The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Scaling accurate genetic variant discovery to tens of thousands of samples
Open accessAbstract
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 HC-RCM is a novel, scalable, assembly-based algorithm with abundant applications for population genetics and clinical studies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Self-citationFast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587
Self-citationAn integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Van der Auwera, G. A., Carneiro, M. O., Hartl, C., Poplin, R., del Angel, G., Levy‐Moonshine, A., Jordan, T., Shakir, K., Roazen, D., Thibault, J., et al. (2013) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1110s43 · Cited by 6,687
Self-citationIntegrating common and rare genetic variation in diverse human populations
The International HapMap 3 Consortium (2010) · Nature · doi:10.1038/nature09298 · Cited by 3,026
Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population
Lim, E. T., Würtz, P., Havulinna, A. S., Palta, P., Tukiainen, T., Rehnström, K., Esko, T., Mägi, R., Inouye, M., Lappalainen, T., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004494 · Cited by 49
Self-citation
Research works printed but not linked: 20
- 10.1101/gr.112326.110 · DOI
- 2014, and Ana Cvejic. A loss of function screen of identified genome-wide association study loci reveals new genes controlling hematopoiesis, PLoS Genet, 10, e1004450, 10.1371/journal.pgen.1004450 · DOI
- 10.1038/ng.806 · DOI
- Marisa P. Dolled-Filhart , Michael Lee , Chih-wen Ou-yang , Rajini Rani Haraksingh , and Jimmy Cheng-Ho Lin . Computational and bioinformatics frameworks for next-generation whole exome and genome sequencing. The Scientific World Journal, pages 1–10, 2013. · DOI
- Richard Durbin . Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge university press, 1998. · DOI
- 10.1101/gr.210500.116 · DOI
- Erik Garrison and Gabor Marth . Haplotype-based variant detection from short-read sequencing. ArXiv e-prints, 07 2012.
- 10.1093/bioinformatics/btu368 · DOI
- 10.1093/bioinformatics/17.9.803 · DOI
- 10.1038/ng.1028 · DOI
- 10.1101/gr.078212.108 · DOI · Paper page
- 10.1093/bioinformatics/btp698 · DOI · Paper page
- 10.1186/gb-2011-12-11-r112 · DOI
- 2014, . Accurate de novo and transmitted indel detection in exome-capture data using microassembly, Nature methods, 11, 1033, 10.1038/nmeth.3069 · DOI
- 10.1186/1479-7364-8-14 · DOI
- 10.1038/ng.3036 · DOI
- 10.1101/gr.101360.109 · DOI · Paper page
- 10.1016/0022-2836(81)90087-5 · DOI
- 10.1016/j.tig.2014.07.001 · DOI
- Justin M Zook , Brad Chapman , Jason Wang , David Mittelman , Oliver Hofmann , Winston Hide , and Marc Salit . Integrating human sequence data sets provides a resource of benchmark snp and indel genotype calls. Nature biotechnology, 2014. · DOI
Cited by
A draft human pangenome reference.
Liao, W.-W., Asri, M., Ebler, J., Doerr, D., Haukness, M., Hickey, G., Lu, S., Lucas, J. K., Monlong, J., Abel, H. J., et al. (2023) · Nature · doi:10.1038/s41586-023-05896-x · Cited by 1,129
Pan-cancer whole-genome analyses of metastatic solid tumours
Priestley, P., Baber, J., Lolkema, M. P., Steeghs, N., de Bruijn, E., Shale, C., Duyvesteyn, K., Haidari, S., van Hoeck, A., Onstenk, W., et al. (2019) · Nature · doi:10.1038/s41586-019-1689-y · Cited by 1,106
Byrska-Bishop, M., Evani, U. S., Zhao, X., Basile, A. O., Abel, H. J., Regier, A. A., Corvelo, A., Clarke, W. E., Musunuri, R., Nagulapalli, K., et al. (2022) · Cell · doi:10.1016/j.cell.2022.08.004 · Cited by 1,094
Haplotype-resolved diverse human genomes and integrated analysis of structural variation
Ebert, P., Audano, P. A., Zhu, Q., Rodriguez-Martin, B., Porubsky, D., Bonder, M. J., Sulovari, A., Ebler, J., Zhou, W., Serra Mari, R., et al. (2021) · Science · doi:10.1126/science.abf7117 · Cited by 702
Organ aging signatures in the plasma proteome track health and disease
Oh, H. S.-H., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06802-1 · Cited by 634
Metadata
- Authors
- Poplin, R., Ruano-Rubio, V., DePristo, M. A., Fennell, T. J., Carneiro, M. O., Van der Auwera, G. A., Kling, D. E., Gauthier, L. D., Levy-Moonshine, A., Roazen, D., Shakir, K., Thibault, J., Chandran, S., Whelan, C., Lek, M., Gabriel, S., Daly, M. J., Neale, B., MacArthur, D. G., & Banks, E.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2017-11-14
- OpenAlex
- W2770095154
- Cited by
- 1,915
- References
- 7
- Type
- posted content
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Scaling accurate genetic variant discovery to tens of thousands of samples. https://app.cochat.ai/papers/10.1101/201178
- The paper
- Poplin, R., Ruano-Rubio, V., DePristo, M. A., Fennell, T. J., Carneiro, M. O., Van der Auwera, G. A., Kling, D. E., Gauthier, L. D., Levy-Moonshine, A., Roazen, D., Shakir, K., Thibault, J., Chandran, S., Whelan, C., Lek, M., Gabriel, S., Daly, M. J., Neale, B., MacArthur, D. G., & Banks, E. (2017). Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/201178
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.