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Geisinger
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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.

BTI is comprised of scientists with expertise in clinical informatics, bioinformatics, translational informatics, human genetics, genetic epidemiology and biostatistics.  In addition to research, BTI also houses several research cores, which are available throughout the organization to provide specific services.  These include a biostatistics core, phenomics and clinical data core, and a data science core. 

Leadership:

Marylyn D. Ritchie, PhD is the founding chair of the Department of Biomedical and Translational Informatics at Geisinger Health System.  Dr. Ritchie is a statistical and computational geneticist with a focus on understanding genetic architecture of complex human disease.  She has expertise in developing novel bioinformatics tools for complex analysis of big data in genetics, genomics and clinical databases, in particular in the area of pharmacogenomics.  Some of her methods include multifactor dimensionality reduction (MDR), the Analysis Tool for Heritable and Environmental Network Associations (ATHENA), and the biosoftware suite for annotating/ filtering variants and genomic regions as well as building models of biological relevance for gene-gene interactions and rare-variant burden/dispersion tests.  Dr. Ritchie has over 15 years of experience in the analysis of complex data and has authored over 250 publications.  She was named one of Genome Technology PIs of Tomorrow in 2006, awarded a Sloan Fellowship in 2010 and she was named one of Thomas Reuters Most Highly Cited Researchers for 2014.  

Investigators:

Kirchner, H. Lester, PhD
Pendergrass, Sarah A., PhD
Kim, Dokyoon, PhD

Post-doctoral Fellows:

Bauer, Christopher, PhD
Byrska-Bishop, Marta, PhD
Ingram, Wendy, PhD

Staff Scientist

Abedi, Vida, PhD

Staff:

Ritchie Lab

Bradford, Yukiko
Byrskabishop, Marta
Dudek, Scott
Frase, Alex
Li, Victoria
Lucas, Anastasia
Okula-Basile, Anna
Setia, Shelali
Unger, Susan
Verma, Anurag
Zhang, Blair

Pendergrass Lab

Bauer, Christopher
Cha, Elliott
Josyula, Navya

Kim Lab

Bang, Lisa
Jason Miller

Biostatistics Core

Berger, Andrea
Norberg, Cara
Sun, Haiyan
Young, Amanda

Data Science Core

Misra, Debdipto
Challa, Sashi
Lytle, Joshua
Person, Thomas
Shivakumar, Manu

Phenomic Analystics & Clinical Data Core

Adams, Lance
Biegley, Preston
Boris, Derek
Borthwick, Ken
Brown, Jason
Geise, Brandon
Hartzel, Dustin
Kolinovsky, Amy
Kost, Korey
Lavage, Dan
Leader, Joe
Lewis, Meredith
Manney, Catarina
Manus, Neil
Snyder, John

Administrative staff

Smith, Kristin - senior administrative assistant to the department
Wallace, John​ - IT Professional

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.

MyCode®
The MyCode® Community Health Initiative has consented more than 110,000 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.

PA-CURE
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.