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.

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.

Corrigendum: American Foulbrood in the Czech Republic: ERIC II Genotype of Paenibacillus Larvae Is Prevalent.

[This corrects the article DOI: 10.3389/fvets.2021.698976.].

American Foulbrood in the Czech Republic: ERIC II Genotype of Paenibacillus Larvae Is Prevalent.

American foulbrood (AFB) is a dangerous disease of honeybees (Apis mellifera) caused by the spore-forming bacterium Paenibacillus larvae. According to the ERIC (enterobacterial repetitive intergenic consensus) classification, five genotypes are distinguished, i.e., I, II, III, IV, and V, which differ in their virulence and prevalence in colonies. In the Czech Republic, AFB prevalence is monitored by the State Veterinary Administration; however, the occurrence of specific P. larvae genotypes within the country remains unknown. In this study, our aim was to genotype field P. larvae strains collected in the Czech Republic according to the ERIC classification. In total, 102 field isolates from colonies with AFB clinical symptoms were collected from various locations in the Czech Republic, and the PCR genotypization was performed using ERIC primers. We confirmed the presence of both ERIC I and II genotypes, while ERIC III, IV, and V were not detected. The majority of samples (n = 82, 80.4%) were identified as ERIC II, while the ERIC I genotype was confirmed only in 20 samples (19.6%). In contrast to other European countries, the ERIC II genotype is predominant in Czech honeybee colonies. The ERIC I genotype was mostly detected in border regions close to Poland, Slovakia, and Austria.

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.