Accuracy of within‐ and among‐family genomic prediction in triticale

Genomic prediction has emerged as a powerful genomic tool to assist breeding of complex traits. In this study, we employed a population of 647 triticale doubled haploid lines derived from four families to assess the potential of this approach for triticale breeding. All lines were phenotyped for grain yield, thousand‐kernel weight, biomass yield, plant height, frost tolerance and Fusarium head blight resistance. The obtained prediction accuracies were moderate to high and consisted to varying degrees of within‐ and among‐family variance, in line with the different degrees of phenotypic differences between family means. The prediction accuracy within individual families also varied with the genetic complexity of the traits and was generally highest based on effect estimation with lines from the respective family, whereas the prediction accuracy decreased with decreasing relatedness among the families. Taken together, our results illustrate the potential of genomic prediction to increase selection gain in triticale breeding, but the composition of the training set is of utmost importance, and consequently, the implementation of this approach in applied breeding programmes is not straightforward.     

Choice of shrinkage parameter and prediction of genomic breeding values in elite maize breeding populations

Genomic selection (GS) is a promising alternative to marker‐assisted selection particularly for quantitative traits. In this study, we examined the prediction accuracy of genomic breeding values by using ridge regression best linear unbiased prediction in combination with fivefold cross‐validation based on empirical data of a commercial maize breeding programme. The empirical data is composed of 930 testcross progenies derived from 11 segregating families evaluated at six environments for grain yield and grain moisture. Accuracy to predict genomic breeding values was affected by the choice of the shrinkage parameter λ², by unbalanced family size, by size of the training population and to a lower extent by the number of markers. Accuracy of genomic breeding values was high suggesting that the selection gain can be improved implementing GS in elite maize breeding programmes.     

Hybrid breeding for biomass yield in winter triticale: II. Combining ability and hybrid prediction

Accurate hybrid prediction and knowledge about the relative contribution of general (GCA) and specific combining ability (SCA) are of utmost importance for efficient hybrid breeding. We therefore evaluated 91 triticale single-cross hybrids in field trials at seven environments for plant height, heading time, fresh biomass, dry matter content and dry biomass. Fresh and dry biomass showed the highest proportion (23%) of variance due to SCA. Prediction accuracies based on GCA were slightly higher than based on mid-parent values. Utilizing parental kinship information yielded the highest prediction accuracies when both parental lines have been tested in other hybrid combinations, but still moderate-to-low prediction accuracies for two untested parents. Thus, hybrid prediction for biomass traits in triticale is currently promising based on mid-parent values as emphasized by our simulation study, but can be expected to shift to GCA-based prediction with an increasing importance of GCA due to selection in hybrid breeding. Moreover, the performance of potential hybrids between newly developed lines can be predicted with moderate accuracy using genomic relationship information.     

Prospects for genomic selection in forage plant species

Genomic selection (GS) is a powerful method for exploitation of DNA sequence polymorphisms in breeding improvement, through the prediction of breeding values based on all markers distributed genome‐wide. Forage grasses and legumes provide important targets for GS implementation, as many key traits are difficult or expensive to assess, and are measured late in the breeding cycle. Generic attributes of forage breeding programmes are described, along with status of genomic resources for a representative species group (ryegrasses). Two schemes for implementing GS in ryegrass breeding are described. The first requires relatively little modification of current schemes, but could lead to significant reductions in operating cost. The second scheme would allow two rounds of selection for key agronomic traits within a time period previously required for a single round, potentially leading to doubling of genetic gain rate, but requires a purpose‐designed reference population. In both schemes, the limited extent of linkage disequilibrium (LD), which is the major challenge for GS implementation in ryegrass breeding, is addressed. The strategies also incorporate recent advances in DNA sequencing technology to minimize costs.     

