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

Abstract

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution; scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N=341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states; subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region; and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

Integrative Single-Cell Meta-Analysis Reveals Disease-Relevant Vascular Cell States and Markers in Human Atherosclerosis

“…Given the limitations of relying solely on cluster-specific genes, we used an orthogonal approach to determine GWAS trait enrichment at single-cell resolution. Overall, estimation of single-cell disease relevance scores (scDRSs) 73 corroborated our findings using level 1 annotations but also prioritized pro-inflammatory macrophages and foamy macrophages in WBC counts and Alzheimer's disease (AD), respectively (Figure 4E). Beyond showing that fibromyocytes and foam-like SMCs had slightly larger weights compared to other SMC clusters, this analysis also revealed CAD enrichments in ECs and foamy macrophages, which could have been previously missed when surveying only highly specific genes.…”

“…The black line depicts the FDR significance threshold (FDR < 0.05 at -log10(p) = 1.301). (E) Meta-analysis UMAP embeddings showing normalized scDRS 73 scores for CAD and immune traits (WBC count and AD) previously shown as highly enriched in level 1 myeloid annotations. Red indicates cells enriched for the above-mentioned traits, while non-relevant cells are denoted in dark blue.…”

“…Cell prioritization p values (from the linear regression model) can be interpreted as the positive contribution of a cell type-specific expression program to a GWAS trait/disease gene-level Z-statistics. scDRS enrichment of GWAS-tagged heritability We used the single-cell disease relevance score (scDRS) framework 73 to identify cell subtypes driving the GWAS enrichments previously shown in level 1 annotations. scDRS integrates gene expression profiles from scRNA-seq with polygenic disease information from GWASs to associate individual cells to disease by assessing the excess expression of GWAS putative disease genes in a given cell relative to other genes with similar expression across all cells.…”

Shared genetic etiology between hypothyroidism and complex diseases: a large-scale genome-wide cross-trait analysis

“…To identify cell types underlying complex traits, the scRNA-seq data from four tissues (whole blood [36], spleen [37], small intestinal [38], and lung [39]) and GWAS summary statistics of four diseases we studied were integrated using three different genetic prioritization models: LDSC applied to specifically expressed genes (LDSC-SEG), MAGMA, and single-cell disease relevance score (scDRS) [40][41][42].…”