Jiebiao Wang
University of Pittsburgh, PA·Mathematics & Statistics
Alzheimer's disease does not affect everyone, or every brain cell, in the same way. The data meant to explain those differences are messy: brain tissue samples blend many cell types together, single-cell measurements are expensive and cover few people, and patients differ in genetics, other illnesses and demographics.
- Fields
Mathematics & Statistics
Genetics
Neuroscience
- Topics
Alzheimer's disease
Down syndrome
trisomy 21
- Grants
- 2 known grants, through Nov 2027
What they work on
- Improve estimation of cell-type fractions in bulk brain tissue by building an ensemble over existing deconvolution methods and incorporating the hierarchical relationships among brain cell types.
- Identify cell-type-specific differentially methylated regions associated with Alzheimer's disease, modeling spatial correlation among neighboring CpG sites in AMP-AD brain cohorts.
- Integrate cell-type-specific gene expression, methylation and protein estimates through omics-wide association studies and causal mediation analysis, validating results against single-cell multi-omics data.
- Develop a Bayesian additive regression tree method to capture person-to-person variation in how APOE genotype affects amyloid-beta levels in Down syndrome, handling missing covariates.
- Build a heterogeneous causal mediation model to estimate how much of trisomy 21's effect on amyloid-beta is transmitted through cardiovascular disease, using clinical, biospecimen and imaging data from the ABC-DS cohort.
More about the research
Two projects develop statistical and machine-learning methods that squeeze more reliable biology out of existing large datasets, one focused on cell-type resolution and one on individual-level differences in people with Down syndrome.
Why it matters
Alzheimer's disease causes most dementia cases and is expected to affect 13.8 million Americans by 2050, while prevention and treatment remain only modestly effective. Pinpointing which genes and proteins act in which vulnerable brain cell types could sharpen the search for drug targets, and clarifying whether cardiovascular disease mediates Alzheimer's risk in Down syndrome could identify a treatable contributor in a high-risk group. The resulting open software lets other labs apply these analyses to their own data.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Karunya Kandimalla
University of Minnesota, MN·Pharmacology
Build NUGENT, a deep-learning framework that infers cell-type composition and cell-type-specific gene expression from brain tissue data, to recover signatures of scarce endothelial cells and pericytes that standard deconvolution methods miss.
Thierry Chekouo Tekougang
University of Minnesota, MN·Mathematics & Statistics
Develop Bayesian statistical learning models that capture both linear and nonlinear relationships across genomic, epigenomic, transcriptomic and proteomic measurements so several omics layers can be analyzed jointly.
Matteo Pellegrini
University of California-Los Angeles, CA·Computer Science
Process bulk, single-cell and spatial transcriptomic data for consortium projects using established pipelines, and return interpretable results to collaborators without extensive computational expertise.
The school
University of Pittsburgh: 83 labs on PI Pages, $1.5B a year on research, 403 research doctorates a year.
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