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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.

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Summary

New distribution QTLs (distQTLs) methods using single-cell RNA sequencing data reveal genetic influences on gene expression with higher resolution. This approach overcomes limitations of bulk eQTL analysis, offering deeper insights into transcriptional regulation.

Keywords:
Fréchet regressiongene expression distributionquantitative trait locisingle-cell RNA sequencing

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Area of Science:

  • Genomics
  • Transcriptomics
  • Systems Biology

Background:

  • Expression quantitative trait loci (eQTL) mapping links genetic variation to gene expression.
  • Bulk eQTL methods average expression, masking cell-specific regulatory heterogeneity.
  • Single-cell eQTL (sc eQTL) methods offer higher resolution but require advanced analytical approaches.

Purpose of the Study:

  • To introduce and validate a novel method, distribution QTLs (distQTLs), for identifying genetic influences on gene expression using single-cell RNA sequencing data.
  • To leverage Fréchet regression for analyzing full empirical distributions of gene expression, moving beyond traditional summary statistics.
  • To compare the performance of distQTLs against existing eQTL methods in a large-scale population cohort.

Main Methods:

  • Application of Fréchet regression to population-scale single-cell RNA sequencing (scRNA-seq) data from the OneK1K cohort (982 donors).
  • Comparison of distQTL results with summary statistics-based eQTLs and mixed-effects modeling approaches.
  • Orthogonal validation of distQTL findings using cell-type-specific epigenomic profiles.

Main Results:

  • The distQTL method demonstrates superior performance across various gene expression contexts compared to traditional eQTL approaches.
  • Benchmarking against the Genotype-Tissue Expression Project (GTEx) confirms the robustness and accuracy of distQTL calls.
  • Validation using epigenomic data supports the biological relevance of distQTL identified genetic associations.

Conclusions:

  • Distribution QTLs (distQTLs) represent a significant advancement in dissecting transcriptional regulation at single-cell resolution.
  • The Fréchet regression-based distQTL approach effectively captures gene expression heterogeneity influenced by genetic variation.
  • This method provides a powerful tool for understanding the genetic architecture of gene expression in complex biological systems.