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Fusarium oxysporum f. sp. fragariae (Fof) race 1 is avirulent on cultivars with the dominant resistance gene FW1, while Fof race 2 is virulent on FW1-resistant cultivars. We hypothesized there was a gene-for-gene interaction between a gene at the FW1 locus and an avirulence gene (AvrFW1) in Fof race 1. To identify a candidate AvrFW1, we compared genomes of 24 Fof race 1 and three Fof race 2 isolates. We found one candidate gene that was present in race 1, was absent in race 2, was highly expressed in planta, and was homologous to a known effector, secreted in xylem 6 (SIX6). We knocked out SIX6 in two Fof race 1 isolates by homologous recombination. All SIX6 knockout transformants (ΔSIX6) gained virulence on FW1/fw1 cultivars, whereas ectopic transformants and the wildtype isolates remained avirulent. ΔSIX6 isolates were quantitatively less virulent on FW1/fw1 cultivars Fronteras and San Andreas than fw1/fw1 cultivars. Seedlings from an FW1/fw1 × fw1/fw1 population were genotyped for FW1 and tested for susceptibility to a SIX6 knockout isolate. Results suggested that additional minor-effect quantitative resistance genes could be present at the FW1 locus. This work demonstrates that SIX6 acts as an avirulence factor interacting with a resistance gene at the FW1 locus. The identification of AvrFW1 enables surveillance for Fof race 2 and provides insight into the mechanisms of FW1-mediated resistance. [Graphic: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Abstract The annual production of strawberry has increased by one million tonnes in the US and 8.4 million tonnes worldwide since 1960. Here we show that the US expansion was driven by genetic gains from Green Revolution breeding and production advances that increased yields by 2,755%. Using a California population with a century-long breeding history and phenotypes of hybrids observed in coastal California environments, we estimate that breeding has increased fruit yields by 2,974-6,636%, counts by 1,454-3,940%, weights by 228-504%, and firmness by 239-769%. Using genomic prediction approaches, we pinpoint the origin of the Green Revolution to the early 1950s and uncover significant increases in additive genetic variation caused by transgressive segregation and phenotypic diversification. Lastly, we show that the most consequential Green Revolution breeding breakthrough was the introduction of photoperiod-insensitive, PERPETUAL FLOWERING hybrids in the 1970s that doubled yields and drove the dramatic expansion of strawberry production in California.

Abstract Verticillium wilt (VW), a devastating vascular wilt disease of strawberry (Fragaria × ananassa), has caused economic losses for nearly a century. This disease is caused by the soil‐borne pathogen Verticillium dahliae, which occurs nearly worldwide and causes disease in numerous agriculturally important plants. The development of VW‐resistant cultivars is critically important for the sustainability of strawberry production. We previously showed that a preponderance of the genetic resources (asexually propagated hybrid individuals) preserved in public germplasm collections were moderately to highly susceptible and that genetic gains for increased resistance to VW have been negligible over the last 60 years. To more fully understand the challenges associated with breeding for increased quantitative resistance to this pathogen, we developed and phenotyped a training population of hybrids (n=564) among elite parents with a wide range of resistance phenotypes. When these data were combined with training data from a population of elite and exotic hybrids (n=386), genomic prediction accuracies of 0.47–0.48 were achieved and were predicted to explain 70%–75% of the additive genetic variance for resistance. We concluded that breeding values for resistance to VW can be predicted with sufficient accuracy for effective genomic selection with routine updating of training populations.

