EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing

Abstract

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.

Integration of human organoids single‐cell transcriptomic profiles and human genetics repurposes critical cell type‐specific drug targets for severe COVID‐19

“…Notably, we found that 12 of these interacting drugs, gov; Figure S23A). Consistently, we performed evidence-driven analysis using the RISmed method, 59 and found that a high proportion of these prioritized drugs have been associated with COVID-19…”

“…Multiple lines of evidence 18,19,35,58,59 have demonstrated that integrating scRNA-seq data and polygenic risk signals from GWAS is a promising approach to uncover the cellular mechanisms through which these variants drive complex diseases. In this study, we sought stimulate host innate antiviral responses.…”

“…The paired Student's t ‐test is used to assess the difference in the number of ligand/receptor interactions with other cells between positive cells and negative cells in intestinal tuft and brain endothelial cells. The RISmed (version 2.3.0) 59 is used to perform a PubMed search for resorting to reported evidence supporting the association between COVID‐19 and a given cell type or drug (see Supplementary Methods S1).…”

Large‐scale single‐cell RNA sequencing atlases of human immune cells across lifespan: Possibilities and challenges

“…Meta-analysis of large and even population-scale scRNA-seq datasets is another emerging trend in the field and will provide valuable insights into disease (e.g., source of genetic variants) and demographic differences in immune profiles (e.g., age and sex). Although many studies are limited in biological replicates for true population-scale meta-analysis, a recent surge in tools to link scRNA-seq datasets with genome-wide association study summary statistics may provide a bridge between fields in the meantime [76,77]. In addition, perhaps owing to the size of developmental samples being more feasible, whole organism atlases for developing organisms have been produced, including for mouse gastrula [78], monkey gastrula [79], rabbit embryo [80], and human embryos [81], with immune cells profiled in all.…”

Joint genotypic and phenotypic outcome modeling improves base editing variant effect quantification

“…A trans-ancestry GWAS meta-analysis from the Global Lipids Genetics Consortium (GLGC) has identified >900 genome-wide significant loci associated with blood lipid levels, including >400 loci associated with LDL-C 30 . LDL-C GWAS loci overlap strongly with liver-enriched gene expression, nominating liver as the primary tissue driving LDL-C variant effects 31,32 . Yet, the causal variants and mechanisms by which many of these loci modulate LDL-C levels remain unknown.…”