Scaling accurate genetic variant discovery to tens of thousands of samples

By Ryan Poplin, Valentín Ruano-Rubio, Mark A. DePristo, Tim Fennell, Mauricio O. Carneiro, Géraldine A. Van der Auwera, D. E. Kling, Laura D. Gauthier, Ami Levy-Moonshine, David Roazen and 10 more. bioRxiv, 2017

1,758 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7f4b768032b0f93ea6e8546b2d9db66e33260e93

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Citations in the corpus

High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios (2021, positive): “For variant discovery we used HaplotypeCaller in GVCF mode (Poplin et al., 2017) with sex-dependent ploidy settings on chromosome X and Y.”
Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes (2020, methodology): “We calculated the phasing sensitivity, de ﬁ ned as the fraction of heterozygous variant pairs that have read-based phase information assigned for both variants, and found that it was 87.9% for adjacent heterozygous variant pairs, re ﬂ ecting the stringent haplotype-calling criteria of GATK 21 (Suppl”
Stacks 2: Analytical Methods for Paired-end Sequencing Improve RADseq-based Population Genomics (2019, methodology): “…and genotype calling commonly used for whole genome data. angsd emphasizes the use of genotype likelihoods and prob‐ abilities rather than of explicit genotype calls, and is regarded as more accurate than GATK (Poplin et al., 2018) for the latter (Korneliussen et al., 2014; Maruki & Lynch, 2017).”
Calling Somatic SNVs and Indels with Mutect2 (2019, methodology): “The discussion below will focus on meth-ods unique to Mutect2; methods shared with HaplotypeCaller are deferred to the supplemental material.”