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.     

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.     

Potential of genomic selection in rapeseed (Brassica napus L.) breeding

Genomic selection employs genome‐wide marker data to predict genomic breeding values. In this study, a population consisting of 391 lines of elite winter oilseed rape derived from nine families was used to evaluate the prospects of genomic selection in rapeseed breeding. All lines have been phenotyped for six morphological, quality‐ and yield‐related traits and genotyped with genome‐wide SNP markers. We used ridge regression best linear unbiased prediction in combination with cross‐validation and obtained medium to high prediction accuracies for the studied traits. Our results illustrate that among‐family variance contributes to the prediction accuracy and can lead to an overestimation of the prospects of genomic selection within single segregating families. We also tested a scenario where estimation of effects was carried out without individuals from the family in which breeding values were predicted, which yielded lower but nevertheless attractive prediction accuracies. Taken together, our results suggest that genomic selection can be a valuable genomic approach for complex agronomic traits towards a knowledge‐based breeding in rapeseed.     

Genomic selection allowing for marker‐by‐environment interaction

Genomic selection has been routinely implemented in plant breeding in two stages. The first stage usually omits the marker information and estimates adjusted means of genotypes across environments. The second stage uses the adjusted means to predict genomic breeding values. However, if the effects of markers vary substantially between different environments, it may be important to account for this variation for varieties adapted to different environments. Using two maize data sets, we investigated whether modelling the marker‐by‐environment interaction can improve the predictive ability of genomic selection relative to modelling genotype‐by‐environment interaction alone. Modelling the marker‐by‐environment interaction did not substantially increase the predictive ability relative to modelling only the genotype‐by‐environment interaction for the two tested data sets. Thus, genomic selection, carried out in a stagewise fashion, such that the marker information is omitted until the last stage of the process, may suffice for most practical purposes. Moreover, predictive ability did not reduce substantially even when the number of markers with consistent effects across environments used for genomic prediction was reduced to about 50.     

Genomics-assisted breeding in fruit trees

Recent advancements in genomic analysis technologies have opened up new avenues to promote the efficiency of plant breeding. Novel genomics-based approaches for plant breeding and genetics research, such as genome-wide association studies (GWAS) and genomic selection (GS), are useful, especially in fruit tree breeding. The breeding of fruit trees is hindered by their long generation time, large plant size, long juvenile phase, and the necessity to wait for the physiological maturity of the plant to assess the marketable product (fruit). In this article, we describe the potential of genomics-assisted breeding, which uses these novel genomics-based approaches, to break through these barriers in conventional fruit tree breeding. We first introduce the molecular marker systems and whole-genome sequence data that are available for fruit tree breeding. Next we introduce the statistical methods for biparental linkage and quantitative trait locus (QTL) mapping as well as GWAS and GS. We then review QTL mapping, GWAS, and GS studies conducted on fruit trees. We also review novel technologies for rapid generation advancement. Finally, we note the future prospects of genomics-assisted fruit tree breeding and problems that need to be overcome in the breeding.     

Biotechnology and apple breeding in Japan

Apple is a fruit crop of significant economic importance, and breeders world wide continue to develop novel cultivars with improved characteristics. The lengthy juvenile period and the large field space required to grow apple populations have imposed major limitations on breeding. Various molecular biological techniques have been employed to make apple breeding easier. Transgenic technology has facilitated the development of apples with resistance to fungal or bacterial diseases, improved fruit quality, or root stocks with better rooting or dwarfing ability. DNA markers for disease resistance (scab, powdery mildew, fire-blight, Alternaria blotch) and fruit skin color have also been developed, and marker-assisted selection (MAS) has been employed in breeding programs. In the last decade, genomic sequences and chromosome maps of various cultivars have become available, allowing the development of large SNP arrays, enabling efficient QTL mapping and genomic selection (GS). In recent years, new technologies for genetic improvement, such as trans-grafting, virus vectors, and genome-editing, have emerged. Using these techniques, no foreign genes are present in the final product, and some of them show considerable promise for application to apple breeding.     

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 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.     

