The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.

The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.

Mutations of the catalytic subunit of RAB3GAP cause Warburg Micro syndrome.

Warburg Micro syndrome (WARBM1) is a severe autosomal recessive disorder characterized by developmental abnormalities of the eye and central nervous system and by microgenitalia. We identified homozygous inactivating mutations in RAB3GAP, encoding RAB3 GTPase activating protein, a key regulator of the Rab3 pathway implicated in exocytic release of neurotransmitters and hormones, in 12 families with Micro syndrome. We hypothesize that the underlying pathogenesis of Micro syndrome is a failure of exocytic release of ocular and neurodevelopmental trophic factors.

Recent advances in polygenic scores: translation, equitability, methods and FAIR tools.

Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.

The Polygenic Score Catalog: new functionality and tools to enable FAIR research.

Polygenic scores (PGS) have transformed human genetic research and have multiple potential clinical applications, including risk stratification for disease prevention and prediction of treatment response. Here, we present a series of recent enhancements to the PGS Catalog (www.PGSCatalog.org), the largest findable, accessible, interoperable, and reusable (FAIR) repository of PGS. These include expansions in data content and ancestral diversity as well as the addition of new features. We further present the PGS Catalog Calculator (pgsc_calc, https://github.com/PGScatalog/pgsc_calc), an open-source, scalable and portable pipeline to reproducibly calculate PGS that securely democratizes equitable PGS applications by implementing genetic ancestry estimation and score normalization using reference data. With the PGS Catalog & calculator users can now quantify an individual's genetic predisposition for hundreds of common diseases and clinically relevant traits. Taken together, these updates and tools facilitate the next generation of PGS, thus lowering barriers to the clinical studies necessary to identify where PGS may be integrated into clinical practice.

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Proteomic analysis of the maternal protein restriction rat model for schizophrenia: identification of translational changes in hormonal signaling pathways and glutamate neurotransmission.

Previous studies have found that some first onset schizophrenia patients show signs of impaired insulin signaling. Also, epidemiological studies have shown that periods of suboptimal nutrition including protein deficiencies during pregnancy can lead to increased incidence of metabolic conditions and psychiatric disorders in the offspring. For these reasons, we have carried out a molecular profiling analysis of blood serum and brain tissues from adult offspring produced by the maternal low protein (LP) rat model. The results showed similar changes to those seen in schizophrenia. Multiplex immunoassay profiling identified changes in the levels of insulin, adiponectin, and leptin along with alterations in inflammatory and vascular system-related proteins such as osteopontin, macrophage colony-stimulating factor 1, and vascular cell adhesion molecule 1. LC-MS(E) proteomic profiling showed that glutamatergic pathways were altered in frontal cortex, while signaling pathways and cytoskeletal proteins involved in hormonal secretion and synaptic remodeling were altered in the hypothalamus. Taken together, these studies indicate that the LP rat model recapitulates several pathophysiological attributes seen in schizophrenia patients. We propose that the LP model may have utility for drug discovery efforts, especially to identify compounds that modulate the metabolic and glutamatergic systems.

Identification of targeted analyte clusters for studies of schizophrenia.

The search for biomarkers to diagnose psychiatric disorders such as schizophrenia has been underway for decades. Many molecular profiling studies in this field have focused on identifying individual marker signals that show significant differences in expression between patients and the normal population. However, signals for multiple analyte combinations that exhibit patterned behaviors have been less exploited. Here, we present a novel approach for identifying biomarkers of schizophrenia using expression of serum analytes from first onset, drug-naïve patients and normal controls. The strength of patterned signals was amplified by analyzing data in reproducing kernel spaces. This resulted in the identification of small sets of analytes referred to as targeted clusters that have discriminative power specifically for schizophrenia in both human and rat models. These clusters were associated with specific molecular signaling pathways and less strongly related to other neuropsychiatric disorders such as major depressive disorder and bipolar disorder. These results shed new light concerning how complex neuropsychiatric diseases behave at the pathway level and demonstrate the power of this approach in identification of disease-specific biomarkers and potential novel therapeutic strategies.

Analysis of the human pituitary proteome by data independent label-free liquid chromatography tandem mass spectrometry.

