Evidence map›Paper›PMID 42238462›Full record

ArticlemedRxiv : the preprint server for health sciences2026

Wavelet Decomposition-Based Genomic Analysis of the Human Electrocardiogram.

Salma Zainana, Larissa Lauer, Tuomo Kiiskinen, Robert Tibshirani, Trevor Hastie, Euan A Ashley, Jack W O'Sullivan, Manuel A Rivas

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. 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

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.

Salma ZainanaInstitute for Computational & Mathematical Engineering, Stanford, CA, USA, 94305.
Larissa LauerDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA, 94305.
Tuomo KiiskinenDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA, 94305.
Robert TibshiraniDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA, 94305.
Trevor HastieDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA, 94305.ORCID 0000-0002-0164-3142
Euan A AshleyDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA, 94305.
Jack W O'SullivanDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA, 94305.
Manuel A RivasDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA, 94305.ORCID 0000-0003-1457-9925

Funding

SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASESR01HG010140 · NHGRI · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2018 to 2022
$2.0M
NHGRI NIH HHS R01 HG010140
6 · The paper itself

Abstract

The electrocardiogram (ECG) encodes the electrical activity of the heart across multiple timescales, yet standard clinical analysis collapses this rich signal into a handful of scalar measurements that discard most of the waveform's structure. Whether the frequency signals lost in this reduction carry heritable biological information relevant to cardiovascular disease risk remains unclear. Here we decompose resting 12-lead ECGs from 47,052 White British UK Biobank participants into 84 frequency-specific energy features using Daubechies-6 wavelet analysis across 12 leads and 7 decomposition levels, and perform independent genome-wide association analyses on each feature. We identify 67 independent loci (p < 5 × 10

Identifiers

PMID42238462
PMCPMC13228739

What Socratic holds

Textmetadata
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 Socratic graph.