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eQTL sample size is an important consideration for real trait analysis
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Inferring Causal Cell Types Driving Human Disease and Complex Traits - MPG Primer 2023
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- 1 Intro
- 2 Disease-associated cell types can be identified by integrating GWAS with functional genomics data
- 3 Colocalization of eQTLs with GWAS variants can implicate disease-critical genes and tissues
- 4 This is analogous to the need for fine-mapping GWAS variants
- 5 We want a method that can identify the causal tissues among many tagging tissues
- 6 Transcriptome-wide association studies (TWAS) perform polygenic colocalization of genes with disease
- 7 TWAS association statistics are proportional to the amount of tagged causal effects due to co-regulation
- 8 Co-regulation across tissues and genes can be estimated using gene expression prediction models and a reference panel
- 9 Visualization of multivariate regression in TCSC
- 10 TCSC is powerful, well-calibrated, and unbiased in simulations
- 11 TCSC power is modest but can improve by modifying certain parameters
- 12 eQTL sample size is an important consideration for real trait analysis
- 13 Applying TCSC to real gene expression and trait data
- 14 TCSC identifies causal tissue-trait pairs
- 15 TCSC performs well when model assumptions are violated
- 16 Getting started with TCSC
- 17 Data access provided on the TCSC Repo
