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Detection and quantification of rare mutations with massively parallel sequencing.

ABSTRACT: The identification of mutations that are present in a small fraction of DNA templates is essential for progress in several areas of biomedical research. Although massively parallel sequencing instruments are in principle well suited to this task, the error rates in such instruments are generally too high to allow confident identification of rare variants. We here describe an approach that can substantially increase the sensitivity of massively parallel sequencing instruments for this purpose. The keys to this approach, called the Safe-Sequencing System ("Safe-SeqS"), are (i) assignment of a unique identifier (UID) to each template molecule, (ii) amplification of each uniquely tagged template molecule to create UID families, and (iii) redundant sequencing of the amplification products. PCR fragments with the same UID are considered mutant ("supermutants") only if ?95% of them contain the identical mutation. We illustrate the utility of this approach for determining the fidelity of a polymerase, the accuracy of oligonucleotides synthesized in vitro, and the prevalence of mutations in the nuclear and mitochondrial genomes of normal cells.

SUBMITTER: Kinde I

PROVIDER: S-EPMC3111315 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Detection and quantification of rare mutations with massively parallel sequencing.

Kinde Isaac I Wu Jian J Papadopoulos Nick N Kinzler Kenneth W KW Vogelstein Bert B

Proceedings of the National Academy of Sciences of the United States of America 20110517 23

The identification of mutations that are present in a small fraction of DNA templates is essential for progress in several areas of biomedical research. Although massively parallel sequencing instruments are in principle well suited to this task, the error rates in such instruments are generally too high to allow confident identification of rare variants. We here describe an approach that can substantially increase the sensitivity of massively parallel sequencing instruments for this purpose. Th ...[more]

PMID: 21586637

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Project description:There have been two major eras in the history of gene discovery. The first was the era of linkage analysis, with approximately 1,300 disease-related genes identified by positional cloning by the turn of the millennium. The second era has been powered by two major breakthroughs: the publication of the human genome and the development of massively parallel sequencing (MPS). MPS has greatly accelerated disease gene identification, such that disease genes that would have taken years to map previously can now be determined in a matter of weeks. Additionally, the number of affected families needed to map a causative gene and the size of such families have fallen: de novo mutations, previously intractable by linkage analysis, can be identified through sequencing of the parent-child trio, and genes for recessive disease can be identified through MPS even of a single affected individual. MPS technologies include whole exome sequencing (WES), whole genome sequencing (WGS), and panel sequencing, each with their strengths. While WES has been responsible for most gene discoveries through MPS, WGS is superior in detecting copy number variants, chromosomal rearrangements, and repeat-rich regions. Panels are commonly used for diagnostic purposes as they are extremely cost-effective and generate manageable quantities of data, with no risk of unexpected findings. However, in instances of diagnostic uncertainty, it can be challenging to choose the right panel, and in these circumstances WES has a higher diagnostic yield. MPS has ethical, social, and legal implications, many of which are common to genetic testing generally but amplified due to the magnitude of data (e.g., relationship misattribution, identification of variants of uncertain significance, and genetic discrimination); others are unique to WES and WGS technologies (e.g., incidental or secondary findings). Nonetheless, MPS is rapidly translating into clinical practice as an extremely useful part of the clinical armamentarium.

Project description:BackgroundCirculating cell-free (ccf) fetal DNA comprises 3-20% of all the cell-free DNA present in maternal plasma. Numerous research and clinical studies have described the analysis of ccf DNA using next generation sequencing for the detection of fetal aneuploidies with high sensitivity and specificity. We sought to extend the utility of this approach by assessing semi-automated library preparation, higher sample multiplexing during sequencing, and improved bioinformatic tools to enable a higher throughput, more efficient assay while maintaining or improving clinical performance.MethodsWhole blood (10mL) was collected from pregnant female donors and plasma separated using centrifugation. Ccf DNA was extracted using column-based methods. Libraries were prepared using an optimized semi-automated library preparation method and sequenced on an Illumina HiSeq2000 sequencer in a 12-plex format. Z-scores were calculated for affected chromosomes using a robust method after normalization and genomic segment filtering. Classification was based upon a standard normal transformed cutoff value of z = 3 for chromosome 21 and z = 3.95 for chromosomes 18 and 13.ResultsTwo parallel assay development studies using a total of more than 1900 ccf DNA samples were performed to evaluate the technical feasibility of automating library preparation and increasing the sample multiplexing level. These processes were subsequently combined and a study of 1587 samples was completed to verify the stability of the process-optimized assay. Finally, an unblinded clinical evaluation of 1269 euploid and aneuploid samples utilizing this high-throughput assay coupled to improved bioinformatic procedures was performed. We were able to correctly detect all aneuploid cases with extremely low false positive rates of 0.09%, <0.01%, and 0.08% for trisomies 21, 18, and 13, respectively.ConclusionsThese data suggest that the developed laboratory methods in concert with improved bioinformatic approaches enable higher sample throughput while maintaining high classification accuracy.

Dataset Information

Detection and quantification of rare mutations with massively parallel sequencing.

Publications

Detection and quantification of rare mutations with massively parallel sequencing.

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