Conventional and contemporary approaches for drought tolerance rice breeding: Progress and prospects

Drought is becoming a major threat to rice farming across the globe owing to the depletion of water tables in rice-growing belts. Drought affects rice plants at multiple stages, causing damage at morphological and physio-biochemical levels, leading to severe losses that exceed losses from all other stresses. The amalgamation of conventional breeding methods with modern molecular biology tools and biometrical methods could help accelerate the genetic gain for drought tolerance in rice. Many drought-tolerance traits with genetic determinants have been identified and exploited for tolerance rice variety breeding. The integration of genome-wide association study and genomic selection tools with speed breeding shortened the breeding cycle and aided in rapid improvement of genetic gain. In this review, we emphasized the progress made through classical breeding as well as the limitations and usefulness of current genomic methods in improving drought tolerance. We briefly addressed methods for identifying genetic determinants for drought tolerance and deploying them through genomics-assisted breeding programmes to develop high-yielding drought-tolerant rice cultivars.     

Identification and comparative analysis of quantitative trait loci associated with parthenocarpy in processing cucumber

Parthenocarpy (seedless fruit) is an economically important yield‐related trait in cucumber (Cucumis sativus L.; 2n = 2x = 14). However, the genomic locations of factors controlling parthenocarpic fruit development in this species are not known. Therefore, an F_{2 : 3} mating design was utilized to map quantitative trait loci (QTL) for parthenocarpy using a narrow cross employing two gynoecious, indeterminate and normal leaf lines [2A (parthenocarpic) and Gy8 (non‐parthenocarpic)]. QTL detection was performed employing 2A‐ and Gy8‐coupling phase data using the parthenocarpic yield of 126 F₃ families grown at two locations at Hancock, WI in 2000. The QTLs detected in this study were compared with the map locations of QTLs conditioning first‐harvest yield of seeded cucumber characterized in a previous study. There were 10 QTLs for parthenocarpy detected defining four genomic regions, in which three QTLs also mapped to the same genomic regions as QTLs detected for fruit yield at first‐harvest as reported in a previous study. The eight fluorescence amplified fragment length polymorphism (AFLP) markers linked to parthenocarpy through QTL mapping defined herein (four each in linkage groups 1 and 4) are candidates for use in marker‐assisted selection programmes where breeding for increased levels of parthenocarpy is an objective in the elite‐processing cucumber populations.     

The Plabsoft database: a comprehensive database management system for integrating phenotypic and genomic data in academic and commercial plant breeding programs

Besides phenotypic data from field trials and molecular data from lab experiments, modern plant breeding programs generate a wide variety of data, for instance pedigree, randomization, geostatistical or climate data. Due to the lack of an integrated database system, breeders generally exploit only part of these data for selection decisions or retrieve only part of the information present in the data. Most approaches in genomics, however, develop their full power only when they are based on analyses of large numbers of genotypes from multiple crosses and current as well as past generations. We have developed a flexible data management and -analyses system for storage and quality control of plant breeding data. It is implemented using the PostgreSQL database management system and linked to the R software environment for integrated statistical analyses of phenotypic and genomic data. The database structure is capable of managing the following types of data observed in breeding programs of all major crops: (a) germplasm data of any species including pedigree data, (b) phenotypic data of any trait and trait complexity, (c) trial management data for any field and trial design, (d) molecular marker data for all common types of markers, as well as (e) project and study management data.     

Maize participatory breeding in Portugal: Comparison of farmer's and breeder's on‐farm selection

“VASO” is a Portuguese participatory maize breeding project (1984), where several maize landraces such as “Pigarro” have been selected both by a farmer's (phenotypic recurrent selection) and a breeder's approach (S2 lines recurrent selection). The objectives of this study were to determine the phenotypic and genotypic responses to participatory selection using these two different approaches, to clarify to which extent both selection methods preserve genetic diversity, and conclude what is the preferred method to apply in sustainable farming systems. The results, obtained via ANOVA, regression analyses and molecular markers, indicate that for both selection methods, genetic diversity was not significantly reduced, even with the most intensive breeder's selection. Although there were some common outputs, such as the determinated versus indeterminated ears, cob and ear weight ratio per ear and rachis 2, specific phenotypic traits evolved in opposite directions between the two selection approaches. Yield increase was only detected during farmer selection, indicating its interest on PPB. Candidate genes were identified for a few of the traits under selection as potential functional markers in participatory plant breeding.     

