The NHGRI-EBI GWAS Catalog has provided data from published genome-wide association studies since 2008. In 2015, the database was redesigned and relocated to EMBL-EBI. The new infrastructure includes ...a new graphical user interface (www.ebi.ac.uk/gwas/), ontology supported search functionality and an improved curation interface. These developments have improved the data release frequency by increasing automation of curation and providing scaling improvements. The range of available Catalog data has also been extended with structured ancestry and recruitment information added for all studies. The infrastructure improvements also support scaling for larger arrays, exome and sequencing studies, allowing the Catalog to adapt to the needs of evolving study design, genotyping technologies and user needs in the future.
The National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (GWAS) Catalog provides a publicly available manually curated collection of published GWAS ...assaying at least 100,000 single-nucleotide polymorphisms (SNPs) and all SNP-trait associations with P <1 × 10(-5). The Catalog includes 1751 curated publications of 11 912 SNPs. In addition to the SNP-trait association data, the Catalog also publishes a quarterly diagram of all SNP-trait associations mapped to the SNPs' chromosomal locations. The Catalog can be accessed via a tabular web interface, via a dynamic visualization on the human karyotype, as a downloadable tab-delimited file and as an OWL knowledge base. This article presents a number of recent improvements to the Catalog, including novel ways for users to interact with the Catalog and changes to the curation infrastructure.
Abstract
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/.
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.
The analysis of DNA methylation has become routine in the pipeline for diagnosis of imprinting disorders, with many publications reporting aberrant methylation associated with imprinted ...differentially methylated regions (DMRs). However, comparisons between these studies are routinely hampered by the lack of consistency in reporting sites of methylation evaluated. To avoid confusion surrounding nomenclature, special care is needed to communicate results accurately, especially between scientists and other health care professionals. Within the European Network for Human Congenital Imprinting Disorders we have discussed these issues and designed a nomenclature for naming imprinted DMRs as well as for reporting methylation values. We apply these recommendations for imprinted DMRs that are commonly assayed in clinical laboratories and show how they support standardized database submission. The recommendations are in line with existing recommendations, most importantly the Human Genome Variation Society nomenclature, and should facilitate accurate reporting and data exchange among laboratories and thereby help to avoid future confusion.
Background
Variant interpretation is dependent on transcript annotation and remains time consuming and challenging. There are major obstacles for historical data reuse and for interpretation of new ...variants. First, both RefSeq and Ensembl/GENCODE produce transcript sets in common use, but there is currently no easy way to translate between the two. Second, the resources often used for variant interpretation (e.g. ClinVar, gnomAD, UniProt) do not use the same transcript set, nor default transcript or protein sequence.
Method
Ensembl ran a survey in 2018 to sample attitudes to choosing one default transcript per locus, and to gather data on reference sequences used by the scientific community. This was publicised on the Ensembl and UCSC genome browsers, by email and on social media.
Results
The survey had 788 responses from 32 different countries, the results of which we report here.
Conclusions
We present our roadmap to create an effective default set of transcripts for resources, and for reporting interpretation of clinical variants.
After decades of avoiding the demand to highlight one transcript per locus in Ensembl, we ran a survey 2018 to assay opinions across the scientific community. Ignoring the problem of ‘one transcript’ was not making the issue go away; many important genomic resources had instead adopted their own methods of selecting one transcript (e.g. HGMD, Ensembl, gnomAD, UniProt, ClinVar, etc.). Here we report our results and roadmap to create an effective default set of transcripts for resources, and for reporting interpretation of clinical variants.
Mental retardation is a pervasive societal problem, 25 times more common than blindness for example. Fragile X syndrome, the most common form of inherited mental retardation, is caused by mutations ...in the
FMR1 gene. Fragile X patients display neurite morphology defects in the brain, suggesting that this may be the basis of their mental retardation.
Drosophila contains a single homolog of FMR1,
dfxr (also called
dfmr1). We analyzed the role of
dfxr in neurite development in three distinct neuronal classes. We find that DFXR is required for normal neurite extension, guidance, and branching.
dfxr mutants also display strong eclosion failure and circadian rhythm defects. Interestingly, distinct neuronal cell types show different phenotypes, suggesting that
dfxr differentially regulates diverse targets in the brain.
Clinical genetic testing of protein-coding regions identifies a likely causative variant in only around half of developmental disorder (DD) cases. The contribution of regulatory variation in ...non-coding regions to rare disease, including DD, remains very poorly understood. We screened 9,858 probands from the Deciphering Developmental Disorders (DDD) study for de novo mutations in the 5′ untranslated regions (5′ UTRs) of genes within which variants have previously been shown to cause DD through a dominant haploinsufficient mechanism. We identified four single-nucleotide variants and two copy-number variants upstream of MEF2C in a total of ten individual probands. We developed multiple bespoke and orthogonal experimental approaches to demonstrate that these variants cause DD through three distinct loss-of-function mechanisms, disrupting transcription, translation, and/or protein function. These non-coding region variants represent 23% of likely diagnoses identified in MEF2C in the DDD cohort, but these would all be missed in standard clinical genetics approaches. Nonetheless, these variants are readily detectable in exome sequence data, with 30.7% of 5′ UTR bases across all genes well covered in the DDD dataset. Our analyses show that non-coding variants upstream of genes within which coding variants are known to cause DD are an important cause of severe disease and demonstrate that analyzing 5′ UTRs can increase diagnostic yield. We also show how non-coding variants can help inform both the disease-causing mechanism underlying protein-coding variants and dosage tolerance of the gene.
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