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Who's Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy.

ABSTRACT: The potential for genetic discovery in human DNA sequencing studies is greatly diminished if DNA samples from a cohort are mislabeled, swapped, or contaminated or if they include unintended individuals. Unfortunately, the potential for such errors is significant since DNA samples are often manipulated by several protocols, labs, or scientists in the process of sequencing. We have developed a software package, peddy, to identify and facilitate the remediation of such errors via interactive visualizations and reports comparing the stated sex, relatedness, and ancestry to what is inferred from the individual genotypes derived from whole-genome (WGS) or whole-exome (WES) sequencing. Peddy predicts a sample's ancestry using a machine learning model trained on individuals of diverse ancestries from the 1000 Genomes Project reference panel. Peddy facilitates both automated and interactive, visual detection of sample swaps, poor sequencing quality, and other indicators of sample problems that, if left undetected, would inhibit discovery.

SUBMITTER: Pedersen BS

PROVIDER: S-EPMC5339084 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Who's Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy.

Pedersen Brent S BS Quinlan Aaron R AR

American journal of human genetics 20170209 3

The potential for genetic discovery in human DNA sequencing studies is greatly diminished if DNA samples from a cohort are mislabeled, swapped, or contaminated or if they include unintended individuals. Unfortunately, the potential for such errors is significant since DNA samples are often manipulated by several protocols, labs, or scientists in the process of sequencing. We have developed a software package, peddy, to identify and facilitate the remediation of such errors via interactive visual ...[more]

PMID: 28190455

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Project description:BACKGROUND:Recent multisite trials reveal striking heterogeneities in results between trial sites. These may be because of population differences indicating different treatment benefits among different types of participants or site anomalies, such as failures to adhere to study protocols that could negatively affect study validity. We sought to determine whether a new data analysis strategy-transportability methods-could suggest site anomalies not readily identified through standard methods. METHODS AND RESULTS:We applied transportability methods to 2 large, multicenter cardiovascular disease treatment trials: the TOPCAT trial (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist; n=3445) comparing spironolactone to placebo for heart failure (for which site anomalies were suspected) and the ACCORD BP trial (Action to Control Cardiovascular Risk in Diabetes-Blood Pressure; n=4733) comparing intensive-to-standard blood pressure treatment (for which site anomalies were not suspected). The transportability methods give expected results by standardizing from one site to another using data on participant covariates. The difference between the expected and observed results was assessed using calibration tests to identify whether treatment-effect differences between sites could be explained by participant population characteristics. Standard regression methods did not detect heterogeneities in TOPCAT between Russia/Georgia study sites suspected of study protocol violations and sites in the Americas ( P=0.12 for difference in primary cardiovascular outcome; P=0.20 for difference in total mortality). The transportability methods, however, detected the difference between Russia/Georgia sites and sites in the Americas ( P<0.001) and found that measured participant characteristics did not explain the between-site discrepancies. The transport methods found no such discrepancies between sites in ACCORD BP, suggesting participant characteristics explained between-site differences. CONCLUSIONS:Transportability methods may be superior to standard approaches for detecting anomalies within multicenter randomized trials and assist data monitoring boards to determine whether important treatment-effect heterogeneities can be attributed to participant differences or potentially to site performance differences requiring further investigation.

Dataset Information

Who's Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy.

Publications

Who's Who? Detecting and Resolving Sample Anomalies in Human DNA Sequencing Studies with Peddy.

Similar Datasets

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