Evidence map›Paper›PMID 38915639›Full record

ArticlebioRxiv : the preprint server for biology2024

Exploring penetrance of clinically relevant variants in over 800,000 humans from the Genome Aggregation Database.

Sanna Gudmundsson, Moriel Singer-Berk, Sarah L Stenton, Julia K Goodrich, Michael W Wilson, Jonah Einson, Nicholas A Watts, Genome Aggregation Database Consortium, Tuuli Lappalainen, Heidi L Rehm and 2 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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 it

What 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 registry

The 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 literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Sanna GudmundssonProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0002-2332-074X
Moriel Singer-BerkProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Sarah L StentonProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Julia K GoodrichProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Michael W WilsonProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Jonah EinsonNew York Genome Center, New York, NY, USA.
Nicholas A WattsProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Genome Aggregation Database Consortium
Tuuli LappalainenScience for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
Heidi L RehmProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Daniel G MacArthurProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Anne O'Donnell-LuriaProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.ORCID 0000-0001-6418-9592

Funding

The Genome Aggregation Database (gnomAD)U24HG011450 · NHGRI · BROAD INSTITUTE, INC. · PI Mark Joseph Daly, Konrad Karczewski · 2021 to 2026
$22.3M
NHGRI NIH HHS U24 HG011450
6 · The paper itself

Abstract

Incomplete penetrance, or absence of disease phenotype in an individual with a disease-associated variant, is a major challenge in variant interpretation. Studying individuals with apparent incomplete penetrance can shed light on underlying drivers of altered phenotype penetrance. Here, we investigate clinically relevant variants from ClinVar in 807,162 individuals from the Genome Aggregation Database (gnomAD), demonstrating improved representation in gnomAD version 4. We then conduct a comprehensive case-by-case assessment of 734 predicted loss of function variants (pLoF) in 77 genes associated with severe, early-onset, highly penetrant haploinsufficient disease. We identified explanations for the presumed lack of disease manifestation in 701 of the variants (95%). Individuals with unexplained lack of disease manifestation in this set of disorders rarely occur, underscoring the need and power of deep case-by-case assessment presented here to minimize false assignments of disease risk, particularly in unaffected individuals with higher rates of secondary properties that result in rescue.

Identifiers

PMID38915639
PMCPMC11195293

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.