Department of Biomedical and Translational Informatics

The Department of Biomedical and Translational Informatics (BTI) was founded January 1, 2015, to develop and apply state-of-the-art technologies in informatics and genomics to link clinical information from electronic health records (EHR) with genomic information from the MyCode Community Health Initiative to make discoveries about the genetic architecture of common, complex disease.  In this era of precision medicine, Geisinger is well poised to make significant contributions to the discovery of important predictors of disease risk and treatment response.  BTI will build a faculty with the appropriate research background to tackle these important challenges.

Our Team

Staff Scientists

  • Vida Abedi, PhD
  • Mariusz Butkiewcz, PhD
  • Chris Haggerty, PhD
  • Matt Oetgens
  • Nicole Restrepo
  • Geetha Chittoor, PhD

Projects underway

eMERGE: electronic Medical Records & Genomics Network
Geisinger is one of nine sites that make up the electronic Medical Records and Genomics (eMERGE) Network, which uses genomics, statistics, ethics, informatics and clinical medicine to study the relationship between genetic variations and common human traits. Learn More

The MyCode Community Health Initiative has consented more than 150,000 Geisinger patients who will provide blood and/or tissue samples to be genetically sequenced. That data will be combined with each participant's medical record to provide Geisinger researchers with information to investigate new approaches to disease control, diagnosis and treatment. Learn More

The purpose of this research is (1) to develop and validate advanced methodologies for elucidating features and patterns in clinical data for integration with genomic and environmental data to explore the genetic architecture of complex traits; (2) to develop a strategy for Phenome-Wide Association Studies using low frequency genetic variants and the full spectrum of available clinical, environmental, and behavioral data; and (3)  to develop and apply data integration methods for maximizing signal to noise in complex analyses and to facilitate interpretation of results.  All of these tools will be applied in the context of obesity and its comorbid diseases.

Phenomic Analytics & Clinical Data Core

Provides collaborative phenotypic development and research data support.

Biostatistics Core

Provides biostatistical analysis support and is a resource for interpreting and understanding clinical data.

Pendergrass Lab

Focuses on high-throughput data analyses and data-mining projects for uncovering genetic architecture.