Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.

AbstractBackground and ObjectivesThere has been a significant increase in the use of fermentation for protein modification by the food industry. This research aimed to investigate the use of solid‐state fermentation (SSF) by Aspergillus oryzae NRRL 5590 on pea and navy bean protein isolates (PPI and NBPI, respectively) to enhance their physicochemical and functional properties.FindingsThe impact of fermentation was more profound on PPI than NBPI with a higher degree of hydrolysis achieved for the former (9.3% vs. 4.4%). Fermented PPI had significantly increased protein content, surface charge and hydrophobicity, solubility, and foaming properties, but decreased emulsion stability. For NBPI, modifications were only observed for surface hydrophobicity and water hydration capacity (WHC), which both increased after fermentation. Overall, navy bean proteins were less susceptible to protein hydrolysis than pea proteins upon fermentation, possibly due to the phaseolin protein in navy bean.ConclusionsIn summary, fermentation may be used to enhance the solubility and foaming properties of PPI and WHC of NBPI for their use as ingredients in applications where such higher functionalities are favorable.Significance and NoveltyThe results provide insights into pulse protein modification by bioprocessing, specifically fermentation, and opportunities for potential value‐added applications for pea and navy bean proteins.

AbstractBackground and ObjectivesFermentation has been increasingly used as a “clean” processing technique to modify proteins. The goal of this research was to assess the use of solid‐state fermentation (SSF) by Aspergillus oryzae on pea (pea protein isolate [PPI]) and navy bean protein isolates (NBPI) for different time periods (0–48 or 0−72 h, respectively) and its impact on their nutrition and volatile profile.FindingsThe SSF process resulted in higher total phenolic content and lower protein digestibility, and consequently, the protein quality was reduced for both pulses. The quantity of the volatile compounds initially present in the samples did not change substantially after SSF; however, many new compounds were identified in fermented PPI, which have been reported to have pleasant sensory properties.ConclusionsThe protein quality of PPI or NBPI was not improved by A. oryzae SSF; however, the results suggest the potential for using SSF to positively modify the volatile profile of PPI.Significance and NoveltyThe findings of this research strengthened our knowledge base of fermented pulse ingredients with the use of protein isolates and navy beans, narrowing the somewhat limited gap for fermentation application to high protein substrates.

Tetraploid wheats (Triticum turgidum L.), including durum wheat (T. turgidum ssp. durum (Desf.) Husn.), are important crops with high nutritional and cultural values. However, their production is constrained by sensitivity to environmental conditions. In search of adaptive genetic signatures tracing historical selection and hybridization events, we performed genome scans on two datasets: (1) Durum Global Diversity Panel comprising a total of 442 tetraploid wheat and wild progenitor accessions including durum landraces (n=286), domesticated emmer (T. turgidum ssp. dicoccum (Schrank) Thell.; n=103) and wild emmer (T. turgidum ssp. dicoccoides (Korn. ex Asch. & Graebn.) Thell.; n=53) wheats genotyped using the 90K single nucleotide polymorphism (SNP) array, and (2) a second dataset comprising a total 121 accessions of nine T. turgidum subspecies including wild emmer genotyped with>100M SNPs from whole-genome resequencing. The genome scan on the first dataset detected six outlier loci on chromosomes 1A, 1B, 3A (n=2), 6A, and 7A. These loci harbored important genes for adaptation to abiotic stresses, phenological responses, such as seed dormancy, circadian clock, flowering time, and key yield-related traits, including pleiotropic genes, such as HAT1, KUODA1, CBL1, and ZFN1. The scan on the second dataset captured a highly differentiated region on chromosome 2B that shows significant differentiation between two groups: one group consists of Georgian (T. turgidum ssp.paleocolchicum A. Love & D. Love) and Persian (T. turgidum ssp. carthlicum (Nevski) A. Love & D. Love) wheat accessions, while the other group comprises all the remaining tetraploids including wild emmer. This is consistent with a previously reported introgression in this genomic region from T. timopheevii Zhuk. which naturally cohabit in the Georgian and neighboring areas. This region harbored several adaptive genes, including the thermomorphogenesis gene PIF4, which confers temperature-resilient disease resistance and regulates other biological processes. Genome scans can be used to fast-track germplasm housed in gene banks and in situ; which helps to identify environmentally resilient accessions for breeding and/or to prioritize them for conservation.

Durum wheat is more susceptible to Fusarium head blight (FHB) than other types or classes of wheat. The disease is one of the most devastating in wheat; it reduces yield and end-use quality and contaminates the grain with fungal mycotoxins such as deoxynivalenol (DON). A panel of 265 Canadian and European durum wheat cultivars, as well as breeding and experimental lines, were tested in artificially inoculated field environments (2019-2022, inclusive) and two greenhouse trials (2019 and 2020). The trials were assessed for FHB severity and incidence, visual rating index, Fusarium-damaged kernels, DON accumulation, anthesis or heading date, maturity date, and plant height. In addition, yellow pigment and protein content were analyzed for the 2020 field season. To capture loci underlying FHB resistance and related traits, GWAS was performed using single-locus and several multi-locus models, employing 13,504 SNPs. Thirty-one QTL significantly associated with one or more FHB-related traits were identified, of which nine were consistent across environments and associated with multiple FHB-related traits. Although many of the QTL were identified in regions previously reported to affect FHB, the QTL QFhb-3B.2, associated with FHB severity, incidence, and DON accumulation, appears to be novel. We developed KASP markers for six FHB-associated QTL that were consistently detected across multiple environments and validated them on the Global Durum Panel (GDP). Analysis of allelic diversity and the frequencies of these revealed that the lines in the GDP harbor between zero and six resistance alleles. This study provides a comprehensive assessment of the genetic basis of FHB resistance and DON accumulation in durum wheat. Accessions with multiple favorable alleles were identified and will be useful genetic resources to improve FHB resistance in durum breeding programs through marker-assisted recurrent selection and gene stacking.

