Neptune: An environment for the delivery of genomic medicine

Abstract

PurposeGenomic medicine holds great promise for improving healthcare, but integrating searchable and actionable genetic data into electronic health records remains a challenge. Here, we describe Neptune, a system for managing the interaction between a clinical laboratory and an electronic health record system.MethodsWe developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis and EHR integration.ResultsNeptune enabled the analysis of data for generation of and delivery to EHR systems of over 15,000 clinical genomic reports. These projects demanded customizable clinical reports that contained a variety of genetic data types including SNVs, CNVs, pharmacogenomics and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow.ConclusionsMethods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open and available at https://gitlab.com/bcm-hgsc/neptune.

The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities

“…To understand the rates of previously-known pathogenic variants broken down by predicted ancestry groups in the All of Us data, we used the 'VIP' database to annotate P/LP variants 15 present in WGS data in All of Us participants across 73 genes with actionable secondary findings 12 . This database contains variants curated by the HGSC-CL variant interpretation group during projects such as eMERGE III 13 and HeartCare 14 as well as an initial assessment of de-identified variants from the All of Us cohort itself.…”

The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities

“…To understand the rates of previously-known pathogenic variants broken down by predicted ancestry groups in the All of Us data, we used the 'VIP' database to annotate P/LP variants 15 present in WGS data in All of Us participants across 73 genes with actionable secondary findings 12 . This database contains variants curated by the HGSC-CL variant interpretation group during projects such as eMERGE III 13 and HeartCare 14 as well as an initial assessment of de-identified variants from the All of Us cohort itself.…”

The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities

“…To understand the rates of previously-known pathogenic variants broken down by predicted ancestry groups in the All of Us data, we used the 'VIP' database to annotate P/LP variants 15 present in WGS data in All of Us participants across 73 genes with actionable secondary findings 12 . This database contains variants curated by the HGSC-CL variant interpretation group during projects such as eMERGE III 13 and HeartCare 14 as well as an initial assessment of de-identified variants from the All of Us cohort itself.…”

The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities

“…To understand the rates of previously-known pathogenic variants broken down by predicted ancestry groups in the All of Us data, we used the 'VIP' database to annotate P/LP variants 15 present in WGS data in All of Us participants across 73 genes with actionable secondary findings 12 . This database contains variants curated by the HGSC-CL variant interpretation group during projects such as eMERGE III 13 and HeartCare 14 as well as an initial assessment of de-identified variants from the All of Us cohort itself.…”