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.

Christopher S Bond Life Sciences Center University of Missouri Columbia MO 65212 USA

Department of Biochemistry Faculty of Science Palacký University in Olomouc 78371 Olomouc Czech Republic

Department of Electrical Engineering and Computer Science University of Missouri Columbia MO 65212 USA

Department of Health Management and Informatics School of Medicine University of Missouri Columbia MO 65212 USA

Division of Plant Sciences University of Missouri Columbia MO 65201 USA

MU Data Science and Informatics Institute University of Missouri Columbia MO 65212 USA

Plant Genetics Research Unit United States Department of Agriculture Agricultural Research Service University of Missouri Columbia MO 65211 USA

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