Accuracy and responses of genomic selection on key traits in apple breeding

Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review

To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01497-2.

Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses

KEY MESSAGE

Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources. Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.

Advancing tree genomics to future proof next generation orchard production

The challenges facing tree orchard production in the coming years will be largely driven by changes in the climate affecting the sustainability of farming practices in specific geographical regions. Identifying key traits that enable tree crops to modify their growth to varying environmental conditions and taking advantage of new crop improvement opportunities and technologies will ensure the tree crop industry remains viable and profitable into the future. In this review article we 1) outline climate and sustainability challenges relevant to horticultural tree crop industries, 2) describe key tree crop traits targeted for improvement in agroecosystem productivity and resilience to environmental change, and 3) discuss existing and emerging genomic technologies that provide opportunities for industries to future proof the next generation of orchards.

Investigation of potential genetic factors for growth traits in yellow-feather broilers using weighted single-step genome-wide association study

Yellow-feather broilers take a large portion of poultry industry in China due to its meat characteristics. Improving the growth traits of yellow-feathered broilers will have great significance for the Chinese poultry market. The current study was designed to investigate the potential genetic factors using the weighted single-step genome-wide association study (wssGWAS) method, which takes consideration of more factors including pedigree, sex, environment and has more accuracy than traditional GWAS. The yellow-feather dwarf chickens from Wens Nanfang Poultry Breeding Co. Ltd. were revolved to recode 9 growth traits: Average daily gain (ADG), body weight (BW) at 45 d, 49 d, 56 d, 63 d, 70 d, 77 d, 84 d, 91 d for analysis. For the results, the region 4.63 to 5.03 Mb on chromosome 15, which was the QTL overlapped in BW45, BW49, BW56, BW63, BW84, might be the crucial genetic region for growth traits. Seven GO terms and 3 KEGG pathways, GO:0005200, GO:0005882, GO:0045111, GO:0099513, GO:0099081, GO:0099512, GO:0099080, KEGG:gga04020, KEGG:gga04540, KEGG:gga04210, were detected to relevant with growth traits. The genes enriched in these biological processes (NRAS, TUBB1, ADORA2B, NTRK3, NGF, TNNC2, F-KER, LOC429492, LOC431325, LOC431324, LOC396480) might have the function in growth of yellow-feather broilers.

Genome-wide association mapping in a sweet cherry germplasm collection (Prunus avium L.) reveals candidate genes for fruit quality traits

In sweet cherry (Prunus avium L.), large variability exists for various traits related to fruit quality. There is a need to discover the genetic architecture of these traits in order to enhance the efficiency of breeding strategies for consumer and producer demands. With this objective, a germplasm collection consisting of 116 sweet cherry accessions was evaluated for 23 agronomic fruit quality traits over 2-6 years, and characterized using a genotyping-by-sequencing approach. The SNP coverage collected was used to conduct a genome-wide association study using two multilocus models and three reference genomes. We identified numerous SNP-trait associations for global fruit size (weight, width, and thickness), fruit cracking, fruit firmness, and stone size, and we pinpointed several candidate genes involved in phytohormone, calcium, and cell wall metabolisms. Finally, we conducted a precise literature review focusing on the genetic architecture of fruit quality traits in sweet cherry to compare our results with potential colocalizations of marker-trait associations. This study brings new knowledge of the genetic control of important agronomic traits related to fruit quality, and to the development of marker-assisted selection strategies targeted towards the facilitation of breeding efforts.

Multiple-statistical genome-wide association analysis and genomic prediction of fruit aroma and agronomic traits in peaches

'Chinese Cling' is an important founder in peach breeding history due to the pleasant flavor. Genome-wide association studies (GWAS) combined with genomic selection are promising tools in fruit tree breeding, as there is a considerable time lapse between crossing and release of a cultivar. In this study, 242 peaches from Shanghai germplasm were genotyped with 145 456 single-nucleotide polymorphisms (SNPs). The six agronomic traits of fruit flesh color, fruit shape, fruit hairiness, flower type, pollen sterility, and soluble solids content, along with 14 key volatile odor compounds (VOCs), were recorded for multiple-statistical GWAS. Except the reported candidate genes, six novel genes were identified as associated with these traits. Thirty-nine significant SNPs were associated with eight VOCs. The putative candidate genes were confirmed for VOCs by RNA-seq, including three genes in the biosynthesis pathway found to be associated with linalool, soluble solids content, and cis-3-hexenyl acetate. Multiple-trait genomic prediction enhanced the predictive ability for γ-decalactone to 0.7415 compared with the single-trait model value of 0.1017. One PTS1-SSR marker was designed to predict the linalool content, and the favorable genotype 187/187 was confirmed, mainly existing in the 'Shanghai Shuimi' landrace. Overall, our findings will be helpful in determining peach accessions with the ideal phenotype and show the potential of multiple-trait genomic prediction to improve accuracy for highly correlated genetic traits. The diagnostic marker will be valuable for the breeder to bridge the gap between quantitative trait loci and marker-assisted selection for developing strong-aroma cultivars.

Genomewide selection for fruit quality traits in apple: breeding insights gained from prediction and postdiction

Many fruit quality traits in apple (Malus domestica Borkh.) are controlled by multiple small-effect quantitative trait loci (QTLs). Genomewide selection (genomic selection) might be an effective breeding approach for highly quantitative traits in woody perennial crops with long generation times like apple. The goal of this study was to determine if genomewide prediction is an effective breeding approach for fruit quality traits in an apple scion breeding program. Representative apple scion breeding germplasm (n_individuals = 955), high-quality single nucleotide polymorphism (SNP) data (n_SNPs = 977), and breeding program fruit quality trait data at harvest were analyzed. Breeding parents `Honeycrisp' and `Minneiska' were highly represented. Moderate to high predictive abilities were observed for most fruit quality traits at harvest. For example, when 25% random subsets of the germplasm set were used as training sets, mean predictive abilities ranged from 0.35 to 0.54 across traits. Trait, training and test sets, family size for within family prediction, and number of SNPs per chromosome affected model predictive ability. Inclusion of large-effect QTLs as fixed effects resulted in higher predictive abilities for some traits (e.g. percent red overcolor). Postdiction (i.e. retrospective) analyses demonstrated the impact of culling threshold on selection decisions. The results of this study demonstrate that genomewide selection is a useful breeding approach for certain fruit quality traits in apple.

