Biology subjects

Pullin, J. M.

Publications and source records attributed to Pullin, J. M..

4 recordsLinked to original sources

bioRxiv2025

Systematic comparison of colocalization methods using protein quantitative trait loci

Colocalization is frequently performed as a step to triage findings from genetic investigations linking molecular and disease data. However, the reliability and consistency of the various colocalization methods is not well understood. In particular, it is unclear whether a non-colocalization result should be taken as a definitive sign that two traits do not share a genetic cause in the region of interest, or merely as suggestive evidence. We use protein QTL data to benchmark four colocalization methods (coloc, coloc-SuSiE, prop-coloc, and colocPropTest), considering whether the methods conclude there is colocalization between datasets representing associations with the same protein. We consider a baseline scenario in which the associations come from the same dataset split in half at random, and scenarios in which the associations are with the same protein, but measured on different platforms or in different populations. In the baseline scenario, all methods report colocalization for the majority of proteins. In other scenarios, methods do not consistently report colocalization, they often report non-colocalization, and they often disagree. In the worst-case scenario, colocalization was only agreed by all four methods for 20% of proteins, despite our experiment being constructed to select for cases where colocalization is expected. This suggests caution in performing and interpreting colocalization analyses is warranted.

genetics↗

bioRxiv2025

Frataxin deficiency drives cardiac dysfunction and transcriptional dysregulation in Friedreich ataxia iPSC model

Friedreich ataxia (FRDA) is a progressive neuromuscular degenerative disorder caused by GAA repeat expansions in the FXN gene, leading to frataxin deficiency and multisystem pathology. Cardiomyopathy is the leading cause of mortality in individuals with FRDA. To investigate the cellular and molecular mechanisms underlying FRDA-associated cardiac dysfunction, we employed induced pluripotent stem cell (iPSC) lines derived from three individuals with FRDA, each paired with an isogenic control line generated through CRISPR/Cas9-mediated excision of the pathogenic GAA repeat expansion. Correction of the mutation restored FXN expression to levels comparable to healthy donor iPSCs, and all lines differentiated efficiently into cardiomyocytes. Functional analysis revealed significant contractile abnormalities in FRDA cardiomyocytes and multicellular cardiac microtissues, including prolonged contraction and relaxation times and faster beating rates, consistent with clinical observations of cardiac contractile dysfunction. FRDA cardiomyocytes also exhibited pathological features such as increased cell size, irregular calcium transients, elevated mitochondrial reactive oxygen species levels, increased mitochondrial fission and increased cell death. These phenotypes were exacerbated by pathological levels of iron supplementation in culture media, highlighting the heightened sensitivity of frataxin-deficient cardiomyocytes to iron-induced metabolic stress. RNA sequencing revealed a distinct transcriptional profile associated with frataxin deficiency. MEG3 and PCDHGA10 were consistently dysregulated across all three FRDA-iPSC lines and may represent early molecular markers of FRDA cardiomyopathy. Together, these findings establish a robust human iPSC model of FRDA cardiomyopathy that captures early disease phenotypes and reveals novel molecular targets. This preclinical human model provides valuable insight into the pathogenesis of FRDA and provides a platform for developing early-stage therapeutic interventions.

pathology↗

bioRxiv2024

Variant-specific priors in colocalisation analysis

Linking GWAS variants to their causal gene remains an ongoing challenge. A widely used method for performing this analysis is the coloc package for statistical colocalisation analysis, which can be used to link GWAS and eQTL associations. Currently, coloc assumes that all variants in a region are equally likely to be causal, despite the success of fine-mapping methods that use additional information to adjust their prior probabilities. In this paper we propose and implement an approach for specifying variant-specific prior probabilities in the coloc method. We describe and compare six source of information for specifying prior probabilities: non-coding constraint, enhancer-gene link scores, the output of the PolyFun method and three estimates of eQTL-TSS distance densities. Using simulations and analysis of ground-truth pQTL-eQTL colocalisations we show that variant-specific priors, particularly the eQTL-TSS distance density priors, can improve colocalisation performance. Furthermore, across GWAS- eQTL colocalisations variant-specific priors changed colocalisation significance in up to 13.5% of colocalisations, at some loci revealing the likely causal gene.

genetics↗

bioRxiv2022

A comparison of marker gene selection methods for single-cell RNA sequencing data

The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalogue and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper we benchmarked 56 computational methods for selecting marker genes in scRNA-seq data. The performance of the methods was compared using 10 real scRNA-seq datasets and over 170 additional simulated datasets. Methods were compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed and their implementation quality. In addition, various case studies were used to scrutinise the most commonly used methods, highlighting issues and inconsistencies. Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Students t-test and logistic regression. All code used in the evaluation, including an extensible Snakemake pipeline, is available at: https://gitlab.svi.edu.au/biocellgen-public/mage_2020_marker-gene-benchmarking.

bioinformatics↗