Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data
Open accessAbstract
DNA sample contamination is a serious problem in DNA sequencing studies and may result in systematic genotype misclassification and false positive associations. Although methods exist to detect and filter out cross-species contamination, few methods to detect within-species sample contamination are available. In this paper, we describe methods to identify within-species DNA sample contamination based on (1) a combination of sequencing reads and array-based genotype data, (2) sequence reads alone, and (3) array-based genotype data alone. Analysis of sequencing reads allows contamination detection after sequence data is generated but prior to variant calling; analysis of array-based genotype data allows contamination detection prior to generation of costly sequence data. Through a combination of analysis of in silico and experimentally contaminated samples, we show that our methods can reliably detect and estimate levels of contamination as low as 1%. We evaluate the impact of DNA contamination on genotype accuracy and propose effective strategies to screen for and prevent DNA contamination in sequencing studies.
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They excluded 118 sequences with VerifyBAMID freemix ≥4% and 5 sequences with less than 94.5% of CCDS achieving tenfold read depth.
“We excluded from analyses 118 (0.025%) sequences that achieved a VerifyBAMID freemix (contamination) level of 4% or higher68, and an additional five sequences (0.001%) where less than 94.5% of the CCDS (release 22) achieved a minimum of tenfold read depth69.”
From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [68]
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- Authors
- Jun, G., Flickinger, M., Hetrick, K. N., Romm, J. M., Doheny, K. F., Abecasis, G. R., Boehnke, M., & Kang, H. M.
- Affiliations
- University of Michigan (US)
- Johns Hopkins University (US)
- Corresponding author
- Kang, Hyun Min
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 91
- Issue
- 5
- Pages
- 839-848
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC3487130
- OpenAlex
- W2099328768
- Cited by
- 484
- References
- 1
- Citations by year
- 2026: 30 · 2025: 29 · 2024: 38 · 2023: 54 · 2022: 48 · 2021: 53 · 2020: 51 · 2019: 50 · 2018: 41 · 2017: 36 · 2016: 24 · 2015: 10 · 2014: 14 · 2013: 4 · 2012: 2
- FWCI
- 3.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (HHSN268201100011C, HHSN268201100011I, R01HL117626)
- National Institute of Mental Health (MH084698, R01MH084698)
- National Human Genome Research Institute (R56HG000376, HG005214, HG006513, R01HG000376, U01HG006513, U01HG005214, R01HG007022, HG000376, HHSN268200782096C, T32HG000040)
- National Institute of Diabetes and Digestive and Kidney Diseases (R13DK088398, DK088398, P30DK020572)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- contamination detection, contamination estimation, sequencing reads, array-based genotyping, genotype misclassification, false positive association, contaminant screening, variant calling
- MeSH terms
- Diabetes Mellitus, Type 2, Genotype, Humans, Sequence Analysis, DNA, DNA Contamination
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