Biomedical and Translational Informatics Laboratory

About Lab Research

The mission of the Ritchie Lab is to improve our understanding of the underlying genetic architecture of common diseases such as cancer, diabetes, cardiovascular disease, and pharmacogenomic traits among others. The approaches we explore will involve the development and application of new statistical and computational methods with a focus on the detection of gene-gene interactions, gene-environment interactions, and network and/or pathway effects associated with human disease.  Systems Genomics approaches, which involve the integration of multiple types of ‘omics data, is also a driving focus of the laboratory.  These meta-dimensional approaches hold the promise of providing a more comprehensive view of genetic and genomic information. 

All of these tools and methodologies that the Ritchie Lab develops focus on Big Data applications and emphasize improvements in visual analytics as we embrace the new horizons of genomic information.

Latest Software Releases

  • BioBin 2.3.0:  The latest update for BioBin is now available.  New in this version:
    • Improved genomic build detection and handling
    • Improved output formatting
    • New runtime options to better control the processing of sample, phenotype and VCF files
  • PLATO 2.0.0:  The latest update for PLATO is now available.  This version is a complete rewite of PLATO, which provides the following key benefits:
    • Advanced model generation for regression models
    • Added support for more input formats, including VCF and Beagle
    • Enhanced parallelization options, including MPI

Recent Publications

Verma A, Bradford Y, Dudek SM, Verma SS, Pendergrass SA, Ritchie MD. A simulation study investigating power of Phenome-Wide Association Studies. BMC Bioinformatics, 2018 Apr 4;19(1):120. doi: 10.1186/s12859-018-2135-0. PMID:29618318

Verma SS, Josyula N, Verma A, Zhang X, Veturi Y, Dewey FE, Hartzel DN, Lavage DR, Leader J, Ritchie MD, Pendergrass SA. Rare variants in drug target genes contributing to complex diseases, phenome-wide. Sci Rep. 2018 Mar 15;8(1):4624. doi: 10.1038/s41598-018-22834-4. PMID:29545597; PMCID:PMC5854600

Verma A, Lucas A, Verma SS, Zhang Y, Josyula N, Khan A, Hartzel DN, Lavage DR, Leader J, Ritchie MD, Pendergrass SA. PheWAS and Beyond: The Landscape of Associations with Medical Diagnoses and Clinical Measures across 38,662 Individuals from Geisinger. Am J Hum Genet. 2018 Mar 19. pii: S0002-9297(18)30062-4. doi: 10.1016/j.ajhg.2018.02.017. PMID:29606303

Basile AO, Ritchie MD. Informatics and machine learning to define the phenotype.  Expert Rev Mol Diagn. 2018 Mar;18(3):219-226. doi: 10.1080/14737159.2018.1439380. Epub 2018 Feb 16. PMID:29431517

Verma SS, Ritchie MD. Another Round of "Clue" to Uncover the Mystery of Complex Traits. Genes (Basel). 2018 Jan 25;9(2). pii: E61. PMID: 29370075