GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies.

The GWAS Central resource gathers and curates extensive summary-level genome-wide association study (GWAS) data and puts a range of user-friendly but powerful website tools for the comparison and visualisation of GWAS data at the fingertips of researchers. Through our continued efforts to harmonise and import data received from GWAS authors and consortia, and data sets actively collected from public sources, the database now contains over 72.5 million P-values for over 5000 studies testing over 7.4 million unique genetic markers investigating over 1700 unique phenotypes. Here, we describe an update to integrate this extensive data collection with mouse disease model data to support insights into the functional impact of human genetic variation. GWAS Central has expanded to include mouse gene-phenotype associations observed during mouse gene knockout screens. To allow similar cross-species phenotypes to be compared, terms from mammalian and human phenotype ontologies have been mapped. New interactive interfaces to find, correlate and view human and mouse genotype-phenotype associations are included in the website toolkit. Additionally, the integrated browser for interrogating multiple association data sets has been updated and a GA4GH Beacon API endpoint has been added for discovering variants tested in GWAS. The GWAS Central resource is accessible at https://www.gwascentral.org/.

Beacon v2 and Beacon networks: A "lingua franca" for federated data discovery in biomedical genomics, and beyond.

Beacon is a basic data discovery protocol issued by the Global Alliance for Genomics and Health (GA4GH). The main goal addressed by version 1 of the Beacon protocol was to test the feasibility of broadly sharing human genomic data, through providing simple "yes" or "no" responses to queries about the presence of a given variant in datasets hosted by Beacon providers. The popularity of this concept has fostered the design of a version 2, that better serves real-world requirements and addresses the needs of clinical genomics research and healthcare, as assessed by several contributing projects and organizations. Particularly, rare disease genetics and cancer research will benefit from new case level and genomic variant level requests and the enabling of richer phenotype and clinical queries as well as support for fuzzy searches. Beacon is designed as a "lingua franca" to bridge data collections hosted in software solutions with different and rich interfaces. Beacon version 2 works alongside popular standards like Phenopackets, OMOP, or FHIR, allowing implementing consortia to return matches in beacon responses and provide a handover to their preferred data exchange format. The protocol is being explored by other research domains and is being tested in several international projects.

GWAS Central: a comprehensive resource for the discovery and comparison of genotype and phenotype data from genome-wide association studies.

The GWAS Central resource provides a toolkit for integrative access and visualization of a uniquely extensive collection of genome-wide association study data, while ensuring safe open access to prevent research participant identification. GWAS Central is the world's most comprehensive openly accessible repository of summary-level GWAS association information, providing over 70 million P-values for over 3800 studies investigating over 1400 unique phenotypes. The database content comprises direct submissions received from GWAS authors and consortia, in addition to actively gathered data sets from various public sources. GWAS data are discoverable from the perspective of genetic markers, genes, genome regions or phenotypes, via graphical visualizations and detailed downloadable data reports. Tested genetic markers and relevant genomic features can be visually interrogated across up to sixteen multiple association data sets in a single view using the integrated genome browser. The semantic standardization of phenotype descriptions with Medical Subject Headings and the Human Phenotype Ontology allows the precise identification of genetic variants associated with diseases, phenotypes and traits of interest. Harmonization of the phenotype descriptions used across several GWAS-related resources has extended the phenotype search capabilities to enable cross-database study discovery using a range of ontologies. GWAS Central is updated regularly and available at https://www.gwascentral.org.

Array-based dynamic allele specific hybridization (Array-DASH): Optimization-free microarray processing for multiple simultaneous genomic assays.

We report proof-of-principle experiments regarding a dynamic microarray protocol enabling accurate and semi-quantitative DNA analysis for re-sequencing, fingerprinting and genotyping. Single-stranded target molecules hybridise to surface-bound probes during initial gradual cooling with high-fidelity. Real-time tracking of target denaturation (via fluorescence) during a 'dynamic' gradual heating phase permits 'melt-curve' analysis. The probe most closely matching the target sequence is identified based on the highest melting temperature. We demonstrated a >99% re-sequencing accuracy and a potential detection rate of 1% for SNPs. Experiments employing Hypericum ribosomal ITS regions and HIV genomes illustrated a reliable detection level of 5% plus simultaneous re-sequencing and genotyping. Such performance suggests a range of potential real-world applications involving rapid sequence interrogation, for example, in the Covid-19 pandemic. Guidance is offered towards the development of a commercial platform and dedicated software required to bring this technique into mainstream science.