Abstract Genomic prediction in breeding populations containing hundreds to thousands of parents and seedlings is prohibitively expensive with current high‐density genetic marker platforms designed for strawberry. We developed mid‐density panels of molecular inversion probes (MIPs) to be deployed with the “DArTag” marker platform to provide a low‐cost, high‐throughput genotyping solution for strawberry genomic prediction. In total, 7742 target single nucleotide polymorphism (SNP) regions were used to generate MIP assays that were tested with a screening panel of 376 octoploid Fragaria accessions. We evaluated the performance of DArTag assays based on genotype segregation, amplicon coverage, and their ability to produce subgenome‐specific amplicon alignments to the FaRR1 assembly and subsequent alignment‐based variant calls with strong concordance to DArT's alignment‐free, count‐based genotype reports. We used a combination of marker performance metrics and physical distribution in the FaRR1 assembly to select 3K and 5K production panels for genotyping of large strawberry populations. We show that the 3K and 5K DArTag panels are able to target and amplify homologous alleles within subgenomic sequences with low‐amplification bias between reference and alternate alleles, supporting accurate genotype calling while producing marker genotypes that can be treated as functionally diploid for quantitative genetic analysis. The 3K and 5K target SNPs show high levels of polymorphism in diverse F. × ananassa germplasm and UC Davis cultivars, with mean pairwise diversity (π) estimates of 0.40 and 0.32 and mean heterozygous genotype frequencies of 0.35 and 0.33, respectively.

Abstract The development of strawberry (Fragaria × ananassa Duchesne ex Rozier) cultivars resistant to Phytophthora crown rot (PhCR), a devastating disease caused by the soil‐borne pathogen Phytophthora cactorum (Lebert & Cohn) J. Schröt., has been challenging partly because the resistance phenotypes are quantitative and only moderately heritable. To develop deeper insights into the genetics of resistance and build the foundation for applying genomic selection, a genetically diverse training population was screened for resistance to California isolates of the pathogen. Here we show that genetic gains in breeding for resistance to PhCR have been negligible (3% of the cultivars tested were highly resistant and none surpassed early 20th century cultivars). Narrow‐sense genomic heritability for PhCR resistance ranged from 0.41 to 0.75 among training population individuals. Using multivariate genome‐wide association studies (GWAS), we identified a large‐effect locus (predicted to be RPc2) that explained 43.6–51.6% of the genetic variance, was necessary but not sufficient for resistance, and was associated with calcium channel and other candidate genes with known plant defense functions. The addition of underutilized gene bank resources to our training population doubled additive genetic variance, increased the accuracy of genomic selection, and enabled the discovery of individuals carrying favorable alleles that are either rare or not present in modern cultivars. The incorporation of an RPc2‐associated single‐nucleotide polymorphism (SNP) as a fixed effect increased genomic prediction accuracy from 0.40 to 0.55. Finally, we show that parent selection using genomic‐estimated breeding values, genetic variances, and cross usefulness holds promise for enhancing resistance to PhCR in strawberry.

Abstract Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi‐view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer‐grade components for capturing, calibrating, and reconstructing the 3D form of small‐to‐moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground‐truth object and the reconstructed models was >0.97 and root‐mean square error (RMSE) was < 3 mm for objects without locally concave regions. Measurements from 1 mm and 2 mm resolution reconstructions were consistent (R2 > 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, five grapes, seven peppers, and four Bosc and two red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form.

Abstract Understanding the genetic basis of plant traits requires comprehensive and quantitative descriptions of the phenotypic variation that exists within populations. Cameras and other sensors have made high‐throughput phenotyping possible, but image‐based phenotyping procedures involve a step where a researcher selects the traits to be measured. This feature selection step is inherently prone to human biases. Recently, a set of phenotyping approaches, which are referred to collectively as latent phenotyping techniques, have arisen in the literature. Latent phenotyping techniques isolate a latent source of variance in the data, such as stress or genotype, and then quantify the effect of this latent source of variance using latent variables without defining any conventional traits. In this review, we discuss the differences between, and challenges of, both traditional and latent phenotyping.

