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

生物 全基因组关联研究 电池类型 细胞 疾病 遗传学 基因 RNA序列 转录组 计算生物学
作者
Martin J. Zhang,Kangcheng Hou,Kushal K. Dey,Saori Sakaue,Karthik A. Jagadeesh,Kathryn Weinand,Aris Taychameekiatchai,Poorvi Rao,Angela Oliveira Pisco,James Zou,Bruce Wang,Michael J. Gandal,Soumya Raychaudhuri,Bogdan Pasaniuc,Nick Patterson
出处
期刊:Nature Genetics [Springer Nature]
标识
DOI:10.1038/s41588-022-01167-z
摘要

Single-cell RNA sequencing (scRNA-seq) provides unique insights into the pathology and cellular origin of disease. We introduce single-cell disease relevance score (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 (GWASs). We applied scDRS to 74 diseases/traits and 1.3 million 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; and 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 coexpression with GWAS disease-associated genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.
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