Human genotype-phenotype databases: aims, challenges and opportunities.

Genotype-phenotype databases provide information about genetic variation, its consequences and its mechanisms of action for research and health care purposes. Existing databases vary greatly in type, areas of focus and modes of operation. Despite ever larger and more intricate datasets--made possible by advances in DNA sequencing, omics methods and phenotyping technologies--steady progress is being made towards integrating these databases rather than using them as separate entities. The consequential shift in focus from single-gene variants towards large gene panels, exomes, whole genomes and myriad observable characteristics creates new challenges and opportunities in database design, interpretation of variant pathogenicity and modes of data representation and use.

International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases.

Provision of a molecularly confirmed diagnosis in a timely manner for children and adults with rare genetic diseases shortens their "diagnostic odyssey," improves disease management, and fosters genetic counseling with respect to recurrence risks while assuring reproductive choices. In a general clinical genetics setting, the current diagnostic rate is approximately 50%, but for those who do not receive a molecular diagnosis after the initial genetics evaluation, that rate is much lower. Diagnostic success for these more challenging affected individuals depends to a large extent on progress in the discovery of genes associated with, and mechanisms underlying, rare diseases. Thus, continued research is required for moving toward a more complete catalog of disease-related genes and variants. The International Rare Diseases Research Consortium (IRDiRC) was established in 2011 to bring together researchers and organizations invested in rare disease research to develop a means of achieving molecular diagnosis for all rare diseases. Here, we review the current and future bottlenecks to gene discovery and suggest strategies for enabling progress in this regard. Each successful discovery will define potential diagnostic, preventive, and therapeutic opportunities for the corresponding rare disease, enabling precision medicine for this patient population.

The sputum microbiome is distinct between COPD and health, independent of smoking history.

BACKGROUND: Airway bacterial dysbiosis is a feature of chronic obstructive pulmonary disease (COPD). However, there is limited comparative data of the lung microbiome between healthy smokers, non-smokers and COPD. METHODS: We compared the 16S rRNA gene-based sputum microbiome generated from pair-ended Illumina sequencing of 124 healthy subjects (28 smokers and 96 non-smokers with normal lung function), with single stable samples from 218 COPD subjects collected from three UK clinical centres as part of the COPDMAP consortium. RESULTS: In healthy subjects Firmicutes, Bacteroidetes and Actinobacteria were the major phyla constituting 88% of the total reads, and Streptococcus, Veillonella, Prevotella, Actinomyces and Rothia were the dominant genera. Haemophilus formed only 3% of the healthy microbiome. In contrast, Proteobacteria was the most dominant phylum accounting for 50% of the microbiome in COPD subjects, with Haemophilus and Moraxella at genus level contributing 25 and 3% respectively. There were no differences in the microbiome profile within healthy and COPD subgroups when stratified based on smoking history. Principal coordinate analysis on operational taxonomic units showed two distinct clusters, representative of healthy and COPD subjects (PERMANOVA, p = 0·001). CONCLUSION: The healthy and COPD sputum microbiomes are distinct and independent of smoking history. Our results underline the important role for Gammaproteobacteria in COPD.

Consent Codes: Upholding Standard Data Use Conditions.

A systematic way of recording data use conditions that are based on consent permissions as found in the datasets of the main public genome archives (NCBI dbGaP and EMBL-EBI/CRG EGA).

VariantValidator: Accurate validation, mapping, and formatting of sequence variation descriptions.

The Human Genome Variation Society (HGVS) variant nomenclature is widely used to describe sequence variants in scientific publications, clinical reports, and databases. However, the HGVS recommendations are complex and this often results in inaccurate variant descriptions being reported. The open-source hgvs Python package (https://github.com/biocommons/hgvs) provides a programmatic interface for parsing, manipulating, formatting, and validating of variants according to the HGVS recommendations, but does not provide a user-friendly Web interface. We have developed a Web-based variant validation tool, VariantValidator (https://variantvalidator.org/), which utilizes the hgvs Python package and provides additional functionality to assist users who wish to accurately describe and report sequence-level variations that are compliant with the HGVS recommendations. VariantValidator was designed to ensure that users are guided through the intricacies of the HGVS nomenclature, for example, if the user makes a mistake, VariantValidator automatically corrects the mistake if it can, or provides helpful guidance if it cannot. In addition, VariantValidator has the facility to interconvert genomic variant descriptions in HGVS and Variant Call Format with a degree of accuracy that surpasses most competing solutions.

