Network and pathway analysis tools are traditionally used to interrogate gene expression data in order to understand the biological processes affected by a particular manipulation or disease/condition of interest. A systems-level understanding of the biological processes affected in particular disease states can allow one to identify candidates not only for pharmaceutical intervention but also for potential prognostic and diagnostic markers for the disease. However, network and pathway analyses are currently underutilized in the interpretation of large-scale genetic association study results. While simple monogenic, overtly Mendelian diseases are easily understood in the context of a single genetic aberration, the vast majority of diseases follow more complex patterns of inheritance and are influenced by a large number of genes and environmental stimuli. Genetic association studies investigating complex diseases that exploit network and pathway analysis tools can shed light on the genetic networks affected by particular genetic variations and sequence polymorphisms, just as gene expression studies can reveal genes dysregulated in a particular disease state. In this chapter, we describe the steps required to undertake network analysis of large-scale genetic association data - in particular single nucleotide polymorphism (SNP)-based genetic association data - in terms of data organization/preparation, SNP weighting schemes, and pathway analysis methods. We provide two illustrative examples that demonstrate the application of this approach: one involving the analysis of cancer tumor resequencing studies and another involving a genome-wide association study (GWAS).