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.

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.

Breeding programs are often constrained by the genetic diversity of the parental lines, even though these lines can be a rare source of unique alleles not found elsewhere. Therefore, identifying these rare alleles is crucial for keeping them in the breeding programs while introducing new genetic resources. The growing amount of whole genome sequenced data has made Genome‐wide Association Study (GWAS) dominant in investigations to find causal genes for all crops, including legumes. However, GWAS often fails to predict more than one causative mutation (CM) in multiple alleles of a single causal gene. Consequently, multiple alleles complicate breeding when not recognized by a single associated marker, which typically identifies only the most frequent CM and discriminates against the others. In this work, we focus on adopting recent applied genomics methods to identify multiple independent alleles and rare alleles in soybean as a model for other legumes. We predicted, identified, and confirmed a new and extremely rare CM for the loss of black pigmentation in the soybean seed coat and hilum color R gene, the Q25fs. The deletion of eight bases leads to a frameshift, a premature stop codon, and a truncated, nonfunctional protein. Our results also suggest a possibly new gene or an allele of the seed coat color inhibitor I gene. Using a soybean model, we demonstrate how applied genomics methods can accelerate pre‐breeding, and additionally, we discuss the potential for adopting these methods for application to other legumes.