Establishing the GWAS Catalog as a resource for large-scale association studies

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PROJECT SUMMARY: Accelerating access and sharing of COVID-19 human host genetic and phenotype data Early evidence from twin studies suggests that approximately 50% of COVID-19 disease burden is determined by host genetics. The identification of host factors for COVID-19 will directly influence the development of public health intervention strategies and the identification of drug targets. There are a variety of existing cohort longitudinal studies with existing genetic and clinical data, e.g. UK Biobank, AllofUs, 23andMe, Ancestry.com who are engaging existing cohort participants for information on COVID-19 disease burden. The COVID-19 Host Genetics Initiative (COVID-19-HGI) is an international consortium that aims to identify host genetic associations of COVID-19 by combining data from human cohorts. The European Genome-phenome Archive (EGA) and the NHGRI Analysis, Visualization, and Informatics Lab-space AnVIL/Terra platforms are founding partners that form the data sharing and analysis platform. The EGA is a GA4GH driver project and can rapidly acquire these data enabling ethical genomic data sharing. This extends the international data sharing infrastructure and processes enabling access to human controlled access data relevant to addressing the COVID-19 pandemic. Aim 1: Host submissions to the COVID-19-HGI data sharing platform The EGA has previously received submissions from over 144 US submitters and US based users represent 33% of the total user community which streams 8.6 PB of data last year. The COVID-19 pandemic is expected to significantly increase this number through planned new industrial collaboration (e.g. Ancestry.com, Regeneron Pharmaceuticals). There is an opportunity to develop new submission templates and processes to enable more rapid submission of genetic and phenotype data. Aim 2: COVID-19 host metadata harmonisation Recording and collection of clinical patient data of COVID-19 disease burden is a critical requirement. Phenotype information is collected using a variety of formats, coding schemes, surveys, and ontologies. Using the COVID-19-HGI data dictionary, we will construct a common minimal metadata model that will map across COVID-19 studies for genetic association studies. Aim 3: Rapid integrated data access and flow into COVID-19-HGI analysis platform Rapid integration of new human genotypes and phenotyping will be essential to determine reliable and well supported genetic associations. The NHGRI AnVIL and Terra platform will be the analysis platform for the COVID-19-HGI. We will use GA4GH standards to provide rapid data access and integration of US COVID- 19 data. This will result in more rapid and seamless human data flow between EGA and AnVIL to provide additional power to COVID-19 host association studies