Identification of SNPs in rice GPAT genes and in silico analysis of their functional impact on GPAT proteins

SNPs are the most common nucleotide variations in the genome. Functional SNPs in the coding region, known as nonsynonymous SNPs (nsSNPs), change amino acid residues and affect protein function. Identifying functional SNPs is an uphill task as it is difficult to correlate between variation and phenotypes in association studies. Computational in silico analysis provides an opportunity to understand the SNPs functional impact to proteins and facilitate experimental approaches in understanding the relationship between the phenotype and genotype. Advancement in sequencing technologies contributed to sequencing thousands of genomes. As a result, many public databases have been designed incorporating this sequenced data to explore nucleotide variations. In this study, we explored functional SNPs in the rice GPAT family (as a model plant gene family), using 3000 Rice Genome Sequencing Project data. We identified 1056 SNPs, among hundred rice varieties in 26 GPATgenes, and filtered 98 nsSNPs. We further investigated the structural and functional impact of these nsSNPs using various computational tools and shortlisted 13 SNPs having high damaging effects on protein structure. We found that rice GPATgenes can be influenced by nsSNPs and they might have a major effect on regulation and function of GPAT genes. This information will be useful to understand the possible relationships between genetic mutation and phenotypic variation, and their functional implication on rice GPAT proteins. The study will also provide a computational pathway to identify SNPs in other rice gene families.