Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences.

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.

Cataloging SCN resistance loci in North American public soybean breeding programs.

Soybean cyst nematode (SCN) is a destructive pathogen of soybeans responsible for annual yield loss exceeding $1.5 billion in the United States. Here, we conducted a series of genome-wide association studies (GWASs) to understand the genetic landscape of SCN resistance in the University of Missouri soybean breeding programs (Missouri panel), as well as germplasm and cultivars within the United States Department of Agriculture (USDA) Uniform Soybean Tests-Northern Region (NUST). For the Missouri panel, we evaluated the resistance of breeding lines to SCN populations HG 2.5.7 (Race 1), HG 1.2.5.7 (Race 2), HG 0 (Race 3), HG 2.5.7 (Race 5), and HG 1.3.6.7 (Race 14) and identified seven quantitative trait nucleotides (QTNs) associated with SCN resistance on chromosomes 2, 8, 11, 14, 17, and 18. Additionally, we evaluated breeding lines in the NUST panel for resistance to SCN populations HG 2.5.7 (Race 1) and HG 0 (Race 3), and we found three SCN resistance-associated QTNs on chromosomes 7 and 18. Through these analyses, we were able to decipher the impact of seven major genetic loci, including three novel loci, on resistance to several SCN populations and identified candidate genes within each locus. Further, we identified favorable allelic combinations for resistance to individual SCN HG types and provided a list of available germplasm for integration of these unique alleles into soybean breeding programs. Overall, this study offers valuable insight into the landscape of SCN resistance loci in U.S. public soybean breeding programs and provides a framework to develop new and improved soybean cultivars with diverse plant genetic modes of SCN resistance.

Genomic Variations Explorer (GenVarX): a toolset for annotating promoter and CNV regions using genotypic and phenotypic differences.

The rapid growth of sequencing technology and its increasing popularity in biology-related research over the years has made whole genome re-sequencing (WGRS) data become widely available. A large amount of WGRS data can unlock the knowledge gap between genomics and phenomics through gaining an understanding of the genomic variations that can lead to phenotype changes. These genomic variations are usually comprised of allele and structural changes in DNA, and these changes can affect the regulatory mechanisms causing changes in gene expression and altering the phenotypes of organisms. In this research work, we created the GenVarX toolset, that is backed by transcription factor binding sequence data in promoter regions, the copy number variations data, SNPs and Indels data, and phenotypes data which can potentially provide insights about phenotypic differences and solve compelling questions in plant research. Analytics-wise, we have developed strategies to better utilize the WGRS data and mine the data using efficient data processing scripts, libraries, tools, and frameworks to create the interactive and visualization-enhanced GenVarX toolset that encompasses both promoter regions and copy number variation analysis components. The main capabilities of the GenVarX toolset are to provide easy-to-use interfaces for users to perform queries, visualize data, and interact with the data. Based on different input windows on the user interface, users can provide inputs corresponding to each field and submit the information as a query. The data returned on the results page is usually displayed in a tabular fashion. In addition, interactive figures are also included in the toolset to facilitate the visualization of statistical results or tool outputs. Currently, the GenVarX toolset supports soybean, rice, and Arabidopsis. The researchers can access the soybean GenVarX toolset from SoyKB via https://soykb.org/SoybeanGenVarX/, rice GenVarX toolset, and Arabidopsis GenVarX toolset from KBCommons web portal with links https://kbcommons.org/system/tools/GenVarX/Osativa and https://kbcommons.org/system/tools/GenVarX/Athaliana, respectively.

Urea derivative MTU improves stress tolerance and yield in wheat by promoting cyclic electron flow around PSI.

Increasing crop productivity under optimal conditions and mitigating yield losses under stressful conditions is a major challenge in contemporary agriculture. We have recently identified an effective anti-senescence compound (MTU, [1-(2-methoxyethyl)-3-(1,2,3-thiadiazol-5yl)urea]) in in vitro studies. Here, we show that MTU delayed both age- and stress-induced senescence of wheat plants (Triticum aestivum L.) by enhancing the abundance of PSI supercomplex with LHCa antennae (PSI-LHCa) and promoting the cyclic electron flow (CEF) around PSI. We suppose that this rarely-observed phenomenon blocks the disintegration of photosynthetic apparatus and maintains its activity as was reflected by the faster growth rate of wheat in optimal conditions and under drought and heat stress. Our multiyear field trial analysis further shows that the treatment with 0.4 g ha-1 of MTU enhanced average grain yields of field-grown wheat and barley (Hordeum vulgare L.) by 5-8%. Interestingly, the analysis of gene expression and hormone profiling confirms that MTU acts without the involvement of cytokinins or other phytohormones. Moreover, MTU appears to be the only chemical reported to date to affect PSI stability and activity. Our results indicate a central role of PSI and CEF in the onset of senescence with implications in yield management at least for cereal species.

The Allele Catalog Tool: a web-based interactive tool for allele discovery and analysis.

BACKGROUND: The advancement of sequencing technologies today has made a plethora of whole-genome re-sequenced (WGRS) data publicly available. However, research utilizing the WGRS data without further configuration is nearly impossible. To solve this problem, our research group has developed an interactive Allele Catalog Tool to enable researchers to explore the coding region allelic variation present in over 1,000 re-sequenced accessions each for soybean, Arabidopsis, and maize. RESULTS: The Allele Catalog Tool was designed originally with soybean genomic data and resources. The Allele Catalog datasets were generated using our variant calling pipeline (SnakyVC) and the Allele Catalog pipeline (AlleleCatalog). The variant calling pipeline is developed to parallelly process raw sequencing reads to generate the Variant Call Format (VCF) files, and the Allele Catalog pipeline takes VCF files to perform imputations, functional effect predictions, and assemble alleles for each gene to generate curated Allele Catalog datasets. Both pipelines were utilized to generate the data panels (VCF files and Allele Catalog files) in which the accessions of the WGRS datasets were collected from various sources, currently representing over 1,000 diverse accessions for soybean, Arabidopsis, and maize individually. The main features of the Allele Catalog Tool include data query, visualization of results, categorical filtering, and download functions. Queries are performed from user input, and results are a tabular format of summary results by categorical description and genotype results of the alleles for each gene. The categorical information is specific to each species; additionally, available detailed meta-information is provided in modal popups. The genotypic information contains the variant positions, reference or alternate genotypes, the functional effect classes, and the amino-acid changes of each accession. Besides that, the results can also be downloaded for other research purposes. CONCLUSIONS: The Allele Catalog Tool is a web-based tool that currently supports three species: soybean, Arabidopsis, and maize. The Soybean Allele Catalog Tool is hosted on the SoyKB website ( https://soykb.org/SoybeanAlleleCatalogTool/ ), while the Allele Catalog Tool for Arabidopsis and maize is hosted on the KBCommons website ( https://kbcommons.org/system/tools/AlleleCatalogTool/Zmays and https://kbcommons.org/system/tools/AlleleCatalogTool/Athaliana ). Researchers can use this tool to connect variant alleles of genes with meta-information of species.

AccuCalc: A Python Package for Accuracy Calculation in GWAS.

The genome-wide association study (GWAS) is a popular genomic approach that identifies genomic regions associated with a phenotype and, thus, aims to discover causative mutations (CM) in the genes underlying the phenotype. However, GWAS discoveries are limited by many factors and typically identify associated genomic regions without the further ability to compare the viability of candidate genes and actual CMs. Therefore, the current methodology is limited to CM identification. In our recent work, we presented a novel approach to an empowered "GWAS to Genes" strategy that we named Synthetic phenotype to causative mutation (SP2CM). We established this strategy to identify CMs in soybean genes and developed a web-based tool for accuracy calculation (AccuTool) for a reference panel of soybean accessions. Here, we describe our further development of the tool that extends its utilization for other species and named it AccuCalc. We enhanced the tool for the analysis of datasets with a low-frequency distribution of a rare phenotype by automated formatting of a synthetic phenotype and added another accuracy-based GWAS evaluation criterion to the accuracy calculation. We designed AccuCalc as a Python package for GWAS data analysis for any user-defined species-independent variant calling format (vcf) or HapMap format (hmp) as input data. AccuCalc saves analysis outputs in user-friendly tab-delimited formats and also offers visualization of the GWAS results as Manhattan plots accentuated by accuracy. Under the hood of Python, AccuCalc is publicly available and, thus, can be used conveniently for the SP2CM strategy utilization for every species.

Natural and artificial selection of multiple alleles revealed through genomic analyses.

