Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A precision metric for clinical genome sequencing

View ORCID ProfileRachel L. Goldfeder, View ORCID ProfileEuan A. Ashley
doi: https://doi.org/10.1101/051490
Rachel L. Goldfeder
Stanford University, Department of Medicine
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Rachel L. Goldfeder
Euan A. Ashley
Stanford University, Department of Medicine
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Euan A. Ashley
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

A requisite precondition for the application of next-generation sequencing to clinical medicine is the ability to confidently call genotype at each coding/splicing position of every gene of interest. Current gold standard technologies, such as Sanger sequencing and microarrays, allow confident identification of the genomic origin of the DNA of interest. A commonly used minimum standard for the adoption of new technology in medicine is non-inferiority. We developed a metric to quantify the extent to which current sequencing technologies reach this clinical grade reporting standard. This metric, the rationale for which we present here, is defined as the absolute number of base pairs per gene not callable with confidence, as specified by the presence of 20 high quality (Q20) bases from uniquely mapped (mapq>0) reads per locus. To illustrate the utility of this metric, we apply it across data from several commercially available clinical sequencing products. We present specific examples of coverage for genes known to be important for clinical medicine. We derive data from a variety of platforms including whole genome sequencing (Illumina Hiseq and X chemistry) and exome capture (including medically optimized capture from Agilent, Baylor Clinical Lab, and Personalis). We observe that compared to whole genomes (with ˜30x average coverage), augmented exomes perform far better for known disease causing genes, but less well for other genes and in untranslated regions. Increasing whole genome coverage improves this discrepancy with an average coverage of ˜45x representing the cross over point where performance equals that of exome capture for disease causing genes. A combination of some genome-wide coverage and augmented exon coverage may offer the most cost effective solution for clinical grade genome sequencing today. In summary, this coverage metric provides transparency regarding the current state of next-generation sequencing for clinical medicine and will inform genotype interpretation, technology improvement, and sequencing platform choices for physicians and laboratories. We provide an application on precision.fda.gov (Coverage of Key Genes app) to calculate this metric.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Back to top
PreviousNext
Posted May 24, 2016.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A precision metric for clinical genome sequencing
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A precision metric for clinical genome sequencing
Rachel L. Goldfeder, Euan A. Ashley
bioRxiv 051490; doi: https://doi.org/10.1101/051490
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A precision metric for clinical genome sequencing
Rachel L. Goldfeder, Euan A. Ashley
bioRxiv 051490; doi: https://doi.org/10.1101/051490

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4246)
  • Biochemistry (9184)
  • Bioengineering (6808)
  • Bioinformatics (24072)
  • Biophysics (12167)
  • Cancer Biology (9570)
  • Cell Biology (13847)
  • Clinical Trials (138)
  • Developmental Biology (7666)
  • Ecology (11742)
  • Epidemiology (2066)
  • Evolutionary Biology (15548)
  • Genetics (10676)
  • Genomics (14372)
  • Immunology (9523)
  • Microbiology (22923)
  • Molecular Biology (9140)
  • Neuroscience (49175)
  • Paleontology (358)
  • Pathology (1488)
  • Pharmacology and Toxicology (2584)
  • Physiology (3851)
  • Plant Biology (8357)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2302)
  • Systems Biology (6207)
  • Zoology (1304)