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.

Christopher S Bond Life Sciences Center University of Missouri Columbia MO 65212 USA; MU Data Science and Informatics Institute University of Missouri Columbia MO 65212 USA

Department of Biochemistry Faculty of Science Palacky University Olomouc Olomouc 78371 Czech Republic

Department of Electrical Engineering and Computer Science University of Missouri Columbia MO 65212 USA; Christopher S Bond Life Sciences Center University of Missouri Columbia MO 65212 USA

Department of Electrical Engineering and Computer Science University of Missouri Columbia MO 65212 USA; Christopher S Bond Life Sciences Center University of Missouri Columbia MO 65212 USA; MU Data Science and Informatics Institute 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

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

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Cataloging SCN resistance loci in North American public soybean breeding programs

Natural and artificial selection of multiple alleles revealed through genomic analyses

AccuCalc: A Python Package for Accuracy Calculation in GWAS