Autochthonous Apple Cultivars from the Campania Region (Southern Italy): Bio-Agronomic and Qualitative Traits

Apple (Malus × domestica Borkh.) is an important fruit crop widely spread in the cold and mild climates of temperate regions in the world, with more than 93 million tons harvested worldwide in 2021. The object of this work was to analyze thirty-one local apple cultivars of the Campania region (Southern Italy) using agronomic, morphological (UPOV descriptors) and physicochemical (solid soluble content, texture, pH and titratable acidity, skin color, Young's modulus and browning index) traits. UPOV descriptors highlighted similarities and differences among apple cultivars with a depth phenotypic characterization. Apple cultivars showed significant differences in fruit weight (31.3-236.02 g) and physicochemical trait ranging from 8.0 to 14.64° Brix for solid soluble content, 2.34-10.38 g malic acid L^-1 for titratable acidity, and 15-40% for browning index. Furthermore, different percentages in apple shape and skin color have been detected. Similarities among the cultivars based on their bio-agronomic and qualitative traits have been evaluated by cluster analyses and principal component analyses. This apple germplasm collection represents an irreplaceable genetic resource with considerable morphological and pomological variabilities among several cultivars. Nowadays, some local cultivars, widespread only in restricted geographical areas, could be reintroduced in cultivation contribution to improving the diversity of our diets and contemporary to preserve knowledge on traditional agricultural systems.

Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat

Individuals within a common environment experience variations due to unique and non-identifiable micro-environmental factors. Genetic sensitivity to micro-environmental variation (i.e. micro-environmental sensitivity) can be identified in residuals, and genotypes with lower micro-environmental sensitivity can show greater resilience towards environmental perturbations. Micro-environmental sensitivity has been studied in animals; however, research on this topic is limited in plants and lacking in wheat. In this article, we aimed to (i) quantify the influence of genetic variation on residual dispersion and the genetic correlation between genetic effects on (expressed) phenotypes and residual dispersion for wheat grain yield using a double hierarchical generalized linear model (DHGLM); and (ii) evaluate the predictive performance of the proposed DHGLM for prediction of additive genetic effects on (expressed) phenotypes and its residual dispersion. Analyses were based on 2,456 advanced breeding lines tested in replicated trials within and across different environments in Denmark and genotyped with a 15K SNP-Illumina-BeadChip. We found that micro-environmental sensitivity for grain yield is heritable, and there is potential for its reduction. The genetic correlation between additive effects on (expressed) phenotypes and dispersion was investigated, and we observed an intermediate correlation. From these results, we concluded that breeding for reduced micro-environmental sensitivity is possible and can be included within breeding objectives without compromising selection for increased yield. The predictive ability and variance inflation for predictions of the DHGLM and a linear mixed model allowing heteroscedasticity of residual variance in different environments (LMM-HET) were evaluated using leave-one-line-out cross-validation. The LMM-HET and DHGLM showed good and similar performance for predicting additive effects on (expressed) phenotypes. In addition, the accuracy of predicting genetic effects on residual dispersion was sufficient to allow genetic selection for resilience. Such findings suggests that DHGLM may be a good choice to increase grain yield and reduce its micro-environmental sensitivity.

A single QTL harboring multiple genetic variations leads to complicated phenotypic segregation in apple flesh firmness and crispness

Within a QTL, the genetic recombination and interactions among five and two functional variations at MdbHLH25 and MdWDR5A caused much complicated phenotype segregation in apple FFR and FCR. The storability of climacteric fruit like apple is a quantitative trait. We previously identified 62 quantitative trait loci (QTLs) associating flesh firmness retainability (FFR) and flesh crispness retainability (FCR), but only a few functional genetic variations were identified and validated. The genetic variation network controlling fruit storability is far to be understood and diagnostic markers are needed for molecular breeding. We previously identified overlapped QTLs F16.1/H16.2 for FFR and FCR using an F1 population derived from 'Zisai Pearl' × 'Red Fuji'. In this study, five and two single-nucleotide polymorphisms (SNPs) were identified on the candidate genes MdbHLH25 and MdWDR5A within the QTL region. The SNP1 A allele at MdbHLH25 promoter reduced the expression and SNP2 T allele and/or SNP4/5 GT alleles at the exons attenuated the function of MdbHLH25 by downregulating the expression of the target genes MdACS1, which in turn led to a reduction in ethylene production and maintenance of higher flesh crispness. The SNPs did not alter the protein-protein interaction between MdbHLH25 and MdWDR5A. The joint effect of SNP genotype combinations by the SNPs on MdbHLH25 (SNP1, SNP2, and SNP4) and MdWDR5A (SNPi and SNPii) led to a much broad spectrum of phenotypic segregation in FFR and FCR. Together, the dissection of these genetic variations contributes to understanding the complicated effects of a QTL and provides good potential for marker development in molecular breeding.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.

Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine

The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression-were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates.

Breeding and genetics of disease resistance in temperate fruit trees: challenges and new opportunities

Climate change, large monocultures of disease-susceptible cultivars, overuse of pesticides, and the emergence of new pathogens or pathogenic strains causing economic losses are all major threats to our environment, health, food, and nutritional supply. Temperate tree fruit crops belonging to the Rosaceae family are the most economically important and widely grown fruit crops. These long-lived crops are under attack from many different pathogens, incurring major economic losses. Multiple chemical sprays to control various diseases annually is a common practice, resulting in significant input costs, as well as environmental and health concerns. Breeding for disease resistance has been undertaken primarily in pome fruit crops (apples and pears) for a few fungal and bacterial diseases, and to a lesser extent in some stone fruit crops. These breeding efforts have taken multiple decades due to the biological constraints and complex genetics of these tree fruit crops. Over the past couple of decades, major advances have been made in genetic and physical mapping, genomics, biotechnology, genome sequencing, and phenomics, along with accumulation of large germplasm collections in repositories. These valuable resources offer opportunities to make significant advances in greatly reducing the time needed to either develop new cultivars or modify existing economic cultivars for enhanced resistance to multiple diseases. This review will cover current knowledge, challenges, and opportunities in breeding for disease resistance in temperate tree fruit crops.

Genome-Wide Analysis of SAUR Gene Family Identifies a Candidate Associated with Fruit Size in Loquat (Eriobotrya japonica Lindl.)

