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distQTL: distribution quantitative trait loci identification by population-scale single-cell data.

Alexander Coulter1, Chun Yip Tong2, Yang Ni1,3

  • 1Department of Statistics, College of Arts and Sciences, Texas A&M University, College Station, TX 77843, United States.

NAR Genomics and Bioinformatics
|December 1, 2025
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Summary

We introduce distribution QTLs (distQTLs), a novel method using single-cell RNA sequencing data to uncover genetic influences on gene expression heterogeneity. This approach surpasses traditional methods by analyzing full expression distributions, offering finer resolution in regulatory studies.

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Area of Science:

  • Genomics
  • Transcriptomics
  • Computational Biology

Background:

  • Expression quantitative trait loci (eQTLs) link genetic variation to gene expression.
  • Bulk eQTL analysis averages expression, masking cell-specific regulatory differences.
  • Single-cell eQTL methods offer higher resolution but require advanced analytical techniques.

Purpose of the Study:

  • To develop and apply a novel method, distribution QTLs (distQTLs), for identifying genetic effects on gene expression heterogeneity using single-cell RNA-sequencing data.
  • To leverage metric space regression, specifically Fréchet regression, for analyzing full empirical expression distributions.

Main Methods:

  • Application of Fréchet regression to population-scale single-cell RNA-sequencing (scRNA-seq) data from the OneK1K cohort.
  • Comparison of distQTL performance against traditional eQTL methods (summary statistics, mixed-effects modeling).
  • Orthogonal validation of distQTL findings using cell-type-specific epigenomic profiles.

Main Results:

  • DistQTLs demonstrate superior performance across various gene expression contexts compared to existing eQTL approaches.
  • The method effectively identifies regulatory heterogeneity masked by bulk analysis.
  • Validation confirms the accuracy and utility of distQTL calls.

Conclusions:

  • DistQTLs provide a powerful new framework for dissecting genetic regulation at single-cell resolution.
  • This method enhances our understanding of gene expression variability and its genetic underpinnings.
  • The approach is validated and applicable to large-scale scRNA-seq datasets.