sarah-pendergrass

Assistant Professor

LOCATION(S)
Geisinger Research
155 Gibbs Street, #420
Rockville, MD 20850

spendergrass@geisinger.edu

https://www.pendergrasslab.com

Research Interests

Dr. Pendergrass is a Genetic Bioinformatician focusing on high-throughput data analysis and data-mining projects for uncovering the genetic architecture of complex traits. The data for these projects have come from a range of sources, where genotypic data has been coupled with the following: de-identified electronic health record data, population survey based data, clinical study data, and pharmacological study data. She has an interest in incorporating environmental exposure data in analyses of disease susceptibility, and analyses across ancestry. She has extensive experience developing novel methodologies and performing high-throughput analyses for discovery, such as those for Phenome-Wide Association Studies (PheWAS), an approach investigating the association between genetic variation and wide range of phenotypic variables.  She also has expertise in the development of unique software tools for data analysis, with software designed to enable researchers to access and analyze data in new ways. In addition, she has developed several unique software tools for data visualization: PhenoGram, PhenoGram-Genie, Synthesis-View, and PheWAS-View. Through her PhD she has expertise in gene-expression analyses and bioinformatics, with projects leveraging the complexity of gene-expression data for biomarker and biological discovery for the disease Systemic Sclerosis. Her masters work in Biomedical Engineering, and undergraduate degree in Physics, have provided her with additional technical and analytical expertise for complex data-driven projects.

Recent Publications

  • Dewey FE, Murray MF, Overton JD, Habegger L, Leader JB, Fetterolf SN, O’Dushlaine C, Hout CVV, Staples J, Gonzaga-Jauregui C, Metpally R, Pendergrass SA, Giovanni MA, Kirchner HL, Balasubramanian S, Abul-Husn NS, Hartzel DN, Lavage DR, Kost KA, Packer JS, Lopez AE, Penn J, Mukherjee S, Gosalia N, Kanagaraj M, Li AH, Mitnaul LJ, Adams LJ, Person TN, Praveen K, Marcketta A, Lebo MS, Austin-Tse CA, Mason-Suares HM, Bruse S, Mellis S, Phillips R, Stahl N, Murphy A, Economides A, Skelding KA, Still CD, Elmore JR, Borecki IB, Yancopoulos GD, Davis FD, Faucett WA, Gottesman O, Ritchie MD, Shuldiner AR, Reid JG, Ledbetter DH, Baras A, Carey DJ. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science. 2016 Dec 23;354(6319):aaf6814. PMID: 28008009
  • Bauer CR, Lavage D, Snyder J, Leader J, Mahoney JM, Pendergrass SA. Opening the Door to the Large Scale Use of Clinical Lab Measures for Association Testing: Exploring Different Methods for Defining Phenotypes. Pac Symp Biocomput Pac Symp Biocomput. 2016;22:356–367. PMID: 27896989
  • Verma A, Verma SS, Pendergrass SA, Crawford DC, Crosslin DR, Kuivaniemi H, Bush WS, Bradford Y, Kullo I, Bielinski SJ, Li R, Denny JC, Peissig P, Hebbring S, De Andrade M, Ritchie MD, Tromp G. eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants. BMC Med Genomics. 2016;9 Suppl 1:32. PMID: 27535653
  • Verma A, Basile AO, Bradford Y, Kuivaniemi H, Tromp G, Carey D, Gerhard GS, Crowe JE, Ritchie MD, Pendergrass SA. Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases. PloS One. 2016;11(8):e0160573. PMID: 27508393
  • Verma A, Leader JB, Verma SS, Frase AT, Wallace J, Dudek S, Lavage D, Van Hout C, Dewey FE, Penn J, Lopez A, Overton JD, Carey DJ, Ledbetter DH, Kirchner LH, Ritchie MD, Pendergrass SA. Integrating clinical laboratory measures and ICD-9 code diagnoses in phenome-wide association studies. Pac Symp Biocomput. 2016;

Education

MS in Biomedical Engineering, Thayer School of Engineering, Dartmouth College, 2001-2004
PhD in Genetics, Dartmouth College, 2004-2009
Postdoctoral Fellowship, Human Genetics, Vanderbilt University 2009-2011