Rare and common genetic variants shape single-cell immune gene expression

Key numbers

• 83% of all 21
• 47% of those genes

Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases1,2. Single-cell-resolved measures of RNA levels and single-cell expression quantitative trait loci have revealed genetic regulation that drives cell states and types across diverse tissues3. Here we describe phase 1 of TenK10K, which leverages single-cell RNA sequencing data from more than 5 million cells across 28 immune cell types, along with matched whole-genome sequencing data from 1,925 individuals. This provides power to detect associations for rare and low-frequency genetic variants that have largely been uncharacterized in their impact on cell-specific gene expression. We map the effects of both common and rare variants in a cell-type-specific manner using a recent method that increases power by modelling single cells directly rather than aggregated 'pseudobulk' counts. We identify putative common regulatory variants for 83% of all 21,404 genes tested and cumulative rare-variant signals for 47% of those genes. We explore how genetic effects vary across cell types and states, develop a framework to determine the degree of single-cell expression quantitative trait loci cell-type specificity and show that about half of the effects are observed only in one or a few cell types. By integrating our results with functional annotations and disease information, we further characterize the likely molecular modes of action for many disease-variant associations. Finally, we explore the effects that genetic variants have on gene expression across continuous cell states, and effects that vary cell-state abundance directly.

Anna S E Cuomo, Eleanor Spenceley, Hope A Tanudisastro, Blake Bowen, Albert Henry, Hao Lawrence Huang, Angli Xue, Wei Zhou, Matthew J Welland, Samantha J Bryen, Oscar A Dong, Arthur S Lee, Jeffrey M Pullin, Kristof Wing, Owen Tang, Michael P Gray, Michael Franklin, Michael Harper, Michael Silk, Katalina Bobowik, Alexander Stuckey, John Marshall, Vivian Bakiris, Caitlin Uren, Rachael A McCloy, Bindu Swapna Madala, Amy Miniter, Caitlin Bartie, Drew R Neavin, Zhen Qiao, et al.

Leverage3 / 5

A method or resource many groups across the field will adopt within a year.

10% of this desk’s rubric

Magnitude3 / 5

Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.

20% of this desk’s rubric

Evidence4 / 5

Strong: large scale, preregistered, independently replicated, or a well-powered randomized trial.

32% of this desk’s rubric

Novelty4 / 5

Challenges a prevailing assumption with evidence.

8% of this desk’s rubric

Trajectory2 / 5

Some room to improve with obvious engineering.

5% of this desk’s rubric

Stakes3 / 5

Meaningful benefit to many people within a few years.

25% of this desk’s rubric

• Citations3 (reference 20, via openalex, Oct 30, 2026, 05:48 UTC)

• Reviewed by heuristic-v10 on Oct 30, 2026, 06:17 UTC. Paper type: method.
• Categories: Journal Article
• TOP, No.5 in the Front page edition of October 30, 2026.
• TOP, No.1 in the Biology edition of October 30, 2026.