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Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data.
Zhang, Martin Jinye; Hou, Kangcheng; Dey, Kushal K; Sakaue, Saori; Jagadeesh, Karthik A; Weinand, Kathryn; Taychameekiatchai, Aris; Rao, Poorvi; Pisco, Angela Oliveira; Zou, James; Wang, Bruce; Gandal, Michael; Raychaudhuri, Soumya; Pasaniuc, Bogdan; Price, Alkes L.
Afiliação
  • Zhang MJ; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. jinyezhang@hsph.harvard.edu.
  • Hou K; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. jinyezhang@hsph.harvard.edu.
  • Dey KK; Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA. houkc@ucla.edu.
  • Sakaue S; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. houkc@ucla.edu.
  • Jagadeesh KA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA. houkc@ucla.edu.
  • Weinand K; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Taychameekiatchai A; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Rao P; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Pisco AO; Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.
  • Zou J; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Wang B; Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Gandal M; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Raychaudhuri S; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Pasaniuc B; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Price AL; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Nat Genet ; 54(10): 1572-1580, 2022 10.
Article em En | MEDLINE | ID: mdl-36050550
ABSTRACT
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Estudo de Associação Genômica Ampla / Análise de Célula Única Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Genet Assunto da revista: GENETICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Estudo de Associação Genômica Ampla / Análise de Célula Única Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Genet Assunto da revista: GENETICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos