Review on blueberry drought tolerance from the perspective of cultivar improvement.

Blueberry (Vaccinium spp.) is an increasingly popular fruit around the world for their attractive taste, appearance, and most importantly their many health benefits. Global blueberry production was valued at $2.31 billion with the United States alone producing $1.02 billion of cultivated blueberries in 2021. The sustainability of blueberry production is increasingly threatened by more frequent and extreme drought events caused by climate change. Blueberry is especially prone to adverse effects from drought events due to their superficial root system and lack of root hairs, which limit blueberry's ability to intake water and nutrients from the soil especially under drought stress conditions. The goal of this paper is to review previous studies on blueberry drought tolerance focusing on physiological, biochemical, and molecular drought tolerance mechanisms, as well as genetic variability present in cultivated blueberries. We also discuss limitations of previous studies and potential directions for future efforts to develop drought-tolerant blueberry cultivars. Our review showed that the following areas are lacking in blueberry drought tolerance research: studies of root and fruit traits related to drought tolerance, large-scale cultivar screening, efforts to understand the genetic architecture of drought tolerance, tools for molecular-assisted drought tolerance improvement, and high-throughput phenotyping capability for efficient cultivar screening. Future research should be devoted to following areas: (1) drought tolerance evaluation to include a broader range of traits, such as root architecture and fruit-related performance under drought stress, to establish stronger association between physiological and molecular signals with drought tolerance mechanisms; (2) large-scale drought tolerance screening across diverse blueberry germplasm to uncover various drought tolerance mechanisms and valuable genetic resources; (3) high-throughput phenotyping tools for drought-related traits to enhance the efficiency and affordability of drought phenotyping; (4) identification of genetic architecture of drought tolerance using various mapping technologies and transcriptome analysis; (5) tools for molecular-assisted breeding for drought tolerance, such as marker-assisted selection and genomic selection, and (6) investigation of the interactions between drought and other stresses such as heat to develop stress resilient genotypes.

Multivariate analysis methods improve the selection of strawberry genotypes with low cold requirement.

Methods of multivariate analysis is a powerful approach to assist the initial stages of crops genetic improvement, particularly, because it allows many traits to be evaluated simultaneously. In this study, heat-tolerant genotypes have been selected by analyzing phenotypic diversity, direct and indirect relationships among traits were identified, and four selection indices compared. Diversity was estimated using K-means clustering with the number of clusters determined by the Elbow method, and the relationship among traits was quantified by path analysis. Parametric and non-parametric indices were applied to selected genotypes using the magnitude of genotypic variance, heritability, genotypic coefficient of variance, and assigned economic weight as selection criteria. The variability among materials led to the formation of two non-overlapping clusters containing 40 and 154 genotypes. Strong to moderate correlations were found between traits with direct effect of the number of commercial fruit on the mass of commercial fruit. The Smith and Hazel index showed the greatest total gains for all criteria; however, concerning the biochemical traits, the Mulamba and Mock index showed the highest magnitudes of predicted gains. Overall, the K-means clustering, correlation analysis, and path analysis complement the use of selection indices, allowing for selection of genotypes with better balance among the assessed traits.

As It Stands: The Palouse Wild Cider Apple Breeding Program.

Providing hands-on education for the next generation of plant breeders would help maximize effectiveness of future breeding efforts. Such education should include training in introgression of crop wild relative alleles, which can increase genetic diversity while providing cultivar attributes that meet industry and consumer demands in a crop such as cider apple. Incorporation of DNA information in breeding decisions has become more common and is another skill future plant breeders need. The Palouse Wild Cider apple breeding program (PWCabp) was established at Washington State University in early 2014 as a student-run experiential learning opportunity. The objectives of this study were to describe the PWCabp's approaches, outcomes, and student involvement to date that has relied on a systematic operational structure, utilization of wild relatives, and incorporation of DNA information. Students chose the crop (cider apple) and initial target market and stakeholders (backyard growers and hobbyists of the Palouse region). Twelve target attributes were defined including high phenolics and red flesh. Phase one and two field trials were established. Two promising high-bitterness selections were identified and propagated. By running the PWCabp, more than 20 undergraduate and graduate students gained experience in the decisions and operations of a fruit breeding program. PWCabp activities have produced desirable new germplasm via utilization of highly diverse Malus germplasm and trained new plant breeding professionals via experiential learning.

