Abstract
Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented a significant step forward to test the mediating role of gene expression in GWAS associations. However, it is hard to disentangle causal cell types using eQTL data alone, and other methods generally do not use the large amounts of publicly available RNA-seq data. Here we introduce PhenoPLIER, a polygenic approach that maps both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same tissues. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, and our approach was accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we found that functionally important players lacked TWAS associations but were prioritized in phenotype-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets within associated processes that are missed by single-gene strategies.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵Funded by The Gordon and Betty Moore Foundation (GBMF 4552); The National Human Genome Research Institute (R01 HG010067); The National Cancer Institute (R01 CA237170)
miltondp · miltondp
sckinta
kirylukk
bvoight · bvoight28
CarstenSkarke
MarylynRitchie
STRUANGRANT
cgreene · GreeneScientist
Incorporate feedback from first version of preprint, add missed co-author.