Dataset Information

SMaSH: Sample matching using SNPs in humans.

ABSTRACT: BACKGROUND:Inadvertent sample swaps are a real threat to data quality in any medium to large scale omics studies. While matches between samples from the same individual can in principle be identified from a few well characterized single nucleotide polymorphisms (SNPs), omics data types often only provide low to moderate coverage, thus requiring integration of evidence from a large number of SNPs to determine if two samples derive from the same individual or not. METHODS:We select about six thousand SNPs in the human genome and develop a Bayesian framework that is able to robustly identify sample matches between next generation sequencing data sets. RESULTS:We validate our approach on a variety of data sets. Most importantly, we show that our approach can establish identity between different omics data types such as Exome, RNA-Seq, and MethylCap-Seq. We demonstrate how identity detection degrades with sample quality and read coverage, but show that twenty million reads of a fairly low quality RNA-Seq sample are still sufficient for reliable sample identification. CONCLUSION:Our tool, SMASH, is able to identify sample mismatches in next generation sequencing data sets between different sequencing modalities and for low quality sequencing data.

SUBMITTER: Westphal M

PROVIDER: S-EPMC6936078 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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SMaSH: Sample matching using SNPs in humans.

Westphal Maximillian M Frankhouser David D Sonzone Carmine C Shields Peter G PG Yan Pearlly P Bundschuh Ralf R

BMC genomics 20191230 Suppl 12

<h4>Background</h4>Inadvertent sample swaps are a real threat to data quality in any medium to large scale omics studies. While matches between samples from the same individual can in principle be identified from a few well characterized single nucleotide polymorphisms (SNPs), omics data types often only provide low to moderate coverage, thus requiring integration of evidence from a large number of SNPs to determine if two samples derive from the same individual or not.<h4>Methods</h4>We select ...[more]

PMID: 31888490

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Project description:Gene expression patterns in the brain are strongly influenced by the severity of physiological stress at death. This agonal effect, if not well controlled, can lead to spurious findings in case-control comparisons. While many recent studies match samples by tissue pH and clinically recorded agonal conditions, we found that these commonly used indicators were sometimes at odds with observed stress-related patterns of gene expression, and that matching by these criteria still sometimes results in identifying differences between cases and controls that are primarily driven by residual agonal effects. This problem is analogous to the one in genetic studies, where race and ethnicity are often imprecise proxies for complex environmental and genetic factors. We developed an Agonal Stress Rating (ASR) system that evaluates each sampleâs degree of stress based on gene expression data, and used ASRs in post hoc sample matching or covariate analysis. While we found that gene expression patterns are generally correlated across different regions of the same brain, we also found strong region-region differences in empirical ASRs in many subjects that are likely due to inter-individual variabilities in local structure or function, resulting in region-specific vulnerability to agonal stress. Variation of agonal stress from one region of the brain to another differs between individuals, revealing a new level of complexity for gene expression studies of brain tissues. The Agonal Stress Ratings provide a direct assessment of the regulatory responses to agonal stress in individual samples, and allow a strong control of this important confounder. Our strategy is analogous to sample matching by inferred ancestral proportions in genetic association studies to control subtle confounding by ancestry. Keywords: Agonal Stress Rating comparison We examined the relationship between the Agonal Stress Ratings (ASRs) and conventional pre hoc indicators such as pH and clinically derived Agonal Factor Scores (AFS), compared the stress ratings across six brain regions in up t0 126 samples, and assessed the performance of different sample matching strategies.

Dataset Information

SMaSH: Sample matching using SNPs in humans.

Publications

SMaSH: Sample matching using SNPs in humans.

Similar Datasets

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