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Joint Estimation of Contamination, Error and Demography for Nuclear DNA from Ancient Humans.

ABSTRACT: When sequencing an ancient DNA sample from a hominin fossil, DNA from present-day humans involved in excavation and extraction will be sequenced along with the endogenous material. This type of contamination is problematic for downstream analyses as it will introduce a bias towards the population of the contaminating individual(s). Quantifying the extent of contamination is a crucial step as it allows researchers to account for possible biases that may arise in downstream genetic analyses. Here, we present an MCMC algorithm to co-estimate the contamination rate, sequencing error rate and demographic parameters-including drift times and admixture rates-for an ancient nuclear genome obtained from human remains, when the putative contaminating DNA comes from present-day humans. We assume we have a large panel representing the putative contaminant population (e.g. European, East Asian or African). The method is implemented in a C++ program called 'Demographic Inference with Contamination and Error' (DICE). We applied it to simulations and genome data from ancient Neanderthals and modern humans. With reasonable levels of genome sequence coverage (>3X), we find we can recover accurate estimates of all these parameters, even when the contamination rate is as high as 50%.

SUBMITTER: Racimo F

PROVIDER: S-EPMC4822957 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Correction: Joint Estimation of Contamination, Error and Demography for Nuclear DNA from Ancient Humans.

Project description:[This corrects the article DOI: 10.1371/journal.pgen.1005972.].

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Project description:Detecting and estimating DNA sample contamination are important steps to ensure high-quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes. We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele frequencies projected from reference genotypes onto principal component coordinates. Our method can also be used for estimating genetic ancestries, similar to LASER or TRACE, but simultaneously accounting for potential contamination. We demonstrate that our method robustly estimates contamination rates and genetic ancestries across populations and contamination scenarios. We further demonstrate that, in the presence of contamination, genetic ancestry inference can be substantially biased with existing methods that ignore contamination, while our method corrects for such biases.

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Estimation of DNA contamination and its sources in genotyped samples.

Project description: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.

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Separating endogenous ancient DNA from modern day contamination in a Siberian Neandertal.

Project description:One of the main impediments for obtaining DNA sequences from ancient human skeletons is the presence of contaminating modern human DNA molecules in many fossil samples and laboratory reagents. However, DNA fragments isolated from ancient specimens show a characteristic DNA damage pattern caused by miscoding lesions that differs from present day DNA sequences. Here, we develop a framework for evaluating the likelihood of a sequence originating from a model with postmortem degradation-summarized in a postmortem degradation score-which allows the identification of DNA fragments that are unlikely to originate from present day sources. We apply this approach to a contaminated Neandertal specimen from Okladnikov Cave in Siberia to isolate its endogenous DNA from modern human contaminants and show that the reconstructed mitochondrial genome sequence is more closely related to the variation of Western Neandertals than what was discernible from previous analyses. Our method opens up the potential for genomic analysis of contaminated fossil material.

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Monitoring DNA contamination in handled vs. directly excavated ancient human skeletal remains.

Project description:Bones, teeth and hair are often the only physical evidence of human or animal presence at an archaeological site; they are also the most widely used sources of samples for ancient DNA (aDNA) analysis. Unfortunately, the DNA extracted from ancient samples, already scarce and highly degraded, is widely susceptible to exogenous contaminations that can affect the reliability of aDNA studies. We evaluated the molecular effects of sample handling on five human skeletons freshly excavated from a cemetery dated between the 11 to the 14(th) century. We collected specimens from several skeletal areas (teeth, ribs, femurs and ulnas) from each individual burial. We then divided the samples into two different sets: one labeled as "virgin samples" (i.e. samples that were taken by archaeologists under contamination-controlled conditions and then immediately sent to the laboratory for genetic analyses), and the second called "lab samples"(i.e. samples that were handled without any particular precautions and subject to normal washing, handling and measuring procedures in the osteological lab). Our results show that genetic profiles from "lab samples" are incomplete or ambiguous in the different skeletal areas while a different outcome is observed in the "virgin samples" set. Generally, all specimens from different skeletal areas in the exception of teeth present incongruent results between "lab" and "virgin" samples. Therefore teeth are less prone to contamination than the other skeletal areas we analyzed and may be considered a material of choice for classical aDNA studies. In addition, we showed that bones can also be a good candidate for human aDNA analysis if they come directly from the excavation site and are accompanied by a clear taphonomic history.

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