Open esources in Statistical Genomics arsenal of analytic strategies, R code and teaching materials

Defining best practices for genomic data pre-processing, analysis and post-analytic interrogation is essential for standardizing methods and ensuring reproducible findings. This website aims to facilitate statistical learning in the free, open-source R computing environment through various teaching materials as well as easily accessible tutorials. We provide well-documented code, methods, and bioinformatic resources that are commonly used in practice as well as descriptions on how to conduct your own genome-wide association (GWA) analysis. Topics covered include (but are not limited to) understanding genetic data formats, filtering data based on established criteria, understanding the UCSC Genome Browser and much more.

This growing resource hub equips users with teaching materials found under the statsTeachR tab. Each "module" contains slides covering basic concepts in the scope of genetic data analysis, as well as a series of lab assignments that walk the user through basic R coding, theory, and On Your Own exercises. The GWAS modules, for example, cover topics related to conducting your own GWA data analysis as well as methods for interpretting results.

Explore the tutorial tab to find our full tutorial in an easy to read and reproducible format. This feature, while similar to the modules, is unique in that it allows users to seemlessly run a full GWA analysis on real data without the extra bells and whistles of a lesson plan.