Biology subjects

van der Wijst, M.

Publications and source records attributed to van der Wijst, M..

5 recordsLinked to original sources

bioRxiv2026

The enhanced multi-tissue atlas of regulatory effects in cattle

Cattle are integral to global food security, yet the molecular architecture of their complex traits remains poorly understood. Here, we present the Cattle Genotype-Tissue Expression (CattleGTEx) Phase 1 resource (https://cattlegtex.farmgtex.org/), a substantial expansion of the pilot study. By leveraging 12,422 RNA-seq profiles across 43 tissues and 82 breeds, we characterized 433,972 primary and 161,428 non-primary regulatory effects spanning seven molecular phenotypes. This high-resolution atlas resolves 75% of GWAS signals for 44 complex traits, significantly addressing the "missing regulation" in livestock. We propose a genetic regulatory model demonstrating how variants across multiple biological layers interact with specific biological contexts to shape phenotypic variation. Furthermore, CattleGTEx elucidates mechanisms underlying adaptive evolution between Bos taurus and Bos indicus, as well as artificial selection in dairy and beef breeds. Finally, by mapping evolutionary constraints on these regulatory effects, we demonstrate the translational value of this resource for prioritizing causal variants in human complex diseases. Together, Phase 1 of CattleGTEx provides a transformative framework for functional genomics, precision breeding, and comparative genetics.

genetics↗

bioRxiv2026

Sex-specific Genetic Regulatory Effects in Chickens

Sexual dimorphism is a defining vertebrate feature, yet its sex-specific molecular architecture remains poorly understood. Here we established a sex-balanced, uniformly reared chicken cohort to map this landscape, integrating individual whole-genome sequencing with 7,969 bulk and 779,380 single nucleus transcriptomes across 32 tissues from 280 birds. We identified 495,098 independent expression quantitative trait loci for 20,194 genes, including 10,937 loci modulated by cell-type composition. Notably, 340 genes were regulated by 449 loci in a sex-dependent manner, significantly enrichment in endocrine tissues like adipose and the adrenal gland. Furthermore, we fine-mapped 1,219 structural variants, demonstrating their unique roles to tissue- and sex-specific expression beyond SNPs. Ultimately, we showed the utility of these regulatory effects in elucidating the molecular basis of metabolism and complex traits in both chickens and humans. This comprehensive atlas of regulatory effects provides profound insights into the genomic and molecular basis of sexual dimorphism in vertebrates.

genomics↗

bioRxiv2025

High cell-type specificity of eQTLs revealed by single-nucleus analyses of brain and blood

Identifying causal mechanisms from genome-wide association studies (GWAS) requires an understanding of how disease-associated genetic variants influence gene expression in specific cell types. Here, we present scMetaBrain, a large-scale single-nucleus RNA-sequencing (snRNA-seq) resource derived using 1,260 samples from 785 individuals spanning 10 brain datasets. By analyzing 3.9 million transcriptomes, we identified 19,371 unique expression quantitative trait locus (eQTL) genes (eGenes) at a major cell type level, with the largest number of eQTLs observed in excitatory neurons. Notably, 31% of the eQTLs detected were highly cell-type-specific, with most restricted to excitatory neurons (69%). We compared the eQTLs with bulk RNA-seq datasets across different tissues and with a newly generated single nucleus dataset of 123 donors from peripheral blood mononuclear cells. We observed that differences in eQTL effect sizes between brain cell types are often as large as comparing eQTLs between brain tissue and non-brain tissue from bulk RNA-seq studies. Furthermore, we observe that eQTL effect size agreement was highest for cell types with similar function, even when comparing brain to blood cells. This suggests that that bulk analyses substantially overestimate eQTL agreement, likely due to tissue-level averaging of cellular regulatory effects. Through colocalization, we prioritized 662 genes for 11 brain-related traits and prioritized a single cell type in 68% of genes. Our findings demonstrate that eQTL effects are far more cell-type-specific than previously recognized, underscoring the need to expand single-cell eQTL studies across diverse tissues and cell types to fully capture the regulatory architecture of genetic variants.

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↗