RT Journal Article
SR Electronic
T1 Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.07.05.450786
DO 10.1101/2021.07.05.450786
A1 Milton Pividori
A1 Sumei Lu
A1 Binglan Li
A1 Chun Su
A1 Matthew E. Johnson
A1 Wei-Qi Wei
A1 Qiping Feng
A1 Bahram Namjou
A1 Krzysztof Kiryluk
A1 Iftikhar Kullo
A1 Yuan Luo
A1 Blair D. Sullivan
A1 Benjamin F. Voight
A1 Carsten Skarke
A1 Marylyn D. Ritchie
A1 Struan F.A. Grant
A1 Casey S. Greene
YR 2021
UL http://biorxiv.org/content/early/2021/08/05/2021.07.05.450786.abstract
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.