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Nat Commun. 2018 Oct 2;9(1):4038. doi: 10.1038/s41467-018-06159-4.

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

Author information

1
McDonnell Genome Institute, Washington University School of Medicine, St. Louis, 63108, MO, USA.
2
Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.
3
Human Genome Sequencing Center, Baylor College of Medicine, Houston, 77030, TX, USA.
4
Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA.
5
New York Genome Center, New York, 10013, NY, USA.
6
Google, New York, 10011, NY, USA.
7
DNAnexus Inc, Mountain View, 94040, CA, USA.
8
Spiral Genetics, Seattle, 98104, WA, USA.
9
Department of Genetics, Rutgers University, Piscataway, 08854, NJ, USA.
10
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, 02142, MA, USA.
11
Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, 02114, MA, USA.
12
McDonnell Genome Institute, Washington University School of Medicine, St. Louis, 63108, MO, USA. ihall@wustl.edu.

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.

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