Symposium Speaker: Marylyn D. Ritchie, PhD, MS

Dr. Marylyn Ritchie is Professor in the Department of Biochemistry and Molecular Biology and Director, Center for Systems Genomics at the Pennsylvania State University.  Dr. Ritchie is also Director of the new Biomedical and Translational Informatics at Geisinger Clinic.  Dr. Ritchie’s research interests as a statistical geneticist include the development and application of novel statistical and computational methods to identify genetic variants associated with human diseases.  Dr. Ritchie’s lab places a special emphasis on the development of methods to detect gene-gene interactions, gene-environment interactions, and network/pathway effects associated with disease.  Dr. Ritchie has extensive experience in Big Data science and the use of electronic health records in genomic research.  Dr. Ritchie has been the electronic MEdical Records & GEnomics (eMERGE) Coordinating Center genomics lead for the past eight years.  Dr. Ritchie’s other accomplishments include being named Genome Technology’s “Rising Young Investigator” (2006), a Sloan Research Fellow (2010), and a Kavli Frontiers in Sciences fellow by the National Academy of Science (2011-2014).  Dr. Ritchie was most recently named Thomas Reuters Most Highly Cited Researchers in 2014.

Recent Publications

  1. Rajagopalan, A, Nguyen, TA, Guare, LA, Garao Rico, AL, Venkatesh, R, Caruth, L, Regeneron Genetics Center, Penn Medicine BioBank, Verma, A, Ritchie, MD et al.. DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs. medRxiv 2025; : . PubMed PMID:40894144 PubMed Central PMC12393615.
  2. Venkatesh, R, Cherlin, T, Penn Medicine BioBank, Ritchie, MD, Guerraty, MA, Verma, SS. Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction. medRxiv 2025; : . PubMed PMID:40894134 PubMed Central PMC12393626.
  3. Tavolinejad, H, Beeche, C, Dib, MJ, Pourmussa, B, Damrauer, SM, DePaolo, J, Azzo, JD, Salman, O, Duda, J, Gee, J et al.. Ascending Aortic Dimensions and Body Size: Allometric Scaling, Normative Values, and Prognostic Performance. JACC Cardiovasc Imaging 2025; : . PubMed PMID:40844449 .
  4. Kim, YG, Nam, Y, Westbrook, TM, Joo, J, Woerner, J, Deo, R, Ritchie, MD, Kim, D. Protein risk scores enable precise prediction of cardiovascular events in chronic kidney disease patients. medRxiv 2025; : . PubMed PMID:40778120 PubMed Central PMC12330408.
  5. Khan, A, Gould, PA, Luo, Y, Prens, EP, Wheless, L, Hung, AM, VA Million Veteran Program, Drivas, TG, Ritchie, MD, Saeidian, AH et al.. The polygenic architecture of hidradenitis suppurativa reveals signaling mechanisms that implicate epithelial remodeling. medRxiv 2025; : . PubMed PMID:40766141 PubMed Central PMC12324615.
  6. Kim, J, Chae, A, Duda, J, Borthakur, A, Rader, DJ, Gee, JC, Kahn, CE Jr, Penn Medicine BioBank, Witschey, WR, Sagreiya, H et al.. Automated characterization of abdominal MRI exams using deep learning. Sci Rep 2025; 15 (1): 27044. PubMed PMID:40715356 PubMed Central PMC12297695.
  7. Smit, RAJ, Wade, KH, Hui, Q, Arias, JD, Yin, X, Christiansen, MR, Yengo, L, Preuss, MH, Nakabuye, M, Rocheleau, G et al.. Polygenic prediction of body mass index and obesity through the life course and across ancestries. Nat Med 2025; : . PubMed PMID:40691366 .
  8. Woerner, J, Westbrook, TM, Joo, J, Shivakumar, M, Venkatesh, R, Cherlin, T, Jung, SH, Jeong, S, Maseda, D, McKeague, M et al.. Large-scale evaluation of proteomic and polygenic risk scores reveals complementary contributions to incident disease prediction. medRxiv 2025; : . PubMed PMID:40672481 PubMed Central PMC12265734.
  9. Beeche, C, Zhao, B, Tavolinejad, H, Pourmussa, B, Kim, J, Duda, J, Gee, J, Witschey, WR, Chirinos, JA, Penn Medicine BioBank et al.. Early Vascular Aging Determined by 3-Dimensional Aortic Geometry: Genetic Determinants and Clinical Consequences. Circulation 2025; : . PubMed PMID:40671674 PubMed Central PMC12278840.
  10. Beeche, C, Kim, J, Tavolinejad, H, Zhao, B, Sharma, R, Duda, J, Gee, J, Dako, F, Verma, A, Morse, C et al.. A Pan-Organ Vision-Language Model for Generalizable 3D CT Representations. medRxiv 2025; : . PubMed PMID:40630577 PubMed Central PMC12236870.
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Dr. Ritchie on Twitter

Dr. Ritchie on Twitter

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The Ritchie Lab Website

Curriculum vitae