학술논문
Genetic sex validation for sample tracking in next-generation sequencing clinical testing
Document Type
article
Author
Jianhong Hu; Viktoriya Korchina; Hana Zouk; Maegan V. Harden; David Murdock; Alyssa Macbeth; Steven M. Harrison; Niall Lennon; Christie Kovar; Adithya Balasubramanian; Lan Zhang; Gauthami Chandanavelli; Divya Pasham; Robb Rowley; Ken Wiley; Maureen E. Smith; Adam Gordon; Gail P. Jarvik; Patrick Sleiman; Melissa A. Kelly; Harris T. Bland; Mullai Murugan; Eric Venner; Eric Boerwinkle; the eMERGE III consortium; Cynthia Prows; Lisa Mahanta; Heidi L. Rehm; Richard A. Gibbs; Donna M. Muzny
Source
BMC Research Notes, Vol 17, Iss 1, Pp 1-8 (2024)
Subject
Language
English
ISSN
1756-0500
Abstract
Abstract Objective Data from DNA genotyping via a 96-SNP panel in a study of 25,015 clinical samples were utilized for quality control and tracking of sample identity in a clinical sequencing network. The study aimed to demonstrate the value of both the precise SNP tracking and the utility of the panel for predicting the sex-by-genotype of the participants, to identify possible sample mix-ups. Results Precise SNP tracking showed no sample swap errors within the clinical testing laboratories. In contrast, when comparing predicted sex-by-genotype to the provided sex on the test requisition, we identified 110 inconsistencies from 25,015 clinical samples (0.44%), that had occurred during sample collection or accessioning. The genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density genotyping arrays. It was determined that discrepancies resulted from clerical errors (49.09%), samples from transgender participants (3.64%) and stem cell or bone marrow transplant patients (7.27%) along with undetermined sample mix-ups (40%) for which sample swaps occurred prior to arrival at genome centers, however the exact cause of the events at the sampling sites resulting in the mix-ups were not able to be determined.