Single Nucleotide Polymorphism (SNP) is a significant point mutation that happens often in genomes and has a wide range of applications. SNPs are the preferred marker in genetic analysis and are helpful in identifying genes linked to specific traits or diseases. The identification of functional SNPs for complex diseases or important economic traits is one of the active research areas in animals, especially in veterinary science. SNPs present in gene coding regions can alter the protein sequence; such SNPs are called missense or non-synonymous SNPs (nsSNPs). nsSNPs are important factors leading to the functional diversity of the encoded proteins. All nsSNPs are not structurally or functionally influencing, although many harmful variants can have an impact on the physiology of an organism. According to experimental investigations, one-third of nsSNP mutations are harmful therefore, the identification of deleterious nsSNPs is still a major challenge. Thus, in this post-genomic era, computational approaches are established as potential strategies for the screening of most deleterious nsSNPs and their structural and functional consequences. There are various computational tools available for the prediction of damaging or functional nsSNPs. In this chapter, we have provided systematic information about the use of such tools, for the screening of nsSNPs along with available databases. Moreover, we have provided comprehensive details about nsSNPs studied in livestock animals along with their effect on protein structural and conformational dynamics. The application of reported nsSNPs that serve as potential targets in veterinary science and livestock productivity has also been summarized.