Metabolomic selection for enhanced fruit flavor.

Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.

Soluble and insoluble α-glucan synthesis in yeast by enzyme suites derived exclusively from maize endosperm.

Molecular mechanisms that distinguish the synthesis of semi-crystalline α-glucan polymers found in plant starch granules from the synthesis of water-soluble polymers by nonplant species are not well understood. To address this, starch biosynthetic enzymes from maize (Zea mays L.) endosperm were isolated in a reconstituted environment using yeast (Saccharomyces cerevisiae) as a test bed. Ninety strains were constructed containing unique combinations of 11 synthetic transcription units specifying maize starch synthase (SS), starch phosphorylase (PHO), starch branching enzyme (SBE), or isoamylase-type starch debranching enzyme (ISA). Soluble and insoluble branched α-glucans accumulated in varying proportions depending on the enzyme suite, with ISA function stimulating distribution into the insoluble form. Among the SS isoforms, SSIIa, SSIII, and SSIV individually supported the accumulation of glucan polymer. Neither SSI nor SSV alone produced polymers; however, synergistic effects demonstrated that both isoforms can stimulate α-glucan accumulation. PHO did not support α-glucan production by itself, but it had either positive or negative effects on polymer content depending on which SS or a combination thereof was present. The complete suite of maize enzymes generated insoluble particles resembling native starch granules in size, shape, and crystallinity. Ultrastructural analysis revealed a hierarchical assembly starting with subparticles of approximately 50 nm diameter that coalesce into discrete structures of approximately 200 nm diameter. These are assembled into semi-crystalline α-glucan superstructures up to 4 µm in length filling most of the yeast cytosol. ISA was not essential for the formation of such particles, but their abundance was increased dramatically by ISA presence.

Hyperspectral signals in the soil: Plant-soil hydraulic connection and disequilibrium as mechanisms of drought tolerance and rapid recovery.

Predicting soil water status remotely is appealing due to its low cost and large-scale application. During drought, plants can disconnect from the soil, causing disequilibrium between soil and plant water potentials at pre-dawn. The impact of this disequilibrium on plant drought response and recovery is not well understood, potentially complicating soil water status predictions from plant spectral reflectance. This study aimed to quantify drought-induced disequilibrium, evaluate plant responses and recovery, and determine the potential for predicting soil water status from plant spectral reflectance. Two species were tested: sweet corn (Zea mays), which disconnected from the soil during intense drought, and peanut (Arachis hypogaea), which did not. Sweet corn's hydraulic disconnection led to an extended 'hydrated' phase, but its recovery was slower than peanut's, which remained connected to the soil even at lower water potentials (-5 MPa). Leaf hyperspectral reflectance successfully predicted the soil water status of peanut consistently, but only until disequilibrium occurred in sweet corn. Our results reveal different hydraulic strategies for plants coping with extreme drought and provide the first example of using spectral reflectance to quantify rhizosphere water status, emphasizing the need for species-specific considerations in soil water status predictions from canopy reflectance.

Prediction of resistance, virulence, and host-by-pathogen interactions using dual-genome prediction models.

KEY MESSAGE: Integrating disease screening data and genomic data for host and pathogen populations into prediction models provides breeders and pathologists with a unified framework to develop disease resistance. Developing disease resistance in crops typically consists of exposing breeding populations to a virulent strain of the pathogen that is causing disease. While including a diverse set of pathogens in the experiments would be desirable for developing broad and durable disease resistance, it is logistically complex and uncommon, and limits our capacity to implement dual (host-by-pathogen)-genome prediction models. Data from an alternative disease screening system that challenges a diverse sweet corn population with a diverse set of pathogen isolates are provided to demonstrate the changes in genetic parameter estimates that result from using genomic data to provide connectivity across sparsely tested experimental treatments. An inflation in genetic variance estimates was observed when among isolate relatedness estimates were included in prediction models, which was moderated when host-by-pathogen interaction effects were incorporated into models. The complete model that included genomic similarity matrices for host, pathogen, and interaction effects indicated that the proportion of phenotypic variation in lesion size that is attributable to host, pathogen, and interaction effects was similar. Estimates of the stability of lesion size predictions for host varieties inoculated with different isolates and the stability of isolates used to inoculate different hosts were also similar. In this pathosystem, genetic parameter estimates indicate that host, pathogen, and host-by-pathogen interaction predictions may be used to identify crop varieties that are resistant to specific virulence mechanisms and to guide the deployment of these sources of resistance into pathogen populations where they will be more effective.

