Biology subjects

Kolberg, P.

Publications and source records attributed to Kolberg, P..

3 recordsLinked to original sources

bioRxiv2025

Integrated QTL mapping and CRISPR screening in pooled iPSC-derived microglia reveals genetic drivers of neurodegenerative risk

Mounting evidence implicates microglia in neurodegeneration, but linking disease-associated genetic variants to target genes and cellular phenotypes is hindered by the inaccessibility of these cells. We differentiated 261 human iPSC lines into microglia-like cells (iMGL) in pools with phenotypic (differentiation, phagocytosis and migration) and single-cell transcriptomic readouts. Burden analysis of deleterious variants detected 36 genes influencing microglial phenotypes. Expression quantitative trait locus (eQTL) analysis found 7,121 eGenes, and 79 colocalizations across four neurodegenerative disease GWAS, half of which had limited prior evidence of causality. Integration of eQTL and phenotypic associations highlighted the role of disease-relevant variants including LRRK2 and TREM2 acting via microglial phagocytosis. A coupled CRISPR screen identified a role of TREM2 in phagocytosis and highlighted the importance of cellular state in directionality of phenotype. By contextualizing variant effects within disease-relevant microglial states, we provide a comprehensive framework for interpreting the function of risk loci in neurodegenerative disorders.

genomics↗

bioRxiv2023

Extensive co-regulation of neighbouring genes complicates the use of eQTLs in target gene prioritisation

Identifying causal genes underlying genome-wide association studies (GWAS) is a fundamental problem in human genetics. Although colocalisation with gene expression quantitative trait loci (eQTLs) is often used to prioritise GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analysed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalogue datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalisation methods (coloc.susie, coloc.abf and CLPP) and five Mendelian randomisation (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalisation methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalisation method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalising QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritising novel targets requires triangulation of evidence from multiple sources.

genetics↗

bioRxiv2023

Systematic visualisation of molecular QTLs reveals variant mechanisms at GWAS loci

Splicing quantitative trait loci (QTLs) have been implicated as a common mechanism underlying complex trait associations. However, utilising splicing QTLs in target discovery and prioritisation has been challenging due to extensive data normalisation which often renders the direction of the genetic effect as well as its magnitude difficult to interpret. This is further complicated by the fact that strong expression QTLs often manifest as weak splicing QTLs and vice versa, making it difficult to uniquely identify the underlying molecular mechanism at each locus. We find that these ambiguities can be mitigated by visualising the association between the genotype and average RNA sequencing read coverage in the region. Here, we generate these QTL coverage plots for 1.7 million molecular QTL associations in the eQTL Catalogue identified with five quantification methods. We illustrate the utility of these QTL coverage plots by performing colocalisation between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. We find that while visually confirmed splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases. All our association summary statistics and QTL coverage plots are freely available at https://www.ebi.ac.uk/eqtl/.

genomics↗