Biology subjects

Vigorito, E.

Publications and source records attributed to Vigorito, E..

4 recordsLinked to original sources

bioRxiv2023

Shared and distinct molecular effects of regulatory genomic variants provide insight into mechanisms of distal enhancer-promoter communication

Gene enhancers often form long-range contacts with promoters, but it remains unclear if enhancer activity and their chromosomal contacts are mediated by the same DNA sequences and recruited factors. We studied the effects of expression quantitative trait loci (eQTLs) on enhancer activity and promoter contacts in primary monocytes isolated from 34 individuals. Using eQTL-Capture Hi-C and a Bayesian approach considering both intra- and inter-individual variation, we initially detected 19 eQTLs associated with enhancer-eGene promoter contacts, most of which also associated with enhancer accessibility and activity. Capitalising on these shared effects, we devised a multi-modality Bayesian strategy, which identified 629 "trimodal QTLs" jointly associated with enhancer accessibility, eGene promoter contact, and gene expression. Causal mediation analysis and CRISPR interference revealed causal relationships between these three modalities. Many detected QTLs overlapped disease susceptibility loci and influenced the predicted binding of myeloid transcription factors, including SPI1, GABPB and STAT3. Additionally, a variant associated with PCK2 promoter contact directly disrupted a CTCF binding motif and impacted promoter insulation from downstream enhancers. Jointly, our findings suggest an inherent genetic link between the activity and connectivity of enhancers with relevance for human disease, and highlight the role of genetically-determined chromatin boundaries in gene control.

genetics↗

bioRxiv2022

BBmix: a Bayesian Beta-Binomial mixture model for accurate genotyping from RNA-sequencing

MotivationWhile many pipelines have been developed for calling genotypes using RNA-sequencing data, they all have adapted DNA genotype callers that do not model biases specific to RNA-sequencing such as reference panel bias or allele specific expression. ResultsHere, we present BBmix, a Bayesian Beta-Binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than one hour is sufficient to call genotypes in a large number of samples. AvailabilityWe implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmix and accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

genomics↗

bioRxiv2020

EPISPOT: an epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies

We present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for both cis and trans actions, including QTL hotspot effects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits, and hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step towards improving the challenging detection and functional interpretation of trans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from > 150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritising cis and trans QTL hits and is tailored to any transcriptomic, proteomic or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress towards a better functional understanding of genetic regulation.

genetics↗

bioRxiv2020

BaseQTL: a Bayesian method to detect eQTLs from RNA-seq data with or without genotypes

Available methods to detect molecular quantitative trait loci (QTL) require study individuals to be genotyped. Here, we describe BaseQTL, a Bayesian method that exploits allele-specific expression to map molecular QTL from sequencing reads even when no genotypes are available. When used with genotypes, BaseQTL has lower error rates and increased power compared with existing QTL mapping methods. Running without genotypes limits how many tests can be performed, but due to the proximity of QTL variants to gene bodies, the 2.8% of variants within a 100kB-window that could be tested, contained 26% of QTL variants detectable with genotypes. eQTL effect estimates were invariably consistent between analyses performed with and without genotypes. Often, sequencing data may be generated in absence of genotypes on patients and controls in differential expression studies, and we identified an apparent psoriasis-specific effect for GSTP1 in one such dataset, providing new insights into disease-dependent gene regulation.

genomics↗