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 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.

Characterization of the TBR1 interactome: variants associated with neurodevelopmental disorders disrupt novel protein interactions.

TBR1 is a neuron-specific transcription factor involved in brain development and implicated in a neurodevelopmental disorder (NDD) combining features of autism spectrum disorder (ASD), intellectual disability (ID) and speech delay. TBR1 has been previously shown to interact with a small number of transcription factors and co-factors also involved in NDDs (including CASK, FOXP1/2/4 and BCL11A), suggesting that the wider TBR1 interactome may have a significant bearing on normal and abnormal brain development. Here, we have identified approximately 250 putative TBR1-interaction partners by affinity purification coupled to mass spectrometry. As well as known TBR1-interactors such as CASK, the identified partners include transcription factors and chromatin modifiers, along with ASD- and ID-related proteins. Five interaction candidates were independently validated using bioluminescence resonance energy transfer assays. We went on to test the interaction of these candidates with TBR1 protein variants implicated in cases of NDD. The assays uncovered disturbed interactions for NDD-associated variants and identified two distinct protein-binding domains of TBR1 that have essential roles in protein-protein interaction.

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.

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 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/.

Functional characterization of TBR1 variants in neurodevelopmental disorder.

Recurrent de novo variants in the TBR1 transcription factor are implicated in the etiology of sporadic autism spectrum disorders (ASD). Disruptions include missense variants located in the T-box DNA-binding domain and previous work has demonstrated that they disrupt TBR1 protein function. Recent screens of thousands of simplex families with sporadic ASD cases uncovered additional T-box variants in TBR1 but their etiological relevance is unclear. We performed detailed functional analyses of de novo missense TBR1 variants found in the T-box of ASD cases, assessing many aspects of protein function, including subcellular localization, transcriptional activity and protein-interactions. Only two of the three tested variants severely disrupted TBR1 protein function, despite in silico predictions that all would be deleterious. Furthermore, we characterized a putative interaction with BCL11A, a transcription factor that was recently implicated in a neurodevelopmental syndrome involving developmental delay and language deficits. Our findings enhance understanding of molecular functions of TBR1, as well as highlighting the importance of functional testing of variants that emerge from next-generation sequencing, to decipher their contributions to neurodevelopmental disorders like ASD.

Proteomic analysis of FOXP proteins reveals interactions between cortical transcription factors associated with neurodevelopmental disorders.

FOXP transcription factors play important roles in neurodevelopment, but little is known about how their transcriptional activity is regulated. FOXP proteins cooperatively regulate gene expression by forming homo- and hetero-dimers with each other. Physical associations with other transcription factors might also modulate the functions of FOXP proteins. However, few FOXP-interacting transcription factors have been identified so far. Therefore, we sought to discover additional transcription factors that interact with the brain-expressed FOXP proteins, FOXP1, FOXP2 and FOXP4, through affinity-purifications of protein complexes followed by mass spectrometry. We identified seven novel FOXP-interacting transcription factors (NR2F1, NR2F2, SATB1, SATB2, SOX5, YY1 and ZMYM2), five of which have well-estabslished roles in cortical development. Accordingly, we found that these transcription factors are co-expressed with FoxP2 in the deep layers of the cerebral cortex and also in the Purkinje cells of the cerebellum, suggesting that they may cooperate with the FoxPs to regulate neural gene expression in vivo. Moreover, we demonstrated that etiological mutations of FOXP1 and FOXP2, known to cause neurodevelopmental disorders, severely disrupted the interactions with FOXP-interacting transcription factors. Additionally, we pinpointed specific regions within FOXP2 sequence involved in mediating these interactions. Thus, by expanding the FOXP interactome we have uncovered part of a broader neural transcription factor network involved in cortical development, providing novel molecular insights into the transcriptional architecture underlying brain development and neurodevelopmental disorders.

Equivalent missense variant in the FOXP2 and FOXP1 transcription factors causes distinct neurodevelopmental disorders.

