Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple.
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ABSTRACT:
SUBMITTER: Roth M
PROVIDER: S-EPMC7459338 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Molecular events regulating apple fruit ripening and sensory quality are largely unknown. Such knowledge is essential for genomic-assisted apple breeding and postharvest quality management. In this study, a parallel transcriptome profile analysis, scanning electron microscopic (SEM) examination and systematic physiological characterization were performed on two apple cultivars, Honeycrisp (HC) and Cripps Pink (CP), which have distinct ripening features and texture attributes. Systematic physiological characterization of fruit ripening based on weekly maturity data indicated substantial differences in fruit crispness and firmness at comparable ripening stages. SEM images of fruit cortex tissues prepared from fruits with equivalent maturity suggested that the cell wall thickness may contribute to the observed phenotypes of fruit firmness and crispness. A high-density long-oligo apple microarray consisting of duplex 190,135 cross-hybridization-free 50-70-mer isothermal probes, and representing 23,997 UniGene clusters, was manufactured on a Nimblegen array platform. Transcriptome profiling identified a total of 1793 and 1209 UniGene clusters differentially expressed during ripening from cortex tissues of HC and CP, respectively. UniGenes implicated in hormone metabolism and response, cell wall biosynthesis and modification and those encoding transcription factors were among the prominent functional groups. Between the two cultivars, most of the identified UniGenes were similarly regulated during fruit ripening; however, a short list of gene families or specific family members exhibited distinct expression patterns between the two cultivars, which may represent candidate genes regulating cultivar-specific apple fruit ripening patterns and quality attributes.
2010-11-05 | GSE24523 | GEO
Project description:Key messagePopulation structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.
| S-EPMC4282691 | biostudies-literature
Project description:Molecular events regulating apple fruit ripening and sensory quality are largely unknown. Such knowledge is essential for genomic-assisted apple breeding and postharvest quality management. In this study, a parallel transcriptome profile analysis, scanning electron microscopic (SEM) examination and systematic physiological characterization were performed on two apple cultivars, Honeycrisp (HC) and Cripps Pink (CP), which have distinct ripening features and texture attributes. Systematic physiological characterization of fruit ripening based on weekly maturity data indicated substantial differences in fruit crispness and firmness at comparable ripening stages. SEM images of fruit cortex tissues prepared from fruits with equivalent maturity suggested that the cell wall thickness may contribute to the observed phenotypes of fruit firmness and crispness. A high-density long-oligo apple microarray consisting of duplex 190,135 cross-hybridization-free 50-70-mer isothermal probes, and representing 23,997 UniGene clusters, was manufactured on a Nimblegen array platform. Transcriptome profiling identified a total of 1793 and 1209 UniGene clusters differentially expressed during ripening from cortex tissues of HC and CP, respectively. UniGenes implicated in hormone metabolism and response, cell wall biosynthesis and modification and those encoding transcription factors were among the prominent functional groups. Between the two cultivars, most of the identified UniGenes were similarly regulated during fruit ripening; however, a short list of gene families or specific family members exhibited distinct expression patterns between the two cultivars, which may represent candidate genes regulating cultivar-specific apple fruit ripening patterns and quality attributes. Using a single color labeling system, a total of 24 microarray slides were utilized, one for each cortex tissue sample, for transcriptome profiling analysis. 2 cultivars x 3 developmental stages x 4 biological replicates.
2010-11-05 | E-GEOD-24523 | biostudies-arrayexpress
Project description:Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F2 panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible.
| S-EPMC7341128 | biostudies-literature
Project description:Improving fruit quality is an important but challenging breeding goal in winter squash. Squash breeding in general is resource-intensive, especially in terms of space, and the biology of squash makes it difficult to practice selection on both parents. These restrictions translate to smaller breeding populations and limited use of greenhouse generations, which in turn, limit genetic gain per breeding cycle and increases cycle length. Genomic selection is a promising technology for improving breeding efficiency; yet, few studies have explored its use in horticultural crops. We present results demonstrating the predictive ability of whole-genome models for fruit quality traits. Predictive abilities for quality traits were low to moderate, but sufficient for implementation. To test the use of genomic selection for improving fruit quality, we conducted three rounds of genomic recurrent selection in a butternut squash (Cucurbita moschata) population. Selections were based on a fruit quality index derived from a multi-trait genomic selection model. Remnant seed from selected populations was used to assess realized gain from selection. Analysis revealed significant improvement in fruit quality index value and changes in correlated traits. This study is one of the first empirical studies to evaluate gain from a multi-trait genomic selection model in a resource-limited horticultural crop.
| S-EPMC7534422 | biostudies-literature
Project description:Genomic prediction is a useful tool for plant and animal breeding programs and is starting to be used to predict human diseases as well. A shortcoming that slows down the genomic selection deployment is that the accuracy of the prediction is not known a priori. We propose EthAcc (Estimated THeoretical ACCuracy) as a method for estimating the accuracy given a training set that is genotyped and phenotyped. EthAcc is based on a causal quantitative trait loci model estimated by a genome-wide association study. This estimated causal model is crucial; therefore, we compared different methods to find the one yielding the best EthAcc. The multilocus mixed model was found to perform the best. We compared EthAcc to accuracy estimators that can be derived via a mixed marker model. We showed that EthAcc is the only approach to correctly estimate the accuracy. Moreover, in case of a structured population, in accordance with the achieved accuracy, EthAcc showed that the biggest training set is not always better than a smaller and closer training set. We then performed training set optimization with EthAcc and compared it to CDmean. EthAcc outperformed CDmean on real datasets from sugar beet, maize, and wheat. Nonetheless, its performance was mainly due to the use of an optimal but inaccessible set as a start of the optimization algorithm. EthAcc's precision and algorithm issues prevent it from reaching a good training set with a random start. Despite this drawback, we demonstrated that a substantial gain in accuracy can be obtained by performing training set optimization.
