PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and environmental factors that mediate eQTLs.
Genome Biol
; 25(1): 29, 2024 01 22.
Article
in En
| MEDLINE
| ID: mdl-38254182
ABSTRACT
Expression quantitative trait loci (eQTL) offer insights into the regulatory mechanisms of trait-associated variants, but their effects often rely on contexts that are unknown or unmeasured. We introduce PICALO, a method for hidden variable inference of eQTL contexts. PICALO identifies and disentangles technical from biological context in heterogeneous blood and brain bulk eQTL datasets. These contexts are biologically informative and reproducible, outperforming cell counts or expression-based principal components. Furthermore, we show that RNA quality and cell type proportions interact with thousands of eQTLs. Knowledge of hidden eQTL contexts may aid in the inference of functional mechanisms underlying disease variants.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain
/
Quantitative Trait Loci
Type of study:
Diagnostic_studies
/
Prognostic_studies
Language:
En
Journal:
Genome Biol
Journal subject:
BIOLOGIA MOLECULAR
/
GENETICA
Year:
2024
Type:
Article
Affiliation country:
Netherlands