title: Applying Ancestry and Sex Computation as a Quality Control Tool in Targeted Next-Generation Sequencing.
abstract: OBJECTIVE
  To apply techniques for ancestry and sex computation from next-generation sequencing (NGS) data as an approach to confirm sample identity and detect sample processing errors.
  
  
  METHODS
  We combined a principal component analysis method with k-nearest neighbors classification to compute the ancestry of patients undergoing NGS testing. By combining this calculation with X chromosome copy number data, we determined the sex and ancestry of patients for comparison with self-report. We also modeled the sensitivity of this technique in detecting sample processing errors.
  
  
  RESULTS
  We applied this technique to 859 patient samples with reliable self-report data. Our k-nearest neighbors ancestry screen had an accuracy of 98.7% for patients reporting a single ancestry. Visual inspection of principal component plots was consistent with self-report in 99.6% of single-ancestry and mixed-ancestry patients. Our model demonstrates that approximately two-thirds of potential sample swaps could be detected in our patient population using this technique.
  
  
  CONCLUSIONS
  Patient ancestry can be estimated from NGS data incidentally sequenced in targeted panels, enabling an inexpensive quality control method when coupled with patient self-report.
authors.affiliation: From the Department of Laboratory Medicine, University of Washington, Seattle. pcm10@uw.edu.
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Mathias
authors.affiliation: From the Department of Laboratory Medicine, University of Washington, Seattle.
authors.forename: Emily H
authors.initials: EH
authors.lastname: Turner
authors.affiliation: From the Department of Laboratory Medicine, University of Washington, Seattle.
authors.forename: Sheena M
authors.initials: SM
authors.lastname: Scroggins
authors.affiliation: From the Department of Laboratory Medicine, University of Washington, Seattle.
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Salipante
authors.affiliation: From the Department of Laboratory Medicine, University of Washington, Seattle.
authors.forename: Noah G
authors.initials: NG
authors.lastname: Hoffman
authors.affiliation: From the Department of Laboratory Medicine, University of Washington, Seattle.
authors.forename: Colin C
authors.initials: CC
authors.lastname: Pritchard
authors.affiliation: From the Department of Laboratory Medicine, University of Washington, Seattle.
authors.forename: Brian H
authors.initials: BH
authors.lastname: Shirts
mesh_terms: D056915:DNA Copy Number Variations; D003951:Diagnostic Errors; D004502:Education, Medical, Continuing; D005260:Female; D059014:High-Throughput Nucleotide Sequencing / Q000592:standards*; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D057089:Pathology, Molecular; D025341:Principal Component Analysis; D011786:Quality Control; D044469:Racial Groups / Q000235:genetics*; D057566:Self Report; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000592:standards; D012737:Sex Factors; D013048:Specimen Handling
keywords: Molecular diagnostics; Next-generation sequencing; Quality control