| S-EPMC6380617 | biostudies-literature
Project description:Key messageA practical approach is developed to determine a cost-effective optimal training set for selective phenotyping in a genomic prediction study. An R function is provided to facilitate the application of the approach. Genomic prediction (GP) is a statistical method used to select quantitative traits in animal or plant breeding. For this purpose, a statistical prediction model is first built that uses phenotypic and genotypic data in a training set. The trained model is then used to predict genomic estimated breeding values (GEBVs) for individuals within a breeding population. Setting the sample size of the training set usually takes into account time and space constraints that are inevitable in an agricultural experiment. However, the determination of the sample size remains an unresolved issue for a GP study. By applying the logistic growth curve to identify prediction accuracy for the GEBVs and the training set size, a practical approach was developed to determine a cost-effective optimal training set for a given genome dataset with known genotypic data. Three real genome datasets were used to illustrate the proposed approach. An R function is provided to facilitate widespread application of this approach to sample size determination, which can help breeders to identify a set of genotypes with an economical sample size for selective phenotyping.
| S-EPMC10011335 | biostudies-literature
Project description:Key messageMaximizing CDmean and Avg_GRM_self were the best criteria for training set optimization. A training set size of 50-55% (targeted) or 65-85% (untargeted) is needed to obtain 95% of the accuracy. With the advent of genomic selection (GS) as a widespread breeding tool, mechanisms to efficiently design an optimal training set for GS models became more relevant, since they allow maximizing the accuracy while minimizing the phenotyping costs. The literature described many training set optimization methods, but there is a lack of a comprehensive comparison among them. This work aimed to provide an extensive benchmark among optimization methods and optimal training set size by testing a wide range of them in seven datasets, six different species, different genetic architectures, population structure, heritabilities, and with several GS models to provide some guidelines about their application in breeding programs. Our results showed that targeted optimization (uses information from the test set) performed better than untargeted (does not use test set data), especially when heritability was low. The mean coefficient of determination was the best targeted method, although it was computationally intensive. Minimizing the average relationship within the training set was the best strategy for untargeted optimization. Regarding the optimal training set size, maximum accuracy was obtained when the training set was the entire candidate set. Nevertheless, a 50-55% of the candidate set was enough to reach 95-100% of the maximum accuracy in the targeted scenario, while we needed a 65-85% for untargeted optimization. Our results also suggested that a diverse training set makes GS robust against population structure, while including clustering information was less effective. The choice of the GS model did not have a significant influence on the prediction accuracies.
| S-EPMC9998580 | biostudies-literature
Project description:Genomic selection (GS) is a breeding approach which exploits genome-wide information and whose unprecedented success has shaped several animal and plant breeding schemes through delivering their genetic progress. This is the first study assessing the potential of GS in apricot (Prunus armeniaca) to enhance postharvest fruit quality attributes. Genomic predictions were based on a F1 pseudo-testcross population, comprising 153 individuals with contrasting fruit quality traits. They were phenotyped for physical and biochemical fruit metrics in contrasting climatic conditions over two years. Prediction accuracy (PA) varied from 0.31 for glucose content with the Bayesian LASSO (BL) to 0.78 for ethylene production with RR-BLUP, which yielded the most accurate predictions in comparison to Bayesian models and only 10% out of 61,030 SNPs were sufficient to reach accurate predictions. Useful insights were provided on the genetic architecture of apricot fruit quality whose integration in prediction models improved their performance, notably for traits governed by major QTL. Furthermore, multivariate modeling yielded promising outcomes in terms of PA within training partitions partially phenotyped for target traits. This provides a useful framework for the implementation of indirect selection based on easy-to-measure traits. Thus, we highlighted the main levers to take into account for the implementation of GS for fruit quality in apricot, but also to improve the genetic gain in perennial species.
| S-EPMC7718743 | biostudies-literature
Project description:1. The pectic substances of apple have been extracted and separated into a pure pectinic acid and a neutral arabinan-galactan complex by precipitation of the acidic component with ethanol and with cetylpyridinium chloride. 2. The composition of the fractions has been determined. The pectinic acid contained galacturonic acid, arabinose, galactose, rhamnose, xylose and several trace sugars. 3. Transelimination degradation of the pectinic acid gave rise to two components completely separable by zone electrophoresis and by Sephadex gel filtration. Analysis of these components confirmed that the pectinic acid molecules contained long chains of esterified galacturonosyl residues, but showed in addition that more neutral portions containing a high proportion of arabinofuranose residues were attached to them. 4. The identification of rhamnose, galactose and xylose in aldobiouronic acids obtained from a partial hydrolysate of pectinic acid has shown that these sugars are covalently linked in the molecule, and it is suggested that the galacturonosyl-(1-->2)-rhamnose link is a general feature of pectinic acid structure. 5. The possible biological significance of pectinic acid structure has been discussed. 6. The arabinan-galactan complex contained nearly equal quantities of arabinose and galactose residues and some of its physical properties have been investigated.
| S-EPMC1206596 | biostudies-other