Who are you? A framework to identify and report genetic sample mix-ups

Area of Science:

• Molecular Ecology
• Genetics
• Bioinformatics

Background:

• Sample mix-ups, including duplication, mislabelling, or swapping, are frequent issues in genetic research.
• These errors can lead to incorrect individual identification, biased research findings, and reduced statistical power.
• A significant majority of researchers (nearly 80%) report encountering sample mix-ups, yet systematic reporting of verification checks is lacking in publications.

Purpose of the Study:

• To present a straightforward sample verification framework for molecular ecology studies.
• To demonstrate the framework's utility in detecting and correcting sample mix-ups using existing metadata.
• To advocate for the routine implementation and reporting of this framework as a quality control measure.

Main Methods:

• Developed a sample verification framework utilizing existing metadata such as species, population structure, sex, and pedigree information.
• Applied the framework to a dataset of hihi (a threatened New Zealand bird species) genotyped using a 50K SNP array.
• Compared genetic data with observed sex and a verified microsatellite pedigree to identify discrepancies.

Main Results:

• The framework successfully confirmed the identities of 488 individuals (39% of the dataset).
• Twenty incorrect bird-genotype links were corrected.
• Hundreds of erroneous sample IDs were detected, highlighting the prevalence of mix-ups.

Conclusions:

• The proposed sample verification framework is effective in identifying and rectifying sample mix-ups in genetic datasets.
• Routine implementation and reporting of such verification steps are crucial for ensuring data accuracy and research integrity in molecular ecology.
• This framework enhances the reliability of genetic and genomic data, supporting robust scientific conclusions.