Plant breeding aims to develop elite crop varieties appropriate for various environments with higher quality and quantity of production. Researchers use quantitative trait loci (QTL) mapping and association studies to identify regions in the genome responsible for the variation of the quantitative traits of interest. However, mapped regions do not always translate to functional proteins, which makes it challenging to identify genes associated with traits of interest. The biological functions of proteins are strongly dependent on their 3D structure. Alternatively, if proteins can be directly linked with the phenotypes, the effect of mutations on phenotypic changes can be assessed. Innovation of deep learning models in biology opens new avenues of exploration. AlphaFold is an AI system that predicts the 3D structure of a protein from its amino acid sequence with near experimental accuracy and was used in this study. Point mutations with a significant influence on the 3D structure of a protein can capture the effect on phenotypes through association study, and this provides insights into the regions that are of functional importance. In the current study, 534 plants were selected based on plant vigor, and 154 missense variants that change amino acid sequences, including 5 significant hits from previous study, were included. The changes in protein 3D structure were assessed by association with the phenotype. The analysis identified five significant associations, four of which were also identified in previous study of SNPs GWAS, however, a new fifth association was also identified which was annotated as disease resistance gene in Medicago truncatula. This study helps to associate SNPs that could be missed by GWAS due to stringent Bonferroni corrected p-values by providing a more robust filter for SNPs using features from predicted protein 3D structures.