Hongzhe Lee

What they work on

1. Develop transfer learning methods that borrow information across ancestry groups and tissues to improve polygenic risk score prediction and increase power to detect trait-associated variants in minority populations.
2. Build meta-learning models that predict ethnicity- and tissue-specific gene expression to strengthen transcriptome-wide association analysis, and colocalization methods combining GTEx expression data with GWAS summary statistics to nominate causal disease genes.
3. Create phylogenomics-based association methods that use a set of universal microbial marker genes and the evolutionary tree to relate metagenomic sequencing data to host traits in large human microbiome studies.
4. Develop phylogenetic-Ising and change-point Ising models to quantify the energy landscape and stability of microbial communities, and time-invariant Ising models for consensus taxon-taxon interactions from longitudinal sampling.
5. Estimate age-dependent, tissue- and cell-type-specific gene expression curves from human dGTEx data using nonparametric B-spline regression, and test which genetic variants shift expression distributions within each tissue.
6. Develop statistical warping methods to align gene expression trajectories between humans and non-human primates, identifying genes under stabilizing selection and quantifying interspecies divergence by tissue and developmental stage.
7. Carry out bioinformatic processing and statistical analysis of human virome data, including long-read sequencing for contig assembly and phage DNA modification, HiC data to assign viruses to hosts, and ViromeQC checks on purification quality.
8. Build a virome database integrating sequencing, imaging, and computational results, and coordinate with the consortium data center on common formats, harmonized workflows, and submission to public repositories.
9. Provide study design, database, and biostatistics support for human and translational digestive and liver disease research spanning multiple omics data types through the Biomedical Data Science Core.

More about the research

Why it matters

Better statistics for genomic data could improve genomics-based risk prediction and point to causal genes and pathways in Alzheimer's disease, cardiometabolic syndrome, and chronic kidney disease, with transfer learning aimed specifically at closing the accuracy gap for minority populations underrepresented in genetic studies. Microbiome methods may clarify how community stability relates to childhood obesity, kidney disease progression, and response to inflammatory bowel disease treatment, informing microbiome-based prevention. Developmental and virome analyses add reference knowledge about gene regulation in early life and the viruses humans carry.

Known grants

From NIH and NSF research grants. Labs often have other funding we can't see.

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The school

University of Pennsylvania: 90 labs on PI Pages, $2.2B a year on research, 750 research doctorates a year.

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