Identification and Correction of Sample Mix-Ups in Expression Genetic Data: A Case Study.

Area of Science:

• Genetics
• Genomics
• Bioinformatics

Background:

• Sample mix-ups in genetic studies can compromise data integrity and study outcomes.
• Accurate genotype data is crucial for identifying genetic associations and understanding biological mechanisms.

Purpose of the Study:

• To develop and validate a method for identifying and correcting sample mix-ups in genotype data using gene expression information.
• To assess the prevalence of sample mix-ups in a large mouse intercross dataset.

Main Methods:

• Utilized genome-wide gene expression data from six tissues in a mouse intercross (>500 animals).
• Developed a classifier based on expression quantitative trait loci (eQTL) with large effects to predict individual genotypes from expression data.
• Compared predicted eQTL genotypes with observed genotypes to identify discrepancies indicative of sample mix-ups.

Main Results:

• Identified a high proportion (18%) of sample mix-ups within the genotype data.
• The eQTL-based classifier successfully identified individuals with mismatched genotypes.
• Concordance across six tissues confirmed genotype mix-ups, with some minor sample mix-ups also noted in expression data.
• Analysis of sample plate positions suggested pipetting errors as a likely cause.

Conclusions:

• Expression quantitative trait loci (eQTL) data provide a powerful tool for detecting and correcting sample mix-ups in genetic studies.
• The developed methodology, implemented in the R/lineup package, can improve the reliability of genetic datasets.
• Addressing sample mix-ups is essential for robust genetic research and accurate biological interpretation.