Software

You can find software developed by the group for new statistical methods and applications here.

  1. Orchid is a framework for performing machine learning while automatically integrating a large number of input features. From the manuscript “Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations" (Cario and Witte, Bioinformatics 2018; PMID: 29106441).
  2. CHARM is a Bayesian approach to jointly analyze common and rare variant effects on traits written by Niall Cardin in conjunction with the paper Cardin, Mefford, and Witte 2012. This approach takes into account the linkage disequilibrium among variants and allows for different effect sizes and directionality.
  3. Step-Up was written by Tom Hoffmann as a companion to the rare variant analysis paper Hoffmann, Marini, and Witte 2010. This provides a flexible, user-specified aggregation of rare variants and a data-directed optimization method to choose a subset of variants for aggregation and their weights.
  4. Valid is software to formally combine association results and LD and display them in the same figure, as described in Jorgenson, Kvale & Witte 2010.
  5. R code from the manuscript “Enriching Genome-wide Association Studies with Hierarchical Modeling” (Chen and Witte, Am J Hum Genet 2007;81:397-404).
  6. R code from the manuscript “Hierarchical modeling of linkage disequilibrium: genetic structure and spatial relations” (Conti and Witte, Am J Hum Genet 2003;72:351-363).
  7. Example SAS code and Macro from the manuscript “Multilevel Modeling in Epidemiology with GLIMMIX” (Witte et al., Epidemiology 2000;11:684-688).
  8. A SAS IML procedure from the manuscript ”Software for Hierarchical Modeling of Epidemiologic Data” (Witte et al., Epidemiology 1998;9:563-566).
  9. GAUSS procedures written by Sander Greenland. This is an extensive set of procedures for undertaking many different analyses.

Witte Lab GitHub

https://github.com/wittelab