Dataset Information

A taxonomy of medical uncertainties in clinical genome sequencing.

ABSTRACT: Clinical next-generation sequencing (CNGS) is introducing new opportunities and challenges into the practice of medicine. Simultaneously, these technologies are generating uncertainties of an unprecedented scale that laboratories, clinicians, and patients are required to address and manage. We describe in this report the conceptual design of a new taxonomy of uncertainties around the use of CNGS in health care.Interviews to delineate the dimensions of uncertainty in CNGS were conducted with genomics experts and themes were extracted in order to expand on a previously published three-dimensional taxonomy of medical uncertainty. In parallel, we developed an interactive website to disseminate the CNGS taxonomy to researchers and engage them in its continued refinement.The proposed taxonomy divides uncertainty along three axes-source, issue, and locus-and further discriminates the uncertainties into five layers with multiple domains. Using a hypothetical clinical example, we illustrate how the taxonomy can be applied to findings from CNGS and used to guide stakeholders through interpretation and implementation of variant results.The utility of the proposed taxonomy lies in promoting consistency in describing dimensions of uncertainty in publications and presentations, to facilitate research design and management of the uncertainties inherent in the implementation of CNGS.Genet Med advance online publication 19 January 2017.

SUBMITTER: Han PKJ

PROVIDER: S-EPMC5517355 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:As whole exome sequencing (WES) and whole genome sequencing (WGS) transition from research tools to clinical diagnostic tests, it is increasingly critical for sequencing methods and analysis pipelines to be technically accurate. The Genome in a Bottle Consortium has recently published a set of benchmark SNV, indel, and homozygous reference genotypes for the pilot whole genome NIST Reference Material based on the NA12878 genome. METHODS:We examine the relationship between human genome complexity and genes/variants reported to be associated with human disease. Specifically, we map regions of medical relevance to benchmark regions of high or low confidence. We use benchmark data to assess the sensitivity and positive predictive value of two representative sequencing pipelines for specific classes of variation. RESULTS:We observe that the accuracy of a variant call depends on the genomic region, variant type, and read depth, and varies by analytical pipeline. We find that most false negative WGS calls result from filtering while most false negative WES variants relate to poor coverage. We find that only 74.6% of the exonic bases in ClinVar and OMIM genes and 82.1% of the exonic bases in ACMG-reportable genes are found in high-confidence regions. Only 990 genes in the genome are found entirely within high-confidence regions while 593 of 3,300 ClinVar/OMIM genes have less than 50% of their total exonic base pairs in high-confidence regions. We find greater than 77 % of the pathogenic or likely pathogenic SNVs currently in ClinVar fall within high-confidence regions. We identify sites that are prone to sequencing errors, including thousands present in publicly available variant databases. Finally, we examine the clinical impact of mandatory reporting of secondary findings, highlighting a false positive variant found in BRCA2. CONCLUSIONS:Together, these data illustrate the importance of appropriate use and continued improvement of technical benchmarks to ensure accurate and judicious interpretation of next-generation DNA sequencing results in the clinical setting.

Dataset Information

A taxonomy of medical uncertainties in clinical genome sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets