Inferring Causal Cell Types Driving Human Disease and Complex Traits - MPG Primer 2023

Inferring Causal Cell Types Driving Human Disease and Complex Traits - MPG Primer 2023

Broad Institute via YouTube Direct link

Intro

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1 of 17

Intro

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Inferring Causal Cell Types Driving Human Disease and Complex Traits - MPG Primer 2023

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  1. 1 Intro
  2. 2 Disease-associated cell types can be identified by integrating GWAS with functional genomics data
  3. 3 Colocalization of eQTLs with GWAS variants can implicate disease-critical genes and tissues
  4. 4 This is analogous to the need for fine-mapping GWAS variants
  5. 5 We want a method that can identify the causal tissues among many tagging tissues
  6. 6 Transcriptome-wide association studies (TWAS) perform polygenic colocalization of genes with disease
  7. 7 TWAS association statistics are proportional to the amount of tagged causal effects due to co-regulation
  8. 8 Co-regulation across tissues and genes can be estimated using gene expression prediction models and a reference panel
  9. 9 Visualization of multivariate regression in TCSC
  10. 10 TCSC is powerful, well-calibrated, and unbiased in simulations
  11. 11 TCSC power is modest but can improve by modifying certain parameters
  12. 12 eQTL sample size is an important consideration for real trait analysis
  13. 13 Applying TCSC to real gene expression and trait data
  14. 14 TCSC identifies causal tissue-trait pairs
  15. 15 TCSC performs well when model assumptions are violated
  16. 16 Getting started with TCSC
  17. 17 Data access provided on the TCSC Repo

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