BIOINFORMATICS-DRIVEN APPROACHES IN PUBLIC HEALTH GENOMICS: A REVIEW OF COMPUTATIONAL SNP AND MUTATION ANALYSIS

Abstract

The integration of bioinformatics in public health genomics has significantly advanced the capacity to identify, analyze, and interpret genetic variations such as single nucleotide polymorphisms (SNPs) and mutations, which play critical roles in disease susceptibility, progression, and treatment outcomes. This systematic review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, synthesizes the findings of 89 peer-reviewed articles published between 2010 and 2024. The review aimed to explore the evolution, application, and effectiveness of computational tools in SNP detection, variant annotation, mutation analysis, and their translational relevance in public health and clinical settings. Specifically, the review examines widely adopted variant calling tools (e.g., GATK, SAMtools, FreeBayes), annotation frameworks (e.g., ANNOVAR, SnpEff, VEP), and pathogenicity prediction algorithms (e.g., SIFT, PolyPhen-2, CADD, REVEL). It also reviews the role of genome-wide association studies (GWAS) and the increasing use of polygenic risk scores (PRS) for population-level risk stratification. A focused assessment of curated mutation databases such as ClinVar, HGMD, and OMIM underscores their role in diagnostic interpretation and clinical decision support. Additionally, population-specific SNP mapping and multi-omics integration approaches are analyzed to highlight emerging practices in understanding regulatory variants and non-coding genomic elements. The findings indicate a robust shift toward integrative, high-throughput, and standardized bioinformatics pipelines across both research and clinical domains. This review provides a consolidated perspective on the current landscape and methodological trends in bioinformatics-driven SNP and mutation analysis, offering critical insights for researchers, clinicians, and public health professionals working to leverage genomics in disease prevention, diagnosis, and precision healthcare.