Advances in high-throughput technologies and the pursuant accumulation of a vast array of new sequence and -omics level data offer new opportunity to gain insight into complex disease etiologies. Moreover, for complex traits, we increasingly recognize that the intergenic space between protein-coding genes (PCGs) – previously thought to be non-instrumental – contains highly ordered regulatory elements that control expression and function of PCGs and themselves can be actively transcribed molecules. Analytic paradigms that integrate and combine information from these emerging taxonomies and data elements are essential for focused inferential interrogation in this setting. This tutorial focuses on specific methods and R tools designed to uncover these complex, and largely uncharacterized relationships.
The overall goal of this tutorial is provided participants with fundamental knowledge, resources and a framework for staying abreast with novel developments in the field of statistical genomics. Upon completion of this tutorial, students will: (a) understand the core analytic components of a standard genome-wide association (GWA) study and be familiar with code to complete a “start-to-finish” GWA analysis using R; and (b) understand and be able to apply approaches for more comprehensive interrogation of regional and higher-order associations to discern the relative contributions of gene and regulatory elements.