The Breeding Information Management System (BIMS): an online resource for crop breeding.

In this era of big data, breeding programs are producing ever larger amounts of data. This necessitates access to efficient management systems to keep track of cross, performance, pedigree, geographical and image-based data, as well as genotyping data. In this article, we report the progress on the Breeding Information Management System (BIMS), a free, secure and online breeding management system that allows breeders to store, manage, archive and analyze their private breeding data. BIMS is the first publicly available database system that enables individual breeders to integrate their private phenotypic and genotypic data with public data and, at the same time, have complete control of their own breeding data along with access to tools such as data import/export, data analysis and data archiving. The integration of breeding data with publicly available genomic and genetic data enhances genetic understanding of important traits and maximizes the marker-assisted breeding utility for breeders and allied scientists. BIMS incorporates the use of the Android App Field Book, open-source phenotype data collection software for phones and tablets that allows breeders to replace hard copy field books, thus alleviating the possibility of transcription errors while providing faster access to the collected data. BIMS comes with training materials and support for individual or small group training and is currently implemented in the Genome Database for Rosaceae, CottonGEN, the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. Database URLs: (https://www.rosaceae.org/), (https://www.cottongen.org/), (https://www.citrusgenomedb.org/), (https://www.pulsedb.org/) and (https://www.vaccinium.org/).

Predicted genetic gains from introgressing chromosome segments from exotic germplasm into an elite soybean cultivar.

KEY MESSAGE: To improve an elite soybean line, introgress longer chromosome segments instead of QTL alleles from exotic germplasm. Broadening the diversity of cultivated soybean [Glycine max (L.) Merrill] through introgression of exotic germplasm has been difficult. Our objectives were to (1) determine if introgressing specific chromosome segments (instead of quantitative trait locus alleles) from exotic soybean germplasm has potential for improving an elite cultivar, and (2) identify strategies to introgress and pyramid exotic chromosome segments into an elite cultivar. We estimated genomewide marker effects for yield and other traits in seven crosses between the elite line IA3023 and seven soybean plant introductions (PIs). We then predicted genetic gains from having ≤ 2 targeted recombinations per linkage group. When introgression was modeled for yield while controlling maturity in the seven PI × IA3023 populations, the predicted yield was 8-25% over the yield of IA3023. Correlated changes in maturity, seed traits, lodging, and plant height were generally small but were in the favorable direction. In contrast, selecting the best recombinant inbred (without targeted recombination) in each of the PI × IA3023 populations led to negative or minimal yield gains over IA3023. In one PI × IA3023 population, introgressing and pyramiding only two linkage groups from recombinant inbreds into IA3023 was predicted to achieve an 8% yield gain over IA3023 without sacrificing the performance for other traits. The probability of inheriting intact chromosomes was high enough to allow introgression and pyramiding of chromosome segments in 5-6 generations. Overall, our study suggested that introgressing specific chromosome segments is an effective way to introduce exotic soybean germplasm into an elite cultivar.

Targeted recombination to increase genetic gain in self-pollinated species.

KEY MESSAGE: If we can induce or select for recombination at targeted marker intervals, genetic gains for quantitative traits in self-pollinated species may be doubled. Targeted recombination refers to inducing or selecting for a recombination event at genomic positions that maximize genetic gain in a cross. A previous study indicated that targeted recombination could double the rate of genetic gains in maize (Zea mays L.), a cross-pollinated crop for which historical genetic gains have been large. Our objectives were to determine whether targeted recombination can sufficiently increase predicted gains in self-pollinated species, and whether prospective gains from targeted recombination vary across crops, populations, traits, and chromosomes. Genomewide marker effects were estimated from previously published marker and phenotypic data on 21 biparental populations of soybean [Glycine max (L.) Merr.], wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and pea (Pisum sativum L.). With the predicted gain from nontargeted recombination as the baseline, the relative gains from creating a doubled haploid with up to one targeted recombination [RG(x ≤ 1)] and two targeted recombinations [RG(x ≤ 2)] per chromosome or linkage group were calculated. Targeted recombination significantly (P = 0.05) increased the predicted genetic gain compared to nontargeted recombination for all traits and all populations, except for plant height in barley. The mean RG(x ≤ 1) was 211%, whereas the mean RG(x ≤ 2) was 243%. The predicted gain varied among traits and populations. For most traits and populations, having targeted recombination on less than a third of all the chromosomes led to the same or higher predicted gain than nontargeted recombination. Together with previous findings in maize, our results suggested that targeted recombination could double the genetic gains in both self- and cross-pollinated crops.

15 years of GDR: New data and functionality in the Genome Database for Rosaceae.

The Genome Database for Rosaceae (GDR, https://www.rosaceae.org) is an integrated web-based community database resource providing access to publicly available genomics, genetics and breeding data and data-mining tools to facilitate basic, translational and applied research in Rosaceae. The volume of data in GDR has increased greatly over the last 5 years. The GDR now houses multiple versions of whole genome assembly and annotation data from 14 species, made available by recent advances in sequencing technology. Annotated and searchable reference transcriptomes, RefTrans, combining peer-reviewed published RNA-Seq as well as EST datasets, are newly available for major crop species. Significantly more quantitative trait loci, genetic maps and markers are available in MapViewer, a new visualization tool that better integrates with other pages in GDR. Pathways can be accessed through the new GDR Cyc Pathways databases, and synteny among the newest genome assemblies from eight species can be viewed through the new synteny browser, SynView. Collated single-nucleotide polymorphism diversity data and phenotypic data from publicly available breeding datasets are integrated with other relevant data. Also, the new Breeding Information Management System allows breeders to upload, manage and analyze their private breeding data within the secure GDR server with an option to release data publicly.

Modeling of genetic gain for single traits from marker-assisted seedling selection in clonally propagated crops.

Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest. Traditionally, genetic potential is determined by phenotypic evaluation. With the availability of DNA tests for some agronomically important traits, breeders have the opportunity to include DNA information in their seedling selection operations-known as marker-assisted seedling selection. A major challenge in deploying marker-assisted seedling selection in clonally propagated crops is a lack of knowledge in genetic gain achievable from alternative strategies. Existing models based on additive effects considering seed-propagated crops are not directly relevant for seedling selection of clonally propagated crops, as clonal propagation captures all genetic effects, not just additive. This study modeled genetic gain from traditional and various marker-based seedling selection strategies on a single trait basis through analytical derivation and stochastic simulation, based on a generalized seedling selection scheme of clonally propagated crops. Various trait-test scenarios with a range of broad-sense heritability and proportion of genotypic variance explained by DNA markers were simulated for two populations with different segregation patterns. Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability. Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available.

The Genome Database for Rosaceae (GDR): year 10 update.

The Genome Database for Rosaceae (GDR, http:/www.rosaceae.org), the long-standing central repository and data mining resource for Rosaceae research, has been enhanced with new genomic, genetic and breeding data, and improved functionality. Whole genome sequences of apple, peach and strawberry are available to browse or download with a range of annotations, including gene model predictions, aligned transcripts, repetitive elements, polymorphisms, mapped genetic markers, mapped NCBI Rosaceae genes, gene homologs and association of InterPro protein domains, GO terms and Kyoto Encyclopedia of Genes and Genomes pathway terms. Annotated sequences can be queried using search interfaces and visualized using GBrowse. New expressed sequence tag unigene sets are available for major genera, and Pathway data are available through FragariaCyc, AppleCyc and PeachCyc databases. Synteny among the three sequenced genomes can be viewed using GBrowse_Syn. New markers, genetic maps and extensively curated qualitative/Mendelian and quantitative trait loci are available. Phenotype and genotype data from breeding projects and genetic diversity projects are also included. Improved search pages are available for marker, trait locus, genetic diversity and publication data. New search tools for breeders enable selection comparison and assistance with breeding decision making.