Optimising desired gain indices to maximise selection response.

Introduction: In plant breeding, we often aim to improve multiple traits at once. However, without knowing the economic value of each trait, it is hard to decide which traits to focus on. This is where "desired gain selection indices" come in handy, which can yield optimal gains in each trait based on the breeder's prioritisation of desired improvements when economic weights are not available. However, they lack the ability to maximise the selection response and determine the correlation between the index and net genetic merit. Methods: Here, we report the development of an iterative desired gain selection index method that optimises the sampling of the desired gain values to achieve a targeted or a user-specified selection response for multiple traits. This targeted selection response can be constrained or unconstrained for either a subset or all the studied traits. Results: We tested the method using genomic estimated breeding values (GEBVs) for seven traits in a bread wheat (Triticum aestivum) reference breeding population comprising 3,331 lines and achieved prediction accuracies ranging between 0.29 and 0.47 across the seven traits. The indices were validated using 3,005 double haploid lines that were derived from crosses between parents selected from the reference population. We tested three user-specified response scenarios: a constrained equal weight (INDEX1), a constrained yield dominant weight (INDEX2), and an unconstrained weight (INDEX3). Our method achieved an equivalent response to the user-specified selection response when constraining a set of traits, and this response was much better than the response of the traditional desired gain selection indices method without iteration. Interestingly, when using unconstrained weight, our iterative method maximised the selection response and shifted the average GEBVs of the selection candidates towards the desired direction. Discussion: Our results show that the method is an optimal choice not only when economic weights are unavailable, but also when constraining the selection response is an unfavourable option.

Genetic Gain and Inbreeding in Different Simulated Genomic Selection Schemes for Grain Yield and Oil Content in Safflower.

Safflower (Carthamus tinctorius L.) is a multipurpose minor crop consumed by developed and developing nations around the world with limited research funding and genetic resources. Genomic selection (GS) is an effective modern breeding tool that can help to fast-track the genetic diversity preserved in genebank collections to facilitate rapid and efficient germplasm improvement and variety development. In the present study, we simulated four GS strategies to compare genetic gains and inbreeding during breeding cycles in a safflower recurrent selection breeding program targeting grain yield (GY) and seed oil content (OL). We observed positive genetic gains over cycles in all four GS strategies, where the first cycle delivered the largest genetic gain. Single-trait GS strategies had the greatest gain for the target trait but had very limited genetic improvement for the other trait. Simultaneous selection for GY and OL via indices indicated higher gains for both traits than crossing between the two single-trait independent culling strategies. The multi-trait GS strategy with mating relationship control (GS_GY + OL + Rel) resulted in a lower inbreeding coefficeint but a similar gain compared to that of the GS_GY + OL (without inbreeding control) strategy after a few cycles. Our findings lay the foundation for future safflower GS breeding.

Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat.

KEY MESSAGE: The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Right ventricular preload and afterload challenge induces contractile dysfunction and arrhythmia in isolated hearts of dystrophin-deficient male mice.

Duchenne muscular dystrophy (DMD) is an X-linked recessive myopathy due to mutations in the dystrophin gene. Diaphragmatic weakness in DMD causes hypoventilation and elevated afterload on the right ventricle (RV). Thus, RV dysfunction in DMD develops early in disease progression. Herein, we deliver a 30-min sustained RV preload/afterload challenge to isolated hearts of wild-type (Wt) and dystrophic (Dmdmdx-4Cv) mice at both young (2-6 month) and middle-age (8-12 month) to test the hypothesis that the dystrophic RV is susceptible to dysfunction with elevated load. Young dystrophic hearts exhibited greater pressure development than wild type under baseline (Langendorff) conditions, but following RV challenge exhibited similar contractile function as wild type. Following the RV challenge, young dystrophic hearts had an increased incidence of premature ventricular contractions (PVCs) compared to wild type. Hearts of middle-aged wild-type and dystrophic mice had similar contractile function during baseline conditions. After RV challenge, hearts of middle-aged dystrophic mice had severe RV dysfunction and arrhythmias, including ventricular tachycardia. Following the RV load challenge, dystrophic hearts had greater lactate dehydrogenase (LDH) release than wild-type mice indicative of damage. Our data indicate age-dependent changes in RV function with load in dystrophin deficiency, highlighting the need to avoid sustained RV load to forestall dysfunction and arrhythmia.

Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.

Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia. We developed an artificial intelligence platform to reduce the tissue preprocessing and histological assessment time through automated grossing recommendations, mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma as a model system, results demonstrate that this approach can address surgical laboratory efficiency bottlenecks for rapid and complete intraoperative margin assessment.

Mapping of a Stripe Rust Resistance Gene Yr72 in the Common Wheat Landraces AUS27506 and AUS27894 from the Watkins Collection.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is among the major threats to global wheat production. The common wheat landraces AUS27506 and AUS27894 displayed stripe rust resistance against several commercially prevailing Pst pathotypes. These genotypes were crossed with a stripe-rust-susceptible landrace AUS27229 to understand the inheritance of resistance and to determine the genomic location(s) of underlying gene(s). F3 generations of crosses AUS27506/AUS27229 and AUS27894/AUS27229 showed monogenic segregation for stripe rust resistance under greenhouse conditions. The absence of segregation for stripe rust response among the AUS27506/AUS27894-derived F3 population suggested that both genotypes carry the same gene. The stripe rust resistance gene carried by AUS27506 and AUS27894 was tentatively named YrAW4. A bulked segregant analysis placed YrAW4 in the long arm of chromosome 2B. The AUS27506/AUS27229 F3 population was enhanced to develop an F6 recombinant inbred line (RIL) population for detailed mapping of chromosome 2BL. DArT-based SSR, STS and SNP markers were employed to enrich the 2BL map. DArT-based STS markers sun481 and SNP marker IWB12294 flanked YrAW4 proximally (1.8 cM) and distally (1.2 cM), respectively. Deletion mapping placed sun481 in the deletion bin 2BL-5. All stripe rust resistance genes, previously located on chromosome 2BL, neither express an infection type like YrAW4, nor are they mapped in the deletion bin 2BL-5. Hence, YrAW4 represented a new locus and was formally named Yr72. The usefulness of the markers IWB12294 and sun481 in marker-assisted selection was demonstrated by the amplification of alleles that are different to that linked with Yr72 in 19 common wheat and two durum wheat cultivars.

Development of genomic resources for Rhodes grass (Chloris gayana), draft genome and annotated variant discovery.

Genomic resources for grasses, especially warm-season grasses are limited despite their commercial and environmental importance. Here, we report the first annotated draft whole genome sequence for diploid Rhodes grass (Chloris gayana), a tropical C4 species. Generated using long read nanopore sequencing and assembled using the Flye software package, the assembled genome is 603 Mbp in size and comprises 5,233 fragments that were annotated using the GenSas pipeline. The annotated genome has 46,087 predicted genes corresponding to 92.0% of the expected genomic content present via BUSCO analysis. Gene ontology terms and repetitive elements are identified and discussed. An additional 94 individual plant genotypes originating from three diploid and two tetraploid Rhodes grass cultivars were short-read whole genome resequenced (WGR) to generate a single nucleotide polymorphism (SNP) resource for the species that can be used to elucidate inter- and intra-cultivar relationships across both ploidy levels. A total of 75,777 high quality SNPs were used to generate a phylogenetic tree, highlighting the diversity present within the cultivars which agreed with the known breeding history. Differentiation was observed between diploid and tetraploid cultivars. The WGR data were also used to provide insights into the nature and evolution of the tetraploid status of the species, with results largely agreeing with the published literature that the tetraploids are autotetraploid.

Physical Mapping of QTLs for Root Traits in a Population of Recombinant Inbred Lines of Hexaploid Wheat.

