Martin Zhang
Carnegie Mellon University, PA·Genetics
Genome-wide association studies (GWAS) scan the DNA of many people to find genetic variants linked to disease. They have found thousands of such variants, but most lie in non-coding regions, stretches of DNA that do not encode proteins.
- Fields
Genetics
Computer Science
Mathematics & Statistics
- Topics
genome-wide association studies
fine-mapping
statistical genetics
- Grants
- 1 known grant, through May 2030
What they work on
- Use multimodal single-cell and spatial genomics data to pinpoint, with high precision, the cell types and cellular contexts most important for diseases identified through genome-wide association studies (GWAS).
- Combine functional regulatory data with causal inference to identify the specific disease-critical genes and cell types at individual GWAS loci, the genomic regions linked to disease.
- Develop graph foundation models, large machine learning models of biological networks, that integrate multi-scale, multi-modal functional genomics data to interpret GWAS loci comprehensively.
More about the research
That makes it hard to tell how a variant actually affects disease. New functional genomics experiments can measure many molecular features within specific cell types or even single cells. Examples include chromatin accessibility (how open DNA is to regulation), gene expression and regulatory links. Combining these data with GWAS could reveal how diseases arise. Current methods, however, usually use only one data type and rely on generic algorithms that ignore each data type's particular structure.
Why it matters
Most disease-linked genetic variants have unknown functions, which slows the search for treatments. Tools that connect these variants to the specific genes and cell types they affect could help researchers studying many heritable diseases find disease mechanisms. That could speed the development of therapeutic strategies. The tools and data will be public.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Clint Miller
University of Virginia, VA·Genetics
Determine how FHL5, a helper protein that tunes gene activity, causes vascular dysfunction through the actin cytoskeleton, the extracellular matrix and vessel constriction. The work will use human vascular cells and arteries studied ex vivo.
Anat Kreimer
Rutgers University-New Brunswick, NJ·Genetics
Develop methods that integrate massively parallel reporter assay (MPRA) data, which test thousands of DNA sequences at once, with other genomic assays and deep learning to reconstruct cell-type-specific gene regulatory networks and their condition-specific regulators.
Melissa Gymrek
University of California-San Diego, CA·Genetics
Develop scalable methods for tandem repeat (TR)-based GWAS that genotype repeats directly from UK Biobank and All of Us whole genome sequences, producing a catalog of TRs linked to complex traits.
The school
Carnegie Mellon University: 28 labs on PI Pages, $505M a year on research, 346 research doctorates a year.
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