The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease.

The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available.

Sputum microbiome temporal variability and dysbiosis in chronic obstructive pulmonary disease exacerbations: an analysis of the COPDMAP study.

BACKGROUND: Recent studies suggest that lung microbiome dysbiosis, the disease associated disruption of the lung microbial community, might play a key role in chronic obstructive pulmonary disease (COPD) exacerbations. However, characterising temporal variability of the microbiome from large longitudinal COPD cohorts is needed to better understand this phenomenon. METHODS: We performed a 16S ribosomal RNA survey of microbiome on 716 sputum samples collected longitudinally at baseline and exacerbations from 281 subjects with COPD at three UK clinical centres as part of the COPDMAP consortium. RESULTS: The microbiome composition was similar among centres and between stable and exacerbations except for a small significant decrease of Veillonella at exacerbations. The abundance of Moraxella was negatively associated with bacterial alpha diversity. Microbiomes were distinct between exacerbations associated with bacteria versus eosinophilic airway inflammation. Dysbiosis at exacerbations, measured as significant within subject deviation of microbial composition relative to baseline, was present in 41% of exacerbations. Dysbiosis was associated with increased exacerbation severity indicated by a greater fall in forced expiratory volume in one second, forced vital capacity and a greater increase in CAT score, particularly in exacerbations with concurrent eosinophilic inflammation. There was a significant difference of temporal variability of microbial alpha and beta diversity among centres. The variation of beta diversity significantly decreased in those subjects with frequent historical exacerbations. CONCLUSIONS: Microbial dysbiosis is a feature of some exacerbations and its presence, especially in concert with eosinophilic inflammation, is associated with more severe exacerbations indicated by a greater fall in lung function. TRIAL REGISTRATION NUMBER: Results, NCT01620645.

European Prevention of Alzheimer's Dementia Registry: Recruitment and prescreening approach for a longitudinal cohort and prevention trials.

INTRODUCTION: It is a challenge to find participants for Alzheimer's disease (AD) prevention trials within a short period of time. The European Prevention of Alzheimer's Dementia Registry (EPAD) aims to facilitate recruitment by preselecting subjects from ongoing cohort studies. This article introduces this novel approach. METHODS: A virtual registry, with access to risk factors and biomarkers for AD through minimal data sets of ongoing cohort studies, was set up. RESULTS: To date, ten cohorts have been included in the EPAD. Around 2500 participants have been selected, using variables associated with the risk for AD. Of these, 15% were already recruited in the EPAD longitudinal cohort study, which serves as a trial readiness cohort. DISCUSSION: This study demonstrates that a virtual registry can be used for the preselection of participants for AD studies.

Blood and sputum eosinophils in COPD; relationship with bacterial load.

BACKGROUND: Sputum and blood eosinophil counts predict corticosteroid effects in COPD patients. Bacterial infection causes increased airway neutrophilic inflammation. The relationship of eosinophil counts with airway bacterial load in COPD patients is uncertain. We tested the hypothesis that bacterial load and eosinophil counts are inversely related. METHODS: COPD patients were seen at stable state and exacerbation onset. Sputum was processed for quantitative polymerase chain reaction detection of the potentially pathogenic microorganisms (PPM) H. influenzae, M. catarrhalis and S. pneumoniae. PPM positive was defined as total load ≥1 × 104copies/ml. Sputum and whole blood were analysed for differential cell counts. RESULTS: At baseline, bacterial counts were not related to blood eosinophils, but sputum eosinophil % was significantly lower in patients with PPM positive compared to PPM negative samples (medians: 0.5% vs. 1.25% respectively, p = 0.01). Patients with PPM positive samples during an exacerbation had significantly lower blood eosinophil counts at exacerbation compared to baseline (medians: 0.17 × 109/L vs. 0.23 × 109/L respectively, p = 0.008), while no blood eosinophil change was observed with PPM negative samples. CONCLUSIONS: These findings indicate an inverse relationship between bacterial infection and eosinophil counts. Bacterial infection may influence corticosteroid responsiveness by altering the profile of neutrophilic and eosinophilic inflammation.

The BioMart community portal: an innovative alternative to large, centralized data repositories.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

Characteristics and longitudinal progression of chronic obstructive pulmonary disease in GOLD B patients.