Solvent-assisted SAGD (SA-SAGD) has been studied as an alternative to improve the efficiency of SAGD. This paper reports a new set of experimental data for SA-SAGD and analyzes the characteristics of bitumen-solvent mixing in the experiments using history-matched numerical models. The dimensions of the sand pack were 3 in. in diameter and 15 in. in length, and the sand pack was contained in a 25-L cylindrical pressure vessel. An annular void space surrounded the sand pack with a gap thickness of 1 in., within which a steady state flow of the injected vapor phase maintained the controlled pressure, temperature, and composition. Therefore, the gravity drainage in the sand pack occurred at a set of specified thermodynamic conditions for the surrounding annular steam chamber, unlike transient conditions near the edge of a steam chamber in larger-scale steam injection processes. In addition to SAGD as the base case, five sets of SA-SAGD were performed: 20 mol% C4, 40 mol% C4, 10 mol% C8, 20 mol% C8, and 10 mol% condensate coinjected with steam at 3500 kPa.The peak oil production rate was 9.84 cm3/min with SAGD, 14.61 cm3/min with 20 mol% C4-SAGD, 16.34 cm3/min with 40 mol% C4-SAGD, 31.33 cm3/min with 10 mol% C8-SAGD, 12.36 cm3/min with 20 mol% C8-SAGD, and 12.33 cm3/min with 10 mol% condensate-SAGD. SAGD was the least effective in bitumen gravity drainage, while the 10 mol% C8-SAGD was the most effective. Increasing the C4 molar concentration from 20 to 40 mol% slightly increased the peak rate; however, the peak rate in C8-SAGD significantly was decreased by increasing the C8 concentration from 10 to 20 mol%. This counter-intuitive result was reported in a few simulation studies with heavy solvents (e.g., C7 and C12) in the literature, but had not been experimentally confirmed before this research.Although the condensate contained 93 mol% C7-like pseudo component (C7, eq), the peak rate of 10 mol% condensate-SAGD was much smaller than that of 10 mol% C8-SAGD. These experimental observations highlight the practical importance of understanding the sensitivity of bitumen gravity drainage to heavy-solvent concentrations near the edge of a steam chamber in SA-SAGD.

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.

Field-grown crops rarely experience growth conditions in which yield can be maximized. Environmental stresses occur in combination, with advancements in crop tolerance further complicated by its polygenic nature. Strategic targeting of causal genes is required to meet future crop production needs. Here, we employed a systems biology approach in wheat (Triticum aestivum L.) to investigate physio-metabolic adjustments and transcriptome reprogramming involved in acclimations to heat, drought, salinity and all combinations therein. A significant shift in magnitude and complexity of plant response was evident across stress scenarios based on the agronomic losses, increased proline concentrations and 8.7-fold increase in unique differentially expressed transcripts (DETs) observed under the triple stress condition. Transcriptome data from all stress treatments were assembled into an online, open access eFP browser for visualizing gene expression during abiotic stress. Weighted gene co-expression network analysis revealed 152 hub genes of which 32% contained the ethylene-responsive element binding factor-associated amphiphilic repression (EAR) transcriptional repression motif. Cross-referencing against the 31 DETs common to all stress treatments isolated TaWRKY33 as a leading candidate for greater plant tolerance to combinatorial stresses. Integration of our findings with available literature on gene functional characterization allowed us to further suggest flexible gene combinations for future adaptive gene stacking in wheat. Our approach demonstrates the strength of robust multi-omics-based data resources for gene discovery in complex environmental conditions. Accessibility of such datasets will promote cross-validation of candidate genes across studies and aid in accelerating causal gene validation for crop resiliency.

Pyramiding multiple resistant genes has been proposed as the most effective way to control wheat rust diseases globally. Identifying the most effective pyramids is challenged by the large pool of rust resistance genes and limited information about their mechanisms of resistance and interactions. Here, using a high-density genetic map, a double haploid population, and multi-rust field testing, we aimed to systematically characterize the most effective gene pyramids for rust resistance from the durable multi-rust resistant CIMMYT cultivar Parula. We revealed that the Parula resistance gene pyramid contains Lr34/Yr18/Sr57 (Lr34), Lr46/Yr29/Sr58 (Lr46), Lr27/Yr30/Sr2 (Sr2), and Lr68. The efficacy, magnitude of effect, and interactions varied for the three rust diseases. A subpopulation mapping approach was applied to characterize the complex interactions of the resistance genes by controlling for the effect of Lr34. Using this approach, we found that Lr34 and Lr68 have a strong additive effect for leaf rust, whereas no additive effects were observed for any rusts between Lr34 and Lr46. Lr34 combined synergistically with Sr12 from Thatcher for stem rust, whereas the additive effect of Lr34 and Sr2 was dependent on the type of rust and environment. Two novel leaf rust quantitative trait loci (QTLs) from Parula were identified in this study, a stable QTL QLr-7BS and QLr-5AS, which showed Lr34 dependent expression. With these findings, we propose combining two to three high-value genes from Canadian wheat (e.g., Sr12 from Thatcher) with a foundational multi-adult plant resistance cassette for desirable and durable resistance to all three rusts in Canadian wheat.