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Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

Nature Biotechnology volume 32, pages 246251 (2014) | Download Citation

Abstract

Clinical adoption of human genome sequencing requires methods that output genotypes with known accuracy at millions or billions of positions across a genome. Because of substantial discordance among calls made by existing sequencing methods and algorithms, there is a need for a highly accurate set of genotypes across a genome that can be used as a benchmark. Here we present methods to make high-confidence, single-nucleotide polymorphism (SNP), indel and homozygous reference genotype calls for NA12878, the pilot genome for the Genome in a Bottle Consortium. We minimize bias toward any method by integrating and arbitrating between 14 data sets from five sequencing technologies, seven read mappers and three variant callers. We identify regions for which no confident genotype call could be made, and classify them into different categories based on reasons for uncertainty. Our genotype calls are publicly available on the Genome Comparison and Analytic Testing website to enable real-time benchmarking of any method.

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Acknowledgements

We thank J. Johnson and A. Varadarajan from the Archon Genomics X Prize and EdgeBio for contributing their whole-genome sequencing data from SOLiD and Illumina, Complete Genomics and Life Technologies for providing bam files for NA12878, and the Broad Institute and 1000 Genomes Project for making publicly available bam and VCF files for NA12878. The Illumina exome data on GCAT were given to the Mittelman laboratory by M. Linderman at Icahn Institute of Genomics and Multiscale Biology of the Icahn School of Medicine at Mount Sinai. We thank the US Food and Drug Administration High Performance Computing staff for their support in running the bioinformatics analyses. Harvard School of Public Health contributions were funded by the Archon Genomics X PRIZE. Certain commercial equipment, instruments or materials are identified in this document. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the products identified are necessarily the best available for the purpose.

Author information

Affiliations

  1. Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA.

    • Justin M Zook
    •  & Marc Salit
  2. Bioinformatics Core, Department of Biostatistics, Harvard School of Public Health, Cambridge, Massachusetts, USA.

    • Brad Chapman
    • , Oliver Hofmann
    •  & Winston Hide
  3. Arpeggi, Inc., Austin, Texas, USA.

    • Jason Wang
    •  & David Mittelman
  4. Virginia Bioinformatics Institute and Department of Biological Sciences, Blacksburg, Virginia, USA.

    • David Mittelman

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Contributions

J.M.Z., M.S., B.C., O.H. and W.H. conceived the integration methods. J.M.Z. wrote the code for the integration methods and wrote the main manuscript. D.M. and J.W. designed the GCAT platform, implemented comparison to our genotype calls, and generated figures.

Competing interests

D.M. and J.W. are partners and equity holders in Gene by Gene Ltd., which offers clinical and direct-to-consumer genetic testing.

Corresponding author

Correspondence to Justin M Zook.

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    Supplementary Text and Figures

    Supplementary Figures 1–38, Supplementary Discussion and Supplementary Tables 1–7

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DOI

https://doi.org/10.1038/nbt.2835

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