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A method for positive forensic identification of samples from extremely low-coverage sequence data.

ABSTRACT: Determining whether two DNA samples originate from the same individual is difficult when the amount of retrievable DNA is limited. This is often the case for ancient, historic, and forensic samples. The most widely used approaches rely on amplification of a defined panel of multi-allelic markers and comparison to similar data from other samples. When the amount retrievable DNA is low these approaches fail.We describe a new method for assessing whether shotgun DNA sequence data from two samples are consistent with originating from the same or different individuals. Our approach makes use of the large catalogs of single nucleotide polymorphism (SNP) markers to maximize the chances of observing potentially discriminating alleles. We further reduce the amount of data required by taking advantage of patterns of linkage disequilibrium modeled by a reference panel of haplotypes to indirectly compare observations at pairs of linked SNPs. Using both coalescent simulations and real sequencing data from modern and ancient sources, we show that this approach is robust with respect to the reference panel and has power to detect positive identity from DNA libraries with less than 1 % random and non-overlapping genome coverage in each sample.We present a powerful new approach that can determine whether DNA from two samples originated from the same individual even when only minute quantities of DNA are recoverable from each.

SUBMITTER: Vohr SH

PROVIDER: S-EPMC4672566 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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A method for positive forensic identification of samples from extremely low-coverage sequence data.

Vohr Samuel H SH Buen Abad Najar Carlos Fernando CF Shapiro Beth B Green Richard E RE

BMC genomics 20151207

<h4>Background</h4>Determining whether two DNA samples originate from the same individual is difficult when the amount of retrievable DNA is limited. This is often the case for ancient, historic, and forensic samples. The most widely used approaches rely on amplification of a defined panel of multi-allelic markers and comparison to similar data from other samples. When the amount retrievable DNA is low these approaches fail.<h4>Results</h4>We describe a new method for assessing whether shotgun D ...[more]

PMID: 26643904

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Project description:The bacterial microbiota of the mosquito influences numerous physiological processes of the host. As low-microbial-biomass ecosystems, mosquito tissues are prone to contamination from the laboratory environment and from reagents commonly used to isolate DNA from tissue samples. In this report, we analyzed nine 16S rRNA data sets, including new data obtained by us, to gain insight into the impact of potential contaminating sequences on the composition, diversity, and structure of the mosquito tissue microbial community. Using a clustering-free approach based on the relative abundance of amplicon sequence variants (ASVs) in tissue samples and negative controls, we identified candidate contaminating sequences that sometimes differed from, but were consistent with, results found using established methodologies. Some putative contaminating sequences belong to bacterial taxa previously identified as contaminants that are commonly found in metagenomic studies but that have also been identified as part of the mosquito core microbiota, with putative physiological relevance for the host. Using different relative abundance cutoffs, we show that contaminating sequences have a significant impact on tissue microbiota diversity and structure analysis. IMPORTANCE The study of tissue-associated microbiota from mosquitoes (primarily from the gut) has grown significantly in the last several years. Mosquito tissue samples represent a challenge for researchers given their low microbial biomass and similar taxonomic composition commonly found in the laboratory environment and in molecular reagents. Using new and published data sets that identified mosquito tissue microbiota from gut and reproductive tract tissues (and their respective negative controls), we developed a simple method to identify contamination microbiota. This approach uses an initial taxonomic identification without operational taxonomic unit (OTU) clustering and evaluates the relative abundance of control sample sequences, allowing the identification and removal of purported contaminating sequences in data sets obtained from low-microbial-biomass samples. While it was exemplified with the analysis of tissue microbiota from mosquitos, it can be extended to other data sets dealing with similar technical artifacts.

Project description:BackgroundPopulation genetics and association studies usually rely on a set of known variable sites that are then genotyped in subsequent samples, because it is easier to genotype than to discover the variation. This is also true for structural variation detected from sequence data. However, the genotypes at known variable sites can only be inferred with uncertainty from low coverage data. Thus, statistical approaches that infer genotype likelihoods, test hypotheses, and estimate population parameters without requiring accurate genotypes are becoming popular. Unfortunately, the current implementations of these methods are intended to analyse only single nucleotide and short indel variation, and they usually assume that the two alleles in a heterozygous individual are sampled with equal probability. This is generally false for structural variants detected with paired ends or split reads. Therefore, the population genetics of structural variants cannot be studied, unless a painstaking and potentially biased genotyping is performed first.ResultsWe present svgem, an expectation-maximization implementation to estimate allele and genotype frequencies, calculate genotype posterior probabilities, and test for Hardy-Weinberg equilibrium and for population differences, from the numbers of times the alleles are observed in each individual. Although applicable to single nucleotide variation, it aims at bi-allelic structural variation of any type, observed by either split reads or paired ends, with arbitrarily high allele sampling bias. We test svgem with simulated and real data from the 1000 Genomes Project.Conclusionssvgem makes it possible to use low-coverage sequencing data to study the population distribution of structural variants without having to know their genotypes. Furthermore, this advance allows the combined analysis of structural and nucleotide variation within the same genotype-free statistical framework, thus preventing biases introduced by genotype imputation.

Dataset Information

A method for positive forensic identification of samples from extremely low-coverage sequence data.

Publications

A method for positive forensic identification of samples from extremely low-coverage sequence data.

Similar Datasets

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