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

  • 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
  • Biofilter 2.2.0: The latest software update for Biofilter is ready for download. This release resolves the following issues:
    • Critical bug fix: We have identified a bug in Biofilter 2.0 which may have resulted in incorrect SNP-gene annotations and SNP-SNP models for users who used loki-build.py to update dbSNP and/or Entrez Gene sources between January 1st and May 22 of this year. The bug is fixed in Biofilter version 2.2.0

Recent Publications

  • eMERGEing progress in genomics-the first seven years Dana C. Crawford, David R. Crosslin, Gerard Tromp, Iftikhar J. Kullo, Helena Kuivaniemi, M. Geoffrey Hayes, Joshua C. Denny, William S. Bush, Jonathan L. Haines, Dan M. Roden, Catherine A. McCarty, Gail P. Jarvik, Marylyn D. Ritchie, 5, 184, Frontiers in Genetics, 1664-8021, 2014, PMID: 24987407 PMCID: PMC4060012,
  • Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors Timothy H. Ciesielski, Sarah A. Pendergrass, Marquitta J. White, Nuri Kodaman, Rafal S. Sobota, Minjun Huang, Jacquelaine Bartlett, Jing Li, Qinxin Pan, Jiang Gui, Scott B. Selleck, Christopher I. Amos, Marylyn D. Ritchie, Jason H. Moore, Scott M. Williams, 7, 10, BioData Mining, 1756-0381, 2014, PMID: 25071867 PMCID: PMC4112852,
  • Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index Robert M. Cronin, Julie R. Field, Yuki Bradford, Christian M. Shaffer, Robert J. Carroll, Jonathan D. Mosley, Lisa Bastarache, Todd L. Edwards, Scott J. Hebbring, Simon Lin, Lucia A. Hindorff, Paul K. Crane, Sarah A. Pendergrass, Marylyn D. Ritchie, Dana C. Crawford, Jyotishman Pathak, Suzette J. Bielinski, David S. Carrell, David R. Crosslin, David H. Ledbetter, David J. Carey, Gerard Tromp, Marc S. Williams, Eric B. Larson, Gail P. Jarvik, Peggy L. Peissig, Murray H. Brilliant, Catherine A. McCarty, Christopher G. Chute, Iftikhar J. Kullo, Erwin Bottinger, Rex Chisholm, Maureen E. Smith, Dan M. Roden, Joshua C. Denny, 5, 250, Frontiers in Genetics, 1664-8021, 2014, PMID: 25177340 PMCID: PMC4134007,