The closely related paralogues FOXP2 and FOXP1 encode transcription factors with shared functions in the development of many tissues, including the brain. However, while mutations in FOXP2 lead to a speech/language disorder characterized by childhood apraxia of speech (CAS), the clinical profile of FOXP1 variants includes a broader neurodevelopmental phenotype with global developmental delay, intellectual disability, and speech/language impairment. Using clinical whole-exome sequencing, we report an identical de novo missense FOXP1 variant identified in three unrelated patients. The variant, p.R514H, is located in the forkhead-box DNA-binding domain and is equivalent to the well-studied p.R553H FOXP2 variant that cosegregates with CAS in a large UK family. We present here for the first time a direct comparison of the molecular and clinical consequences of the same mutation affecting the equivalent residue in FOXP1 and FOXP2. Detailed functional characterization of the two variants in cell model systems revealed very similar molecular consequences, including aberrant subcellular localization, disruption of transcription factor activity, and deleterious effects on protein interactions. Nonetheless, clinical manifestations were broader and more severe in the three cases carrying the p.R514H FOXP1 variant than in individuals with the p.R553H variant related to CAS, highlighting divergent roles of FOXP2 and FOXP1 in neurodevelopment.

Identification and functional characterization of de novo FOXP1 variants provides novel insights into the etiology of neurodevelopmental disorder.

De novo disruptions of the neural transcription factor FOXP1 are a recently discovered, rare cause of sporadic intellectual disability (ID). We report three new cases of FOXP1-related disorder identified through clinical whole-exome sequencing. Detailed phenotypic assessment confirmed that global developmental delay, autistic features, speech/language deficits, hypotonia and mild dysmorphic features are core features of the disorder. We expand the phenotypic spectrum to include sensory integration disorder and hypertelorism. Notably, the etiological variants in these cases include two missense variants within the DNA-binding domain of FOXP1. Only one such variant has been reported previously. The third patient carries a stop-gain variant. We performed functional characterization of the three missense variants alongside our stop-gain and two previously described truncating/frameshift variants. All variants severely disrupted multiple aspects of protein function. Strikingly, the missense variants had similarly severe effects on protein function as the truncating/frameshift variants. Our findings indicate that a loss of transcriptional repression activity of FOXP1 underlies the neurodevelopmental phenotype in FOXP1-related disorder. Interestingly, the three novel variants retained the ability to interact with wild-type FOXP1, suggesting these variants could exert a dominant-negative effect by interfering with the normal FOXP1 protein. These variants also retained the ability to interact with FOXP2, a paralogous transcription factor disrupted in rare cases of speech and language disorder. Thus, speech/language deficits in these individuals might be worsened through deleterious effects on FOXP2 function. Our findings highlight that de novo FOXP1 variants are a cause of sporadic ID and emphasize the importance of this transcription factor in neurodevelopment.

Fetal akinesia: review of the genetics of the neuromuscular causes.

Fetal akinesia refers to a broad spectrum of disorders in which the unifying feature is a reduction or lack of fetal movement. Fetal akinesias may be caused by defects at any point along the motor system pathway including the central and peripheral nervous system, the neuromuscular junction and the muscle, as well as by restrictive dermopathy or external restriction of the fetus in utero. The fetal akinesias are clinically and genetically heterogeneous, with causative mutations identified to date in a large number of genes encoding disparate parts of the motor system. However, for most patients, the molecular cause remains unidentified. One reason for this is because the tools are only now becoming available to efficiently and affordably identify mutations in a large panel of disease genes. Next-generation sequencing offers the promise, if sufficient cohorts of patients can be assembled, to identify the majority of the remaining genes on a research basis and facilitate efficient clinical molecular diagnosis. The benefits of identifying the causative mutation(s) for each individual patient or family include accurate genetic counselling and the options of prenatal diagnosis or preimplantation genetic diagnosis. In this review, we summarise known single-gene disorders affecting the spinal cord, peripheral nerves, neuromuscular junction or skeletal muscles that result in fetal akinesia. This audit of these known molecular and pathophysiological mechanisms involved in fetal akinesia provides a basis for improved molecular diagnosis and completing disease gene discovery.