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
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Yale School of Medicine: Genetics Seminar Series | Bogdan presents recent insights from Machine Learning and potential impacts
Tuesday, October 6, 2026
Bogdan will discuss "Equitable Genomic Prediction in Medicine: Lessons From Machine Learning"
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New Paper Alert | Systematic comparison of phenome-wide admixture mapping and genome-wide association at biobank-scale
Thursday, September 24, 2026
Admixture mapping and GWAS provide complementary approaches for identifying genetic factors associated with disease in diverse populations. Together, they revealed dozens of genetic signals that were not detected by a single method alone.
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New Paper Alert | All of Us diversity and scale yield context-dependent improvements in polygenic prediction
Monday, September 14, 2026
Using genomic data from diverse populations, multi-ancestry polygenic risk scores improved prediction accuracy and reduced performance gaps across ancestry groups. These results underscore the value of representative datasets for advancing precision medicine.