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Kovar, C.

Publications and source records attributed to Kovar, C..

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Genetic Sex Validation for Sample Tracking in Clinical Testing

BackgroundNext generation DNA sequencing (NGS) has been rapidly adopted by clinical testing laboratories for detection of germline and somatic genetic variants. The complexity of sample processing in a clinical DNA sequencing laboratory creates multiple opportunities for sample identification errors, demanding stringent quality control procedures. MethodsWe utilized DNA genotyping via a 96-SNP PCR panel applied at sample acquisition in comparison to the final sequence, for tracking of sample identity throughout the sequencing pipeline. The 96-SNP PCR panels inclusion of sex SNPs also provides a mechanism for a genotype-based comparison to recorded sex at sample collection for identification. This approach was implemented in the clinical genomic testing pathways, in the multi-center Electronic Medical Records and Genomics (eMERGE) Phase III program ResultsWe identified 110 inconsistencies from 25,015 (0.44%) clinical samples, when comparing the 96-SNP PCR panel data to the test requisition-provided sex. The 96-SNP PCR panel genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density hybridization-based genotyping arrays. Results identified clerical errors, samples from transgender participants and stem cell or bone marrow transplant patients and undetermined sample mix-ups. ConclusionThe 96-SNP PCR panel provides a cost-effective, robust tool for tracking samples within DNA sequencing laboratories, while the ability to predict sex from genotyping data provides an additional quality control measure for all procedures, beginning with sample collections. While not sufficient to detect all sample mix-ups, the inclusion of genetic versus reported sex matching can give estimates of the rate of errors in sample collection systems.

genomics↗

Neptune: An environment for the delivery of genomic medicine

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.

bioinformatics↗