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Correcting for Sample Contamination in Genotype Calling of DNA Sequence Data.
Matthew Flickinger1, Goo Jun2, Gonçalo R Abecasis1
1Department of Biostatistics and Center for Statistical Genetics, University of Michigan, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
American Journal of Human Genetics
|August 4, 2015
Summary
This study introduces DNA contamination correction methods for genotype calling in sequencing studies. These methods significantly reduce genotyping errors, offering an alternative to sample removal and improving data accuracy.
Area of Science:
- Genomics
- Bioinformatics
- Population Genetics
Background:
- DNA sample contamination is a common issue in DNA sequencing, leading to genotyping errors and reduced statistical power in association studies.
- Existing methods focus on identifying and removing contaminated samples, which can reduce study sample size.
Purpose of the Study:
- To develop and evaluate methods for modeling DNA sample contamination during genotype calling.
- To provide an alternative to sample removal by correcting genotypes in the presence of contamination.
Main Methods:
- Proposed methods model contamination during genotype calling using sequencing read data.
- Compared contamination-adjusted genotype calls against calls ignoring contamination and calls from uncontaminated data.
Main Results:
- Contamination-adjusted calls reduced genotyping errors by 48%-77% for moderate contamination levels (5%-20%).
- Methods produced genotypes nearly as accurate as uncontaminated data for lower contamination levels.
- The correction methods are particularly effective for sample contamination ranging from 2% to 20%.
Conclusions:
- Contamination-aware genotype calling is a viable alternative to sample removal.
- These methods improve genotyping accuracy and maintain statistical power in DNA sequencing studies with sample contamination.