Genome-to-phenome research in agriculture aims to improve crops through in silico predictions. Genome-wide association study (GWAS) is potent in identifying genomic loci that underlie important traits. As a statistical method, increasing the sample quantity, data quality, or diversity of the GWAS dataset positively impacts GWAS power. For more precise breeding, concrete candidate genes with exact functional variants must be discovered. Many post-GWAS methods have been developed to narrow down the associated genomic regions and, ideally, to predict candidate genes and causative mutations (CMs). Historical natural selection and breeding-related artificial selection both act to change the frequencies of different alleles of genes that control phenotypes. With higher diversity and more extensive GWAS datasets, there is an increased chance of multiple alleles with independent CMs in a single causal gene. This can be caused by the presence of samples from geographically isolated regions that arose during natural or artificial selection. This simple fact is a complicating factor in GWAS-driven discoveries. Currently, none of the existing association methods address this issue and need to identify multiple alleles and, more specifically, the actual CMs. Therefore, we developed a tool that computes a score for a combination of variant positions in a single candidate gene and, based on the highest score, identifies the best number and combination of CMs. The tool is publicly available as a Python package on GitHub, and we further created a web-based Multiple Alleles discovery (MADis) tool that supports soybean and is hosted in SoyKB (https://soykb.org/SoybeanMADisTool/). We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. Finally, we identified a candidate gene for the pod color L2 locus and predicted the existence of multiple alleles that potentially cause loss of pod pigmentation. In this work, we show how a genomic analysis can be employed to explore the natural and artificial selection of multiple alleles and, thus, improve and accelerate crop breeding in agriculture.

A novel Synthetic phenotype association study approach reveals the landscape of association for genomic variants and phenotypes.

INTRODUCTION: Genome-Wide Association Studies (GWAS) identify tagging variants in the genome that are statistically associated with the phenotype because of their linkage disequilibrium (LD) relationship with the causative mutation (CM). When both low-density genotyped accession panels with phenotypes and resequenced data accession panels are available, tagging variants can assist with post-GWAS challenges in CM discovery. OBJECTIVES: Our objective was to identify additional GWAS evaluation criteria to assess correspondence between genomic variants and phenotypes, as well as enable deeper analysis of the localized landscape of association. METHODS: We used genomic variant positions as Synthetic phenotypes in GWAS that we named "Synthetic phenotype association study" (SPAS). The extreme case of SPAS is what we call an "Inverse GWAS" where we used CM positions of cloned soybean genes. We developed and validated the Accuracy concept as a measure of the correspondence between variant positions and phenotypes. RESULTS: The SPAS approach demonstrated that the genotype status of an associated variant used as a Synthetic phenotype enabled us to explore the relationships between tagging variants and CMs, and further, that utilizing CMs as Synthetic phenotypes in Inverse GWAS illuminated the landscape of association. We implemented the Accuracy calculation for a curated accession panel to an online Accuracy calculation tool (AccuTool) as a resource for gene identification in soybean. We demonstrated our concepts on three examples of soybean cloned genes. As a result of our findings, we devised an enhanced "GWAS to Genes" analysis (Synthetic phenotype to CM strategy, SP2CM). Using SP2CM, we identified a CM for a novel gene. CONCLUSION: The SP2CM strategy utilizing Synthetic phenotypes and the Accuracy calculation of correspondence provides crucial information to assist researchers in CM discovery. The impact of this work is a more effective evaluation of landscapes of GWAS associations.

Melissococcus plutonius Can Be Effectively and Economically Detected Using Hive Debris and Conventional PCR.

European foulbrood (EFB) is an infectious disease of honey bees caused by the bacterium Melissococcus plutonius. A method for DNA isolation and conventional PCR diagnosis was developed using hive debris, which was non-invasively collected on paper sheets placed on the bottom boards of hives. Field trials utilized 23 honey bee colonies with clinically positive symptoms and 21 colonies without symptoms. Bayes statistics were applied to calculate the comparable parameters for EFB diagnostics when using honey, hive debris, or samples of adult bees. The reliability of the conventional PCR was 100% at 6.7 × 103 Colony Forming Unit of M. plutonius in 1 g of debris. The sensitivity of the method for the sampled honey, hive debris, and adult bees was 0.867, 0.714, and 1.000, respectively. The specificity for the tested matrices was 0.842, 0.800, and 0.833. The predictive values for the positive tests from selected populations with 52% prevalence were 0.813, 0.833, and 0.842, and the real accuracies were 0.853, 0.750, and 0.912, for the honey, hive debris, and adult bees, respectively. It was concluded that hive debris can effectively be utilized to non-invasively monitor EFB in honey bee colonies.

