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Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects

Author

Listed:
  • Allison A. Regier

    (McDonnell Genome Institute, Washington University School of Medicine)

  • Yossi Farjoun

    (Broad Institute of MIT and Harvard)

  • David E. Larson

    (McDonnell Genome Institute, Washington University School of Medicine)

  • Olga Krasheninina

    (Human Genome Sequencing Center, Baylor College of Medicine)

  • Hyun Min Kang

    (University of Michigan)

  • Daniel P. Howrigan

    (Broad Institute of MIT and Harvard)

  • Bo-Juen Chen

    (New York Genome Center
    Google)

  • Manisha Kher

    (New York Genome Center)

  • Eric Banks

    (Broad Institute of MIT and Harvard)

  • Darren C. Ames

    (DNAnexus Inc)

  • Adam C. English

    (Spiral Genetics)

  • Heng Li

    (Broad Institute of MIT and Harvard)

  • Jinchuan Xing

    (Rutgers University)

  • Yeting Zhang

    (Rutgers University)

  • Tara Matise

    (Rutgers University)

  • Goncalo R. Abecasis

    (University of Michigan)

  • Will Salerno

    (Human Genome Sequencing Center, Baylor College of Medicine)

  • Michael C. Zody

    (New York Genome Center)

  • Benjamin M. Neale

    (Broad Institute of MIT and Harvard
    Massachusetts General Hospital)

  • Ira M. Hall

    (McDonnell Genome Institute, Washington University School of Medicine)

Abstract

Hundreds of thousands of human whole genome sequencing (WGS) datasets will be generated over the next few years. These data are more valuable in aggregate: joint analysis of genomes from many sources increases sample size and statistical power. A central challenge for joint analysis is that different WGS data processing pipelines cause substantial differences in variant calling in combined datasets, necessitating computationally expensive reprocessing. This approach is no longer tenable given the scale of current studies and data volumes. Here, we define WGS data processing standards that allow different groups to produce functionally equivalent (FE) results, yet still innovate on data processing pipelines. We present initial FE pipelines developed at five genome centers and show that they yield similar variant calling results and produce significantly less variability than sequencing replicates. This work alleviates a key technical bottleneck for genome aggregation and helps lay the foundation for community-wide human genetics studies.

Suggested Citation

  • Allison A. Regier & Yossi Farjoun & David E. Larson & Olga Krasheninina & Hyun Min Kang & Daniel P. Howrigan & Bo-Juen Chen & Manisha Kher & Eric Banks & Darren C. Ames & Adam C. English & Heng Li & J, 2018. "Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06159-4
    DOI: 10.1038/s41467-018-06159-4
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    Cited by:

    1. Yuk Yee Leung & Adam C. Naj & Yi-Fan Chou & Otto Valladares & Michael Schmidt & Kara Hamilton-Nelson & Nicholas Wheeler & Honghuang Lin & Prabhakaran Gangadharan & Liming Qu & Kaylyn Clark & Amanda B., 2024. "Human whole-exome genotype data for Alzheimer’s disease," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Marsha M. Wheeler & Adrienne M. Stilp & Shuquan Rao & Bjarni V. Halldórsson & Doruk Beyter & Jia Wen & Anna V. Mihkaylova & Caitlin P. McHugh & John Lane & Min-Zhi Jiang & Laura M. Raffield & Goo Jun , 2022. "Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    3. Wenan Chen & Shuoguo Wang & Saima Sultana Tithi & David W. Ellison & Daniel J. Schaid & Gang Wu, 2022. "A rare variant analysis framework using public genotype summary counts to prioritize disease-predisposition genes," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    4. Nazia Pathan & Wei Q. Deng & Matteo Di Scipio & Mohammad Khan & Shihong Mao & Robert W. Morton & Ricky Lali & Marie Pigeyre & Michael R. Chong & Guillaume Paré, 2024. "A method to estimate the contribution of rare coding variants to complex trait heritability," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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