Biomedical and Translational Informatics Laboratory

Epistasis Discovery in Genetics and Epidemiology

The Epistasis Discovery in Genetics and Epidemiology (EDGE) conference focuses on the discussion and exploration of the impact of genetic interactions on complex traits and outcomes. The EDGE meeting yearly invites a varying group of experts in epistasis across multiple disciplines from animal models to biostatistics, providing a unique and synergistic environment for sharing ideas, identifying new topics of research, as well as the development of novel methodologies for understanding the impact of genetic interactions on complex traits and outcomes. As a result of EDGE, new manuscripts and algorithms have been developed and shared with the scientific community.

Publications as a result of meeting collaborations:

Ciesielski TH, Pendergrass SA, White MJ, Kodaman N, Sobota RS, Huang M, Bartlett J, Li J, Pan Q, Gui J, Selleck SB, Amos CI, Ritchie MD, Moore JH, Williams SM. Diverse convergent evidence in the genetic analysis of complex disease: coordinating omic, informatic, and experimental evidence to better identify and validate risk factors. BioData Min. 2014 Jun 30;7:10. doi: 10.1186/1756-0381-7-10. PM 25071867, PMC 4112852

Full Article Available Here:


Jason Moore

Professor and Director 
Institute of Biomedical Informatics
University of Pennsylvania

 RILow Marylyn Ritchie 1256
Marylyn D Ritchie

Professor and Chair 
Biomedical and Translational Informatics
Chief Research Informatics Officer 
Geisinger Health System

Meeting Coordinator

suzy unger

Suzy Unger

EDGE Meeting Coordinator
Research Project Manager
Ritchie Lab, Bioinformatics and Translational Informatics
Geisinger Health System,
State College, Pennsylvania

Contact Information: sgunger1 at




moorejh Thanks to all the #EDGE17 participants! I learned a lot from all the speakers and the fabulous discussions about #epistasis . On to #EDGE18 !
12:16PM Feb 11
beaulieujones Re: #edge17 discussion today @moorejh
10:35PM Feb 10
AlonKeinan YC: YETI2 builds on heterogeneity in effects between populations in a meta-analysis, can be applied to summary data alone #EDGE17
06:45PM Feb 10
AlonKeinan YC: Based on their YETI (phylogenY-aware Effect size Tests for Interactions) approach: #EDGE17
06:37PM Feb 10
AlonKeinan Fascinating last #EDGE17 talk by Yong Chen on combining "machine learning" & "classical statistical" approaches in interaction testing
06:33PM Feb 10