Genetic sex validation for sample tracking in next-generation sequencing clinical testing.

Area of Science:

• Genomics
• Clinical Diagnostics
• Bioinformatics

Background:

• Clinical sequencing networks require robust quality control for sample identity.
• Single Nucleotide Polymorphism (SNP) genotyping offers a method for genetic analysis and identity verification.

Purpose of the Study:

• To evaluate a 96-SNP panel for precise sample tracking and sex-by-genotype prediction in a large clinical cohort.
• To identify and investigate discrepancies between predicted genetic sex and reported sex, indicating potential sample mix-ups.

Main Methods:

• Utilized DNA genotyping data from a 96-SNP panel across 25,015 clinical samples.
• Compared predicted sex-by-genotype with sex information on test requisitions.
• Confirmed genetic sex predictions using sequencing data and high-density genotyping arrays.

Main Results:

• SNP tracking confirmed no sample swap errors within clinical testing laboratories.
• Identified 110 inconsistencies (0.44%) between predicted and reported sex, primarily occurring during sample collection or accessioning.
• Discrepancies were attributed to clerical errors, samples from transgender individuals, transplant patients, and undetermined mix-ups prior to genome center arrival.

Conclusions:

• A 96-SNP panel is valuable for quality control and tracking sample identity in clinical sequencing.
• Sex-by-genotype prediction is a sensitive method for detecting pre-accessioning sample mix-ups and errors.
• Investigating sex discrepancies can highlight operational challenges in sample handling and collection processes.