PT  - JOURNAL ARTICLE
AU  - Ryan Poplin
AU  - Valentin Ruano-Rubio
AU  - Mark A. DePristo
AU  - Tim J. Fennell
AU  - Mauricio O. Carneiro
AU  - Geraldine A. Van der Auwera
AU  - David E. Kling
AU  - Laura D. Gauthier
AU  - Ami Levy-Moonshine
AU  - David Roazen
AU  - Khalid Shakir
AU  - Joel Thibault
AU  - Sheila Chandran
AU  - Chris Whelan
AU  - Monkol Lek
AU  - Stacey Gabriel
AU  - Mark J. Daly
AU  - Ben Neale
AU  - Daniel G. MacArthur
AU  - Eric Banks
TI  - Scaling accurate genetic variant discovery to tens of thousands of samples
AID  - 10.1101/201178
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 201178
4099  - http://biorxiv.org/content/early/2017/11/14/201178.1.short
4100  - http://biorxiv.org/content/early/2017/11/14/201178.1.full
AB  - Comprehensive disease gene discovery in both common and rare diseases will require the efficient and accurate detection of all classes of genetic variation across tens to hundreds of thousands of human samples. We describe here a novel assembly-based approach to variant calling, the GATK HaplotypeCaller (HC) and Reference Confidence Model (RCM), that determines genotype likelihoods independently per-sample but performs joint calling across all samples within a project simultaneously. We show by calling over 90,000 samples from the Exome Aggregation Consortium (ExAC) that, in contrast to other algorithms, the HC-RCM scales efficiently to very large sample sizes without loss in accuracy; and that the accuracy of indel variant calling is superior in comparison to other algorithms. More importantly, the HC-RCM produces a fully squared-off matrix of genotypes across all samples at every genomic position being investigated. The HCRCM is a novel, scalable, assembly-based algorithm with abundant applications for population genetics and clinical studies.