Biology subjects

Venner, E.

Publications and source records attributed to Venner, E..

8 recordsLinked to original sources

bioRxiv2026

Genetic and Epigenetic Landscape of Self-Identified Hispanics/Latino in All Of Us

Human genetic studies still underrepresent many populations, limiting discovery and interpretation of medically relevant variation. Extensive admixture in self-identified Hispanic/Latino populations increases the need for representative reference data and ancestry-aware interpretation of medically relevant variation. We used long-read sequencing to analyze genetic variation, ancestry, and DNA methylation in 1,490 self-identified Hispanic/Latino participants from the All of Us Research Program. We identified 10 million novel variants and more than 100 candidate phenotype associations, with fine-mapping prioritizing structural variants over nearby single-nucleotide variants in more than half. We linked 3,329 tandem repeats to DNA methylation, resolved widespread allele-specific methylation, and identified ancestry-linked regulation across paralogous genes. These results reveal previously inaccessible genetic and regulatory variation and show the value of long-read sequencing for diverse populations.

genetics↗

bioRxiv2021

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↗

bioRxiv2021

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↗

bioRxiv2021

Genomic Considerations for FHIR; eMERGE Implementation Lessons

Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Networks Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), to represent clinical genomics results. These new standards improve the utility of HL7 FHIR as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape.

genomics↗

bioRxiv2019

ARBoR: An Identity and Security Solution for Clinical Reporting

MotivationClinical genome sequencing laboratories return reports containing clinical testing results, signed by an Board Certified clinical geneticist, to the ordering physician. This report is often a pdf, but can also be a physical paper copy or a structured data file. The reports are frequently modified and re-issued, due to changes in variant interpretation or clinical attributes. To precisely track report authenticity we developed ARBoR, an application for tracking the lineage of versioned clinical reports even when they are distributed as pdf or paper copies. ARBoR employs a modified blockchain approach and instead of relying on a computationally intensive consensus mechanism for determining authenticity, we allow supervised and digitally signed writes to an encrypted ledger, which is then exactly replicated to many clients.\n\nResultsARBoR was implemented for clinical reporting in the HGSC-CL Clinical Laboratory, initially as part of the NIHs Electronic Medical Record and Genomics (eMERGE) project. To date we have issued 15,205 versioned clinical reports tracked by ARBoR. This system has provided us with a simple and tamper-proof mechanism for tracking clinical reports with a complicated update history.

bioinformatics↗

bioRxiv2018

Harmonizing Clinical Sequencing And Interpretation For The eMERGE III Network

BackgroundThe eMERGE III Network was tasked with harmonizing genetic testing protocols linking multiple sites and investigators.\n\nMethodsDNA capture panels targeting 109 genes and 1551 variants were constructed by two clinical sequencing centers for analysis of 25,000 participant DNA samples collected at 11 sites where samples were linked to patients with electronic health records. Each step from sample collection, data generation, interpretation, reporting, delivery and storage, were developed and validated in CAP/CLIA settings and harmonized across sequencing centers.\n\nResultsA compliant and secure network was built and enabled ongoing review and reconciliation of clinical interpretations while maintaining communication and data sharing between investigators. Mechanisms for sustained propagation and growth of the network were established. An interim data freeze representing 15,574 sequenced subjects, informed the assay performance for a range of variant types, the rate of return of results for different phenotypes and the frequency of secondary findings. Practical obstacles for implementation and scaling of clinical and research findings were identified and addressed. The eMERGE protocols and tools established are now available for widespread dissemination.\n\nConclusionsThis study established processes for different sequencing sites to harmonize the technical and interpretive aspects of sequencing tests, a critical achievement towards global standardization of genomic testing. The network established experience in the return of results and the rate of secondary findings across diverse biobank populations. Furthermore, the eMERGE network has accomplished integration of structured genomic results into multiple electronic health record systems, setting the stage for clinical decision support to enable genomic medicine.

genomics↗

bioRxiv2018

Atlas-CNV: a validated approach to call Single-Exon CNVs in the eMERGESeq gene panel

PurposeTo provide a validated method to confidently identify exon-containing copy number variants (CNVs), with a low false discovery rate (FDR), in targeted sequencing data from a clinical laboratory with particular focus on single-exon CNVs.\n\nMethodsDNA sequence coverage data are normalized within each sample and subsequently exonic CNVs are identified in a batch of samples (midpool), when the target log2 ratio of the sample to the batch median exceeds defined thresholds. The quality of exonic CNV calls is assessed by C-scores (Z-like scores) using thresholds derived from gold standard samples and simulation studies. We integrate an ExonQC threshold to lower FDR and compare performance with alternate software (VisCap).\n\nResultsThirteen CNVs were used as a truth set to validate Atlas-CNV and compared with VisCap. We demonstrated FDR reduction in validation, simulation and 10,926 eMERGESeq samples without sensitivity loss. Sixty-four multi-exon and 29 single-exon CNVs with high C-scores were assessed by MLPA.\n\nConclusionsAtlas-CNV is validated as a method to identify exonic CNVs in targeted sequencing data generated in the clinical laboratory. The ExonQC and C-score assignment can reduce FDR (identification of targets with high variance) and improve calling accuracy of single-exon CNVs respectively. We proposed guidelines and criteria to identify high confidence single-exon CNVs.

genomics↗

bioRxiv2018

xAtlas: Scalable small variant calling across heterogeneous next-generation sequencing experiments

MotivationThe rapid development of next-generation sequencing (NGS) technologies has lowered the barriers to genomic data generation, resulting in millions of samples sequenced across diverse experimental designs. The growing volume and heterogeneity of these sequencing data complicate the further optimization of methods for identifying DNA variation, especially considering that curated highconfidence variant call sets commonly used to evaluate these methods are generally developed by reference to results from the analysis of comparatively small and homogeneous sample sets.\n\nResultsWe have developed xAtlas, an application for the identification of single nucleotide variants (SNV) and small insertions and deletions (indels) in NGS data. xAtlas is easily scalable and enables execution and retraining with rapid development cycles. Generation of variant calls in VCF or gVCF format from BAM or CRAM alignments is accomplished in less than one CPU-hour per 30x short-read human whole-genome. The retraining capabilities of xAtlas allow its core variant evaluation models to be optimized on new sample data and user-defined truth sets. Obtaining SNV and indels calls from xAtlas can be achieved more than 40 times faster than established methods while retaining the same accuracy.\n\nAvailabilityFreely available under a BSD 3-clause license at https://github.com/jfarek/xatlas.\n\nContactfarek@bcm.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