Experimental analyses of disease-associated DNA variants have provided significant insights into the functional implications of sequence variation. However, such experiment-based approaches for identifying functional DNA variants from a pool with a large number of neutral variants are challenging. Computational biology has the opportunity to play an important role in the identification of functional DNA variants in large-scale genotyping studies, ultimately yielding new drug targets and biomarkers. This chapter outlines in silico methods to predict disease-associated functional DNA variants so that the number of DNA variants screened for association with disease can be reduced to those that are most likely to alter gene function. To explore possible relationships between genetic mutations and phenotypic variation, different computational methods like Sorting Intolerant from Tolerant (SIFT, an evolutionary-based approach), Polymorphism Phenotyping (PolyPhen, a structure-based approach) and PupaSuite are discussed for prioritization of high-risk DNA variants. The PupaSuite tool aims to predict the phenotypic effect of DNA variants on the structure and function of the affected protein as well as the effect of variants in the non-coding regions of the same genes. To further investigate the possible causes of disease at the molecular level, deleterious nonsynonymous variants can be mapped to 3D protein structures. An analysis of solvent accessibility and secondary structure can also be performed to understand the impact of a mutation on protein function and stability. This chapter demonstrates a 'real-world' application of some existing bioinformatics tools for DNA variant analysis. © 2011 Springer Science+Business Media, LLC.