Next generation sequencing (NGS) is a rapidly evolving technology that has revolutionized the field of genomics. It allows the rapid and cost-effective sequencing of a large amount of DNA simultaneously, making it a powerful tool for a wide range of applications, from genome-wide association studies to the discovery of disease biomarkers. However, the analysis of massive amounts of data generated by NGS remains a major challenge and there is a need for continued development of tools to optimize data processing and analysis. Variant calling is an important process of DNA sequencing analysis, which identifies variants present in the sequence data. Recently, many variant callers have been developed based on different statistical principles, such as binary and count-outcome models, as well as Bayesian approaches. In this poster, we will review the statistical methods employed by different variant callers and discuss their applications in the study of human diseases.