Statistical Bioinformatics Seminar Series: Martin Jinye Zhang

Title: Polygenic enrichment distinguishes disease associations of individual cells in
single-cell RNA-seq data 

Speaker: Dr Martin Jinye Zhang (Harvard School of Public Health) 

Abstract: Single-cell RNA-sequencing (scRNA-seq) provides unique insights into the
pathology and cellular origin of disease.  We introduce scDRS, an approach that links
scRNA-seq with polygenic disease risk at single-cell resolution, independent of
annotated cell-types.  scDRS identifies cells exhibiting excess expression across
disease-associated genes implicated by genome-wide association studies (GWAS).  We
applied scDRS 74 diseases/traits and 1.3M single-cell gene-expression profiles across 31
tissues/organs.  Cell-type-level results broadly recapitulated known cell-type-disease
associations.  Individual-cell-level results identified subpopulations of
disease-associated cells not captured by existing cell-type labels, including T cell
subpopulations associated with inflammatory bowel disease, partially characterized by
their effector-like states; neuron subpopulations associated with schizophrenia,
partially characterized by their spatial locations; hepatocyte subpopulations associated
with triglyceride levels, partially characterized by their higher ploidy levels.  Genes
whose expression was correlated with the scDRS score across cells (reflecting
co-expression with GWAS disease-associated genes) were strongly enriched for
gold-standard drug target and Mendelian disease genes.  

About the speaker: Dr Martin Jinye Zhang is a research associate at Harvard School of
Public Health, advised by Prof.  Alkes Price.  He obtained a PhD in Electrical
Engineering from Stanford University, advised by Prof.  David Tse and Prof.  James Zou.
He is the recipient of the 2021 ASHG Epstein postdoc semifinalist award, the 2020 Top 50
Life and Biological Sciences Articles in Nature Communications, and the 2019 RECOMB best
paper award.  His research focuses on the development of statistical methods that
integrates GWAS and functional genomics to uncover the genetic basis of human disease.
Areas of interest include functional components of heritability, disease-critical
cellular contexts, and causal inference approaches to identify disease genes and
proteins.