Computational Genomics Concept and Its Utilization

This chapter discusses the evolution of computational genomics from passive gene cataloguing to an engine for precision drug discovery. Reduced sequencing costs and resources such as the UK Biobank, GWAS Catalog, and GTEx have enabled population-scale genotype–phenotype mapping, framing naturally occurring experiments of nature that reveal target liability or protection. The narrative dissects complementary genomic approaches—genome-wide association studies (GWAS) for common variants, exome sequencing for rare coding mutations, and whole-genome sequencing for structural variation—illustrating how each informs target prioritization and safety assessment. Integration with bulk and single-cell transcriptomics localizes disease pathways to tissues and cell types, while CRISPR screens, DepMap, and LINCS perturbation signatures validate functional relevance. A detailed inventory of genetic and expression databases, analytic pipelines, and artificial intelligence (AI)-enhanced variant callers highlights the expanding computational toolkit. This chapter also explores AI’s role in accelerating variant interpretation, reducing error rates, and merging genomic data with electronic health records to forecast prognosis and treatment response. Structural genomics and network pharmacology case studies demonstrate how multi-omics insights guide virtual screening, repurposing, and polypharmacology assessment. Emerging pangenome references and diversity panels promise to close ancestral gaps and democratize genomic therapeutics.