The Genomics Research and Innovation Network: creating an interoperable, federated, genomics learning system

Abstract

the Genomics Research and Innovation NetworkPurpose: Clinicians and researchers must contextualize a patient's genetic variants against population-based references with detailed phenotyping. We sought to establish globally scalable technology, policy, and procedures for sharing biosamples and associated genomic and phenotypic data on broadly consented cohorts, across sites of care.Methods: Three of the nation's leading children's hospitals launched the Genomic Research and Innovation Network (GRIN), with federated information technology infrastructure, harmonized biobanking protocols, and material transfer agreements. Pilot studies in epilepsy and short stature were completed to design and test the collaboration model. Results:Harmonized, broadly consented institutional review board (IRB) protocols were approved and used for biobank enrollment, creating ever-expanding, compatible biobanks. An open source federated query infrastructure was established over genotype-phenotype databases at the three hospitals. Investigators securely access the GRIN platform for prep to research queries, receiving aggregate counts of patients with particular phenotypes or genotypes in each biobank. With proper approvals, de-identified data is exported to a shared analytic workspace. Investigators at all sites enthusiastically collaborated on the pilot studies, resulting in multiple publications. Investigators have also begun to successfully utilize the infrastructure for grant applications. Conclusions:The GRIN collaboration establishes the technology, policy, and procedures for a scalable genomic research network.Genetics in Medicine (2020) 22:371-380; https://doi.

The urgency of utilizing COVID-19 biospecimens for research in the heart of the global pandemic

“…Most importantly, this approach reduces potential exposure by diminishing transfer of biohazardous materials, limiting the number of additional specimens needed for collection, and adhering to biosafety guidelines. Further, it creates the potential to generate a holistic, statistically powered, and standardized cohort characterization by compiling all results in one bank, and it fosters important long-term follow up by overcoming the well-documented issues with decentralized biobank sustainability [6][7][8][9][10][11][12][13][14]. The creation of this resource at NYULH has enriched the community with high-quality patient-linked COVID-19 specimens, which has already contributed to novel findings about SARS-CoV-2 epidemiology [3].…”

The urgency of utilizing COVID-19 biospecimens for research in the heart of the global pandemic

“…Most importantly, this approach reduces potential exposure by diminishing transfer of biohazardous materials, limiting the number of additional specimens needed for collection, and adhering to biosafety guidelines. Further, it creates the potential to generate a holistic, statistically powered, and standardized cohort characterization by compiling all results in one bank, and it fosters important long-term follow up by overcoming the well-documented issues with decentralized biobank sustainability [6][7][8][9][10][11][12][13][14] . The creation of this resource at NYULH has enriched the community with high-quality patientlinked COVID-19 specimens, which has already contributed to novel findings about SARS-CoV-2 epidemiology 3 .…”

Experiences implementing scalable, containerized, cloud-based NLP for extracting biobank participant phenotypes at scale

“…Patients with relevant phenotypes or genotypes can be discovered via the Portal query tool, which relies on the PIC-SURE API 12 to interrogate a PIC-SURE High Performance Data Store (HPDS). 13 Queries can be variant-first, phenotype first, or a combination. 13 To complement the structured EHR data, we performed high throughput processing on clinical notes for 8239 patients (as of February 1, 2019), so that phenotype queries could include SNOMED concepts.…”

“… 13 Queries can be variant-first, phenotype first, or a combination. 13 To complement the structured EHR data, we performed high throughput processing on clinical notes for 8239 patients (as of February 1, 2019), so that phenotype queries could include SNOMED concepts.…”