Genetic variations are the heritable changes in DNA caused by mutation and can be present in both coding and non-coding region of the DNA. They provide great resources for the evolution of an organism in response to environmental and biological changes. Analysis of these variants (such as Single Nucleotide Polymorphism (SNPs), Indels, and other structural variants like Copy Number Variations (CNV)) thus, have a wide range of potential applications. These include identification of causative variants and the genes for genetic diseases, personalized genomics, population and evolutionary genetics, and forensic biology. This study represents two such applications of human variant analysis (particularly the analysis of SNPs and Indels). In the first chapter, SNPs were analyzed to understand the correlation between recombination rate and genetic diversity in the human genome, using a computational modeling program. A simulated human population was used to study the effect of various population level factors such as natural selective forces, the type of mutations, etc., on this correlation. In the second chapter, Next Generation Sequencing (in this case Whole Exome Sequencing) data and associated computational variant analysis tools and software were used to analyze both SNPs and Indels in the human genomes to find a lead candidate genetic variant responsible for Inherited Retinal Dystrophy in a family.