Root architecture is key in determining how effective plants are at intercepting and absorbing nutrients and water. Previously, the wheat (Triticum aestivum) cultivars Spica and Maringa were shown to have contrasting root morphologies. These cultivars were crossed to generate an F6:1 population of recombinant inbred lines (RILs) which was genotyped using a 90 K single nucleotide polymorphisms (SNP) chip. A total of 227 recombinant inbred lines (RILs) were grown in soil for 21 days in replicated trials under controlled conditions. At harvest, the plants were scored for seven root traits and two shoot traits. An average of 7.5 quantitative trait loci (QTL) were associated with each trait and, for each of these, physical locations of the flanking markers were identified using the Chinese Spring reference genome. We also compiled a list of genes from wheat and other monocotyledons that have previously been associated with root growth and morphology to determine their physical locations on the Chinese Spring reference genome. This allowed us to determine whether the QTL discovered in our study encompassed genes previously associated with root morphology in wheat or other monocotyledons. Furthermore, it allowed us to establish if the QTL were co-located with the QTL identified from previously published studies. The parental lines together with the genetic markers generated here will enable specific root traits to be introgressed into elite wheat lines. Moreover, the comprehensive list of genes associated with root development, and their physical locations, will be a useful resource for researchers investigating the genetics of root morphology in cereals.

Mining the Australian Grains Gene Bank for Rust Resistance in Barley.

Global barley production is threatened by plant pathogens, especially the rusts. In this study we used a targeted genotype-by-sequencing (GBS) assisted GWAS approach to identify rust resistance alleles in a collection of 287 genetically distinct diverse barley landraces and historical cultivars available in the Australian Grains Genebank (AGG) and originally sourced from Eastern Europe. The accessions were challenged with seven US-derived cereal rust pathogen races including Puccinia hordei (Ph-leaf rust) race 17VA12C, P. coronata var. hordei (Pch-crown rust) race 91NE9305 and five pathogenically diverse races of P. striiformis f. sp. hordei (Psh-stripe rust) (PSH-33, PSH-48, PSH-54, PSH-72 and PSH-100) and phenotyped quantitatively at the seedling stage. Novel resistance factors were identified on chromosomes 1H, 2H, 4H and 5H in response to Pch, whereas a race-specific QTL on 7HS was identified that was effective only to Psh isolates PSH-72 and PSH-100. A major effect QTL on chromosome 5HL conferred resistance to all Psh races including PSH-72, which is virulent on all 12 stripe rust differential tester lines. The same major effect QTL was also identified in response to leaf rust (17VA12C) suggesting this locus contains several pathogen specific rust resistance genes or the same gene is responsible for both leaf rust and stripe rust resistance. Twelve accessions were highly resistant to both leaf and stripe rust diseases and also carried the 5HL QTL. We subsequently surveyed the physical region at the 5HL locus for across the barley pan genome variation in the presence of known resistance gene candidates and identified a rich source of high confidence protein kinase and antifungal genes in the QTL region.

Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes.

Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.

PlmCas12e (CasX2) cleavage of CCR5: impact of guide RNA spacer length and PAM sequence on cleavage activity.

Gene editing using CRISPR/Cas (clustered regularly interspaced palindromic repeats/CRISPR-associated) is under development as a therapeutic tool for the modification of genes in eukaryotic cells. While much effort has focused on CRISPR/Cas9 systems from Streptococcus pyogenes and Staphylococcus aureus, alternative CRISPR systems have been identified from non-pathogenic microbes, including previously unknown class 2 systems, adding to a diverse toolbox of CRISPR/Cas enzymes. The Cas12e enzymes from non-pathogenic Deltaproteobacteria (CasX1, DpeCas12e) and Planctomycetes (CasX2, PlmCas12e) are smaller than Cas9, have a selective protospacer adjacent motif (PAM), and deliver a staggered cleavage cut with a 5-7 nucleotide overhang. We investigated the impact of guide RNA spacer length and alternative PAM sequences on cleavage activity to determine optimal conditions for PlmCas12e cleavage of the cellular gene CCR5 (CC-Chemokine receptor-5). CCR5 encodes the CCR5 coreceptor used by human immunodeficiency virus-type 1 (HIV-1) to infect target cells. A 32 base-pair deletion in CCR5 (CCR5-[Formula: see text]32) is responsible for HIV-1 resistance and reported cures following bone marrow transplantation. Consequently, CCR5 has been an important target for gene editing utilizing CRISPR/Cas. We determined that CCR5 cleavage activity varied with the target site, spacer length, and the fourth nucleotide in the previously described PAM sequence, TTCN. Our analyses demonstrated a PAM preference for purines (adenine, guanine) over pyrimidines (thymidine, cytosine) in the fourth position of the CasX2 PAM. This improved understanding of CasX2 cleavage requirements facilitates the development of therapeutic strategies to recreate the CCR5-[Formula: see text]32 mutation in haematopoietic stem cells.

Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat.

Soil salinity can impose substantial stress on plant growth and cause significant yield losses. Crop varieties tolerant to salinity stress are needed to sustain yields in saline soils. This requires effective genotyping and phenotyping of germplasm pools to identify novel genes and QTL conferring salt tolerance that can be utilised in crop breeding schemes. We investigated a globally diverse collection of 580 wheat accessions for their growth response to salinity using automated digital phenotyping performed under controlled environmental conditions. The results show that digitally collected plant traits, including digital shoot growth rate and digital senescence rate, can be used as proxy traits for selecting salinity-tolerant accessions. A haplotype-based genome-wide association study was conducted using 58,502 linkage disequilibrium-based haplotype blocks derived from 883,300 genome-wide SNPs and identified 95 QTL for salinity tolerance component traits, of which 54 were novel and 41 overlapped with previously reported QTL. Gene ontology analysis identified a suite of candidate genes for salinity tolerance, some of which are already known to play a role in stress tolerance in other plant species. This study identified wheat accessions that utilise different tolerance mechanisms and which can be used in future studies to investigate the genetic and genic basis of salinity tolerance. Our results suggest salinity tolerance has not arisen from or been bred into accessions from specific regions or groups. Rather, they suggest salinity tolerance is widespread, with small-effect genetic variants contributing to different levels of tolerance in diverse, locally adapted germplasm.

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

Genomic prediction and selection response for grain yield in safflower.

In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272-0.531), and low correlations between grain yield and days to flowering (DF, -0.157-0.201). A 4%-20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties.

Quantifying the role of airborne transmission in the spread of COVID-19.

There is an ongoing debate on the different transmission modes of SARS-CoV-2 and their relative contributions to the pandemic. In this paper, we employ a simple mathematical model, which incorporates both the human-to-human and environment-to-human transmission routes, to study the transmission dynamics of COVID-19. We focus our attention on the role of airborne transmission in the spread of the disease in a university campus setting. We conduct both mathematical analysis and numerical simulation, and incorporate published experimental data for the viral concentration in the air to fit model parameters. Meanwhile, we compare the outcome to that of the standard SIR model, utilizing a perturbation analysis in the presence of multiple time scales. Our data fitting and numerical simulation results show that the risk of airborne transmission for SARS-CoV-2 strongly depends on how long the virus can remain viable in the air. If the time for this viability is short, the airborne transmission route would be inconsequential in shaping the overall transmission risk and the total infection size. On the other hand, if the infectious virus can persist in aerosols beyond a few hours, then airborne transmission could play a much more significant role in the spread of COVID-19.

An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse.

