02113nas a2200241   4500000000100000000000100001008004100002100001800043700001800061700001900079700001600098700001900114700001700133700001800150700001800168700001700186700001400203245007200217856004600289490000700335520151500342022001401857       2024                            d1 aDebasmita Pal1 aKevin Schaper1 aAddie Thompson1 aJessica Guo1 aPankaj Jaiswal1 aCurtis Lisle1 aLaurel Cooper1 aDavid LeBauer1 aAnne Thessen1 aArun Ross00aPost-GWAS Prioritization of Genome–Phenome Association in Sorghum  uhttps://www.mdpi.com/2073-4395/14/12/28940 v143 aGenome-wide association studies (GWAS) are widely used to infer the genetic basis of traits in organisms; however, selecting appropriate thresholds for analysis remains a significant challenge. In this study, we introduce the Sequential SNP Prioritization Algorithm (SSPA) to investigate the genetic underpinnings of two key phenotypes in Sorghum bicolor: maximum canopy height and maximum growth rate. Using a subset of the Sorghum Bioenergy Association Panel cultivated at the Maricopa Agricultural Center in Arizona, we performed GWAS with specific permissive-filtered thresholds to identify genetic markers associated with these traits, enabling the identification of a broader range of explanatory candidate genes. Building on this, our proposed method employed a feature engineering approach leveraging statistical correlation coefficients to unravel patterns between phenotypic similarity and genetic proximity across 274 accessions. This approach helps prioritize Single Nucleotide Polymorphisms (SNPs) that are likely to be associated with the studied phenotype. Additionally, we conducted a complementary analysis to evaluate the impact of SSPA by including all variants (SNPs) as inputs, without applying GWAS. Empirical evidence, including ontology-based gene function, spatial and temporal expression, and similarity to known homologs demonstrates that SSPA effectively prioritizes SNPs and genes influencing the phenotype of interest, providing valuable insights for functional genetics research.  a2073-4395