Biology subjects

Dunham, I.

Publications and source records attributed to Dunham, I..

8 recordsLinked to original sources

bioRxiv2022

FORGEdb: systematic analysis of candidate causal variants to uncover target genes and mechanisms in complex traits.

The majority of disease-associated variants identified through genome-wide association studies (GWAS) are located outside of protein-coding regions and are overrepresented in sequences that regulate gene expression. Prioritizing candidate regulatory variants and potential biological mechanisms for further functional experiments, such as genome editing, can be challenging, especially in regions with a high number of variants in strong linkage disequilibrium or multiple proximal gene targets. Improved annotation of the regulatory genome can help identify promising variants and target genes for functional genomics experiments. To advance this area, we developed FORGEdb (https://forge2.altiusinstitute.org/files/forgedb.html), a web-based tool that can rapidly integrate data for individual genetic variants, providing information on associated regulatory elements, transcription factor (TF) binding sites and target genes for over 37 million variants. FORGEdb uses annotations derived from data across a wide range of biological samples to delineate the regulatory context for each variant at the cell type level. Multiple data types, such as Combined Annotation Dependent Depletion (CADD) scores, expression quantitative trait loci (eQTLs), activity-by-contact (ABC) interactions, Contextual Analysis of TF Occupancy (CATO) scores, transcription factor (TF) motifs, DNase I hotspots, histone mark ChIP-seq peaks and chromatin states, are included in FORGEdb and these annotations are integrated into a FORGEdb score to guide assessment of functional importance. In summary, FORGEdb provides an expansive and unique resource of genomic annotations and an integrated score that can be used to accelerate the translation of identified genetic loci into biological insight.

genomics↗

bioRxiv2021

Network expansion of genetic associations defines a pleiotropy map of human cell biology

Proteins that interact within molecular networks tend to have similar functions and when perturbed influence the same organismal traits. Interaction networks can be used to expand the list of likely trait associated genes from genome-wide association studies (GWAS). Here, we used improvements in SNP-to-gene mapping to perform network based expansion of trait associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share a common genetic basis as well as the biological processes underlying this. We identified 73 pleiotropic gene modules linked to multiple traits that are enriched in genes involved in processes such as protein ubiquitination and RNA processing. We show examples of modules linked to human diseases enriched in genes with pathogenic variants found in patients or relevant mouse knock-out phenotypes and can be used to map targets of approved drugs for repurposing opportunities. Finally, we illustrate the use of the network expansion scores to study genes at inflammatory bowel disease (IBD) GWAS loci, and implicate IBD-relevant genes with strong functional and genetic support.

genetics↗

bioRxiv2020

Open Targets Genetics: An open approach to systematically prioritize causal variants and genes at all published GWAS trait-associated loci

Genome-wide association studies (GWAS) have identified many variants robustly associated with complex traits but identifying the gene(s) mediating such associations is a major challenge. Here we present an open resource that provides systematic fine-mapping and protein-coding gene prioritization across 133,441 published human GWAS loci. We integrate diverse data sources, including genetics (from GWAS Catalog and UK Biobank) as well as transcriptomic, proteomic and epigenomic data across many tissues and cell types. We also provide systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues and identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine mapped genetics and functional genomics data using 445 gold standard curated GWAS loci to distinguish causal genes from background genes at the same loci, outperforming a naive distance based model. Genes prioritized by our model are enriched for known approved drug targets (OR = 8.1, 95% CI: [5.7, 11.5]). These results will be regularly updated and are publicly available through a web portal, Open Targets Genetics (OTG, http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

genetics↗

bioRxiv2019

Immune disease variants modulate gene expression in regulatory CD4+ T cells and inform drug targets

Identifying cellular functions dysregulated by disease associated variants could implicate novel pathways for drug targeting or modulation in cell therapies. Variants associated with immune diseases point towards the role of CD4+ regulatory T cells (Tregs), cell type critical for immune homeostasis. To translate the effects of immune disease alleles on Treg function we mapped genetic regulation (QTL) of gene expression and chromatin activity in Tregs. We identified 123 loci where Treg QTLs colocalized with immune disease variants. These colocalizations indicated that dysregulation of key Treg pathways, including cell activation via CD28 and IL-2 signalling mediated by STAT5A contribute to the shared pathobiology of immune diseases. Finally, using disease GWAS signals colocalizing with Treg QTLs, we identified 34 known drug targets and 270 targets with drug tractability evidence. Our study is the first in-depth characterization of immune disease variant effects on Treg gene expression modulation and dysregulation of Treg function.

genetics↗

bioRxiv2017

Designing an intuitive web application for drug discovery scientists

Although a scientific web application that is intuitive can help scientists utilize data more easily and advance their research, there is little guidance on how to design such an application in the academic literature. We discuss how we designed an intuitive application for bench scientists working in drug discovery following an approach that can be applied to the design and development of scientific resources in a broad range of disciplines.

bioinformatics↗

bioRxiv2017

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body, by undertaking a Human Cell Atlas Project as an international collaborative effort. The aim would be to define all human cell types in terms of distinctive molecular profiles (e.g., gene expression) and connect this information with classical cellular descriptions (e.g., location and morphology). A comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, as well as provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas.

cell biology↗

bioRxiv2017

Literature Evidence in Open Targets - a target validation platform

BackgroundWe present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information and serves the platform regularly with the up-to-date data since December, 2015.\n\nResultsCurrently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining.\n\nConclusionThis component helps the platforms users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data.

bioinformatics↗

bioRxiv2016

GARFIELD - GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction

Loci discovered by genome-wide association studies (GWAS) predominantly map outside protein-coding genes. The interpretation of functional consequences of non-coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages GWAS findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding that current methods do not offer. We further assess enrichment statistics for 27 GWAS traits within regulatory regions from the ENCODE and Roadmap projects. We characterise unique enrichment patterns for traits and annotations, driving novel biological insights. The method is implemented in standalone software and R package to facilitate its application by the research community.

genomics↗