Recovering Misidentified Samples Through Genetic Discordance Clustering

Author:

Huang Jesse12ORCID,Kockum Ingrid123,Stridh Pernilla123

Affiliation:

1. Center of Molecular Medicine Karolinska University Hospital Stockholm Sweden

2. Department of Clinical Neuroscience Karolinska Institutet Stockholm Sweden

3. These authors share last authorship equally

Abstract

AbstractThe many logistical and technical challenges associated with sample and data handling in largescale genotyping studies can increase the risk of sample misidentification, which may compromise subsequent analyses. However, the standard quality assurance methods typical for large genotyping arrays can often be further utilized to identify and recover problematic samples. This article emphasizes the importance of identifying and correcting underlying sample misidentification rather than simply excluding known discrepancies, which may potentially include undetected issues. Lastly, we provide a screening protocol to complement standard quality assessments as a guideline for identifying mismatched samples and a tool for assessing the most common causes of sample misidentification. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.

Publisher

Wiley

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