Fruit size is an important fruit quality trait that influences the production and commodity values of loquats (Eriobotrya japonica Lindl.). The Small Auxin Upregulated RNA (SAUR) gene family has proven to play a vital role in the fruit development of many plant species. However, it has not been comprehensively studied in a genome-wide manner in loquats, and its role in regulating fruit size remains unknown. In this study, we identified 95 EjSAUR genes in the loquat genome. Tandem duplication and segmental duplication contributed to the expansion of this gene family in loquats. Phylogenetic analysis grouped the SAURs from Arabidopsis, rice, and loquat into nine clusters. By analyzing the transcriptome profiles in different tissues and at different fruit developmental stages and comparing two sister lines with contrasting fruit sizes, as well as by functional predictions, a candidate gene (EjSAUR22) highly expressed in expanding fruits was selected for further functional investigation. A combination of Indoleacetic acid (IAA) treatment and virus-induced gene silencing revealed that EjSAUR22 was not only responsive to auxin, but also played a role in regulating cell size and fruit expansion. The findings from our study provide a solid foundation for understanding the molecular mechanisms controlling fruit size in loquats, and also provide potential targets for manipulation of fruit size to accelerate loquat breeding.

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.

Multi-environment genomic prediction for soluble solids content in peach (Prunus persica)

Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.

Integrating a growth degree-days based reaction norm methodology and multi-trait modeling for genomic prediction in wheat

Multi-trait and multi-environment analyses can improve genomic prediction by exploiting between-trait correlations and genotype-by-environment interactions. In the context of reaction norm models, genotype-by-environment interactions can be described as functions of high-dimensional sets of markers and environmental covariates. However, comprehensive multi-trait reaction norm models accounting for marker × environmental covariates interactions are lacking. In this article, we propose to extend a reaction norm model incorporating genotype-by-environment interactions through (co)variance structures of markers and environmental covariates to a multi-trait reaction norm case. To do that, we propose a novel methodology for characterizing the environment at different growth stages based on growth degree-days (GDD). The proposed models were evaluated by variance components estimation and predictive performance for winter wheat grain yield and protein content in a set of 2,015 F6-lines. Cross-validation analyses were performed using leave-one-year-location-out (CV1) and leave-one-breeding-cycle-out (CV2) strategies. The modeling of genomic [SNPs] × environmental covariates interactions significantly improved predictive ability and reduced the variance inflation of predicted genetic values for grain yield and protein content in both cross-validation schemes. Trait-assisted genomic prediction was carried out for multi-trait models, and it significantly enhanced predictive ability and reduced variance inflation in all scenarios. The genotype by environment interaction modeling via genomic [SNPs] × environmental covariates interactions, combined with trait-assisted genomic prediction, boosted the benefits in predictive performance. The proposed multi-trait reaction norm methodology is a comprehensive approach that allows capitalizing on the benefits of multi-trait models accounting for between-trait correlations and reaction norm models exploiting high-dimensional genomic and environmental information.

Weighted single-step GWAS and RNA sequencing reveals key candidate genes associated with physiological indicators of heat stress in Holstein cattle

Background

The study of molecular processes regulating heat stress response in dairy cattle is paramount for developing mitigation strategies to improve heat tolerance and animal welfare. Therefore, we aimed to identify quantitative trait loci (QTL) regions associated with three physiological indicators of heat stress response in Holstein cattle, including rectal temperature (RT), respiration rate score (RS), and drooling score (DS). We estimated genetic parameters for all three traits. Subsequently, a weighted single-step genome-wide association study (WssGWAS) was performed based on 3200 genotypes, 151,486 phenotypic records, and 38,101 animals in the pedigree file. The candidate genes located within the identified QTL regions were further investigated through RNA sequencing (RNA-seq) analyses of blood samples for four cows collected in April (non-heat stress group) and four cows collected in July (heat stress group).

Results

The heritability estimates for RT, RS, and DS were 0.06, 0.04, and 0.03, respectively. Fourteen, 19, and 20 genomic regions explained 2.94%, 3.74%, and 4.01% of the total additive genetic variance of RT, RS, and DS, respectively. Most of these genomic regions are located in the Bos taurus autosome (BTA) BTA3, BTA6, BTA8, BTA12, BTA14, BTA21, and BTA24. No genomic regions overlapped between the three indicators of heat stress, indicating the polygenic nature of heat tolerance and the complementary mechanisms involved in heat stress response. For the RNA-seq analyses, 2627 genes were significantly upregulated and 369 downregulated in the heat stress group in comparison to the control group. When integrating the WssGWAS, RNA-seq results, and existing literature, the key candidate genes associated with physiological indicators of heat stress in Holstein cattle are: PMAIP1, SBK1, TMEM33, GATB, CHORDC1, RTN4IP1, and BTBD7.

Conclusions

Physiological indicators of heat stress are heritable and can be improved through direct selection. Fifty-three QTL regions associated with heat stress indicators confirm the polygenic nature and complex genetic determinism of heat tolerance in dairy cattle. The identified candidate genes will contribute for optimizing genomic evaluation models by assigning higher weights to genetic markers located in these regions as well as to the design of SNP panels containing polymorphisms located within these candidate genes.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s40104-022-00748-6.

Construction of a high-density bin-map and identification of fruit quality-related quantitative trait loci and functional genes in pear

Pear (Pyrus spp.) is one of the most common fruit crops grown in temperate regions worldwide. Genetic enhancement of fruit quality is a fundamental goal of pear breeding programs. The genetic control of pear fruit quality traits is highly quantitative, and development of high-density genetic maps can facilitate fine-mapping of quantitative trait loci (QTLs) and gene identification. Bin-mapping is a powerful method of constructing high-resolution genetic maps from large-scale genotyping datasets. We performed whole-genome sequencing of pear cultivars 'Niitaka' and 'Hongxiangsu' and their 176 F ₁ progeny to identify genome-wide single-nucleotide polymorphism (SNP) markers for constructing a high-density bin-map of pear. This analysis yielded a total of 1.93 million SNPs and a genetic bin-map of 3190 markers spanning 1358.5 cM, with an average adjacent interval of 0.43 cM. This bin-map, along with other high-density genetic maps in pear, improved the reference genome assembly from 75.5 to 83.7% by re-anchoring the scaffolds. A quantitative genetic analysis identified 148 QTLs for 18 fruit-related traits; among them, QTLs for stone cell content, several key monosaccharides, and fruit pulp acids were identified for the first time in pear. A gene expression analysis of six pear cultivars identified 399 candidates in the identified QTL regions, which showed expression specific to fruit developmental stages in pear. Finally, we confirmed the function of PbrtMT1, a tonoplast monosaccharide transporter-related gene responsible for the enhancement of fructose accumulation in pear fruit on linkage group 16, in a transient transformation experiment. This study provides genomic and genetic resources as well as potential candidate genes for fruit quality improvement in pear.

