Del Azodi, C. B. | bioRxiv Science

bioRxiv2025

Orchestrating multi-state QTL analysis with Bioconductor

MotivationThe scope of many Quantitative Trait Loci (QTL) mapping studies has increased to include different cellular and environmental states. However, drawing biologically relevant conclusions from the large, high-dimensional data that come from multi-state QTL mapping studies is not straightforward. ResultsTo address this problem, we introduce two R packages, QTLExperiment and multistateQTL. The QTLExperiment package provides a robust container for storing and manipulating QTL summary statistics and associated metadata. Building upon existing Bioconductor infrastructure and conventions, this object class is consistent, user-friendly, and well-documented. The multistateQTL package introduces tools to facilitate the analysis of multi-state QTL data stored in a QTLExperiment container. This package provides methods for statistical analysis, quantification of sharing, classification of multi-state QTL associations, visualization of the data, and more. It also provides flexible methods for simulating multi-state QTL summary statistics with user-defined properties. Availability and implementationThe packages QTLExperiment and multistateQTL are available on Bioconductor (https://www.bioconductor.org/packages/QTLExperiment and https://bioconductor.org/packages/multistateQTL).

bioinformatics↗

bioRxiv2023

Cell type-specific and disease-associated eQTL in the human lung

Common genetic variants confer substantial risk for chronic lung diseases, including pulmonary fibrosis (PF). Defining the genetic control of gene expression in a cell-type-specific and context-dependent manner is critical for understanding the mechanisms through which genetic variation influences complex traits and disease pathobiology. To this end, we performed single-cell RNA-sequencing of lung tissue from 67 PF and 49 unaffected donors. Employing a pseudo-bulk approach, we mapped expression quantitative trait loci (eQTL) across 38 cell types, observing both shared and cell type-specific regulatory effects. Further, we identified disease-interaction eQTL and demonstrated that this class of associations is more likely to be cell-type specific and linked to cellular dysregulation in PF. Finally, we connected PF risk variants to their regulatory targets in disease-relevant cell types. These results indicate that cellular context determines the impact of genetic variation on gene expression, and implicates context-specific eQTL as key regulators of lung homeostasis and disease.

genomics↗