PICALO: Principal Interaction Component Analysis for the Identification of Discrete Technical, Cell-type, and Environmental Factors That Mediate EQTLs

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2024 Jan 23

PMID 38254182

Authors

Martijn Vochteloo

Patrick Deelen

Britt Vink

Ellen A Tsai

Heiko Runz

Sergio Andreu-Sanchez

Jingyuan Fu

Alexandra Zhernakova

Harm-Jan Westra

Lude Franke

Affiliations

Soon will be listed here.

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.

Citing Articles

gsQTL: Associating genetic risk variants with gene sets by exploiting their shared variability.

Bouland G, Tesi N, Mahfouz A, Reinders M bioRxiv. 2024; .

PMID: 39345521 PMC: 11429704. DOI: 10.1101/2024.09.13.612853.

Genotype × environment interactions in gene regulation and complex traits.

Boye C, Nirmalan S, Ranjbaran A, Luca F Nat Genet. 2024; 56(6):1057-1068.

PMID: 38858456 PMC: 11492161. DOI: 10.1038/s41588-024-01776-w.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci.

Kang J, Raveane A, Nathan A, Soranzo N, Raychaudhuri S Annu Rev Genomics Hum Genet. 2023; 24:277-303.

PMID: 37196361 PMC: 10784788. DOI: 10.1146/annurev-genom-101422-100437.

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