Researchers from the TenK10K phase‑1 initiative have released a comprehensive map of genetic variants that influence gene expression at single‑cell resolution in human immune cells. The study leveraged single‑cell RNA sequencing of 5.4 million peripheral blood mononuclear cells (PBMCs) drawn from 1,925 individuals of European ancestry, combined with whole‑genome sequencing for each donor.
Scope of the dataset
After quality control, the cohort comprised an average of 2,825 cells per participant, which were classified into 28 distinct immune cell types using a reference‑based and hierarchical clustering workflow. Whole‑genome sequencing identified 66,658,242 variant sites, of which 53,784,018 (80.6%) were rare (minor allele frequency < 1%). This depth of rare‑variant data is roughly 4.5 times greater than that obtained from conventional SNP arrays.
Mapping common and rare eQTLs
Using the SAIGE‑QTL method, which models single‑cell expression as Poisson counts rather than aggregating data into pseudobulk profiles, the team tested cis‑eQTLs within ±100 kb of each gene. At a false discovery rate below 5%, they identified 154,932 common‑variant eQTLs affecting 17,674 unique genes, representing 83 % of the 21,404 genes examined. Fine‑mapping added 24,925 secondary and tertiary signals, expanding the catalog of independent regulatory variants.
Rare‑variant analysis uncovered cumulative signals for 47 % of the tested genes, highlighting regulatory effects that have been largely inaccessible in prior studies limited to common variants.
Cell‑type specificity and disease relevance
The investigators observed that roughly half of the eQTL effects were confined to a single cell type or a small subset of the 28 immune populations, with almost none being universal across all cell types. By integrating functional annotations and performing statistical colocalization with 60 disease phenotypes and blood traits, the study linked more than 60,000 eQTLs to over 5,000 genes, suggesting potential molecular mechanisms for nearly 900 disease‑associated loci.
In addition, a deep‑learning framework called scDeepID was applied to define continuous cell‑state gradients related to cell function. Over 15,000 eQTLs were found to vary dynamically along these states, and 164 independent genetic variants were identified that modulate the abundance of specific cell states.
When compared with the large‑scale eQTLGen consortium blood study, TenK10K identified a comparable number of eGenes (17,674 versus 16,987) despite having a sample size that is 15‑fold smaller. Notably, 25.9 % of the eGenes discovered were absent from the eQTLGen catalog, tending to be lower‑expressed and more cell‑type‑specific, underscoring the value of single‑cell resolution for uncovering hidden regulatory relationships.
The TenK10K phase‑1 resource provides an unprecedented view of how both common and rare genetic variation shapes immune gene expression at the cellular level, offering new avenues for understanding disease mechanisms and identifying therapeutic targets.
Helene Elliott is the Lead Science & Space Reporter at News Raise. She reports on aerospace missions, astrophysics discoveries, quantum research, and environmental technology.
