The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Variant interpretation using population databases: Lessons from gnomAD
Open accessAbstract
Reference population databases are an essential tool in variant and gene interpretation. Their use guides the identification of pathogenic variants amidst the sea of benign variation present in every human genome, and supports the discovery of new disease-gene relationships. The Genome Aggregation Database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data. The data is available through the online gnomAD browser (https://gnomad.broadinstitute.org/) that enables rapid and intuitive variant analysis. This review provides guidance on the content of the gnomAD browser, and its usage for variant and gene interpretation. We introduce key features including allele frequency, per-base expression levels, constraint scores, and variant co-occurrence, alongside guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease.
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
Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W. W., Hegde, M., Lyon, E., Spector, E., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.30 · Cited by 32,594
Self-citationInitial sequencing and analysis of the human genome
International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216
The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400
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-citationThe mutational constraint spectrum quantified from variation in 141,456 humans
Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., et al. (2020) · Nature · doi:10.1038/s41586-020-2308-7 · Cited by 9,434
Self-citationThe Ensembl Variant Effect Predictor
McLaren, W., Gil, L., Hunt, S. E., Riat, H. S., Ritchie, G. R. S., Thormann, A., Flicek, P., & Cunningham, F. (2016) · Genome Biology · doi:10.1186/s13059-016-0974-4 · Cited by 8,099
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
dbSNP: the NCBI database of genetic variation
Sherry, S. T. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.308 · Cited by 7,502
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
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
Self-citation
Cited by
Efficient in vivo genome editing prevents hypertrophic cardiomyopathy in mice
Reichart, D., Newby, G. A., Wakimoto, H., Lun, M., Gorham, J. M., Curran, J. J., Raguram, A., DeLaughter, D. M., Conner, D. A., Marsiglia, J. D. C., et al. (2023) · Nature Medicine · doi:10.1038/s41591-022-02190-7 · Cited by 203
A deep catalogue of protein-coding variation in 983,578 individuals
Sun, K. Y., Bai, X., Chen, S., Bao, S., Zhang, C., Kapoor, M., Backman, J., Joseph, T., Maxwell, E., Mitra, G., et al. (2024) · Nature · doi:10.1038/s41586-024-07556-0 · Cited by 127
RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding
(2022) · doi:10.1002/pro.4527 · Cited by 90
Dawood, M., Fayer, S., Pendyala, S., Post, M., Kalra, D., Patterson, K., Venner, E., Muffley, L. A., Fowler, D. M., Rubin, A. F., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01392-7 · Cited by 31
Somatic mutations alter the differentiation outcomes of iPSC-derived neurons
Puigdevall, P., Jerber, J., Danecek, P., Castellano, S., & Kilpinen, H. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100280 · Cited by 29
Metadata
- Authors
- Gudmundsson, S., Singer‐Berk, M., Watts, N. A., Phu, W., Goodrich, J. K., Solomonson, M., Genome Aggregation Database Consortium, Rehm, H. L., MacArthur, D. G., & O'Donnell‐Luria, A.
- Venue
- Human Mutation
- Publisher
- Wiley
- Published
- 2021-12-16
- Volume
- 43
- Issue
- 8
- Pages
- 1012-1030
- ISSN
- 1059-7794 (ISSN-L)
- 1098-1004
- OpenAlex
- W3185808000
- Cited by
- 674
- References
- 48
- FWCI
- 39.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Variant interpretation using population databases: Lessons from gnomAD. https://app.cochat.ai/papers/10.1002/humu.24309
- The paper
- Gudmundsson, S., Singer‐Berk, M., Watts, N. A., Phu, W., Goodrich, J. K., Solomonson, M., Genome Aggregation Database Consortium, Rehm, H. L., MacArthur, D. G., & O'Donnell‐Luria, A. (2022). Variant interpretation using population databases: Lessons from gnomAD. Human Mutation. https://doi.org/10.1002/humu.24309
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.