Pigeonpea improvement: An amalgam of breeding and genomic research

In the past five decades, constant research has been directed towards yield improvement in pigeonpea resulting in the deployment of several commercially acceptable cultivars in India. Though, the genesis of hybrid technology, the biggest breakthrough, enigma of stagnant productivity still remains unsolved. To sort this productivity disparity, genomic research along with conventional breeding was successfully initiated at ICRISAT. It endowed ample genomic resource providing insight in the pigeonpea genome combating production constraints in a precise and speedy manner. The availability of the draft genome sequence with a large‐scale marker resource, oriented the research towards trait mapping for flowering time, determinacy, fertility restoration, yield attributing traits and photo‐insensitivity. Defined core and mini‐core collection, still eased the pigeonpea breeding being accessible for existing genetic diversity and developing stress resistance. Modern genomic tools like next‐generation sequencing, genome‐wide selection helping in the appraisal of selection efficiency is leading towards next‐generation breeding, an awaited milestone in pigeonpea genetic enhancement. This paper emphasizes the ongoing genetic improvement in pigeonpea with an amalgam of conventional breeding as well as genomic research.     

The potential of genomic‐assisted breeding to improve Fusarium head blight resistance in winter durum wheat

Durum wheat is the most important tetraploid wheat mainly used for semolina and pasta production, but is notorious for its high susceptibility to Fusarium head blight (FHB). Our objectives were to identify and characterize quantitative trait loci (QTL) in winter durum and to evaluate the potential of genomic approaches for the improvement of FHB resistance. Here, we employed an international panel of 170 winter and 14 spring durum lines, phenotyped for Fusarium culmorum resistance at five environments. Heading date, plant height and mean FHB severity showed significant genotypic variation with high heritabilities and FHB resistance was negatively correlated with both heading date and plant height. The dwarfing gene Rht‐B1 significantly affected FHB resistance and the genome‐wide association scan identified eight additional QTL affecting FHB resistance, explaining between 1% and 14% of the genotypic variation. A genome‐wide prediction approach yielded only a slightly improved predictive ability compared to marker‐assisted selection based on the four strongest QTL. In conclusion, FHB resistance in durum wheat is a highly quantitative trait and in breeding programmes may best be tackled by classical high‐throughput recurrent phenotypic selection that can be assisted by genomic prediction if marker profiles are available.     

Genomic prediction of grain yield in commercial Finnish oat (Avena sativa) and barley (Hordeum vulgare) breeding programmes

Genomic selection has been adopted in many plant breeding programmes. In this paper, we cover some aspects of information necessary before starting genomic selection. Spring oat and barley breeding data sets from commercial breeding programmes were studied using single, multitrait and trait-assisted models for predicting grain yield. Heritabilities were higher when estimated using multitrait models compared to single-trait models. However, no corresponding increase in prediction accuracy was observed in a cross-validation scenario. On the other hand, forward prediction showed a slight, but not significant, increase in accuracy of genomic estimated breeding values for breeding cohorts when a multitrait model was applied. When a correlated trait was used in a trait-assisted model, on average the accuracies increased by 9%–14% for oat and by 11%–28% for barley compared with a single-trait model. Overall, accuracies in forward validation varied between breeding cohorts and years for grain yield. Forward prediction accuracies for multiple cohorts and multiple years’ data are reported for oat for the first time.     

Genomic prediction for yields,processing and nutritional quality traits in cultivated potato (Solanum tuberosum L.)

Current potato breeding approaches are hampered by several factors including costly seed tubers, tetrasomic inheritance and inbreeding depression. Genomic selection (GS) demonstrated interesting results regardless of the ploidy level, and can be harnessed to circumvent these problems. In this work, three GS models were evaluated using 50,107 informative SilicoDArT markers and 11 traits in two values for cultivation and use (VCU) potato trials. Two key breeding problems modelled included predicting the performance of (i) new and unphenotyped clones (cross‐validation) and (ii) a VCU using another as training set (TS). GS models performed comparably. Cross‐validation accuracy was high for D35, D45, DMW and BVAL, in ascending order. Prediction accuracies of the VCUs were highly correlated, but the best prediction was obtained for the smaller VCU using the bigger as TS. Cross‐validation and VCU prediction accuracies were higher when bigger TSs were used. The findings herein indicate that GS can be attractively integrated in potato breeding, particularly in early clonal generations to predict and select for traits with low heritability which would otherwise require more testing years, environments and resources.     

