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Keywords: Genomics; Clinical testing; SNP analysis/discovery; Methodology

J. Hu 1; A. Balasubramanian 1; L. Zhang 1; V. Korchina 1; C. Kovar 1; E. Venner 1; M. Murugan 1; D. Murdock 1,2; H. Zouk 3; M. Harden 4; A. Macbeth 4; S. Harrison 4; N. Lennon 4; R. Rowley 5; M. Smith 6; A. Gordon 6; G. Jarvik 7; P. Sleiman 8; M. Kelly 9; S. Bland 10; E. Boerwinkle 1,11; C. Prows 12; D. Crosslin 13; I. Stanaway 14; L. Mahanta 3; H. Rehm 3; R. Gibbs 1; D. Muzny 1; the eMERGE III consortium

1) Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX.; 2) Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX; 3) Partners Healthcare Laboratory for Molecular Medicine (LMM), The Broad Institute of MIT and Harvard, Cambridge, MA; 4) The Broad Institute of MIT and Harvard, Cambridge, MA; 5) Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD; 6) Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL; 7) Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, WA; 8) Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA; 9) Geisinger Genomic Medicine Institute, Danville, PA; 10) Vanderbilt University Medical Center, Nashville, TN; 11) Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX,; 12) Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; 13) Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA; 14) Kaiser Permanente Washington Health Research Institute, Seattle, WA

Next generation sequencing (NGS) has been rapidly adopted in clinical laboratories. Stringent quality control of sample identity for each patient-derived specimen is critical, however the large number of steps and complexity of the clinical sequencing workflow create the potential for sample identification errors at all phases, from sample collection to the sequencing and return of results. Here, we demonstrate the power of monitoring assigned sample sex assignment, to assure process quality. Methods were developed for the NIH Electronic Medical Records and Genomics (eMERGE) Network, that has collected >25,000 samples from 11 clinical sites and sequenced each using a ‘gene panel’, at two sequencing core sites, the Human Genome Sequencing Center Clinical Laboratory (HGSC-CL) at Baylor College of Medicine (BCM) and Partners Healthcare Laboratory for Molecular Medicine (LMM) in partnership with the Clinical Research Sequencing Platform (CRSP) at the Broad Institute. Following sample transfer to the sequencing sites, an aliquot of each was immediately tested with a Fluidigm SNP array panel, providing a unique genotype based upon 96 single base sites, which can be later compared with the gene panel sequence data, to ensure no sample swaps occurred while at the sequencing centers. As six of these SNP markers are assigned to sex chromosomes, they also were used to identify putative sample sex-assignment discrepancies that were subsequently confirmed by either (or both) the gene panel data, or hybridization to an Illumina exome array with 14k sex chromosome sites. The two sequencing sites have collectively confirmed 110/25,015 samples (0.44%) with inconsistencies between data at the genome centers versus test submission-provided sex assignment. Further investigation in collaboration with eMERGE clinical sites resolved most inconsistencies as the samples were from transgender participants, stem cell or bone marrow transplant patients, or else mis-assignments by traceable clerical errors. As a result, process improvements were made at each collection site. The SNP array assay was demonstrated to be a cost-effective and robust tool that can be implemented in the clinical laboratory for validating process integrity. Although checking sex assignment detects just 50% of possible sample swaps and a similar proportion of transplant recipients, it will detect all transgender participants and identify requirements for process improvements in new pipelines.