BACKGROUND: The characteristics and natural history of GOLD B COPD patients are not well described. The clinical characteristics and natural history of GOLD B patients over 1 year in a multicentre cohort of COPD patients in the COPDMAP study were assessed. We aimed to identify the subgroup of patients who progressed to GOLD D (unstable GOLD B patients) and identify characteristics associated with progression. METHODS: Three hundred seventy COPD patients were assessed at baseline and 12 months thereafter. Demographics, lung function, health status, 6 min walk tests and levels of systemic inflammation were assessed. Students t tests and Mann Whitney-U tests were used. RESULTS: One hundred seven (28.9%) of patients were categorised as GOLD B at baseline. These GOLD B patients had similar FEV1 to GOLD A patients (66% predicted). More GOLD B patients were current smokers (p = 0.031), had chronic bronchitis (p = 0.0003) and cardiovascular comorbidities (p = 0.019) compared to GOLD A. At 12 months, 25.3% of GOLD B patients progressed to GOLD D. These patients who progressed (unstable patients) had worse health status and symptoms (SGRQ-C Total, 50.0 v 41.1, p = 0.019 and CAT, 21.0 v 14.0, p = 0.006) and lower FEV1 (60% v 69% p = 0.014) at baseline compared to stable patients who remained in GOLD B. CONCLUSIONS: Unstable GOLD B patients who progressed to GOLD D had a higher level of symptoms at baseline. A high symptom burden may predict an increased likelihood of disease progression in GOLD B patients.

Clinical Interpretation of Variants from Next-Generation Sequencing: The 2016 Scientific Meeting of the Human Genome Variation Society.

The 2016 scientific meeting of the Human Genome Variation Society (HGVS; http://www.hgvs.org) was held on the 20th of May in Barcelona, Spain, with the theme of "Clinical Interpretation of Variants from Next-Generation Sequencing."

Data Safe Havens in health research and healthcare.

MOTIVATION: The data that put the 'evidence' into 'evidence-based medicine' are central to developments in public health, primary and hospital care. A fundamental challenge is to site such data in repositories that can easily be accessed under appropriate technical and governance controls which are effectively audited and are viewed as trustworthy by diverse stakeholders. This demands socio-technical solutions that may easily become enmeshed in protracted debate and controversy as they encounter the norms, values, expectations and concerns of diverse stakeholders. In this context, the development of what are called 'Data Safe Havens' has been crucial. Unfortunately, the origins and evolution of the term have led to a range of different definitions being assumed by different groups. There is, however, an intuitively meaningful interpretation that is often assumed by those who have not previously encountered the term: a repository in which useful but potentially sensitive data may be kept securely under governance and informatics systems that are fit-for-purpose and appropriately tailored to the nature of the data being maintained, and may be accessed and utilized by legitimate users undertaking work and research contributing to biomedicine, health and/or to ongoing development of healthcare systems. RESULTS: This review explores a fundamental question: 'what are the specific criteria that ought reasonably to be met by a data repository if it is to be seen as consistent with this interpretation and viewed as worthy of being accorded the status of 'Data Safe Haven' by key stakeholders'? We propose 12 such criteria. CONTACT: paul.burton@bristol.ac.uk.

Human Genome Variation 2006: emerging views on structural variation and large-scale SNP analysis.

The eighth annual Human Genome Variation Meeting was held in September 2006 in the Hong Kong Special Administrative Region, China. The meeting highlighted recent advances in characterization of genetic variation, including genome-wide association studies and structural variation.

Cafe Variome: general-purpose software for making genotype-phenotype data discoverable in restricted or open access contexts.

Biomedical data sharing is desirable, but problematic. Data "discovery" approaches-which establish the existence rather than the substance of data-precisely connect data owners with data seekers, and thereby promote data sharing. Cafe Variome (http://www.cafevariome.org) was therefore designed to provide a general-purpose, Web-based, data discovery tool that can be quickly installed by any genotype-phenotype data owner, or network of data owners, to make safe or sensitive content appropriately discoverable. Data fields or content of any type can be accommodated, from simple ID and label fields through to extensive genotype and phenotype details based on ontologies. The system provides a "shop window" in front of data, with main interfaces being a simple search box and a powerful "query-builder" that enable very elaborate queries to be formulated. After a successful search, counts of records are reported grouped by "openAccess" (data may be directly accessed), "linkedAccess" (a source link is provided), and "restrictedAccess" (facilitated data requests and subsequent provision of approved records). An administrator interface provides a wide range of options for system configuration, enabling highly customized single-site or federated networks to be established. Current uses include rare disease data discovery, patient matchmaking, and a Beacon Web service.