The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene–drug associations and genotype–phenotype relationships. Curators assign levels of evidence to variant–drug associations using well-defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high-quality information supporting personalized medicine–implementation projects.

Open Targets Genetics (https://genetics.opentargets.org) is an open-access integrative resource that aggregates human GWAS and functional genomics data including gene expression, protein abundance, chromatin interaction and conformation data from a wide range of cell types and tissues to make robust connections between GWAS-associated loci, variants and likely causal genes. This enables systematic identification and prioritisation of likely causal variants and genes across all published trait-associated loci. In this paper, we describe the public resources we aggregate, the technology and analyses we use, and the functionality that the portal offers. Open Targets Genetics can be searched by variant, gene or study/phenotype. It offers tools that enable users to prioritise causal variants and genes at disease-associated loci and access systematic cross-disease and disease-molecular trait colocalization analysis across 92 cell types and tissues including the eQTL Catalogue. Data visualizations such as Manhattan-like plots, regional plots, credible sets overlap between studies and PheWAS plots enable users to explore GWAS signals in depth. The integrated data is made available through the web portal, for bulk download and via a GraphQL API, and the software is open source. Applications of this integrated data include identification of novel targets for drug discovery and drug repurposing.

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The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine’s Standards for Developing Trustworthy Clinical Practice Guidelines.

The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicly available and the underlying code is open source. Since our last update two years ago, we have had 10 releases to maintain and continuously improve evidence for target–disease relationships from 20 different data sources. In addition, we have integrated new evidence from key datasets, including prioritised targets identified from genome-wide CRISPR knockout screens in 300 cancer models (Project Score), and GWAS/UK BioBank statistical genetic analysis evidence from the Open Targets Genetics Portal. We have evolved our evidence scoring framework to improve target identification. To aid the prioritisation of targets and inform on the potential impact of modulating a given target, we have added evaluation of post-marketing adverse drug reactions and new curated information on target tractability and safety. We have also developed the user interface and backend technologies to improve performance and usability. In this article, we describe the latest enhancements to the Platform, to address the fundamental challenge that developing effective and safe drugs is difficult and expensive.

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Background The UK 100,000 Genomes Project is in the process of investigating the role of genome sequencing of patients with undiagnosed rare disease following usual care, and the alignment of research with healthcare implementation in the UK’s national health service. (Other parts of this Project focus on patients with cancer and infection.) Methods We enrolled participants, collected clinical features with human phenotype ontology terms, undertook genome sequencing and applied automated variant prioritization based on virtual gene panels (PanelApp) and phenotypes (Exomiser), alongside identification of novel pathogenic variants through research analysis. We report results on a pilot study of 4660 participants from 2183 families with 161 disorders covering a broad spectrum of rare disease. Results Diagnostic yields varied by family structure and were highest in trios and larger pedigrees. Likely monogenic disorders had much higher diagnostic yields (35%) with intellectual disability, hearing and vision disorders, achieving yields between 40 and 55%. Those with more complex etiologies had an overall 25% yield. Combining research and automated approaches was critical to 14% of diagnoses in which we found etiologic non-coding, structural and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohort-wide burden testing across 57,000 genomes enabled discovery of 3 new disease genes and 19 novel associations. Of the genetic diagnoses that we made, 24% had immediate ramifications for the clinical decision-making for the patient or their relatives. Conclusion Our pilot study of genome sequencing in a national health care system demonstrates diagnostic uplift across a range of rare diseases. (Funded by National Institute for Health Research and others)

The chemokine receptor CXCR3 is expressed on the surface of both resting and activated T- lymphocytes. We describe here a study of the endocytosis of CXCR3 using T-lymphocytes and CXCR3 transfectants. Chemokine-induced CXCR3 downregulation occurred in a rapid, dose-dependent manner, with CXCL11 the most potent and efficacious ligand. Endocytosis was mediated in part by arrestins, but appeared to occur independently of clathrin and caveolae. In contrast to other chemokine receptors, which are largely recycled to the cell surface within an hour, cell surface replenishment of CXCR3 occurred over several hours and was dependent upon mRNA transcription, de novo protein synthesis and transport through the ER and Golgi. Confocal microscopy and Western blotting confirmed the fate of endocytosed CXCR3 to be degradation, mediated in part by lysosomes and proteosomes. Site-directed mutagenesis of the CXCR3 C-terminus revealed that internalization and degradation were independent of phophorylation, ubiquitination or a conserved LL motif. CXCR3 was found to be efficiently internalized in the absence of ligand, a process involving a YXXL motif at the extreme of the C-terminus. Although freshly isolated T-lymphocytes expressed moderate cell surface levels of CXCR3, they were only responsive to CXCL11 with CXCL9 and CXCL10 only having significant activity on activated T-lymphocytes. Thus, the activities of CXCR3 are tightly controlled following mRNA translation. Since CXCR3+ cells are themselves a source of IFN-γ, which potently induces the expression of CXCR3 ligands, such tight regulation of CXCR3 may serve as a control to avoid the unnecessary amplification of activated T-lymphocyte recruitment.