Introduction: Individual host susceptibility to coronavirus disease 2019 (COVID-19) can be attributed in part to single-nucleotide polymorphisms (SNPs), which may be exist at exonic sites of the genome. The objective of this work was to analyze, in silico, the functional and structural impact of exonic SNPs that are related to susceptibility to COVID-19 in the literature. Methods: Literature data were retrieved from PubMed and Science Direct in relation to COVID-19 risk-associated SNPs, and a separate analysis was performed between synonymous (sSNP) and non-synonymous (nsSNP) SNPs. To characterize the sSNPs, the following predictions were made: effects on mRNA structure (with RNAfold; CycleFold; Kinefold); splicing effects on mRNA (MaxEnt Scan; Ex Skip); and effects on miRNA binding (TargetScan Score). Regarding the nsSNPs, a functional analysis of protein damage (with SIFT, PolyPhen 2, PhD-SNP, SNPs&GO, Predict SNP 2) was performed. After passing the pathogenicity criteria, 8 nsSNPs were selected to predict their impacts on stability (CUPSAT), functionality, and residual evolution (MutPred, ConSurf). Results: The sample consisted of 16 exonic SNPs, 4 sSNP, and 12 nsSNP. Among the sSNPs, the SNP rs12252 of IFITM3 had the greatest potential impact on mRNA structure, alternative splicing, and miRNA binding and indicated a moderate impact on post-transcriptional regulation. Regarding the nsSNPs, the TYK2 SNP rs34536443 was predicted to be deleterious/damaging by all the tools used. The SNPs predicted to be destabilizing by CUPSAT, such as the PLSCR1 SNP rs343320, appear to have a greater negative impact on protein stability. The molecular, structural, and evolutionary impacts of each SNP were described. Conclusion: A total of nine exonic SNPs (one sSNP and eight nsSNPs) were indicated here as potential candidates for further in vivo studies for COVID-19, as they may alter protein stability, interactions, and functional motifs that may be associated with antiviral response pathways.