A bulked segregant analysis tool for out-crossing species (BSATOS) and QTL-based genomics-assisted prediction of complex traits in apple

INTRODUCTION

Genomic heterozygosity, self-incompatibility, and rich-in somatic mutations hinder the molecular breeding efficiency of outcrossing plants.

OBJECTIVES

We attempted to develop an efficient integrated strategy to identify quantitative trait loci (QTLs) and trait-associated genes, to develop gene markers, and to construct genomics-assisted prediction (GAP) modes.

METHODS

A novel protocol, bulked segregant analysis tool for out-crossing species (BSATOS), is presented here, which is characterized by taking full advantage of all segregation patterns (including AB × AB markers) and haplotype information. To verify the effectiveness of the protocol in dealing with the complex traits of outbreeding species, three apple cross populations with 9,654 individuals were adopted.

RESULTS

By using BSATOS, 90, 60, and 77 significant QTLs were identified successfully and candidate genes were predicted for apple fruit weight (FW), fruit ripening date (FRD), and fruit soluble solid content (SSC), respectively. The gene-based markers were developed and genotyped for 1,396 individuals in a training population, including 145 Malus accessions and 1,251 F1 plants of the three full-sib families. GAP models were trained using marker genotype effect estimates of the training population. The prediction accuracy was 0.7658, 0.6455, and 0.3758 for FW, FRD, and SSC, respectively.

CONCLUSION

The BSATOS and GAP models provided a convenient and efficient methodology for candidate gene mining and molecular breeding in out-crossing plant species. The BSATOS pipeline can be freely downloaded from: https://github.com/maypoleflyn/BSATOS.

Genetic architecture and genomic predictive ability of apple quantitative traits across environments

Implementation of genomic tools is desirable to increase the efficiency of apple breeding. Recently, the multi-environment apple reference population (apple REFPOP) proved useful for rediscovering loci, estimating genomic predictive ability, and studying genotype by environment interactions (G × E). So far, only two phenological traits were investigated using the apple REFPOP, although the population may be valuable when dissecting genetic architecture and reporting predictive abilities for additional key traits in apple breeding. Here we show contrasting genetic architecture and genomic predictive abilities for 30 quantitative traits across up to six European locations using the apple REFPOP. A total of 59 stable and 277 location-specific associations were found using GWAS, 69.2% of which are novel when compared with 41 reviewed publications. Average genomic predictive abilities of 0.18-0.88 were estimated using main-effect univariate, main-effect multivariate, multi-environment univariate, and multi-environment multivariate models. The G × E accounted for up to 24% of the phenotypic variability. This most comprehensive genomic study in apple in terms of trait-environment combinations provided knowledge of trait biology and prediction models that can be readily applied for marker-assisted or genomic selection, thus facilitating increased breeding efficiency.

Across-population genomic prediction in grapevine opens up promising prospects for breeding

Crop breeding involves two selection steps: choosing progenitors and selecting individuals within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.

Dissecting Breeders' Sense via Explainable Machine Learning Approach: Application to Fruit Peelability and Hardness in Citrus

"Genomics-assisted breeding", which utilizes genomics-based methods, e.g., genome-wide association study (GWAS) and genomic selection (GS), has been attracting attention, especially in the field of fruit breeding. Low-cost genotyping technologies that support genome-assisted breeding have already been established. However, efficient collection of large amounts of high-quality phenotypic data is essential for the success of such breeding. Most of the fruit quality traits have been sensorily and visually evaluated by professional breeders. However, the fruit morphological features that serve as the basis for such sensory and visual judgments are unclear. This makes it difficult to collect efficient phenotypic data on fruit quality traits using image analysis. In this study, we developed a method to automatically measure the morphological features of citrus fruits by the image analysis of cross-sectional images of citrus fruits. We applied explainable machine learning methods and Bayesian networks to determine the relationship between fruit morphological features and two sensorily evaluated fruit quality traits: easiness of peeling (Peeling) and fruit hardness (FruH). In each of all the methods applied in this study, the degradation area of the central core of the fruit was significantly and directly associated with both Peeling and FruH, while the seed area was significantly and directly related to FruH alone. The degradation area of albedo and the area of flavedo were also significantly and directly related to Peeling and FruH, respectively, except in one or two methods. These results suggest that an approach that combines explainable machine learning methods, Bayesian networks, and image analysis can be effective in dissecting the experienced sense of a breeder. In breeding programs, collecting fruit images and efficiently measuring and documenting fruit morphological features that are related to fruit quality traits may increase the size of data for the analysis and improvement of the accuracy of GWAS and GS on the quality traits of the citrus fruits.

Identifying and testing marker-trait associations for growth and phenology in three pine species: Implications for genomic prediction

In tree species, genomic prediction offers the potential to forecast mature trait values in early growth stages, if robust marker-trait associations can be identified. Here we apply a novel multispecies approach using genotypes from a new genotyping array, based on 20,795 single nucleotide polymorphisms (SNPs) from three closely related pine species (Pinus sylvestris, Pinus uncinata and Pinus mugo), to test for associations with growth and phenology data from a common garden study. Predictive models constructed using significantly associated SNPs were then tested and applied to an independent multisite field trial of P. sylvestris and the capability to predict trait values was evaluated. One hundred and eighteen SNPs showed significant associations with the traits in the pine species. Common SNPs (MAF > 0.05) associated with bud set were only found in genes putatively involved in growth and development, whereas those associated with growth and budburst were also located in genes putatively involved in response to environment and, to a lesser extent, reproduction. At one of the two independent sites, the model we developed produced highly significant correlations between predicted values and observed height data (YA, height 2020: r = 0.376, p < 0.001). Predicted values estimated with our budburst model were weakly but positively correlated with duration of budburst at one of the sites (GS, 2015: r = 0.204, p = 0.034; 2018: r = 0.205, p = 0.034-0.037) and negatively associated with budburst timing at the other (YA: r = -0.202, p = 0.046). Genomic prediction resulted in the selection of sets of trees whose mean height was taller than the average for each site. Our results provide tentative support for the capability of prediction models to forecast trait values in trees, while highlighting the need for caution in applying them to trees grown in different environments.

