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Updated: Jul 5, 2025
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
Published on: August 21, 2019
PICALO: principal interaction component analysis for the identification of discrete technical, cell-type, and
Martijn Vochteloo1,2, Patrick Deelen1,2, Britt Vink1,3
1Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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.
