Evidence map›Paper›PMID 31452258›Full record

ArticleGenetic epidemiology2019

Estimation of DNA contamination and its sources in genotyped samples.

Gregory J M Zajac, Lars G Fritsche, Joshua S Weinstock, Susan L Dagenais, Robert H Lyons, Chad M Brummett, Gonçalo R Abecasis

Abstract read
In one paragraph

Article in Genetic epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Gregory J M ZajacDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan.ORCID 0000-0001-6411-9666
Lars G FritscheDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan.ORCID 0000-0002-2110-1690
Joshua S WeinstockDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan.ORCID 0000-0001-7013-1899
Susan L DagenaisDepartment of Biological Chemistry and DNA Sequencing Core, University of Michigan, Ann Arbor, Michigan.
Robert H LyonsDepartment of Biological Chemistry and DNA Sequencing Core, University of Michigan, Ann Arbor, Michigan.
Chad M BrummettDepartment of Anesthesiology, Division of Pain Medicine, University of Michigan Medical School, Ann Arbor, Michigan.ORCID 0000-0003-0974-7242
Gonçalo R AbecasisDepartment of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan.ORCID 0000-0003-1509-1825

Funding

University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Computational and statistical models for human geneticsR01HG007022 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2012 to 2016
$3.0M
NHGRI NIH HHS R01 HG007022NHGRI NIH HHS T32 HG000040NHLBI NIH HHS HHSN268201700006C
6 · The paper itself

Abstract

Array genotyping is a cost-effective and widely used tool that enables assessment of up to millions of genetic markers in hundreds of thousands of individuals. Genotyping array data are typically highly accurate but sensitive to mixing of DNA samples from multiple individuals before or during genotyping. Contaminated samples can lead to genotyping errors and consequently cause false positive signals or reduce power of association analyses. Here, we propose a new method to identify contaminated samples and the sources of contamination within a genotyping batch. Through analysis of array intensity and genotype data from intentionally mixed samples and 22,366 samples of the Michigan Genomics Initiative, an ongoing biobank-based study, we show that our method can reliably estimate contamination. We also show that identifying sources of contamination can implicate problematic sample processing steps and guide process improvements. Compared to existing methods, our approach can estimate the proportion of contaminating DNA more accurately, eliminate the need for external databases of allele frequencies, and provide contamination estimates that are more robust to the ancestral origin of the contaminating sample.

Indexed as

DNA ContaminationGenotyping TechniquesDNAGene FrequencyGenetic MarkersGenomicsGenotypeHumansPolymorphism, Single NucleotideDNAGenetic MarkersbiobankDNA contaminationgenome-wide association studygenotyping arrayquality control

Identifiers

PMID31452258
PMCPMC6829038

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.