Genomic changes in generations of synthetic rapeseed-like allopolyploid grown under selection

Resynthesized Brassica napus L. is an important source for broadening genetic diversity and producing lines with desired characteristics. It is also a fine model to study the processes of genomic reorganizations in recently formed polyploids. We firstly performed molecular cytogenetic characterization of newly resynthesized rapeseed (B. rapa ssp. narinosa × B. oleracea ssp. capitata) and its parental species, and also examined genomic changes in hybrids of the succeeding generations grown under pressure of selection of yellow-seeded progeny. For karyotype studies, FISH/GISH with 45S, 5S rDNA, C genome specific BoB014O06 BAC clone and genomic DNA of parental B. rapa was performed. Synthetic S0–S2 hybrids had common rapeseed karyotypes (2n = 38) including 14 loci of 45S rDNA sites and 10 loci of 5S rDNA. Progeny selection led to gradual deletion of C genome chromosomes in hybrid karyotypes. So, in karyotypes of S6 and S7 hybrids, the chromosome number was reduced to 2n = 20–22, and only chromosomes of A genome bearing 10–13 loci of 45S rDNA and 8–10 loci of 5S rDNA, variations in chromosome number, chromosome rearrangements as well as examples of trisomy and monosomy were revealed. Our findings indicate an enhanced genome instability in resynthesized rapeseed lines developed under the pressure of selection which might lead to chromosome rearrangements or/and deletions and even elimination of the whole parental genome in hybrids in succeeding generations. The approach can be useful for the development of rapeseed lines with trisomy, chromosome addition/substitution lines important for genetics and plant breeding.     

Optimum allocation of test resources and relative efficiency of alternative procedures of within‐family selection in hybrid breeding

Selection within families can be conducted as family deviation (FDS) or strict within‐family selection (WFS). Our objectives were to (i) investigate two breeding schemes combining selection among families with FDS or WFS and (ii) compare the optimum allocation of test resources for these breeding schemes. We focused on selection among S₁ families and doubled haploid (DH) lines within S₁ families and used Monte Carlo simulations to determine the selection gain (Δ?), its standard deviation (SD_Δ?), and the average coefficient of coancestry among the selected DH lines (). For breeding schemes focusing only on within‐family selection, as employed in animal breeding, the maximum Δ?, its SD_Δ?, and were larger for FDS than for WFS. However, for breeding schemes combining among‐ and within‐family selection, as employed commonly in plant breeding programmes, the maximum Δ?, SD_Δ?, and were almost equal for FDS and WFS. Furthermore, the optimum allocation of test resources was similar for FDS and WFS. We conclude that FDS and WFS are equally suited for short‐ and long‐term success in breeding schemes where among‐family selection is followed by within‐family selection.     

Crop breeding for salt tolerance in the era of molecular markers and marker‐assisted selection

Crop salt tolerance (ST) is a complex trait affected by numerous genetic and non‐genetic factors, and its improvement via conventional breeding has been slow. Recent advancements in biotechnology have led to the development of more efficient selection tools to substitute phenotype‐based selection systems. Molecular markers associated with genes or quantitative trait loci (QTLs) affecting important traits are identified, which could be used as indirect selection criteria to improve breeding efficiency via marker‐assisted selection (MAS). While the use of MAS for manipulating simple traits has been streamlined in many plant breeding programmes, MAS for improving complex traits seems to be at infancy stage. Numerous QTLs have been reported for ST in different crop species; however, few commercial cultivars or breeding lines with improved ST have been developed via MAS. We review genes and QTLs identified with positive effects on ST in different plant species and discuss the prospects for developing crop ST via MAS. With the current advances in marker technology and a better handling of genotype by environment interaction effects, the utility of MAS for breeding for ST will gain momentum.     

Detection of donor effects in a rye introgression population with genome‐wide prediction

Introgression populations are developed to make genetic resources for breeding purposes available. In the case that the number of donor segments exceeds the number of lines, genome‐wide prediction (GWP) methods are suggested as promising for the analysis of such populations. Our objectives were to characterize a rye introgression population with the Rye5K SNP assay and to apply a GWP model with a modification of the restricted maximum likelihood procedure that yields heteroscedastic variances to detect significant donor effects. The introgression lines (ILs) carried on average 4.6 donor segments with a mean length of 27 cM and represented 94% of the donor genome. Two donor effects were detected that significantly increased thousand‐kernel weight. We found four donor effects for protein, total pentosan and starch content that can improve baking quality. Three donor effects for protein content were observed for improving feeding purposes and one donor effect for starch content to improve ethanol production. The effects were localized to small genomic regions. Consequently, these ILs can improve rye breeding by directly employing them in breeding programmes for variety development.     

Genomic selection in hybrid breeding

While hybrid breeding is widely applied in outbreeding species, for many self‐pollinating crop plants, it has only recently been established. This may have had its reason in the limitations of methods available for hybrid performance prediction, in particular when established heterotic pools were absent. Genomic selection has been suggested as a promising approach to resolve these limitations. In our review, we briefly introduce the principles of genomic selection as an extension of marker‐assisted selection using genome‐wide high‐density molecular marker data and discuss the advantages and limitations of currently used algorithms. Including the outcome from a recent extended approach to hybrid wheat as a timely example, we summarize current progress in empirical studies on the application of genomic selection for prediction of hybrid performance. Here, we put emphasis on the factors affecting the accuracy of prediction, pointing in particular to the relevance of relatedness, genotype x environment interaction and experimental design. Finally, we discuss future research needs and potential applications.     

Prediction of F6 line performance from F3 families in spring oilseed rape. I. Yield and other agronomic traits

Summary The reliability of a selection among crosses based on a cross prediction in early generations was investigated in spring rapeseed. The performance of the parents, the F₂ generation, and random F₃ lines from four crosses were used to predict the probability of finding superior recombinant lines. These predictions were made for two years and compared with the observed performance of F₆ lines in the second of these two years and in an additional year. Predicted and observed performances coincided reasonably for the characters plant height, standability, maturity and an index calculated from seed yield, oil content and protein content. For seed yield and flowering time, the predictions were very unreliable. In conclusion, prediction methods may be useful in rapeseed breeding, if quality traits are of major commercial interest.     

Evaluation of genomic approaches for marker‐based improvement of lodging tolerance in triticale

Lodging tolerance is an important agronomic trait as it can have a severe negative impact on grain yield and quality. Here, we used a large mapping population of 647 doubled haploid triticale lines derived from four families to dissect the genetic architecture underlying lodging tolerance and to assess different approaches for a genomics‐based improvement of the trait. The plants were evaluated for lodging in two environments and genotyped with 1710 genomewide DArT markers. We observed a large genotypic variation for lodging and transgressive segregation in all families. Employing two complementary QTL mapping approaches, we identified both main effect and epistatic QTL. Using cross‐validation, we showed that the proportion of genotypic variance explained by the detected QTL is low, thus limiting the efficiency of marker‐assisted selection to improve this trait. By contrast, the cross‐validated predictive ability of genomic prediction was approximately twice as high as that of the QTL‐based selection approaches. In conclusion, our results show that lodging tolerance is a complex trait that can be improved by classical breeding but also assisted by marker‐based approaches.     