Genotype-by-environment interactions and local adaptation shape selection in the US National Chip Processing Trial.

KEY MESSAGE: We find evidence of selection for local adaptation and extensive genotype-by-environment interaction in the potato National Chip Processing Trial (NCPT). We present a novel method for dissecting the interplay between selection, local adaptation and environmental response in plant breeding schemes. Balancing local adaptation and the desire for widely adapted cultivars is challenging for plant breeders and makes genotype-by-environment interactions (GxE) an important target of selection. Selecting for GxE requires plant breeders to evaluate plants across multiple environments. One way breeders have accomplished this is to test advanced materials across many locations. Public potato breeders test advanced breeding material in the National Chip Processing Trial (NCPT), a public-private partnership where breeders from ten institutions submit advanced chip lines to be evaluated in up to ten locations across the country. These clones are genotyped and phenotyped for important agronomic traits. We used these data to interrogate the NCPT for GxE. Further, because breeders submitting clones to the NCPT select in a relatively small geographic range for the first 3 years of selection, we examined these data for evidence of incidental selection for local adaptation, and the alleles underlying it, using an environmental genome-wide association study (envGWAS). We found genomic regions associated with continuous environmental variables and discrete breeding programs, as well as regions of the genome potentially underlying GxE for yield.

Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis in Populus deltoides.

Despite the growing resources and tools for high-throughput characterization and analysis of genomic information, the discovery of the genetic elements that regulate complex traits remains a challenge. Systems genetics is an emerging field that aims to understand the flow of biological information that underlies complex traits from genotype to phenotype. In this study, we used a systems genetics approach to identify and evaluate regulators of the lignin biosynthesis pathway in Populus deltoides by combining genome, transcriptome, and phenotype data from a population of 268 unrelated individuals of P. deltoides The discovery of lignin regulators began with the quantitative genetic analysis of the xylem transcriptome and resulted in the detection of 6706 and 4628 significant local- and distant-eQTL associations, respectively. Among the locally regulated genes, we identified the R2R3-MYB transcription factor MYB125 (Potri.003G114100) as a putative trans-regulator of the majority of genes in the lignin biosynthesis pathway. The expression of MYB125 in a diverse population positively correlated with lignin content. Furthermore, overexpression of MYB125 in transgenic poplar resulted in increased lignin content, as well as altered expression of genes in the lignin biosynthesis pathway. Altogether, our findings indicate that MYB125 is involved in the control of a transcriptional coexpression network of lignin biosynthesis genes during secondary cell wall formation in P. deltoides.

Genomic prediction applied to multiple traits and environments in second season maize hybrids.

Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.

Characterizing the Physical Properties and Cell Compatibility of Phytoglycogen Extracted from Different Sweet Corn Varieties.

Owing to its unique structure and properties, the glucose dendrimer phytoglycogen is gaining interest for medical and biotechnology applications. Although many maize variants are available from commercial and academic breeding programs, most applications rely on phytoglycogen extracted from the common maize variant, sugary1. Here we characterized the solubility, hydrodynamic diameter, water-binding properties, protein contaminant concentration, and cytotoxicity of phytoglycogens from different maize sources, A632su1, A619su1, Wesu7, and Ia453su1, harboring various sugary1 mutants. A619su1-SW phytoglycogen was cytotoxic while A632su1-SW phytoglycogen was not. A632su1-Pu phytoglycogen promoted cell growth, whereas extracts from A632su1-NE, A632su1-NC, and A632su1-CM were cytotoxic. Phytoglycogen extracted from Wesu7su1-NE using ethanol precipitation was cytotoxic. Acid-treatment improved Wesu7 phytoglycogen cytocompatibility. Protease-treated Wesu7 extracts promoted cell growth. Phytoglycogen extracted from Ia453su1 21 days after pollination ("Ia435su1 21DAP") was cytotoxic, whereas phytoglycogen extracted at 40 days ("Ia435su1 40DAP") was not. In general, size and solubility had no correlation with cytocompatibility, whereas protein contaminant concentration and water-binding properties did. A632su1-CM had the highest protein contamination among A632 mutants, consistent with its higher cytotoxicity. Likewise, Ia435su1 21DAP phytoglycogen had higher protein contamination than Ia435su1 40DAP. Conversely, protease-treated Wesu7 extracts had lower protein contamination than the other Wesu7 extracts. A632su1-NE, A632su1-NC, and A632su1-CM had similar water-binding properties which differed from those of A632su1-Pu and A632su1-SW. Likewise, water binding differed between Ia435su1 21DAP and Ia435su1 40DAP. Collectively, these data demonstrate that maize phytoglycogen extracts are not uniformly cytocompatible. Rather, maize variant, plant genotype, protein contaminants, and water-binding properties are determinants of phytoglycogen cytotoxicity.

Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus.

BACKGROUND: The advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses. RESULTS: Predictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000-10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study. CONCLUSIONS: This study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.

Genome-wide association study reveals putative regulators of bioenergy traits in Populus deltoides.

Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. These polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.

Discovering candidate genes that regulate resin canal number in Pinus taeda stems by integrating genetic analysis across environments, ages, and populations.

Genetically improving constitutive resin canal development in Pinus stems may enhance the capacity to synthesize terpenes for bark beetle resistance, chemical feedstocks, and biofuels. To discover genes that potentially regulate axial resin canal number (RCN), single nucleotide polymorphisms (SNPs) in 4027 genes were tested for association with RCN in two growth rings and three environments in a complex pedigree of 520 Pinus taeda individuals (CCLONES). The map locations of associated genes were compared with RCN quantitative trait loci (QTLs) in a (P. taeda × Pinus elliottii) × P. elliottii pseudo-backcross of 345 full-sibs (BC1). Resin canal number was heritable (h(2) ˜ 0.12-0.21) and positively genetically correlated with xylem growth (rg ˜ 0.32-0.72) and oleoresin flow (rg ˜ 0.15-0.51). Sixteen well-supported candidate regulators of RCN were discovered in CCLONES, including genes associated across sites and ages, unidirectionally associated with oleoresin flow and xylem growth, and mapped to RCN QTLs in BC1. Breeding is predicted to increase RCN 11% in one generation and could be accelerated with genomic selection at accuracies of 0.45-0.52 across environments. There is significant genetic variation for RCN in loblolly pine, which can be exploited in breeding for elevated terpene content.

Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology.

Genomic selection and doubled haploids hold significant potential to enhance genetic gains and shorten breeding cycles across various crops. Here, we utilized stochastic simulations to investigate the best strategies for optimize a sweet corn breeding program. We assessed the effects of incorporating varying proportions of old and new parents into the crossing block (3:1, 1:1, 1:3, and 0:1 ratio, representing different degrees of parental substitution), as well as the implementation of genomic selection in two distinct pipelines: one calibrated using the phenotypes of testcross parents (GSTC scenario) and another using F1 individuals (GSF1). Additionally, we examined scenarios with doubled haploids, both with (DH) and without (DHGS) genomic selection. Across 20 years of simulated breeding, we evaluated scenarios considering traits with varying heritabilities, the presence or absence of genotype-by-environment effects, and two program sizes (50 vs 200 crosses per generation). We also assessed parameters such as parental genetic mean, average genetic variance, hybrid mean, and implementation costs for each scenario. Results indicated that within a conventional selection program, a 1:3 parental substitution ratio (replacing 75% of parents each generation with new lines) yielded the highest performance. Furthermore, the GSTC model outperformed the GSF1 model in enhancing genetic gain. The DHGS model emerged as the most effective, reducing cycle time from 5 to 4 years and enhancing hybrid gains despite increased costs. In conclusion, our findings strongly advocate for the integration of genomic selection and doubled haploids into sweet corn breeding programs, offering accelerated genetic gains and efficiency improvements.

Utilizing genomic prediction to boost hybrid performance in a sweet corn breeding program.

