In one paragraphArticle in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from itWhat it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed, 1 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
12 authors at 7 institutions in 1 country.
Sarah L StentonProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.ORCID 0000-0003-4071-449X Vikas PejaverInstitute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0002-1943-0284 Timothy BergquistInstitute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.ORCID 0000-0001-5614-8977 Leslie G BieseckerCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Alicia B ByrneProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.ORCID 0000-0002-8141-1818 Emily NadeauDepartment of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA.ORCID 0000-0002-7711-0342 Marc S GreenblattDepartment of Medicine and University of Vermont Cancer Center, University of Vermont, Larner College of Medicine, Burlington, VT 05405, USA.ORCID 0000-0002-1729-8355 Steven HarrisonProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.ORCID 0000-0002-9614-9111 Sean TavtigianDepartment of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.ORCID 0000-0002-7543-8221 Predrag RadivojacKhoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.ORCID 0000-0002-6769-0793 Steven E BrennerDepartment of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0000-0001-7559-6185 Anne O'Donnell-LuriaProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.ORCID 0000-0001-6418-9592 Broad Institute · USGenomic Health (United States) · USUniversity of Vermont · USNational Institutes of Health · USNortheastern University · USUniversity of California, Berkeley · USUniversity of Utah · US
Funding
Baylor College of Medicine/Stanford University Clinical Genome Resource (CLINGEN)U24HG009649 · NHGRI · BAYLOR COLLEGE OF MEDICINE · PI TERI Ellen KLEIN, Aleksandar Milosavljevic · 2021 to 2026
$31.5MThe Clinical Genome Resource – Advancing genomic medicine through biocuration and expert assessment of genes and variants at scaleU24HG009650 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI JONATHAN S BERG, Jessica Ezzell Hunter · 2021 to 2026
$30.0MClinGen AI Data Delivery SupplementU24HG006834 · NHGRI · BROAD INSTITUTE, INC. · PI Marina DiStefano, CHRISTA LESE MARTIN · 2021 to 2026
$26.7MJoint Center for Mendelian GenomicsUM1HG008900 · NHGRI · BROAD INSTITUTE, INC. · PI O'DONNELL-LURIA, ANNE, REHM, HEIDI L · 2016 to 2020
$16.5MBroad Institute Mendelian Genomic Research CenterU01HG011755 · NHGRI · BROAD INSTITUTE, INC. · PI Anne O'Donnell-Luria, MICHAEL E TALKOWSKI · 2021 to 2026
$14.6MClinical and Molecular Studies of MalformationsZ01HG200328 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI BIESECKER, LESLIE G · 2005 to 2008
$5.5MSupporting IGVF by modeling genetics, function, and phenotype with machine learningU01HG012022 · NHGRI · NORTHEASTERN UNIVERSITY · PI Predrag Radivojac · 2021 to 2026
$3.4MUpgrading rigor and efficiency of germline cancer gene variant classification for the 2020sR01CA264971 · NCI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Sean Vahram Tavtigian · 2022 to 2026
$2.9MIntegrative data science approaches for rare disease discovery in health recordsR00LM012992 · NLM · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI PEJAVER, VIKAS RAO · 2022 to 2024
$709kIntramural NIH HHS Z01 HG200328NCI NIH HHS R01 CA264971NHGRI NIH HHS U01 HG011755NHGRI NIH HHS U01 HG012022NHGRI NIH HHS U24 HG006834NHGRI NIH HHS U24 HG009649NHGRI NIH HHS U24 HG009650NHGRI NIH HHS UM1 HG008900NLM NIH HHS R00 LM012992
6 · The paper itselfAbstract
Purpose: To investigate the number of rare missense variants observed in human genome sequences by ACMG/AMP PP3/BP4 evidence strength, following the calibrated PP3/BP4 computational recommendations. Methods: Missense variants from the genome sequences of 300 probands from the Rare Genomes Project with suspected rare disease were analyzed using computational prediction tools able to reach PP3_Strong and BP4_Moderate evidence strengths (BayesDel, MutPred2, REVEL, and VEST4). The numbers of variants at each evidence strength were analyzed across disease-associated genes and genome-wide. Results: From a median of 75.5 rare (≤1% allele frequency) missense variants in disease-associated genes per proband, a median of one reached PP3_Strong, 3-5 PP3_Moderate, and 3-5 PP3_Supporting. Most were allocated BP4 evidence (median 41-49 per proband) or were indeterminate (median 17.5-19 per proband). Extending the analysis to all protein-coding genes genome-wide, the number of PP3_Strong variants increased approximately 2.6-fold compared to disease-associated genes, with a median per proband of 1-3 PP3_Strong, 8-16 PP3_Moderate, and 10-17 PP3_Supporting. Conclusion: A small number of variants per proband reached PP3_Strong and PP3_Moderate in 3,424 disease-associated genes, and though not the intended use of the recommendations, also genome-wide. Use of PP3/BP4 evidence as recommended from calibrated computational prediction tools in the clinical diagnostic laboratory is unlikely to inappropriately contribute to the classification of an excessive number of variants as Pathogenic or Likely Pathogenic by ACMG/AMP rules.
Identifiers
PMID38496501
PMCPMC10942508
OpenAlexW4392544611
What OpenQuestion holds
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