Bogdan Pasaniuc, PhD

Professor of Genetics

• Director, Center for Computational Biomedicine, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
• Senior Fellow, Institute of Biomedical Informatics, Perelman School of Medicine
• Senior Fellow, Leonard Davis Institute of Health Economics, University of Pennsylvania
• Professor of Medicine (Translational Medicine and Human Genetics) , Department of Medicine, Perelman School of Medicine
• Professor of Pediatrics, Perelman School of Medicine
• Professor of Informatics in Biostatistics and Epidemiology , Perelman School of Medicine
• Professor, Department of Computer and Information Science, School of Engineering and Applied Science
• Professor, Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia (CHOP) Research Institute

Department: Genetics

Description of Research Expertise

Professor Bogdan Pasaniuc joined UPenn in 2024 as the founding Director of the Center for Computational Biomedicine (CCB). For the previous 12 years, Dr. Pasaniuc led research in statistical and computational genomics at UCLA across the Departments of Computational Medicine, Pathology and Laboratory Medicine, and Human Genetics. Dr. Pasaniuc’s research focuses genomically informed learning health systems, in which AI, genomic, multi-omic, clinical, and healthcare-delivery data are continuously integrated with real-world patient outcomes to improve predictive models, genomic interpretation, and clinical decision-making.

Dr. Pasaniuc leads a computational genomics for precision health research program that integrates AI with statistical genetics to understand the genetic architecture of human disease, with a particular focus on multi-omics, admixed and diverse populations, and precision-health biobanks linking genomic information with electronic health record data. Dr. Pasaniuc has developed and introduced widely used computational methods for multi-ancestry studies, AI for EHR-signatures, polygenic scoring, and multi-omic analyses, including transcriptome-wide association studies (TWAS), which use genetically predicted gene expression as a principled approach to identify genes underlying complex human diseases and traits, including schizophrenia, ovarian cancer, and prostate cancer.

Dr. Pasaniuc directs the Center for Computational Biomedicine (CCB), a home for quantitative and computational biosciences research, training, and education. Its affiliated faculty span multiple departments and a broad range of computational, statistical, genomic, and biomedical research areas. A hallmark of CCB faculty and their laboratories is a commitment to quantitative reasoning and to the development of innovative statistical and computational methods, often enabled by the latest genomic and biomedical technologies.

Dr. Pasaniuc directs the MyPennGenome (MPG) translational research program, which evaluates preventive whole-genome sequencing as a scalable, equitable, and medically effective strategy for disease interception in healthy adults. By returning medically actionable genomic results through an integrated healthcare workflow, MPG seeks to determine whether genome sequencing can enable earlier prevention, surveillance, diagnosis, and treatment before disease symptoms emerge. MPG builds on extensive evidence from basic science, precision-health initiatives, and genomic screening programs demonstrating that genome sequencing can identify medically actionable findings in generally healthy individuals.

Dr. Pasaniuc co-directs the Penn Medicine Data Commons (PMDC), a strategic partnership between the school and health system designed to democratize access to health-system data, advanced analytics, and AI platforms across the Penn Medicine community. PMDC aims to serve as the foundational data and AI layer for a learning health system, integrating clinical, genomic, imaging, and operational data to develop, validate, and deploy AI models and generate insights that improve biomedical research and patient care.

Dr. Pasaniuc co-leads the Center for Data Science (CDS) as part of the Penn-CHOP Clinical and Translational Science Award (CTSA), integrating major data science efforts across Penn and CHOP and developing systematic approaches to better leverage data science for clinical and translational research, thereby enabling lifespan research.

Dr. Pasaniuc has a strong interest in education and training serving as program co-director of the T32 Postdoctoral Training Program in Genomic Medicine at UPenn.

Selected Publications

• Ding Y, Hou K, Xu Z, Pimplaskar A, Petter E, Boulier K, Privé F, Vilhjálmsson BJ, Olde Loohuis LM, Pasaniuc B. : Polygenic scoring accuracy varies across the genetic ancestry continuum Nature 618(7966) Jun 2023
• Hou K, Ding Y, Xu Z, Wu Y, Bhattacharya A, Mester R, Belbin GM, Buyske S, Conti DV, Darst BF, Fornage M, Gignoux C, Guo X, Haiman C, Kenny EE, Kim M, Kooperberg C, Lange L, Manichaikul A, North KE, Peters U, Rasmussen-Torvik LJ, Rich SS, Rotter JI, Wheeler HE, Wojcik GL, Zhou Y, Sankararaman S, Pasaniuc B. : Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals Nat Genet 55(4) : 549-558, Apr 2023
• Johnson R, Stephens AV, Mester R, Knyazev S, Kohn LA, Freund MK, Bondhus L, Hill BL, Schwarz T, Zaitlen N, Arboleda VA, A Bastarache L, Pasaniuc B* Butte MJ* : Electronic health record signatures identify undiagnosed patients with common variable immunodeficiency disease Sci Transl Med 16(745) May 2024 Notes: 10.1126/scitranslmed.ade4510
• Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B. : Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification Nat Genet 54(1) Dec 2022
• Zhang MJ, Hou K, Dey KK, Sakaue S, Jagadeesh KA, Weinand K, Taychameekiatchai A, Rao P, Pisco AO, Zou J, Wang B, Gandal M, Raychaudhuri S, Pasaniuc B, Price AL. : Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data Nat Genet 54 : 1572–1580, Sept 2022
• Hou K, Bhattacharya A, Mester R, Burch KS, Pasaniuc B. : On powerful GWAS in admixed populations Nat Genet 53(12) : 1631-1633, Dec 2021
• Mancuso N, Freund MK, Johnson R, Shi H, Kichaev G, Gusev A, Pasaniuc B. : Probabilistic fine-mapping of transcriptome-wide association studies Nat Genet 51(4) : 675-682, Apr 2019
• Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BW, Jansen R, de Geus EJ, Boomsma DI, Wright FA, Sullivan PF, Nikkola E, Alvarez M, Civelek M, Lusis AJ, Lehtimäki T, Raitoharju E, Kähönen M, Seppälä I, Raitakari OT, Kuusisto J, Laakso M, Price AL, Pajukanta P, Pasaniuc B. : Integrative approaches for large-scale transcriptome-wide association studies Nat Genet 48(3) : 245-52, Feb 2016
• Hou K, Xu Z, Ding Y, Mandla R, Shi Z, Boulier K, Harpak A, Pasaniuc B : Calibrated prediction intervals for polygenic scores across diverse contexts Nat Genet 56(7) : 1386-1396, Jul 2024
• Johnson R, Ding Y, Bhattacharya A, Knyazev S, Chiu A, Lajonchere C, Geschwind DH, Pasaniuc B. : The UCLA ATLAS Community Health Initiative: Promoting precision health research in a diverse biobank Cell Genom 3(1) : 100243, Jan 2023
• Hou K, Burch KS, Majumdar A, Shi H, Mancuso N, Wu Y, Sankararaman S, Pasaniuc B. : Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture Nat Genet 51(8) : 1244-1251, Aug 2019