Studies of pituitary-related disorders would be facilitated by enhanced knowledge of the pituitary proteome. To construct a data set of human pituitary proteins, separate protein extracts were prepared from 15 post-mortem pituitaries and analyzed by data independent label-free nanoflow liquid chromatography mass spectrometry (nLC-MS(E) ). The detected mass/time features were aligned and quantified using the Rosetta Elucidator(®) system and annotated using results from ProteinLynx Global Server. The resulting data set comprised 1007 unique proteins, with stringent identification by a minimum of two distinct peptides. These proteins consisted predominantly of enzymes, transporters, transcription/translation factors, cell structure and secreted proteins.

Characterizing the proteome of the human dorsolateral prefrontal cortex by shotgun mass spectrometry.

The studies of neuropsychiatric disorders would be facilitated by enhanced knowledge of the dorsolateral prefrontal cortex (DLPFC) proteome. To construct a data set of human DLPFC proteins, protein extracts were prepared from 12 postmortem brains focussing on the DLPFC region (Brodmann area 9) and analyzed using a combined gel electrophoresis and shotgun mass spectrometry approach, featuring data-independent label-free nanoflow liquid chromatography mass spectrometry (nLC-MS(E)). The detected mass/time features were aligned and annotated using the results from ProteinLynx Global Server. The resulting data set comprised 488 unique and accurately identified proteins, with stringent identification by a minimum of two distinct peptides detected at least in >75% of samples. These proteins were involved predominantly in cytoskeletal architecture, metabolism, transcription/translation, and synaptic function. Combination of this data set with that obtained by our previous characterization of the same brain region results in a total of 755 unique proteins, making this the most comprehensive analysis of this important brain region to date.

Sequencing-based genome-wide association studies reporting standards.

Genome sequencing has recently become a viable genotyping technology for use in genome-wide association studies (GWASs), offering the potential to analyze a broader range of genome-wide variation, including rare variants. To survey current standards, we assessed the content and quality of reporting of statistical methods, analyses, results, and datasets in 167 exome- or genome-wide-sequencing-based GWAS publications published from 2014 to 2020; 81% of publications included tests of aggregate association across multiple variants, with multiple test models frequently used. We observed a lack of standardized terms and incomplete reporting of datasets, particularly for variants analyzed in aggregate tests. We also find a lower frequency of sharing of summary statistics compared with array-based GWASs. Reporting standards and increased data sharing are required to ensure sequencing-based association study data are findable, interoperable, accessible, and reusable (FAIR). To support that, we recommend adopting the standard terminology of sequencing-based GWAS (seqGWAS). Further, we recommend that single-variant analyses be reported following the same standards and conventions as standard array-based GWASs and be shared in the GWAS Catalog. We also provide initial recommended standards for aggregate analyses metadata and summary statistics.

Workshop proceedings: GWAS summary statistics standards and sharing.

Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.

The role of proteomics in depression research.

Depression is a severe neuropsychiatric disorder affecting approximately 10% of the world population. Despite this, the molecular mechanisms underlying the disorder are still not understood. Novel technologies such as proteomic-based platforms are beginning to offer new insights into this devastating illness, beyond those provided by the standard targeted methodologies. Here, we will show the potential of proteome analyses as a tool to elucidate the pathophysiological mechanisms of depression as well as the discovery of potential diagnostic, therapeutic and disease course biomarkers.

Denser brain capillary network with preserved pericytes in Alzheimer's disease.

Pericytes are vascular mural cells that surround capillaries of the central nervous system (CNS). They are crucial for brain development and contribute to CNS homeostasis by regulating blood-brain barrier function and cerebral blood flow. It has been suggested that pericytes are lost in Alzheimer's disease (AD), implicating this cell type in disease pathology. Here, we have employed state-of-the-art stereological morphometry techniques as well as tissue clearing and two-photon imaging to assess the distribution of pericytes in two independent cohorts of AD (n = 16 and 13) and non-demented controls (n = 16 and 4). Stereological quantification revealed increased capillary density with a normal pericyte population in the frontal cortex of AD brains, a region with early amyloid ß deposition. Two-photon analysis of cleared frontal cortex tissue confirmed the preservation of pericytes in AD cases. These results suggest that pericyte demise is not a general hallmark of AD pathology.

A standardized framework for representation of ancestry data in genomics studies, with application to the NHGRI-EBI GWAS Catalog.