Inheritance of Yield Components and Morphological Traits in Avocado cv. Hass From "Criollo" "Elite Trees" via Half-Sib Seedling Rootstocks

Grafting induces precocity and maintains clonal integrity in fruit tree crops. However, the complex rootstock × scion interaction often precludes understanding how the tree phenotype is shaped, limiting the potential to select optimum rootstocks. Therefore, it is necessary to assess (1) how seedling progenies inherit trait variation from elite 'plus trees', and (2) whether such family superiority may be transferred after grafting to the clonal scion. To bridge this gap, we quantified additive genetic parameters (i.e., narrow sense heritability-h ², and genetic-estimated breeding values-GEBVs) across landraces, "criollo", "plus trees" of the super-food fruit tree crop avocado (Persea americana Mill.), and their open-pollinated (OP) half-sib seedling families. Specifically, we used a genomic best linear unbiased prediction (G-BLUP) model to merge phenotypic characterization of 17 morpho-agronomic traits with genetic screening of 13 highly polymorphic SSR markers in a diverse panel of 104 avocado "criollo" "plus trees." Estimated additive genetic parameters were validated at a 5-year-old common garden trial (i.e., provenance test), in which 22 OP half-sib seedlings from 82 elite "plus trees" served as rootstocks for the cv. Hass clone. Heritability (h ²) scores in the "criollo" "plus trees" ranged from 0.28 to 0.51. The highest h ² values were observed for ribbed petiole and adaxial veins with 0.47 (CI 95%0.2-0.8) and 0.51 (CI 0.2-0.8), respectively. The h ² scores for the agronomic traits ranged from 0.34 (CI 0.2-0.6) to 0.39 (CI 0.2-0.6) for seed weight, fruit weight, and total volume, respectively. When inspecting yield variation across 5-year-old grafted avocado cv. Hass trees with elite OP half-sib seedling rootstocks, the traits total number of fruits and fruits' weight, respectively, exhibited h ² scores of 0.36 (± 0.23) and 0.11 (± 0.09). Our results indicate that elite "criollo" "plus trees" may serve as promissory donors of seedling rootstocks for avocado cv. Hass orchards due to the inheritance of their outstanding trait values. This reinforces the feasibility to leverage natural variation from "plus trees" via OP half-sib seedling rootstock families. By jointly estimating half-sib family effects and rootstock-mediated heritability, this study promises boosting seedling rootstock breeding programs, while better discerning the consequences of grafting in fruit tree crops.

Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine

Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.

Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population

Background

Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations.

Results

Narrow-sense heritability of yield and yield stability was low (h²=0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r=0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474g/year, more than double that of traditional breeding methods (226g/year), due to the halving of generation length from 8 to 4years.

Conclusions

The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07694-z.

Role of MdERF3 and MdERF118 natural variations in apple flesh firmness/crispness retainability and development of QTL-based genomics-assisted prediction

Retention of flesh texture attributes during cold storage is critical for the long-term maintenance of fruit quality. The genetic variations determining flesh firmness and crispness retainability are not well understood. The objectives of this study are to identify gene markers based on quantitative trait loci (QTLs) and to develop genomics-assisted prediction (GAP) models for apple flesh firmness and crispness retainability. Phenotype data of 2664 hybrids derived from three Malus domestica cultivars and a M. asiatica cultivar were collected in 2016 and 2017. The phenotype segregated considerably with high broad-sense heritability of 83.85% and 83.64% for flesh firmness and crispness retainability, respectively. Fifty-six candidate genes were predicted from the 62 QTLs identified using bulked segregant analysis and RNA-seq. The genotype effects of the markers designed on each candidate gene were estimated. The genomics-predicted values were obtained using pyramiding marker genotype effects and overall mean phenotype values. Fivefold cross-validation revealed that the prediction accuracy was 0.5541 and 0.6018 for retainability of flesh firmness and crispness, respectively. An 8-bp deletion in the MdERF3 promoter disrupted MdDOF5.3 binding, reduced MdERF3 expression, relieved the inhibition on MdPGLR3, MdPME2, and MdACO4 expression, and ultimately decreased flesh firmness and crispness retainability. A 3-bp deletion in the MdERF118 promoter decreased its expression by disrupting the binding of MdRAVL1, which increased MdPGLR3 and MdACO4 expression and reduced flesh firmness and crispness retainability. These results provide insights regarding the genetic variation network regulating flesh firmness and crispness retainability, and the GAP models can assist in apple breeding.

Integration of Infinium and Axiom SNP array data in the outcrossing species Malus × domestica and causes for seemingly incompatible calls

Background

Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array. Next, the accuracy of the SNP calls was evaluated on additional germplasm (n = 3141) from both arrays using Mendelian inconsistent and consistent errors across thousands of pedigree links. While performing this work, we took the opportunity to evaluate reasons for probe failure and observed discordant SNP calls.

Results

Concordance among the duplicate individuals was on average of 97.1% across 10,295 SNPs. Of these SNPs, 35% had discordant call(s) that were further curated, leading to a final set of 8412 (81.7%) SNPs that were deemed compatible. Compatibility was highly influenced by the presence of alternate probe binding locations and secondary polymorphisms. The impact of the latter was highly influenced by their number and proximity to the 3′ end of the probe.

Conclusions

The Infinium and Axiom SNP array data were mostly compatible. However, data integration required intense data filtering and curation. This work resulted in a workflow and information that may be of use in other data integration efforts. Such an in-depth analysis of array concordance and accuracy as ours has not been previously described in the literature and will be useful in future work on SNP array data integration and interpretation, and in probe/platform development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07565-7.

Tracing founder haplotypes of Japanese apple varieties: application in genomic prediction and genome-wide association study

Haplotypes provide useful information for genomics-based approaches, genomic prediction, and genome-wide association study. As a small number of superior founders have contributed largely to the breeding history of fruit trees, the information of founder haplotypes may be relevant for performing the genomics-based approaches in these plants. In this study, we proposed a method to estimate 14 haplotypes from 7 founders and automatically trace the haplotypes forward to apple parental (185 varieties) and breeding (659 F1 individuals from 16 full-sib families) populations based on 11,786 single-nucleotide polymorphisms, by combining multiple algorithms. Overall, 92% of the single-nucleotide polymorphisms information in the parental and breeding populations was characterized by the 14 founder haplotypes. The use of founder haplotype information improved the accuracy of genomic prediction in 7 traits and the resolution of genome-wide association study in 13 out of 27 fruit quality traits analyzed in this study. We also visualized the significant propagation of the founder haplotype with the largest genetic effect in genome-wide association study over the pedigree tree of the parental population. These results suggest that the information of founder haplotypes can be useful for not only genetic improvement of fruit quality traits in apples but also for understanding the selection history of founder haplotypes in the breeding program of Japanese apple varieties.

Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass

Grafting is typically utilized to merge adapted seedling rootstocks with highly productive clonal scions. This process implies the interaction of multiple genomes to produce a unique tree phenotype. However, the interconnection of both genotypes obscures individual contributions to phenotypic variation (rootstock-mediated heritability), hampering tree breeding. Therefore, our goal was to quantify the inheritance of seedling rootstock effects on scion traits using avocado (Persea americana Mill.) cv. Hass as a model fruit tree. We characterized 240 diverse rootstocks from 8 avocado cv. Hass orchards with similar management in three regions of the province of Antioquia, northwest Andes of Colombia, using 13 microsatellite markers simple sequence repeats (SSRs). Parallel to this, we recorded 20 phenotypic traits (including morphological, biomass/reproductive, and fruit yield and quality traits) in the scions for 3 years (2015-2017). Relatedness among rootstocks was inferred through the genetic markers and inputted in a "genetic prediction" model to calculate narrow-sense heritabilities (h 2) on scion traits. We used three different randomization tests to highlight traits with consistently significant heritability estimates. This strategy allowed us to capture five traits with significant heritability values that ranged from 0.33 to 0.45 and model fits (r) that oscillated between 0.58 and 0.73 across orchards. The results showed significance in the rootstock effects for four complex harvest and quality traits (i.e., total number of fruits, number of fruits with exportation quality, and number of fruits discarded because of low weight or thrips damage), whereas the only morphological trait that had a significant heritability value was overall trunk height (an emergent property of the rootstock-scion interaction). These findings suggest the inheritance of rootstock effects, beyond root phenotype, on a surprisingly wide spectrum of scion traits in "Hass" avocado. They also reinforce the utility of polymorphic SSRs for relatedness reconstruction and genetic prediction of complex traits. This research is, up to date, the most cohesive evidence of narrow-sense inheritance of rootstock effects in a tropical fruit tree crop. Ultimately, our work highlights the importance of considering the rootstock-scion interaction to broaden the genetic basis of fruit tree breeding programs while enhancing our understanding of the consequences of grafting.

Adoption and Optimization of Genomic Selection To Sustain Breeding for Apricot Fruit Quality

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.

The apple REFPOP-a reference population for genomics-assisted breeding in apple

Breeding of apple is a long-term and costly process due to the time and space requirements for screening selection candidates. Genomics-assisted breeding utilizes genomic and phenotypic information to increase the selection efficiency in breeding programs, and measurements of phenotypes in different environments can facilitate the application of the approach under various climatic conditions. Here we present an apple reference population: the apple REFPOP, a large collection formed of 534 genotypes planted in six European countries, as a unique tool to accelerate apple breeding. The population consisted of 269 accessions and 265 progeny from 27 parental combinations, representing the diversity in cultivated apple and current European breeding material, respectively. A high-density genome-wide dataset of 303,239 SNPs was produced as a combined output of two SNP arrays of different densities using marker imputation with an imputation accuracy of 0.95. Based on the genotypic data, linkage disequilibrium was low and population structure was weak. Two well-studied phenological traits of horticultural importance were measured. We found marker-trait associations in several previously identified genomic regions and maximum predictive abilities of 0.57 and 0.75 for floral emergence and harvest date, respectively. With decreasing SNP density, the detection of significant marker-trait associations varied depending on trait architecture. Regardless of the trait, 10,000 SNPs sufficed to maximize genomic prediction ability. We confirm the suitability of the apple REFPOP design for genomics-assisted breeding, especially for breeding programs using related germplasm, and emphasize the advantages of a coordinated and multinational effort for customizing apple breeding methods in the genomics era.

Quantitative trait loci-based genomics-assisted prediction for the degree of apple fruit cover color

Apple fruit cover color is an important appearance trait determining fruit quality, high degree of fruit cover color or completely red fruit skin is also the ultimate breeding goal. MdMYB1 has repeatedly been reported as a major gene controlling apple fruit cover color. There are also multiple minor-effect genes affecting degree of fruit cover color (DFC). This study was to identify genome-wide quantitative trait loci (QTLs) and to develop genomics-assisted prediction for apple DFC. The DFC phenotype data of 9,422 hybrids from five full-sib families of Malus asiatica 'Zisai Pearl', M. domestica 'Red Fuji', 'Golden Delicious', and 'Jonathan' were collected in 2014-2017. The phenotype varied considerably among hybrids with the same MdMYB1 genotype. Ten QTLs for DFC were identified using MapQTL and bulked segregant analysis via sequencing. From these QTLs, ten candidate genes were predicted, including MdMYB1 from a year-stable QTL on chromosome 9 of 'Zisai Pearl' and 'Red Fuji'. Then, kompetitive allele-specific polymerase chain reaction (KASP) markers were designed on these candidate genes and 821 randomly selected hybrids were genotyped. The genotype effects of the markers were estimated. MdMYB1-1 (represented by marker H162) exhibited a partial dominant allelic effect on MdMYB1-2 and showed non-allelic epistasis on markers H1245 and G6. Finally, a non-additive QTL-based genomics assisted prediction model was established for DFC. The Pearson's correlation coefficient between the genomic predicted value and the observed phenotype value was 0.5690. These results can be beneficial for apple genomics-assisted breeding and may provide insights for understanding the mechanism of fruit coloration.

Genomic Prediction and Selection for Fruit Traits in Winter Squash

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.

Genomic prediction of fruit texture and training population optimization towards the application of genomic selection in apple

Texture is a complex trait and a major component of fruit quality in apple. While the major effect of MdPG1, a gene controlling firmness, has already been exploited in elite cultivars, the genetic basis of crispness remains poorly understood. To further improve fruit texture, harnessing loci with minor effects via genomic selection is therefore necessary. In this study, we measured acoustic and mechanical features in 537 genotypes to dissect the firmness and crispness components of fruit texture. Predictions of across-year phenotypic values for these components were calculated using a model calibrated with 8,294 SNP markers. The best prediction accuracies following cross-validations within the training set of 259 genotypes were obtained for the acoustic linear distance (0.64). Predictions for biparental families using the entire training set varied from low to high accuracy, depending on the family considered. While adding siblings or half-siblings into the training set did not clearly improve predictions, we performed an optimization of the training set size and composition for each validation set. This allowed us to increase prediction accuracies by 0.17 on average, with a maximal accuracy of 0.81 when predicting firmness in the 'Gala' × 'Pink Lady' family. Our results therefore identified key genetic parameters to consider when deploying genomic selection for texture in apple. In particular, we advise to rely on a large training population, with high phenotypic variability from which a 'tailored training population' can be extracted using a priori information on genetic relatedness, in order to predict a specific target population.

