Bogdan Research Group - at University of Pennsylvania

We are a research group in the Department of Genetics and Institute of Biomedical Informatics at UPenn. After 12 amazing years at UCLA (2012-2024), our research group moved back to the east coast at Penn to establish a research programme focused on computational genomics for precision health. We develop computational and statistical methods to understand the genetic basis of disease and to translate such findings into the clinical domain. We focus on methods for integrative genomics, biobank and electronic health record studies. We have a strong emphasis on translational genomic studies leveraging novel computational and statistical methods for EHR-linked biobank studies.

Some of our representative works include:

Calibrated prediction intervals for polygenic scores across diverse contexts. Hou et al. Nat Genet. 2024.
Polygenic scoring accuracy varies across the genetic ancestry continuum. Ding et al. Nature. 2023.
Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Hou et al. Nat Genet. 2023.
Probabilistic fine-mapping of transcriptome-wide association studies. Mancuso et al Nat Genet 2019
Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture. Hou*, Burch*, et al. Nat Genet. 2019.
Integrative approaches for large-scale transcriptome-wide association studies. Gusev et al. Nat Genet. 2016.

We are recruiting kind and motivated quantitatively oriented trainees at all levels (undergraduate, graduate and postdoctoral); contact Bogdan for details.

Bogdan's recently updated resume and short bio.

Latest News

6th Annual MGI Symposium 2026 | Bogdan Pasaniuc, Ph.D. Keynote Speaker Friday, September 18, 2026
The Michigan Genomics Initiative (MGI) Annual Symposium is an opportunity for faculty, researchers and students with interest in genomics and genetics to gather, learn and connect with one another.

Bogdan will discuss Biobanks for precision health promises and challenges
New Paper | Intergration of polygenic risk with single cell methylation data using met-scDRS Monday, September 21, 2026
met-scDRS is a our new statistical method that integrates GWAS with single-cell methylome to identify disease-associated cells and genes. By resolving polygenic risk at single-cell resolution, it identifies disease-relevant cell types, and functional pathways that may be missed by bulk analyses.
Genetics Friday Research Talks | Zixuan (Eleanor) Zhang on behalf of Brown/Pasaniuc Labs Friday, October 2, 2026
The postdoctoral and graduate student research in progress talks take place every Friday at 1 pm in the RRB Austrian Auditorium.

Eleanor will discuss "Efficient mapping of context-specific gentic effects in single-cell data using deep kernel mixed effect models"