Winter honeybee (Apis mellifera) populations show greater potential to induce immune responses than summer populations after immune stimuli.

In the temperate climates of central Europe and North America, two distinct honeybee (Apis mellifera) populations are found in colonies: short-living summer bees emerge in spring and survive until summer, whereas long-living winter bees emerge in late August and overwinter. Besides the difference in their life spans, each of these populations fulfils a different role in the colonies and individual bees have distinct physiological and immunological adaptations depending on their roles. For instance, winter worker bees have higher vitellogenin levels and larger reserves of nutrients in the fat body than summer bees. The differences between the immune systems of both populations are well described at the constitutive level; however, our knowledge of its inducibility is still very limited. In this study, we focus on the response of 10-day-old honeybee workers to immune challenges triggered in vivo by injecting heat-killed bacteria, with particular focus on honeybees that emerge and live under hive conditions. Responses to bacterial injections differed between summer and winter bees. Winter bees exhibited a more intense response, including higher expression of antimicrobial genes and antimicrobial activity, as well as a significant decrease in vitellogenin gene expression and its concentration in the hemolymph. The intense immune response observed in winter honeybees may contribute to our understanding of the relationships between colony fitness and infection with pathogens, as well as its association with successful overwintering.

Blue light suppression alters cytokinin homeostasis in wheat leaves senescing under shading stress.

Blue light (BL) suppression accelerates the senescence rate of wheat (Triticum aestivum L.) leaves exposed to shading. In order to study whether this effect involves the alteration of different cytokinin (CK) metabolites, CK-degradation, as well as the expression profile of genes responsible of CK-perception, -inactivation, -reactivation and/or -turnover, leaf segments of 30 day-old plants were placed in boxes containing bi-distilled water and covered with blue (B) or green (G) light filters, which supplied a similar irradiance but differed in the percentage of BL transmitted (G << B). A neutral (N) filter was used as control. When appropriate, different CK metabolites or an inhibitor of CK-degradation were added in order to alter the endogenous CK levels. A rapid decrement of trans-zeatin (tZ) and cis-zeatin (cZ) content was observed after leaf excision, which progressed at a higher rate in treatment G than in the control and B treatments. Senescence progression correlated with an accumulation of glycosylated forms (particularly cZ-derivatives), and an increment of CK-degradation, both of which were slowed in the presence of BL. On the contrary, CK-reactivation (analyzed through TaGLU1-3 expression) was delayed in the absence of BL. When different CK were exogenously supplied, tZ was the only natural free base capable to emulate the senescence-retarding effect of BL. Even though the signaling components involved in the regulation of senescence rate and CK-homeostasis by BL remain elusive, our data suggest that changes in the expression profile and/or functioning of the transcription factor HY5 might play an important role.

Does the Pollen Diet Influence the Production and Expression of Antimicrobial Peptides in Individual Honey Bees?

We investigated the importance of protein nutrition for honey bee immunity. Different protein diets (monofloral pollen of Helianthus spp., Sinapis spp., Asparagus spp., Castanea spp., a mixture of the four different pollen and the pollen substitute FeedbeeTM) were fed to honey bees in cages ad libitum. After 18 days of feeding, apidaecin 1 isoforms concentration in the thorax were measured using nanoflow liquid chromatography coupled with mass spectrometry. Expression levels of genes, coding for apidaecins and abaecin in the abdomen were determined using quantitative PCR. The results indicate that protein-containing nutrition in adult worker honey bees can trigger certain metabolic responses. Bees without dietary protein showed lower apidaecin 1 isoforms concentrations. The significantly lowest concentration of apidaecin 1 isoforms was found in the group that was fed no pollen diet when compared to Asparagus, Castanea, Helianthus, and Sinapis pollen or the pollen supplement FeedBeeTM. Expression levels of the respective genes were also affected by the protein diets and different expression levels of these two antimicrobial peptides were found. Positive correlation between concentration and gene expression of apidaecins was found. The significance of feeding bees with different protein diets, as well as the importance of pollen nutrition for honey bee immunity is demonstrated.