植物基因组选择及其应用研究进展

为促进基因组选择在植物遗传改良中的应用,笔者总结了基因组选择的原理与方法、分析了基因组选择育种的统计方法,指出了各种因素对基因组选择的影响,总结了基因组选择面临的挑战,并讨论了基因组选择在植物数量性状分子育种研究中可能的应用。认为随着基因型分析成本的降低和统计方法的发展,植物基因组选择将会逐步完善,将在植物基因组育种中发挥重要的作用。     

Towards an objective evaluation of persistency of <Emphasis Type="Italic">Lolium perenne</Emphasis> swards using UAV imagery

Perennial ryegrass (Lolium perenne) is a perennial crop used in temperate regions as forage. In L. perenne breeding programs, persistency is an important trait. Poor persistency results in sward degradation and associated yield and nutritive value losses. Breeders assess persistency of accessions using visual scoring in field plots during the 2nd or 3rd growing season. This evaluation system is easy and cheap but is not free from human bias. In this study, the correlation between the scoring done by different breeders was only 0.243. As an alternative we have developed a methodology to assess persistency of L. perenne breeding materials based on vegetation indices (VIs) derived from Unmanned Aerial Vehicle (UAV) imagery. The VIs Excess green (ExG2), Green Leaf Index and Normalized green intensity (GCC) were found to provide consistent results for flights carried out under different light conditions and were validated by ground reference information. The correlation between the VIs and the percentage of ground cover extracted from on-ground imagery was 0.885. To test the implementation of the method we compared the ExG2 value based approach to selection with a visual score based selection methodology as applied by two breeders. The breeding decisions of Breeder A agreed well with decisions based on ExG2 values (74.6%), but those of Breeder B displayed a lower agreement (54.0%). In contrast, agreement between decisions based on different flights was very high (91.6%). The methodology was validated for general applicability. In summary, the results demonstrate that basing persistency selection in L. perenne breeding programs on ExG2 values from UAV imagery is likely to be more objective in comparison to the currently-used visual scoring method.     

Genome-wide selection in cassava

The main objective of this study was to estimate the selection accuracy and to predict the genetic gain in cassava breeding using genomic selection methodologies. We evaluated 358 cassava genotypes for the following traits: shoot weight (SW), fresh root yield (FRY), starch fraction amylose content (AC), dry matter content (DMC), and starch yield (S-Y). Genotyping was performed using 390 single nucleotide polymorphisms (SNPs), which were used as covariates in the random regression-best linear unbiased prediction model for genomic selection. The heritability values detected by markers for the SW, FRY, AC, DMC, and S-Y traits were 0.25, 0.25, 0.03, 0.20, and 0.26, respectively. Because the low heritability detected for AC, this trait was eliminated from further analysis. Using only the most informative SNPs (118, 92, 56, and 97 SNPs for SW, FRY, DMC, and S-Y, respectively) we observed higher selection accuracy which were 0.83, 0.76, 0.67, and 0.77, respectively to SW, FRY, DMC, and S-Y. With these levels of accuracy and considering a selection cycle reduced by half the time, the theoretical gains with genomic selection compared to phenotypic selection for DMC, FRY, and SW would be 39.42 %, 56.90 %, and 73.96 %, respectively. These results indicate that in the cassava, genomic selection can substantially speed up selection cycles, thereby increasing gains per unit time. Although there are high expectations for incorporating this strategy into breeding programs, we still need to validate the model for other traits and evaluate whether the selection accuracy can be improved using more SNPs.     

