PT  - JOURNAL ARTICLE
AU  - Milton Pividori
AU  - Sumei Lu
AU  - Binglan Li
AU  - Chun Su
AU  - Matthew E. Johnson
AU  - Wei-Qi Wei
AU  - Qiping Feng
AU  - Bahram Namjou
AU  - Krzysztof Kiryluk
AU  - Iftikhar Kullo
AU  - Yuan Luo
AU  - Blair D. Sullivan
AU  - Benjamin F. Voight
AU  - Carsten Skarke
AU  - Marylyn D. Ritchie
AU  - Struan F.A. Grant
AU  - Casey S. Greene
TI  - Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms
AID  - 10.1101/2021.07.05.450786
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.07.05.450786
4099  - http://biorxiv.org/content/early/2021/08/05/2021.07.05.450786.short
4100  - http://biorxiv.org/content/early/2021/08/05/2021.07.05.450786.full
AB  - 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 StatementThe authors have declared no competing interest.