Genomics-assisted prediction of salt and alkali tolerances and functional marker development in apple rootstocks

Background

Saline, alkaline, and saline-alkaline stress severely affect plant growth and development. The tolerance of plants to these stressors has long been important breeding objectives, especially for woody perennials like apple. The aims of this study were to identify quantitative trait loci (QTLs) and to develop genomics-assisted prediction models for salt, alkali, and salt-alkali tolerance in apple rootstock.

Results

A total of 3258 hybrids derived from the apple rootstock cultivars ‘Baleng Crab’ (Malus robusta Rehd., tolerant) × ‘M9’ (M. pumila Mill., sensitive) were used to identify 17, 13, and two QTLs for injury indices of salt, alkali, and salt–alkali stress via bulked segregant analysis. The genotype effects of single nucleotide polymorphism (SNP) markers designed on candidate genes in each QTL interval were estimated. The genomic predicted value of an individual hybrid was calculated by adding the sum of all marker genotype effects to the mean phenotype value of the population. The prediction accuracy was 0.6569, 0.6695, and 0.5834 for injury indices of salt, alkali, and salt–alkali stress, respectively. SNP182G on MdRGLG3, which changes a leucine to an arginine at the vWFA-domain, conferred tolerance to salt, alkali, and salt-alkali stress. SNP761A on MdKCAB, affecting the Kv_beta domain that cooperated with the linked allelic variation SNP11, contributed to salt, alkali, and salt–alkali tolerance in apple rootstock.

Conclusions

The genomics-assisted prediction models can potentially be used in breeding saline, alkaline, and saline-alkaline tolerant apple rootstocks. The QTLs and the functional markers may provide insight for future studies into the genetic variation of plant abiotic stress tolerance.

Genomic Prediction with Genotype by Environment Interaction Analysis for Kernel Zinc Concentration in Tropical Maize Germplasm

Zinc (Zn) deficiency is a major risk factor for human health, affecting about 30% of the world's population. To study the potential of genomic selection (GS) for maize with increased Zn concentration, an association panel and two doubled haploid (DH) populations were evaluated in three environments. Three genomic prediction models, M (M1: Environment + Line, M2: Environment + Line + Genomic, and M3: Environment + Line + Genomic + Genomic x Environment) incorporating main effects (lines and genomic) and the interaction between genomic and environment (G x E) were assessed to estimate the prediction ability (rMP ) for each model. Two distinct cross-validation (CV) schemes simulating two genomic prediction breeding scenarios were used. CV1 predicts the performance of newly developed lines, whereas CV2 predicts the performance of lines tested in sparse multi-location trials. Predictions for Zn in CV1 ranged from -0.01 to 0.56 for DH1, 0.04 to 0.50 for DH2 and -0.001 to 0.47 for the association panel. For CV2, rMP values ranged from 0.67 to 0.71 for DH1, 0.40 to 0.56 for DH2 and 0.64 to 0.72 for the association panel. The genomic prediction model which included G x E had the highest average rMP for both CV1 (0.39 and 0.44) and CV2 (0.71 and 0.51) for the association panel and DH2 population, respectively. These results suggest that GS has potential to accelerate breeding for enhanced kernel Zn concentration by facilitating selection of superior genotypes.

Dynamics of ascorbic acid content in apple (Malus x domestica) during fruit development and storage

Vitamin C is a crucial antioxidant and cofactor for both plants and humans. Apple fruits generally contain low levels of vitamin C, making vitamin C content an interesting trait for apple crop improvement. With the aim of breeding high vitamin C apple cultivars it is important to get an insight in the natural biodiversity of vitamin C content in apple fruits. In this study, quantification of ascorbic acid (AsA), dehydroascorbic acid (DHA), and total AsA (AsA + DHA) in apple pulp of 79 apple accessions at harvest revealed significant variation, indicating a large genetic biodiversity. High density genotyping using an 8 K SNP array identified 21 elite and 58 local cultivars in this germplasm, with local accessions showing similar levels of total AsA but higher amounts of DHA compared to elite varieties. Out of the 79 apple cultivars screened, ten genotypes with either the highest or the lowest concentration of total AsA at harvest were used for monitoring vitamin C dynamics during fruit development and storage. For all these cultivars, the AsA/DHA ratio in both apple pulp and peel increased throughout fruit development, whereas the AsA/DHA balance always shifted towards the oxidized form during storage and shelf life, putatively reflecting an abiotic stress response. Importantly, at any point during apple fruit development and storage, the apple peel contained a higher level of vitamin C compared to the pulp, most likely because of its direct exposure to abiotic and biotic stresses.

Turbocharging introgression breeding of perennial fruit crops: a case study on apple

The allelic diversity of primitive germplasm of fruit crops provides a useful resource for introgressing novel genes to meet consumer preferences and environmental challenges. Pre-breeding facilitates the identification of novel genetic variation in the primitive germplasm and expedite its utilisation in cultivar breeding programmes. Several generations of pre-breeding could be required to minimise linkage drag from the donor parent and to maximise the genomic content of the recipient parent. In this study we investigated the potential of genomic selection (GS) as a tool for rapid background selection of parents for the successive generation. A diverse set of 274 accessions was genotyped using random-tag genotyping-by-sequencing, and phenotyped for eight fruit quality traits. The relationship between 'own phenotypes' of 274 accessions and their general combining ability (GCA) was also examined. Trait heritability influenced the strength of correspondence between own phenotype and the GCA. The average (across eight traits) accuracy of predicting own phenotype was 0.70, and the correlations between genomic-predicted own phenotype and GCA were similar to the observed correlations. Our results suggest that genome-assisted parental selection (GAPS) is a credible alternative to phenotypic parental selection, so could help reduce the generation interval to allow faster accumulation of favourable alleles from donor and recipient parents.

