Evidence map›Paper›PMID 36088354›Full record

ArticleNature communications2022

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes.

Remo Monti, Pia Rautenstrauch, Mahsa Ghanbari, Alva Rani James, Matthias Kirchler, Uwe Ohler, Stefan Konigorski, Christoph Lippert

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 10% of its field
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
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  3. Open biology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 2 countries.

Remo MontiDigital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, 14482, Potsdam, Germany.
Pia RautenstrauchMax-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 13125, Berlin, Germany.
Mahsa GhanbariMax-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 13125, Berlin, Germany.
Alva Rani JamesDigital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, 14482, Potsdam, Germany.
Matthias KirchlerDigital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, 14482, Potsdam, Germany.ORCID http://orcid.org/0000-0003-0616-2740
Uwe Ohler *Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), 13125, Berlin, Germany.ORCID http://orcid.org/0000-0002-0881-3116
Stefan Konigorski *Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, 14482, Potsdam, Germany.ORCID http://orcid.org/0000-0002-9966-6819
Christoph Lippert *Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam, Digital Engineering Faculty, 14482, Potsdam, Germany. christoph.lippert@hpi.de.ORCID http://orcid.org/0000-0001-6363-2556
Hasso Plattner Institute · DEMax Delbrück Center · DE

Funding

Medical Research Council MC_PC_17228Medical Research Council MC_QA137853
6 · The paper itself

Abstract

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

Indexed as

ExomeMutation, MissenseExome SequencingGenetic Association StudiesGenetic MarkersHumansIon ChannelsGenetic MarkersIon ChannelsPIEZO1 protein, human

Identifiers

PMID36088354
PMCPMC9464252
OpenAlexW4295427564

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.