玉米自交系“南99”的选育及配合力分析

针对广西玉米育种材料遗传基础狭窄这一关键问题,此研究采用S1改良法群体内轮回选择与自交系谱选择相结合的育种方法,选育出"南99"自交系;经配合力测定和性状鉴定表明,"南99"是一个一般配合力和特殊配合力都高、综合农艺性状好、抗病虫性强、适应性广、自身产量高的自交系,用它做母本分别选育成南校18号、南校968、南校9665和南校201等四个优良玉米单交种并通过广西品种审定。其有较高的利用价值和广泛的应用前景。     

Predicting breeding values of spring barley accessions by using the singular value decomposition of genetic similarities

As pedigree information among parental lines is often incomplete, selection response can be enhanced if a matrix containing genetic similarities is used in the mixed model equations (MME) to predict breeding values. However, a low number of molecular markers may cause this matrix to be singular. This study was conducted to determine if breeding values are still accurate when a singular value decomposition of genetic similarities was performed setting negative singular values to zero. Two‐year data for four traits of 152 spring barley accessions, which were analyzed by 23 SSR markers, were used in the prediction of breeding values. In general, similar values of Akaike Information Criteria and of overall standard error of difference and a Spearman rank correlation coefficient between breeding values ranging from 0.92 to 0.99 were obtained independent of whether the prediction was based on a singular value decomposition of genetic similarities or not. In conclusion, the singular value decomposition of genetic similarities appears to be a suitable method in the case of singular matrices.     

Genomic prediction of sunflower hybrid performance

Predicting single‐cross performance is of high importance to improve the efficiency of sunflower (Helianthus annuus L.) hybrid breeding programmes. We used experimental data from inter‐ and intragroup sunflower hybrids and their parental lines adapted to Central Europe to (i) study the genetic diversity and combining ability and (ii) examine the accuracy to predict hybrid performance based on phenotypic and genomic data. We evaluated 133 intragroup and 104 intergroup hybrids with their parental lines in replicated trials at four environments for grain yield, oil yield and oil content. Furthermore, the parental lines were fingerprinted with 572 AFLP markers. Variance due to specific combining ability was comparable for intergroup and intragroup crosses. This suggested a lack of clearly defined heterotic groups for the sample of studied sunflower lines. Prediction accuracy of hybrid performance based on general combining ability effects was high and could not be increased using genomic selection approaches. For situations where no information on GCA effects of parental lines was available, hybrid prediction based on genomic selection methods was accurate for groups of related lines. For groups of unrelated lines, however, we observed a strong decrease in the prediction accuracy. This suggests that prediction of hybrid performance for crosses based on genetically distant parents remains challenging.     

Genetics,genomics and breeding of groundnut (Arachis hypogaea L.)

Groundnut is an important food and oil crop in the semiarid tropics, contributing to household food consumption and cash income. In Asia and Africa, yields are low attributed to various production constraints. This review paper highlights advances in genetics, genomics and breeding to improve the productivity of groundnut. Genetic studies concerning inheritance, genetic variability and heritability, combining ability and trait correlations have provided a better understanding of the crop's genetics to develop appropriate breeding strategies for target traits. Several improved lines and sources of variability have been identified or developed for various economically important traits through conventional breeding. Significant advances have also been made in groundnut genomics including genome sequencing, marker development and genetic and trait mapping. These advances have led to a better understanding of the groundnut genome, discovery of genes/variants for traits of interest and integration of marker‐assisted breeding for selected traits. The integration of genomic tools into the breeding process accompanied with increased precision of yield trialing and phenotyping will increase the efficiency and enhance the genetic gain for release of improved groundnut varieties.     

Applications of pedigree-based genome mapping in wheat and barley breeding programs

The aim of the pedigree-based genome mapping project is to investigate and develop systems for implementing marker assisted selection to improve the efficiency of selection and increase the rate of genetic gain in breeding programs. Pedigree-based whole genome marker application provides a vehicle for incorporating marker technologies into applied breeding programs by bridging the gap between marker–trait association and marker implementation. We report on the development of protocols for implementation of pedigree-based whole genome marker analysis in breeding programs within the Australian northern winter cereals region. Examples of applications from the Queensland DPI&F wheat and barley breeding programs are provided, commenting on the use of microsatellites and other types of molecular markers for routine genomic analysis, the integration of genotypic, phenotypic and pedigree information for targeted wheat and barley lines, the genomic impacts of strong selection pressure in case study pedigrees, and directions for future pedigree-based marker development and analysis.