Biology subjects

van der Harst, P.

Publications and source records attributed to van der Harst, P..

4 recordsLinked to original sources

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↗

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↗

bioRxiv2019

The genetic makeup of the electrocardiogram

Since its original description in 1893 by Willem van Einthoven, the electrocardiogram (ECG) has been instrumental in the recognition of a wide array of cardiac disorders1,2. Although many electrocardiographic patterns have been well described, the underlying biology is incompletely understood. Genetic associations of particular features of the ECG have been identified by genome wide studies. This snapshot approach only provides fragmented information of the underlying genetic makeup of the ECG. Here, we follow the effecs of individual genetic variants through the complete cardiac cycle the ECG represents. We found that genetic variants have unique morphological signatures not identfied by previous analyses. By exploiting identified abberations of these morphological signatures, we show that novel genetic loci can be identified for cardiac disorders. Our results demonstrate how an integrated approach to analyse high-dimensional data can further our understanding of the ECG, adding to the earlier undertaken snapshot analyses of individual ECG components. We anticipate that our comprehensive resource will fuel in silico explorations of the biological mechanisms underlying cardiac traits and disorders represented on the ECG. For example, known disease causing variants can be used to identify novel morphological ECG signatures, which in turn can be utilized to prioritize genetic variants or genes for functional validation. Furthermore, the ECG plays a major role in the development of drugs, a genetic assessment of the entire ECG can drive such developments.

genetics↗

bioRxiv2017

Genetic study links components of the autonomous nervous system to heart-rate profile during exercise

Heart rate (HR) response to exercise, as defined by HR-increase upon exercise and HR-recovery after exercise, is an important predictor of mortality and believed to be modulated by the autonomic nervous system. However, the mechanistic basis underlying inter-individual differences remains to be elucidated. To investigate this, we performed a large-scale genome wide analysis of HR-increase and HR-recovery in 58,818 individuals. A total of 25 significant independent SNPs in 23 loci (P<8.3x10-9) were associated with HR-increase or HR-recovery, and 36 candidate causal genes were prioritized that were enriched for pathways related to neuron biology. There was no evidence of a causal relationship with mortality or cardiovascular diseases, however, a nominal association with parental lifespan was observed (5.5x10-4) that requires further study. In conclusion, our findings provide new biological and clinical insight into the mechanistic under-pinning of HR response to exercise, underscoring the role of the autonomous nervous system in HR-recovery.\n\nABBREVIATIONS

genetics↗