic selection (GS) prediction scenarios for grain yield and agronomic traits of one of the tropical maize breeding pipelines of CIMMYT in eastern Africa, based on multi-year empirical data for designing a GS-based strategy at the early stages of the pipeline. We used field data from 3,068 tropical maize DH lines genotyped using rAmpSeq markers and evaluated as test crosses in well-watered (WW) and water-stress (WS) environments in Kenya from 2017 to 2019. Three prediction schemes were compared: (1) 1 year of performance data to predict a second year; (2) 2 years of pooled data to predict performance in the third year, and (3) using individual or pooled data plus converting a certain proportion of individuals from the testing set (TST) to the training set (TRN) to predict the next year's data. Employing five-fold cross-validation, the mean prediction accuracies for grain yield (GY) varied from 0.19 to 0.29 under WW and 0.22 to 0.31 under WS, when the 1-year datasets were used training set to predict a second year's data as a testing set. The mean prediction accuracies increased to 0.32 under WW and 0.31 under WS when the 2-year datasets were used as a training set to predict the third-year data set. In a forward prediction scenario, good predictive abilities (0.53 to 0.71) were found when the training set consisted of the previous year's breeding data and converting 30% of the next year's data from the testing set to the training set. The prediction accuracy for anthesis date and plant height across WW and WS environments obtained using 1-year data and integrating 10, 30, 50, 70, and 90% of the TST set to TRN set was much higher than those trained in individual years. We demonstrate that by increasing the TRN set to include genotypic and phenotypic data from the previous year and combining only 10–30% of the lines from the year of testing, the predicting accuracy can be increased, which in turn could be used to replace the first stage of field-based screening partially, thus saving significant costs associated with the testcross formation and multi-location testcross evaluation.

Diallelic Analysis of Tropical Maize Germplasm Response to Spontaneous Chromosomal Doubling.

Chromosome doubling is an important step in the production of maize doubled haploid (DH) lines to induce fertility in the male and female reproductive organs of haploid plants. Chromosomal doubling is routinely accomplished by treating haploid seedlings with mitosis-inhibiting chemicals. However, chromosomal doubling involves several labor-intensive steps and toxic chemicals. Spontaneous chromosomal doubling without any chemical treatments occurs at high frequency in haploids from a few maize genotypes. This study focused on elucidating the genetic components of two traits important for using spontaneous doubling in maize-breeding programs, namely, haploid male fertility (HMF) and haploid fertility (HF). In two different sets of diallel crosses, haploids were derived and assessed for HMF and HF in two environments in replicated trials. The results revealed significant genotypic variations for both traits. The general combining ability (GCA) and specific combining (SCA) were significant for both traits. Significant and positive GCA effects of up to 14% and 9% were found for HMF and HF, respectively. No significant reciprocal effects and genotype-by-environment (G×E) interactions were found for HF in both experiments, but HMF showed significant effects for both in one of the experiments. The GCA effects were more important than the SCA effects for HMF and HF across environments, implying that selection could facilitate their improvement. The high correlations between F1-hybrid performance and mid-parent values, as well as that between F1-hybrid performance and GCA effects, also supports the assumption that these traits are controlled by a few genes. SCA effects also played a role, especially when lines with low spontaneous doubling were used as parents. Overall, spontaneous doubling can be introgressed and improved in elite germplasm with selection, and it has the potential to be employed in DH pipelines.

Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing

Key messageGenomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year’s data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

Breeding Maize Maternal Haploid Inducers.

Maize doubled haploid (DH) lines are usually created in vivo, through crosses with maternal haploid inducers. These inducers have the inherent ability of generating seeds with haploid embryos when used to pollinate other genotypes. The resulting haploid plants are treated with a doubling agent and self-pollinated, producing completely homozygous seeds. This rapid method of inbred line production reduces the length of breeding cycles and, consequently, increases genetic gain. Such advantages explain the wide adoption of this technique by large, well-established maize breeding programs. However, a slower rate of adoption was observed in medium to small-scale breeding programs. The high price and/or lack of environmental adaptation of inducers available for licensing, or the poor performance of those free of cost, might explain why smaller operations did not take full advantage of this technique. The lack of adapted inducers is especially felt in tropical countries, where inducer breeding efforts are more recent. Therefore, defining optimal breeding approaches for inducer development could benefit many breeding programs which are in the process of adopting the DH technique. In this manuscript, we review traits important to maize maternal haploid inducers, explain their genetic basis, listing known genes and quantitative trait loci (QTL), and discuss different breeding approaches for inducer development. The performance of haploid inducers has an important impact on the cost of DH line production.

I am all ears: Maximize maize doubled haploid success by promoting axillary branch elongation.

The maize doubled haploid (DH) technology plays an important role in accelerating breeding genetic gain. One major challenge in fully leveraging the potential of DH technology to accelerate genetic gain is obtaining a consistent seed return from haploid (DH0) plants after chromosome doubling. Here we demonstrated that DH0 seed production can be increased by increasing the number of mature axillary female inflorescences (ears) at anthesis. To determine the maximum capacity of a maize plant to develop ears, we first characterized the developmental progression of every axillary meristem. We found that all axillary meristems developed to a similar developmental stage before the reproductive transition of the shoot apical meristem (SAM). Upon reproductive transition of the SAM, all axillary meristems are released for reproductive development into ears in a developmental gradient reflective on their positions along the main stem. However, under most circumstances only the top one or two ears can generate silks at anthesis. We found that applying the GA inhibitor paclobutrazol (PAC) during the early reproductive transition of axillary meristems increased the number of silking ears at anthesis, leading to increased success of self‐pollination and seed production. These results provide a blueprint to improve DH efficiency and demonstrate the potential of breeding innovation through understanding crops’ developmental processes.

Development of doubled haploid maize lines by using in vivo haploid technique

The doubled haploid technology is now an integral component of modern maize breeding programs. In this study, the maternal haploid induction (gynogenesis) method was used to derive Doubled-Haploid (DH) lines from elite maize germplasm adapted to Turkey. Temperate haploid inducers (RWS, RWK-76, RWS x RWK-76 and WS14) were used as pollinators, and a set of 30 single-crossses (in FAO 650-700 maturity groups) were used as source materials. Putative haploid seeds were selected based on expression of R1-nj anthocyanin color marker. Highest haploid induction rate (20.42%) was recorded by using RWK-76 as inducer line, and the lowest haploid induction rate (17.75%) was obtained through WS14. Putative haploid seeds were germinated and seedlings were treated with 0.06% colchicine + 0.5% dimethylsulfoxide solution. Following transfer of seedlings into the field, 2178 D0 plants were obtained out of a total of 3012 treated haploids. Live plants were from 89% of 2178 seedlings which are planted to the field. Fertile plants were formed 57% of live plants. Inbreeding was succeeded in 31.23% of fertile plants and only 7.8% of inbreeding plants were able to produce seeds. Consequently, 27 doubled haploid lines were developed.