Breeding selection efficiency for raspberry postharvest shelf life affected by storage temperature and harvest season

Improved postharvest quality is an important goal for fresh-market raspberry breeding programs. To determine if warm or cold storage following harvest would better facilitate the breeding selection process for the assessment of postharvest decay and juice leakage, pesticide-free fruit from cultivars and breeding selections of red, yellow, purple, and black raspberries were stored at two temperatures. Following storage fruits were examined for decay and juice leakage rate at room-temperature (25 °C) and at a cooler temperature (5 °C). The rate of decay was much faster in room-temperature storage than in cooler storage; however, classification of genotypes as parents or discards was not always in agreement between these two temperatures. This suggests that a breeder should determine whether room-temperature storage or cooler storage more closely resembles the postharvest environment for the targeted growers. For many leakage rate comparisons, there was no advantage from either storage temperature. However, when an advantage was evident, cold storage evaluation identified a greater number of classes comparing black raspberry and purple raspberry genotypes, but warm storage evaluation identified a greater number of classes comparing red and yellow raspberry genotypes. There was complete agreement on genotype breeding disposition, indicating that a breeder could evaluate genotypes for leakage in the same storage temperature chosen to evaluate decay. Selection decisions made from evaluating floricane fruit were not always in agreement with decisions made from evaluating primocane fruit, indicating that genotypes should be evaluated in both fruiting seasons.     

Implementing genomic selection in sour passion fruit population

Sour passion fruit is an economically important tropical fruit crop with little explored genetic potential. This study aimed to provide breeders with essential estimates of genomic breeding values in economically important traits in passion fruit, using Bayesian models which may contribute to the implementation of Genomic Selection and develop new strategies for the continuity of sour passion fruit breeding programs. For this, the following Bayesian models were tested using 183 polymorphic marks: Bayesian Ridge regression, Bayes A, Bayes B, Bayes B2, Bayes Cπ and Bayesian Lasso for estimation of genomic breeding values. To achieve this, ninety-five full-sib progenies derived from the third cycle of recurrent selection of the sour passion fruit (Passiflora edulis Sims.) at Universidade Estadual do Norte Fluminense Darcy Ribeiro—UENF were used and eight fruit yield (number of fruit, total yield, mean fruit weight, fruit length, fruit width) and quality(percent pulp, skin thickness, soluble solids) traits were assessed. The Bayes Cπ (smaller deviance information criterion) yield the best genetic predictions for almost all traits. Genetic correlations in this study indicate that the number of fruit can be used as a proxy for yield. The values of genomic heritability obtained were high and ranged from 0.62 to 0.76 and predict accuracy ranged from 0.55 to 0.75, so we can to speculate that the use of two replicates in the present study was an adequate amount to obtain phenotypic mean, which was used to adjust the genomic prediction model.     

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.     

Enhancing onion breeding using molecular tools

Bulb onion (Allium cepa L.) is an ancient crop that is thought to have originated in Central Asia and has been cultivated for over 5000 years. Classical genetic and plant breeding approaches have been used to improve onion yield, quality, and resistance against biotic and abiotic stresses. However, its biennial life cycle, cross‐pollinated nature and high inbreeding depression have proved challenging for the characterization and breeding of improved traits. New technologies, notably next‐generation sequencing, are providing researchers with the genomic resources and approaches to overcome these challenges. Using these genomic technologies, molecular markers are being rapidly developed and utilized for germplasm analysis and mapping in onion. These new tools and knowledge are allowing the integration of molecular and conventional breeding to speed up onion improvement programmes. In this review, we outline recent progress in onion genomics and molecular genetics and prospects for enhancing onion yield and quality in the future.     

Genome-enabled prediction models for black tea (Camellia sinensis) quality and drought tolerance traits

Genomic selection in tea plant (Camellia sinensis) breeding has the potential to accelerate efficiency of choosing parents with desirable traits at the seedling stage. The study evaluated different genome-enabled prediction models for black tea quality and drought tolerance traits in discovery and validation populations. The discovery population comprised of two segregating tea populations (TRFK St. 504 and TRFK St. 524) with 255 F₁ progeny and 56 individual tea cultivars in validation population genotyped using 1,421 DArTseq markers. Twofold cross-validation was used for training the prediction models in the discovery population on eight different phenotypic traits. The best prediction models in the discovery population were consequently fitted to the validation population. Of all the four model-based prediction approaches, putative QTLs (Quantitative Trait Loci) + annotated proteins + KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathway-based prediction approach showed more robustness. The findings have for the first time opened up a new avenue for future application of genomic selection in tea breeding.