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scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies.

Peilin JiaRuifeng HuFangfang YanYulin DaiZhong-Ming Zhao
Published in: Genome biology (2022)
We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.
Keyphrases
  • single cell
  • genome wide
  • rna seq
  • dna methylation
  • high throughput
  • poor prognosis
  • genome wide association
  • copy number
  • gene expression
  • electronic health record
  • machine learning
  • big data
  • artificial intelligence