Biology subjects

Korshevniuk, M.

Publications and source records attributed to Korshevniuk, M..

4 recordsLinked to original sources

bioRxiv2026

Cell-specific regulatory circuits connect genetic variation to disease susceptibility

Genome-wide association studies have identified thousands of variants associated with immune-related diseases, yet most lie in non-coding regions, complicating mechanistic interpretation. Regulatory quantitative trait loci (QTLs), such as expression QTLs (eQTLs) and chromatin accessibility QTLs (caQTLs), offer a powerful framework for prioritization and interpretation of these disease-associated genetic variants. When analyzed together, they offer deeper insights into the regulatory architecture underlying disease. We generated same-cell, single-cell multi-omics data, integrating transcriptomic and chromatin accessibility information, from 563,100 matched peripheral blood mononuclear cells collected from 264 individuals, either unstimulated or stimulated for 24h with C. albicans (CA). Across six major immune cell types, we mapped both cis-eQTLs and -caQTLs, identifying 1,571 eGenes and 28,862 caPeaks, with 41% and 11% showing a stimulation-dependent effect. Finally, to dissect the regulatory mechanisms underlying these QTL effects, we applied two complementary strategies: 1. overlapping caQTLs with eQTLs; 2. applying SCENIC+ to identify regulatory triplets containing a transcription factor, the chromatin region it may bind to and the candidate target genes it thereby may regulate. With the first approach, we identified 1,861 dual-acting QTLs. These dual-QTLs showed 1.9-fold stronger enrichment for immune-related disease associations than single-modality QTLs, highlighting their relevance for disease interpretation. With the second approach, we found 62,932 regulatory triplets, of which 1.7% were under genetic control. By then leveraging the SCENIC+-derived TF activity measurements we could study how genetic variants can rewire TF control of gene expression, ultimately shaping inter-individual variation in disease risk. Together, our network-based approach offers new insights into the cellular contexts and gene programs perturbed in disease, providing a foundation for prioritizing therapeutic targets and informing strategies for disease prevention.

immunology↗

bioRxiv2026

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

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.

genetics↗

bioRxiv2025

Disease-associated variants are enriched for altering cell-type-specific gene co-expression relationships

Genes act within complex regulatory networks, and genetic variants can perturb these networks by altering gene co-expression. Here, we performed co-expression quantitative trait locus (co-eQTL) mapping using single-cell RNA-seq from the sc-eQTLGen consortium (1,330 donors, >2 million cells), enabling sensitive detection and prioritization of informative variant-gene-gene triplets. We identified co-eQTLs for 398 eGenes where a nearby genetic variant affected both the genes expression (cis-gene) and its co-expression with other genes, often implicating upstream regulators. For 181 genes, we inferred a likely upstream transcription factor, with motif disruption predicted for 41 genes. These upstream genes are more often loss-of-function intolerant and show more network connections, providing an explanation for why co-eQTL variants are 2.8x more strongly associated with immune diseases than classical eQTLs. These findings position co-eQTLs as mechanistic links between genetic variation and disease, revealing how variants can rewire cell-type-specific gene networks.

genetics↗

bioRxiv2022

Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data

BackgroundExpression quantitative trait loci (eQTL) studies have shown how genetic variants affect downstream gene expression. To identify the upstream regulatory processes, single-cell data can be used. Single-cell data also offers the unique opportunity to reconstruct personalized co-expression networks--by exploiting the large number of cells per individual, we can identify SNPs that alter co-expression patterns (co-expression QTLs, co-eQTLs) using a limited number of individuals. ResultsTo tackle the large multiple testing burden associated with a genome-wide analysis (i.e. the need to assess all combinations of SNPs and gene pairs), we conducted a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets from three studies (reflecting 173 unique participants and 1 million cells) using a novel filtering strategy followed by a permutation-based approach. Before analysis, we evaluated the co-expression patterns to be used for co-eQTL identification using different external resources. The subsequent analysis identified a robust set of cell-type-specific co-eQTLs for 72 independent SNPs that affect 946 gene pairs, which we then replicated in a large bulk cohort. These co-eQTLs provide novel insights into how disease-associated variants alter regulatory networks. For instance, one co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell-activation and autoimmune disease. Among these genes, we identified enrichment for targets of five T-cell-activation-related transcriptional factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. ConclusionOur co-eQTL results highlight the importance of studying gene regulation at the context-specific level to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy--combined with our technical guidelines--will soon identify many more co-eQTLs, further helping to elucidate unknown disease mechanisms.

genetics↗