Advanced plant phenotyping techniques to measure biophysical traits of crops are helping to deliver improved crop varieties faster. Phenotyping of plants using different sensors for image acquisition and its analysis with novel computational algorithms are increasingly being adapted to measure plant traits. Thermal and multispectral imagery provides novel opportunities to reliably phenotype crop genotypes tested for biotic and abiotic stresses under glasshouse conditions. However, optimization for image acquisition, pre-processing, and analysis is required to correct for optical distortion, image co-registration, radiometric rescaling, and illumination correction. This study provides a computational pipeline that optimizes these issues and synchronizes image acquisition from thermal and multispectral sensors. The image processing pipeline provides a processed stacked image comprising RGB, green, red, NIR, red edge, and thermal, containing only the pixels present in the object of interest, e.g., plant canopy. These multimodal outputs in thermal and multispectral imageries of the plants can be compared and analysed mutually to provide complementary insights and develop vegetative indices effectively. This study offers digital platform and analytics to monitor early symptoms of biotic and abiotic stresses and to screen a large number of genotypes for improved growth and productivity. The pipeline is packaged as open source and is hosted online so that it can be utilized by researchers working with similar sensors for crop phenotyping.

CAS12e (CASX2) CLEAVAGE OF CCR5: IMPACT OF GUIDE RNA LENGTH AND PAM SEQUENCE ON CLEAVAGE ACTIVITY.

CRISPR/Cas is under development as a therapeutic tool for the cleavage, excision, and/or modification of genes in eukaryotic cells. While much effort has focused on CRISPR/Cas from Streptococcus pyogenes (SpCas9) and Staphylococcus aureus (SaCas9), alternative CRISPR systems have been identified using metagenomic datasets from non-pathogenic microbes, including previously unknown class 2 systems, adding to a diverse toolbox of gene editors. The Cas12e (CasX1, CasX2) endonucleases from non-pathogenic Deltaproteobacteria (DpeCas12e) and Planctomycetes (PlmCas12e) are more compact than SpCas9, have a more selective protospacer adjacent motif (PAM) requirement, and deliver a staggered cleavage cut with 5-7 base overhangs. We investigated varying guide RNA (spacer) lengths and alternative PAM sequences to determine optimal conditions for PlmCas12e cleavage of the cellular gene CCR5 (CC-Chemokine receptor-5). CCR5 encodes one of two chemokine coreceptors required by HIV-1 to infect target cells, and a mutation of CCR5 (delta-32) is responsible for HIV-1 resistance and reported cures following bone marrow transplantation. Consequently, CCR5 has been an important target for gene editing utilizing CRISPR, TALENs, and ZFNs. We determined that CCR5 cleavage activity varied with the target site, guide RNA length, and the terminal nucleotide in the PAM sequence. Our analyses demonstrated a PlmCas12e PAM preference for purines (A, G) over pyrimidines (T, C) in the fourth position of the CasX2 PAM (TTCN). These analyses have contributed to a better understanding of CasX2 cleavage requirements and will position us more favorably to develop a therapeutic that creates the delta-32 mutation in the CCR5 gene in hematopoietic stem cells.

An image dataset of diverse safflower (Carthamus tinctorius L.) genotypes for salt response phenotyping.

This article describes a dataset of high-resolution visible-spectrum images of safflower (Carthamus tinctorius L.) plants obtained from a LemnaTec Scanalyser automated phenomics platform along with the associated image analysis output and manually acquired biomass data. This series contains 1832 images of 200 diverse safflower genotypes, acquired at the Plant Phenomics Victoria, Horsham, Victoria, Australia. Two Prosilica GT RGB (red-green-blue) cameras were used to generate 6576 × 4384 pixel portable network graphic (PNG) images. Safflower genotypes were either subjected to a salt treatment (250 mM NaCl) or grown as a control (0 mM NaCl) and imaged daily from 15 to 36 days after sowing. Each snapshot consists of four images collected at a point in time; one of which is taken from above (top-view) and the remainder from the side at either 0°, 120° or 240°. The dataset also includes analysis output quantifying traits and describing phenotypes, as well as manually collected biomass and leaf ion content data. The usage of the dataset is already demonstrated in Thoday-Kennedy et al. (2021) [1]. This dataset describes the early growth differences of diverse safflower genotypes and identified genotypes tolerant or susceptible to salinity stress. This dataset provides detailed image analysis parameters for phenotyping a large population of safflower that can be used for the training of image-based trait identification pipelines for a wide range of crop species.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.