Recent advances in marker technologies have led to increasing interest in and investigation on the role of genetic alterations in disease etiology and progression. Allelic or mutational data are used in linkage disequilibrium (i.e., association) studies to identify genes conferring disease risk. The extent of risk conferred by these genetic loci can be immensely useful for disease prevention initiatives and population-based medical intervention. Lately, gene microarrays are used to compare expression levels in different types of diseases, with the goal to identify genes involved in causal or functional pathways leading to tumor or tumor progression. Statistical methods are intrinsic to the successful design of these studies, analysis of the resulting data, and interpretation of the results. This paper provides a brief introductory summary to some of the commonly used statistical methods in genetic studies.