The accurate description of ancestry is essential to interpret, access, and integrate human genomics data, and to ensure that these benefit individuals from all ancestral backgrounds. However, there are no established guidelines for the representation of ancestry information. Here we describe a framework for the accurate and standardized description of sample ancestry, and validate it by application to the NHGRI-EBI GWAS Catalog. We confirm known biases and gaps in diversity, and find that African and Hispanic or Latin American ancestry populations contribute a disproportionately high number of associations. It is our hope that widespread adoption of this framework will lead to improved analysis, interpretation, and integration of human genomics data.

Identification of a molecular profile associated with immune status in first-onset schizophrenia patients.

OBJECTIVES: Alterations in immunological parameters have been reported for schizophrenia although little is known about the effects of inflammatory status on immune-related functional changes at disease onset. Here, we have investigated such T cell-dependent molecular changes in first-onset, antipsychotic-naive schizophrenia patients using a novel ex vivo blood culture system. METHODS: Blood samples from patients (n=17) and controls (n=17) were collected into stimulant-containing or null control TruCulture™ tubes, incubated 24 hours and the concentrations of 107 immune and metabolic molecules measured in the conditioned media using the HumanMAP™ immunoassay system. RESULTS: Nine molecules showed altered release from schizophrenia blood cells compared to those from controls and this was replicated in an independent cohort. In silico pathway analysis showed that these molecules had roles in endothelial cell function, inflammation, acute phase response and fibrinolysis pathways. Importantly, five of these molecules showed altered release only after stimulation. CONCLUSIONS: This study has identified a reproducible peripheral molecular signature associated with altered immune function in first-onset schizophrenia subjects. This suggests that immune status can affect the biomarker profile which could be important for personalized medicine strategies. Furthermore, whole blood culture analysis may be useful in the identification of diagnostic tools or novel treatment strategies due to ease-of-use and clinical accessibility.

Schizophrenia: metabolic aspects of aetiology, diagnosis and future treatment strategies.

Despite decades of research, the pathophysiology and aetiology of schizophrenia remains incompletely understood. The disorder is frequently accompanied by metabolic symptoms including dyslipidaemia, hyperinsulinaemia, type 2 diabetes and obesity. These symptoms are a common side effect of currently available antipsychotic medications. However, reports of metabolic dysfunction in schizophrenia predate the antipsychotic era and have also been observed in first onset patients prior to antipsychotic treatment. Here, we review the evidence for abnormalities in metabolism in schizophrenia patients, both in the central nervous system and periphery. Molecular analysis of post mortem brain tissue has pointed towards alterations in glucose metabolism and insulin signalling pathways, and blood-based molecular profiling analyses have demonstrated hyperinsulinaemia and abnormalities in secretion of insulin and co-released factors at first presentation of symptoms. Nonetheless, such features are not observed for all subjects with the disorder and not all individuals with such abnormalities suffer the symptoms of schizophrenia. One interpretation of these data is the presence of an underlying metabolic vulnerability in a subset of individuals which interacts with environmental or genetic factors to produce the overt symptoms of the disorder. Further investigation of metabolic aspects of schizophrenia may prove critical for diagnosis, improvement of existing treatment based on patient stratification/personalised medicine strategies and development of novel antipsychotic agents.

Metabolic, hormonal and stress-related molecular changes in post-mortem pituitary glands from schizophrenia subjects.

OBJECTIVES: To identify a molecular profile for schizophrenia using post-mortem pituitaries from schizophrenia and control subjects. METHODS: Molecular profiling analysis of pituitaries from schizophrenia (n = 14) and control (n = 15) subjects was carried out using a combination of liquid chromatography tandem mass spectrometry (LC-MS(E)), multiplex analyte profiling (MAP), two-dimensional difference gel electrophoresis (2D-DIGE) and Western blot analysis. RESULTS: This led to identification of differentially expressed molecules in schizophrenia patients including hypothalamic-pituitary-adrenal axis-associated constituents such as cortisol, pro-adrenocorticotropic hormone, arginine vasopressin precursor, agouti-related protein, growth hormone, prolactin and secretagogin, as well as molecules associated with lipid transport and metabolism such as apolipoproteins A1, A2, C3 and H. Altered levels of secretagogin in serum from a cohort of living first onset schizophrenia patients were also detected, suggesting disease association and illustrating the potential for translating some components of this molecular profile to serum-based assays. CONCLUSIONS: Future studies on the molecules identified here may lead to new insights into schizophrenia pathophysiology and pave the way for translation of novel diagnostics for use in a clinical setting.