Allo-octoploid cultivated strawberry (Fragaria × ananassa) originated through a combination of polyploid and homoploid hybridization, domestication of an interspecific hybrid lineage, and continued admixture of wild species over the last 300 years. While genes appear to flow freely between the octoploid progenitors, the genome structures and diversity of the octoploid species remain poorly understood. The complexity and absence of an octoploid genome frustrated early efforts to study chromosome evolution, resolve subgenomic structure, and develop a single coherent linkage group nomenclature. Here, we show that octoploid Fragaria species harbor millions of subgenome-specific DNA variants. Their diversity was sufficient to distinguish duplicated (homoeologous and paralogous) DNA sequences and develop 50K and 850K SNP genotyping arrays populated with co-dominant, disomic SNP markers distributed throughout the octoploid genome. Whole-genome shotgun genotyping of an interspecific segregating population yielded 1.9M genetically mapped subgenome variants in 5,521 haploblocks spanning 3,394 cM in F. chiloensis subsp. lucida, and 1.6M genetically mapped subgenome variants in 3,179 haploblocks spanning 2,017 cM in F. × ananassa. These studies provide a dense genomic framework of subgenome-specific DNA markers for seamlessly cross-referencing genetic and physical mapping information and unifying existing chromosome nomenclatures. Using comparative genomics, we show that geographically diverse wild octoploids are effectively diploidized, nearly completely collinear, and retain strong macro-synteny with diploid progenitor species. The preservation of genome structure among allo-octoploid taxa is a critical factor in the unique history of garden strawberry, where unimpeded gene flow supported its origin and domestication through repeated cycles of interspecific hybridization.

Crop production is becoming an increasing challenge as the global population grows and the climate changes. Modern cultivated crop species are selected for productivity under optimal growth environments and have often lost genetic variants that could allow them to adapt to diverse, and now rapidly changing, environments. These genetic variants are often present in their closest wild relatives, but so are less desirable traits. How to preserve and effectively utilize the rich genetic resources that crop wild relatives offer while avoiding detrimental variants and maladaptive genetic contributions is a central challenge for ongoing crop improvement. This Essay explores this challenge and potential paths that could lead to a solution.

AbstractMany important traits in plants, animals, and microbes are polygenic and challenging to improve through traditional marker-assisted selection. Genomic prediction addresses this by incorporating all genetic data in a mixed model framework. The primary method for predicting breeding values is genomic best linear unbiased prediction, which uses the realized genomic relationship or kinship matrix (K σ g 2 h g 2K σ g 2 h g 2ZKH K ASV σ g 2 h g 2

AbstractGray mold, a disease of strawberry (FragariaananassaBotrytis cinerea

The development of genome-informed methods for identifying quantitative trait loci (QTL) and studying the genetic basis of quantitative variation in natural and experimental populations has been driven by advances in high-throughput genotyping. For many complex traits, the underlying genetic variation is caused by the segregation of one or more 'large-effect' loci, in addition to an unknown number of loci with effects below the threshold of statistical detection. The large-effect loci segregating in populations are often necessary but not sufficient for predicting quantitative phenotypes. They are, nevertheless, important enough to warrant deeper study and direct modelling in genomic prediction problems. We explored the accuracy of statistical methods for estimating the fraction of marker-associated genetic variance (p) and heritability ([Formula: see text]) for large-effect loci underlying complex phenotypes. We found that commonly used statistical methods overestimate p and [Formula: see text]. The source of the upward bias was traced to inequalities between the expected values of variance components in the numerators and denominators of these parameters. Algebraic solutions for bias-correcting estimates of p and [Formula: see text] were found that only depend on the degrees of freedom and are constant for a given study design. We discovered that average semivariance methods, which have heretofore not been used in complex trait analyses, yielded unbiased estimates of p and [Formula: see text], in addition to best linear unbiased predictors of the additive and dominance effects of the underlying loci. The cryptic bias problem described here is unrelated to selection bias, although both cause the overestimation of p and [Formula: see text]. The solutions we described are predicted to more accurately describe the contributions of large-effect loci to the genetic variation underlying complex traits of medical, biological, and agricultural importance.

Cultivated strawberry has a compelling domestication history: the earliest cultivars originated in western Europe as spontaneous hybrids between non-sympatric wild species imported from the New World. The origin of those early hybrids went undiscovered for nearly a half century before conscious domestication began. Here, Pincot, Ledda, Feldmann et al