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Record W4391528656 · doi:10.1101/2024.02.01.24301443

Multiplexed Assays of Variant Effect and Automated Patch-clamping Improve <i>KCNH2</i> -LQTS Variant Classification and Cardiac Event Risk Stratification

2024· preprint· en· W4391528656 on OpenAlexaff
Matthew J. O’Neill, Chai‐Ann Ng, Takanori Aizawa, Luca Sala, Sahej Bains, Annika Winbo, Rizwan Ullah, Qianyi Shen, Chek-Ying Tan, Krystian A. Kozek, Loren Vanags, Devyn Mitchell, Alex Shen, Yuko Wada, Asami Kashiwa, Lia Crotti, Federica Dagradi, Giulia Musu, Carla Spazzolini, Raquel Neves, J. Martijn Bos, John R. Giudicessi, Xavier Bledsoe, Eric R. Gamazon, Megan Lancaster, Andrew M. Glazer, Björn C. Knollmann, Dan M. Roden, Jochen Weile, Frederick P. Roth, Joe‐Elie Salem, Nikki Earle, Rachael Stiles, Taylor Agee, Christopher N. Johnson, Minoru Horie, Jonathan R. Skinner, Michael Ackerman, Peter J. Schwartz, Seiko Ohno, Jamie I. Vandenberg, Brett M. Kroncke

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsUniversity of Toronto
FundersDivision of Materials ResearchVanderbilt Digestive Diseases Research Center, Vanderbilt University Medical CenterNew South Wales GovernmentNational Institutes of HealthVanderbilt UniversityVictor Chang Cardiac Research InstituteAmerican Heart Association
KeywordsRisk stratificationEvent (particle physics)ClampingStratification (seeds)MultiplexingInternal medicineComputer scienceMedicineCardiologyArtificial intelligenceBiologyTelecommunicationsPhysicsComputer vision

Abstract

fetched live from OpenAlex

Abstract Background Long QT syndrome (LQTS) is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by KCNH2 . Variant classification is difficult, often owing to lack of functional data. Moreover, variant-based risk stratification is also complicated by heterogenous clinical data and incomplete penetrance. Here, we sought to test whether variant-specific information, primarily from high-throughput functional assays, could improve both classification and cardiac event risk stratification in a large, harmonized cohort of KCNH2 missense variant heterozygotes. Methods We quantified cell-surface trafficking of 18,796 variants in KCNH2 using a Multiplexed Assay of Variant Effect (MAVE). We recorded KCNH2 current density for 533 variants by automated patch clamping (APC). We calibrated the strength of evidence of MAVE data according to ClinGen guidelines. We deeply phenotyped 1,458 patients with KCNH2 missense variants, including QTc, cardiac event history, and mortality. We correlated variant functional data and Bayesian LQTS penetrance estimates with cohort phenotypes and assessed hazard ratios for cardiac events. Results Variant MAVE trafficking scores and APC peak tail currents were highly correlated (Spearman Rank-order ρ = 0.69). The MAVE data were found to provide up to pathogenic very strong evidence for severe loss-of-function variants. In the cohort, both functional assays and Bayesian LQTS penetrance estimates were significantly predictive of cardiac events when independently modeled with patient sex and adjusted QT interval (QTc); however, MAVE data became non-significant when peak-tail current and penetrance estimates were also available. The area under the ROC for 20-year event outcomes based on patient-specific sex and QTc (AUC 0.80 [0.76-0.83]) was improved with prospectively available penetrance scores conditioned on MAVE (AUC 0.86 [0.83-0.89]) or attainable APC peak tail current data (AUC 0.84 [0.81-0.88]). Conclusion High throughput KCNH2 variant MAVE data meaningfully contribute to variant classification at scale while LQTS penetrance estimates and APC peak tail current measurements meaningfully contribute to risk stratification of cardiac events in patients with heterozygous KCNH2 missense variants. Clinical Perspective What is new? A two-order of magnitude increase in the set of calibrated functional data for KCNH2 -LQTS is provided by two complementary KCNH2 assays Proactively available variant scores are presented for all possible missense variants by using a LQTS penetrance estimation framework conditioned on high-throughput MAVE data Variant functional data, in addition to patient features of corrected QT interval and sex, significantly improve modeling of 20-year cardiac event outcomes What are the clinical implications? Readily available MAVE scores for thousands of variants may facilitate classification of new variants discovered in individuals with suspected LQTS Scores and penetrance estimates are readily searchable at variantbrowser.org for community inquiry Both automated patch-clamp data and quantitative LQTS penetrance estimates, conditioned on MAVE data, improve prediction of 20-year cardiac event outcomes in a large cohort of KCNH2 heterozygotes

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.269
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2024
Admission routes1
Has abstractyes

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