Sweet corn breeding programs, like field corn, focus on the development of elite inbred lines to produce commercial hybrids. For this reason, genomic selection models can help the in silico prediction of hybrid crosses from the elite lines, which is hypothesized to improve the test cross scheme, leading to higher genetic gain in a breeding program. This study aimed to explore the potential of implementing genomic selection in a sweet corn breeding program through hybrid prediction in a within-site across-year and across-site framework. A total of 506 hybrids were evaluated in six environments (California, Florida, and Wisconsin, in the years 2020 and 2021). A total of 20 traits from three different groups were measured (plant-, ear-, and flavor-related traits) across the six environments. Eight statistical models were considered for prediction, as the combination of two genomic prediction models (GBLUP and RKHS) with two different kernels (additive and additive + dominance), and in a single- and multi-trait framework. Also, three different cross-validation schemes were tested (CV1, CV0, and CV00). The different models were then compared based on the correlation between the estimated breeding values/total genetic values and phenotypic measurements. Overall, heritabilities and correlations varied among the traits. The models implemented showed good accuracies for trait prediction. The GBLUP implementation outperformed RKHS in all cross-validation schemes and models. Models with additive plus dominance kernels presented a slight improvement over the models with only additive kernels for some of the models examined. In addition, models for within-site across-year and across-site performed better in the CV0 than the CV00 scheme, on average. Hence, GBLUP should be considered as a standard model for sweet corn hybrid prediction. In addition, we found that the implementation of genomic prediction in a sweet corn breeding program presented reliable results, which can improve the testcross stage by identifying the top candidates that will reach advanced field-testing stages.

Sweet corn phytoglycogen dendrimers as a lyoprotectant for dry-state protein storage.

Protein biotherapeutics typically require expensive cold-chain storage to maintain their fold and function. Packaging proteins in the dry state via lyophilization can reduce these cold-chain requirements. However, formulating proteins for lyophilization often requires extensive optimization of excipients that both maintain the protein folded state during freezing and drying (i.e., "cryoprotection" and "lyoprotection"), and form a cake to carry the dehydrated protein. Here we show that sweet corn phytoglycogens, which are glucose dendrimers, can act as both a protein lyoprotectant and a cake-forming agent. Phytoglycogen (PG) dendrimers from 16 different maize sources (PG1-16) were extracted via ethanol precipitation. PG size was generally consistent at ~70-100 nm for all variants, whereas the colloidal stability in water, protein contaminant level, and maximum density of cytocompatibility varied for PG1-16. 10 mg/mL PG1, 2, 9, 13, 15, and 16 maintained the activity of various proteins, including green fluorescent protein, lysozyme, ß-galactosidase, and horseradish peroxidase, over a broad range of concentrations, through multiple rounds of lyophilization. PG13 was identified as the lead excipient candidate as it demonstrated narrow dispersity, colloidal stability in phosphate-buffered saline, low protein contaminants, and cytocompatibility up to 10 mg/mL in NIH3T3 cell cultures. All dry protein-PG13 mixtures had a cake-like appearance and all frozen protein-PG13 mixtures had a Tg' of ~ -26°C. The lyoprotection and cake-forming properties of PG13 were density-dependent, requiring a minimum density of 5 mg/mL for maximum activity. Collectively these data establish PG dendrimers as a new class of excipient to formulate proteins in the dry state.

Maize terpene synthase 1 impacts insect behavior via the production of monoterpene volatiles ß-myrcene and linalool.

Plant-derived volatiles are important mediators of plant-insect interactions as they can provide cues for host location and quality, or act as direct or indirect defense molecules. The volatiles produced by Zea mays (maize) include a range of terpenes, likely produced by several of the terpene synthases (TPS) present in maize. Determining the roles of specific terpene volatiles and individual TPSs in maize-insect interactions is challenging due to the promiscuous nature of TPSs in vitro and their potential for functional redundancy. In this study, we used metabolite GWAS of a sweetcorn diversity panel infested with Spodoptera frugiperda (fall armyworm) to identify genetic correlations between TPSs and individual volatiles. This analysis revealed a correlation between maize terpene synthase 1 (ZmTPS1) and emission of the monoterpene volatiles linalool and ß-myrcene. Electroantennogram assays showed gravid S. frugiperda could detect both linalool and ß-myrcene. Quantification of headspace volatiles in a maize tps1 loss-of-function mutant confirmed that ZmTPS1 is an important contributor to linalool and ß-myrcene emission in maize. Furthermore, pairwise choice assays between tps1 mutant and wild-type plants showed that ZmTPS1, and by extension its volatile products, aid host location in the chewing insect S. frugiperda, yet repel the sap-sucking pest, Rhopalosiphum maidis (corn leaf aphid). On the other hand, ZmTPS1 had no impact on indirect defense via the recruitment of the parasitoid Cotesia marginiventris. ZmTPS1 is therefore an important mediator of the interactions between maize and its insect pests.