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass

Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

Quantitative trait loci (QTL) underlying phenotypic variation in bioethanol-related processes in Neurospora crassa

Bioethanol production from lignocellulosic biomass has received increasing attention over the past decade. Many attempts have been made to reduce the cost of bioethanol production by combining the separate steps of the process into a single-step process known as consolidated bioprocessing. This requires identification of organisms that can efficiently decompose lignocellulose to simple sugars and ferment the pentose and hexose sugars liberated to ethanol. There have been many attempts in engineering laboratory strains by adding new genes or modifying genes to expand the capacity of an industrial microorganism. There has been less attention in improving bioethanol-related processes utilizing natural variation existing in the natural ecotypes. In this study, we sought to identify genomic loci contributing to variation in saccharification of cellulose and fermentation of glucose in the fermenting cellulolytic fungus Neurospora crassa through quantitative trait loci (QTL) analysis. We identified one major QTL contributing to fermentation of glucose and multiple putative QTL's underlying saccharification. Understanding the natural variation of the major QTL gene would provide new insights in developing industrial microbes for bioethanol production.

Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat

BACKGROUND

Genomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.

RESULTS

Genomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64-70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between - 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was "superior" to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.

CONCLUSIONS

Our results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.

Genome-wide association studies in apple reveal loci of large effect controlling apple polyphenols

Apples are a nutritious food source with significant amounts of polyphenols that contribute to human health and wellbeing, primarily as dietary antioxidants. Although numerous pre- and post-harvest factors can affect the composition of polyphenols in apples, genetics is presumed to play a major role because polyphenol concentration varies dramatically among apple cultivars. Here we investigated the genetic architecture of apple polyphenols by combining high performance liquid chromatography (HPLC) data with ~100,000 single nucleotide polymorphisms (SNPs) from two diverse apple populations. We found that polyphenols can vary in concentration by up to two orders of magnitude across cultivars, and that this dramatic variation was often predictable using genetic markers and frequently controlled by a small number of large effect genetic loci. Using GWAS, we identified candidate genes for the production of quercitrin, epicatechin, catechin, chlorogenic acid, 4-O-caffeoylquinic acid and procyanidins B1, B2, and C1. Our observation that a relatively simple genetic architecture underlies the dramatic variation of key polyphenols in apples suggests that breeders may be able to improve the nutritional value of apples through marker-assisted breeding or gene editing.

Apple whole genome sequences: recent advances and new prospects

In 2010, a major scientific milestone was achieved for tree fruit crops: publication of the first draft whole genome sequence (WGS) for apple (Malus domestica). This WGS, v1.0, was valuable as the initial reference for sequence information, fine mapping, gene discovery, variant discovery, and tool development. A new, high quality apple WGS, GDDH13 v1.1, was released in 2017 and now serves as the reference genome for apple. Over the past decade, these apple WGSs have had an enormous impact on our understanding of apple biological functioning, trait physiology and inheritance, leading to practical applications for improving this highly valued crop. Causal gene identities for phenotypes of fundamental and practical interest can today be discovered much more rapidly. Genome-wide polymorphisms at high genetic resolution are screened efficiently over hundreds to thousands of individuals with new insights into genetic relationships and pedigrees. High-density genetic maps are constructed efficiently and quantitative trait loci for valuable traits are readily associated with positional candidate genes and/or converted into diagnostic tests for breeders. We understand the species, geographical, and genomic origins of domesticated apple more precisely, as well as its relationship to wild relatives. The WGS has turbo-charged application of these classical research steps to crop improvement and drives innovative methods to achieve more durable, environmentally sound, productive, and consumer-desirable apple production. This review includes examples of basic and practical breakthroughs and challenges in using the apple WGSs. Recommendations for "what's next" focus on necessary upgrades to the genome sequence data pool, as well as for use of the data, to reach new frontiers in genomics-based scientific understanding of apple.

Prunus genetics and applications after de novo genome sequencing: achievements and prospects

Prior to the availability of whole-genome sequences, our understanding of the structural and functional aspects of Prunus tree genomes was limited mostly to molecular genetic mapping of important traits and development of EST resources. With public release of the peach genome and others that followed, significant advances in our knowledge of Prunus genomes and the genetic underpinnings of important traits ensued. In this review, we highlight key achievements in Prunus genetics and breeding driven by the availability of these whole-genome sequences. Within the structural and evolutionary contexts, we summarize: (1) the current status of Prunus whole-genome sequences; (2) preliminary and ongoing work on the sequence structure and diversity of the genomes; (3) the analyses of Prunus genome evolution driven by natural and man-made selection; and (4) provide insight into haploblocking genomes as a means to define genome-scale patterns of evolution that can be leveraged for trait selection in pedigree-based Prunus tree breeding programs worldwide. Functionally, we summarize recent and ongoing work that leverages whole-genome sequences to identify and characterize genes controlling 22 agronomically important Prunus traits. These include phenology, fruit quality, allergens, disease resistance, tree architecture, and self-incompatibility. Translationally, we explore the application of sequence-based marker-assisted breeding technologies and other sequence-guided biotechnological approaches for Prunus crop improvement. Finally, we present the current status of publically available Prunus genomics and genetics data housed mainly in the Genome Database for Rosaceae (GDR) and its updated functionalities for future bioinformatics-based Prunus genetics and genomics inquiry.

Genetic Variation and Trait Correlations in an East African Cassava Breeding Population for Genomic Selection

Cassava (Manihot esculenta Crantz) is a major source of dietary carbohydrates for >700 million people globally. However, its long breeding cycle has slowed the rate of genetic gain for target traits. This study aimed to asses genetic variation, the level of inbreeding, and trait correlations in genomic selection breeding cycles. We used phenotypic and genotypic data from the National Crops Resources Research Institute (NaCRRI) foundation population (Cycle 0, C₀) and the progeny (Cycle 1, C₁) derived from crosses of 100 selected C0 clones as progenitors, both to evaluate and optimize genomic selection. The highest broad-sense heritability (H ² = 0.95) and narrow-sense heritability (h ² = 0.81) were recorded for cassava mosaic disease severity and the lowest for root weight per plot (H ² = 0.06 and h ² = 0.00). We observed the highest genetic correlation (r _g= 0.80) between cassava brown streak disease root incidence measured at seedling and clonal stages of evaluation, suggesting the usefulness of seedling data in predicting clonal performance for cassava brown streak root necrosis. Similarly, high genetic correlations were observed between cassava brown streak disease severity (r _g= 0.83) scored at 3 and 6 mo after planting (MAP) and cassava mosaic disease, scored at 3 and 6 MAP (r _g= 0.95), indicating that data obtained on these two diseases at 6 MAP would suffice. Population differentiation between C₀ and C₁ was not well defined, implying that the 100 selected progenitors of C₁ captured the diversity in the C₀. Overall, genetic gain for most traits were observed from C₀ to C₁.