Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.01.20.700519

Federated single-cell QTL meta-analysis reveals novel disease mechanisms

Abstract

Genetic effects on gene expression are often cell type-specific and obscured in bulk analyses. To resolve this context-dependent regulation, we performed a federated cis-eQTL meta-analysis across 12 PBMC datasets (2,032 individuals, 2.5 million cells). Across six immune cell types, we identified cis-eQTLs for 6,592 genes and fine-mapped 14,985 independent loci. Notably, the 42% of eQTLs that were undetected in a bulk eQTL study on 43,301 whole blood samples also showed stronger enrichment for disease GWAS loci. We further identified three genome-wide significant and 65 suggestive loci affecting the abundance of (rare) immune cell types and validated these using previously reported hematological GWAS and bulk-derived trans-eQTLs. Integrating single-cell cis-eQTLs with bulk trans-eQTLs enabled us to anchor 6,382 trans-eGenes (37.2% novel) to upstream regulators and reconstruct directed gene regulatory relationships. For example, a hemorrhoidal disease-associated variant showed a CD4+ T cell-specific cis-eQTL on BACH1 that colocalized with 45 immune and metabolic trans-eGenes. These results demonstrate the power of single-cell QTL meta-analysis in interpreting complex trait genetics.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kaptijn, D., Michielsen, L., Neavin, D., Ripoll-Cladellas, A., Alquicira-Hernandez, J. E., Korshevniuk, M., Lee, J. T. H., Oelen, R., Vochteloo, M., Warmerdam, R., Ando, Y., Ban, M., Bayaraa, O., Berg, M., van Blokland, I., Considine, D., Dieng, M. M., Edahiro, R., Gordon, M. G., Groot, H. E., van der Harst, P., Heinig, M., Hon, C.-C., Idaghdour, Y., Kathail, P., de Klein, N., Li, W., Li, Y., Losert, C., Manikanda, V., Moody, J., Naeem, H., Mokrab, Y., Nawijn, M. C., Netea, M., Niewold, J., Okada, Y., Sawcer, S., Soulama, I., Stegle, O., Tsepilov, Y., Park, W.-Y., Rajagopalan, D., Shahin, T.. 2026-01-23. Federated single-cell QTL meta-analysis reveals novel disease mechanisms. https://doi.org/10.64898/2026.01.20.700519

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Genetic ancestry influences the relative expression of ancestral haplotypes within admixed individuals

Allele-specific expression (ASE) reflects differences between the two haplotypes a person carries, but most ASE studies have been done in European-descent samples. Admixed individuals carry haplotypes from different ancestral backgrounds within the same cell, which allows ancestry effects on regulation to be measured directly. We integrated phased whole-blood RNA-seq with local ancestry inference in 779 participants from the MAGENTA cohort, including non-Hispanic White, African American, Caribbean Hispanic, and Peruvian individuals. Higher West African (YRI) ancestry proportion was associated with more ASE-positive genes per person, and the association remained after adjusting for age, sex, Alzheimer's disease status, APOE genotype, and the number of heterozygous genes. Peruvian ancestry showed no significant association. A gene-level test of imbalance across population groups found 2,053 of 15,008 genes with significant differences, including the major histocompatibility complex in all four groups. A genome-wide scan of 2,213 1 Mb windows compared people carrying two haplotypes of different ancestries with those carrying two of the same ancestry. It identified 52 non-redundant loci that replicated across all four population groups, containing 201, 60, and 110 ancestry-associated genes under the CEU, YRI, and PEL reference baselines. A haplotype-resolved directional analysis showed no genome-wide advantage for either ancestry. It found locus-specific dominance at 11 loci, and only chr8:2-3 Mb (CSMD1) showed the same direction in more than one population group. Genetic ancestry therefore shapes allelic regulation at global, gene, and locus scales.

genetics↗

Assembly-independent measurement of human Yq12 heterochromatin reveals population-scale variation and Yq12-derived RNA in testis

Despite advances in long-read sequencing and telomere-to-telomere assembly, population-scale analysis of large satellite arrays remains challenging. Human Yq12 is a megabase-scale heterochromatic region composed primarily of DYZ1/HSat3A6 and DYZ2/HSat1B repeats, whose near-equal proportions are maintained despite extensive variation in total array length. Here, using complete Y-chromosome assemblies, we developed k-mer-based tools to estimate Yq12 size directly from short- and long-read sequencing data without requiring sample-specific complete assemblies. Across 1,597 males from the 1000 Genomes Project, Yq12 length ranged from ~1 to 52 Mb (mean 26.5 Mb) and varied substantially within and between Y haplogroups. Phylogenetic analyses supported long-term constraint of total Yq12 length near an equilibrium of 26 Mb and strongly coupled evolution of DYZ1 and DYZ2 around near-parity composition. In 303 father-son pairs, fathers had longer estimated Yq12 regions in 70.6% of pairs, while sons more often showed DYZ1:DYZ2 ratios closer to 1:1. Testis Iso-Seq identified transcripts containing DYZ1 and DYZ2 satellite sequences, while single-cell RNA-seq revealed their expression in haploid spermatids. These results establish a scalable framework for studying Yq12 population variation, intergenerational dynamics, and transcription, providing new opportunities to investigate how extreme satellite-array variation relates to Y-chromosome evolution and male germ-cell biology.

genetics↗

Continuous ancestry profiles from genomes and genealogies

Using a single population label to represent an individual's ancestry may offer convenience, but ancestry is multidimensional, continuous, and reflects contributions from multiple ancestral sources. Here, we demonstrate empirically that ancestry profiles outperform categorical ancestry labels in capturing this complexity. Relatedness among 28,332 genotyped individuals was measured using haplotype painting and ordination and was regressed on regional ancestry proportions from the deep genealogies available for one-third of the cohort. When tested on participants in a validation set, predicted profiles placed 78% of documented ancestry in the correct region, against 48% for the training set average. Giving each participant a single regional label placed only 32% of their documented ancestry correctly, and a perfect classifier naming each person's largest documented region would reach only 38%, because most individuals carry ancestry from several regions. Thus, the continuous profile captures substantially more ancestry than any classification, providing support for representing ancestry as a continuum rather than a label.

genetics↗