Seedling root system adaptation to water availability during maize domestication and global expansion.

The maize root system has been reshaped by indirect selection during global adaptation to new agricultural environments. In this study, we characterized the root systems of more than 9,000 global maize accessions and its wild relatives, defining the geographical signature and genomic basis of variation in seminal root number. We demonstrate that seminal root number has increased during maize domestication followed by a decrease in response to limited water availability in locally adapted varieties. By combining environmental and phenotypic association analyses with linkage mapping, we identified genes linking environmental variation and seminal root number. Functional characterization of the transcription factor ZmHb77 and in silico root modeling provides evidence that reshaping root system architecture by reducing the number of seminal roots and promoting lateral root density is beneficial for the resilience of maize seedlings to drought.

Association genetics of oleoresin flow in loblolly pine: discovering genes and predicting phenotype for improved resistance to bark beetles and bioenergy potential.

Rapidly enhancing oleoresin production in conifer stems through genomic selection and genetic engineering may increase resistance to bark beetles and terpenoid yield for liquid biofuels. We integrated association genetic and genomic prediction analyses of oleoresin flow (g 24 h(-1)) using 4854 single nucleotide polymorphisms (SNPs) in expressed genes within a pedigreed population of loblolly pine (Pinus taeda) that was clonally replicated at three sites in the southeastern United States. Additive genetic variation in oleoresin flow (h(2) ≈ 0.12-0.30) was strongly correlated between years in which precipitation varied (r(a) ≈ 0.95), while the genetic correlation between sites declined from 0.8 to 0.37 with increasing differences in soil and climate among sites. A total of 231 SNPs were significantly associated with oleoresin flow, of which 81% were specific to individual sites. SNPs in sequences similar to ethylene signaling proteins, ABC transporters, and diterpenoid hydroxylases were associated with oleoresin flow across sites. Despite this complex genetic architecture, we developed a genomic prediction model to accelerate breeding for enhanced oleoresin flow that is robust to environmental variation. Results imply that breeding could increase oleoresin flow 1.5- to 2.4-fold in one generation.

Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics.

Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of increasing consumer power in the food industry. Cotton-candy grapes, specialty tomatoes, and pineapple-flavored white strawberries provide a few examples. Given the increased demand for flavorful varieties, and pressing need to reduce micronutrient malnutrition, we expect breeding to increase its prioritization toward these traits. Reaching this goal will, in part, necessitate knowledge of the genetic architecture controlling these traits, as well as the development of breeding methods that maximize their genetic gain. Can artificial intelligence (AI) help predict flavor preferences, and can such insights be leveraged by breeding programs? In this Perspective, we outline both the opportunities and challenges for the development of more flavorful and nutritious crops, and how AI can support these breeding initiatives.

Genomic selection for growth and wood quality in Eucalyptus: capturing the missing heritability and accelerating breeding for complex traits in forest trees.

• Genomic selection (GS) is expected to cause a paradigm shift in tree breeding by improving its speed and efficiency. By fitting all the genome-wide markers concurrently, GS can capture most of the 'missing heritability' of complex traits that quantitative trait locus (QTL) and association mapping classically fail to explain. Experimental support of GS is now required. • The effectiveness of GS was assessed in two unrelated Eucalyptus breeding populations with contrasting effective population sizes (N(e) = 11 and 51) genotyped with > 3000 DArT markers. Prediction models were developed for tree circumference and height growth, wood specific gravity and pulp yield using random regression best linear unbiased predictor (BLUP). • Accuracies of GS varied between 0.55 and 0.88, matching the accuracies achieved by conventional phenotypic selection. Substantial proportions (74-97%) of trait heritability were captured by fitting all genome-wide markers simultaneously. Genomic regions explaining trait variation largely coincided between populations, although GS models predicted poorly across populations, likely as a result of variable patterns of linkage disequilibrium, inconsistent allelic effects and genotype × environment interaction. • GS brings a new perspective to the understanding of quantitative trait variation in forest trees and provides a revolutionary tool for applied tree improvement. Nevertheless population-specific predictive models will likely drive the initial applications of GS in forest tree breeding.

Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks.

Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high-quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full-sib and 35 half-sib families was bulk-genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single-nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.