Abstract

Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (4×) 1000 Genomes Project datasets.

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Acknowledgements

Many thanks to our colleagues in Medical and Population Genetics and Cancer Informatics and the 1000 Genomes Project who encouraged and supported us during the development of the Genome Analysis Toolkit and associated tools. This work was supported by grants from the National Human Genome Research Institute, including the Large Scale Sequencing and Analysis of Genomes grant (54 HG003067) and the Joint SNP and CNV calling in 1000 Genomes sequence data grant (U01 HG005208). We would also like to thank our excellent anonymous reviewers for their thoughtful comments.

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Affiliations

  1. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

    • Mark A DePristo
    • , Eric Banks
    • , Ryan Poplin
    • , Kiran V Garimella
    • , Jared R Maguire
    • , Christopher Hartl
    • , Anthony A Philippakis
    • , Guillermo del Angel
    • , Manuel A Rivas
    • , Matt Hanna
    • , Aaron McKenna
    • , Tim J Fennell
    • , Andrew M Kernytsky
    • , Andrey Y Sivachenko
    • , Kristian Cibulskis
    • , Stacey B Gabriel
    • , David Altshuler
    •  & Mark J Daly
  2. Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Anthony A Philippakis
  3. Harvard Medical School, Boston, Massachusetts, USA.

    • Anthony A Philippakis
    • , David Altshuler
    •  & Mark J Daly
  4. Center for Human Genetic Research, Massachusetts General Hospital, Richard B. Simches Research Center, Boston, Massachusetts, USA.

    • Manuel A Rivas
    • , David Altshuler
    •  & Mark J Daly

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Contributions

M.A.D., E.B., R.P., K.V.G., J.R.M., C.H., A.A.P., G.d.A., M.A.R., T.J.F., A.Y.S. and K.C. conceived of, implemented and performed analytic approaches. M.A.D., E.B., R.P., K.V.G., G.d.A., A.M.K. and M.J.D. wrote the manuscript. M.A.D., M.H. and A.M. developed Picard and GATK infrastructure underlying the tools implemented here. M.A.D., S.B.G., D.A. and M.J.D. lead the team.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Mark A DePristo.

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DOI

https://doi.org/10.1038/ng.806

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