Next generation sequencing exome data analysis aids in the discovery of SNP and INDEL patterns in Parkinson's disease

Whole exome sequencing is an adept method to reveal novel and disease-related SNPs and INDELs as it screen the actionable areas of the genome. We evaluated the exome sequenced datasets of patients with Parkinson's disease (PD) in South African ethnic origin. The primary focus of this study was to discover the SNPs and INDELs patterns responsible for PD. The variant discovery was performed with genome analysis tool kit best practices variant detection pipelines. The SNPs were linked to the genes and categorized based on the filter-based annotation from ANNOVAR. We identified a total of 7955 SNPs and 9952 INDELs in all seven datasets together. A total of 130 missense nsSNPs were prioritized based on its damaging effect predicted from SIFT and Polyphen2 annotation. We noticed a novel nsSNP rs111655870 in gene LRRK2 that shows the mutation of a Leucine to Phenylalanine at position 208 which can alter the protein function. The study also filtered seven nsSNPs in genes NAGA, SULT4A1, MYH8, FLNA, TPM3, ATP13A1, CLN8 that have potentially deleterious effects predicted by various computational tools. This analysis suggested that the above filtered nsSNPs and INDELs have a functional impact and provide the footing for genetic studies related to PD. Further screening of these variations provides deeper insight for molecular mechanism of disease progression.