Biology subjects

Buniello, A.

Publications and source records attributed to Buniello, A..

6 recordsLinked to original sources

bioRxiv2026

The Human Pleiotropic Map of GWAS Associations and Therapeutic Implications

Genetic support for drug targets substantially increases clinical success rates, establishing genome-wide association studies (GWAS) as central to therapeutic hypothesis generation. However, the same genetic evidence that reveals causal gene-disease relationships simultaneously exposes organism-level safety liabilities--a dimension requiring principled, genome-wide quantification. Here we systematically analyse 100,526 GWAS to yield 789,453 credible sets and gene prioritisations for 15,641 genes, with discovery showing no saturation as GWAS expand and increase diversity. We find that 64% of GWAS-implicated genes are pleiotropic, associated with traits across multiple diseases and showing a non-linear relationship between the degree of pleiotropy and clinical success. Highly pleiotropic genes--concentrated in immune, inflammatory, and oncogenic signalling programmes--are enriched in safety-terminated clinical programmes, mouse lethal knockouts, and cancer driver genes, establishing gene-level pleiotropy as a potential measure of genetically-informed organism-level safety liability. Protein-altering variant (PAV) support amplifies therapeutic signal (OR = 6.0), yet PAV targets show higher average pleiotropy, introducing a competing safety liability. Combining PAV support with intermediate pleiotropy (2-5 therapeutic areas) resolves this tension, yielding OR = 10.3 and relative success = 4.8--a profile already satisfied by 52 approved therapies. As GWAS continue to expand in scale and resolution, these findings lay the groundwork for increasingly sophisticated target discovery strategies that yield safer and more effective therapeutic hypotheses.

genomics↗

bioRxiv2024

Associations on the Fly, a new feature aiming to facilitate exploration of the Open Targets Platform evidence

MotivationThe Open Targets Platform (https://platform.opentargets.org) is a unique, comprehensive, open-source resource supporting systematic identification and prioritisation of targets for drug discovery. The Platform combines, harmonises and integrates data from >20 diverse sources to provide target-disease associations, covering evidence derived from genetic associations, somatic mutations, known drugs, differential expression, animal models, pathways and systems biology. An in-house target identification scoring framework weighs the evidence from each data source and type, contributing to an overall score for each of the 7.8M target-disease associations. However, the previous infrastructure did not allow user-led dynamic adjustments in the contribution of different evidence types for target prioritisation, a limitation frequently raised by our user community. Furthermore, the previous Platform user interface did not support navigation and exploration of the underlying target-disease evidence on the same page, occasionally making the user journey counterintuitive. ResultsHere, we describe "Associations on the Fly" (AOTF), a new Platform feature - developed as part of a wider product refactoring project - to enable formulation of more flexible and impactful therapeutic hypotheses through dynamic adjustment of the weight of contributing evidence from each source, altering the prioritisation of targets. Availability and implementationAll Open Targets code is available as open source: [https://github.com/opentargets]. This tool was implemented using React v18 and its code is accessible here: [https://github.com/opentargets/ot-ui-apps]. The tools described in the paper are accessible through the Open Targets Platform web interface [https://platform.opentargets.org/] and GraphQL API (https://platform-docs.opentargets.org/data-access/graphql-api). Data is available for download here: [https://platform.opentargets.org/downloads] and from the EMBL-EBI FTP: [https://ftp.ebi.ac.uk/pub/databases/opentargets/platform/]. Supplementary informationAdditional information on this tool can be found on the Platform documentation pages [https://platform-docs.opentargets.org/web-interface, https://platform-docs.opentargets.org/web-interface/associations-on-the-fly, https://platform-docs.opentargets.org/target-prioritisation] and training video [https://youtu.be/2A9bksboAag]. ContactAnnalisa Buniello, EMBL-EBI, buniello@ebi.ac.uk

bioinformatics↗

bioRxiv2024

Lit-OTAR Framework for Extracting Biological Evidences from Literature

The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionise drug discovery by extracting evidence from scientific literature for drug target identification and validation. This novel framework combines Named Entity Recognition (NER) for identifying gene/protein (target), disease, organism, and chemical/drug within scientific texts, and entity normalisation to map these entities to databases like Ensembl, Experimental Factor Ontology (EFO), and ChEMBL. Continuously operational, it has processed over 39 million abstracts and 4.5 million full-text articles and preprints to date, identifying more than 48.5 million unique associations that significantly help accelerate the drug discovery process and scientific research (> 29.9m distinct target-disease, 11.8m distinct target-drug and 8.3m distinct disease-drug relationships). The results are made accessible through the Open Targets Platform (https://platform.opentargets.org/) as well as Europe PMC website (SciLite web app) and annotations API (https://europepmc.org/annotationsapi).

bioinformatics↗

bioRxiv2022

A community driven GWAS summary statistics standard

Summary statistics from genome-wide association studies (GWAS) represent a huge potential for research. A challenge for researchers in this field is the access and sharing of summary statistics data due to a lack of standards for the data content and file format. For this reason, the GWAS Catalog hosted a series of meetings in 2021 with summary statistics stakeholders to guide the development of a standard format. The key requirements from the stakeholders were for a standard that contained key data elements to be able to support a wide range of data analyses, required low bioinformatics skills for file access and generation, to have easily accessible metadata, and unambiguous and interoperable data. Here, we define the specifications for the first version of the GWAS-SSF format, which was developed to meet the requirements discussed with the community. GWAS-SSF consists of a tab-separated data file with well-defined fields and an accompanying metadata file.

bioinformatics↗

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↗

bioRxiv2017

A Standardized Framework For Representation Of Ancestry Data In Genomics Studies

BackgroundThe accurate description of ancestry is essential to interpret and integrate human genomics data, and to ensure that advances in the field of genomics benefit individuals from all ancestral backgrounds. However, there are no established guidelines for the consistent, unambiguous and standardized description of ancestry. To fill this gap, we provide a framework, designed for the representation of ancestry in GWAS data, but with wider application to studies and resources involving human subjects.\n\nResultHere we describe our framework and its application to the representation of ancestry data in a widely-used publically available genomics resource, the NHGRI-EBI GWAS Catalog. We present the first analyses of GWAS data using our ancestry categories, demonstrating the validity of the framework to facilitate the tracking of ancestry in big data sets. We exhibit the broader relevance and integration potential of our method by its usage to describe the well-established HapMap and 1000 Genomes reference populations. Finally, to encourage adoption, we outline recommendations for authors to implement when describing samples.\n\nConclusionsWhile the known bias towards inclusion of European ancestry individuals in GWA studies persists, African and Hispanic or Latin American ancestry populations contribute a disproportionately high number of associations, suggesting that analyses including these groups may be more effective at identifying new associations. We believe the widespread adoption of our framework will increase standardization of ancestry data, thus enabling improved analysis, interpretation and integration of human genomics data and furthering our understanding of disease.

genetics↗